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  • 51.
    Funk, Peter
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Olsson, Erik
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Bengtsson, Marcus
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Case-Based Experience Reuse and Agents for Efficient Health Monitoring, Prevention and Corrective Actions2006Inngår i: Proceedings of the 19th International Congress on Condition, COMADEM 2006, Luleå, Sweden, 2006, s. 445-453Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Experienced staffs acquire their experience during many years of practice, and sometimes also through expensive mistakes. This knowledge is often lost when technicians retire, or if companies need to downsize during periods of reduced sale. When scaling up production, new staff requires training and may repeat similar mistakes. Another issue that may be costly is when monitoring systems repeatedly give false alarms, causing expensive loss of production capacity and resulting in technicians losing trust in the systems and in worst case, switch them off. If monitoring systems could learn from previous experience for both correct and false alarms, the reliability and trust in the monitoring systems would increase. Moreover, connecting alarms to either equipment taking automatic actions or recommend actions based on the current situations and previous experience would be valuable.

    An engineer repeating the same task a second time is often able to perform the task in 1/3 of the time it took at the first time. Most corrective and preventive actions for a particular machine type have been carried out before. This past experience holds a large potential for time savings, predictability and reduced risk if an efficient experience transfer can be accomplished. But building large complex support system is not always the ideal way. We propose instead localized intelligent agents, able to either autonomously perform the necessary actions or aid a human in the decision making process by providing the necessary information needed to make an informed and validated decision.

  • 52.
    Funk, Peter
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Applied Intelligent Psychophysiological Medical Systems: Systems Theory Concretization and Complex Data Analysis2008Konferansepaper (Fagfellevurdert)
  • 53.
    Funk, Peter
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Case Based Reasoning and Knowledge Discovery in Medical Applications with Time Series2006Inngår i: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 22, nr 3/4, s. 238-253Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical application and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.

  • 54.
    Funk, Peter
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Discovering Key Sequences in Time Series Data for Pattern Classification2006Inngår i: Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006. Proceedings, 2006, s. 492-505Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper addresses the issue of discovering key sequences from time series data for pattern classification. The aim is to find from a symbolic database all sequences that are both indicative and non-redundant. A sequence as such is called a key sequence in the paper. In order to solve this problem we first we establish criteria to evaluate sequences in terms of the measures of evaluation base and discriminating power. The main idea is to accept those sequences appearing frequently and possessing high co-occurrences with consequents as indicative ones. Then a sequence search algorithm is proposed to locate indicative sequences in the search space. Nodes encountered during the search procedure are handled appropriately to enable completeness of the search results while removing redundancy. We also show that the key sequences identified can later be utilized as strong evidences in probabilistic reasoning to determine to which class a new time series most probably belongs.

  • 55.
    Funk, Peter
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Discovering Knowledge about Key Sequences for Indexing Time Series Cases2006Inngår i: Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings, 2006, s. 474-488Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Coping with time series cases is becoming an important issue in case based reasoning. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly usable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Three alternate ways to develop case indexes based on key sequences are suggested. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval.

  • 56.
    Funk, Peter
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Extracting knowledge from sensor signals for case-based reasoning with longitudinal time series data2008Inngår i: Case-Based Reasoning in Signals and Images / [ed] Petra Perner, Springer, 2008, s. 247-284Kapittel i bok, del av antologi (Annet vitenskapelig)
    Abstract [en]

    In many industrial and medical diagnosis problems it is essential to investigate time series measurements collected to recognize existing or potential faults/diseases. Today this is usually done manually by humans. However the lengthy and complex nature of signals in practice often makes it a tedious and hard task to analyze and interpret available data properly even by experts with rich experiences. The incorporation of intelligent data analysis method such as case-based reasoning is showing strong benefit in offering decision support to technicians and clinicians for more reliable and efficient judgments. This chapter addresses a general framework enabling more compact and efficient representation of practical time series cases capturing the most important characteristics while ignoring irrelevant trivialities. Our aim is to extract a set of qualitative, interpretable features from original, and usually real-valued time series data. These features should on one hand convey significant information to human experts enabling potential discoveries/findings and on the other hand facilitate much simplified case indexing and imilarity matching in case-based reasoning. The road map to achieve this goal consists of two subsequent stages. In the first stage it is tasked to transform the time series of real numbers into a symbolic series by temporal abstraction or symbolic approximation. A few different methods are available at this stage and they are introduced in this chapter. Then in the second stage we use knowledge discovery method to identify key sequences from the transformed symbolic series in terms of their cooccurrences with certain classes. Such key sequences are valuable in providing concise and important features to characterize dynamic properties of the original time series signals. Four alternative ways to index time series cases using discovered key sequences are discussed in this chapter.

  • 57.
    Funk, Peter
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Why we need to move to intelligent and experience based monitoring and diagnostic systems2010Inngår i: COMADEM 2010, 23th International Conference on Condition Monitoring and Diagnostic Engineering Management, Nara, Japan, 2010, s. 111-115Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Monitoring, quality control and diagnosis is a large cost for production industry. Studies have estimated that the total cost of maintenance in Sweden is 20 billion Euros and the amount spent on maintenance in Europe is around 1500 billion Euros per year. The key to efficient maintenance is monitoring and quality control. Much of this work is today still manual and based on experienced technicians. Today large amounts of data are collected in the production industry but only a fragment of this data is used. Much of the monitoring data from sensors are used for quality control and maintenance which is still interpreted manually or a system monitoring if a threshold value is passed in order to give an alert. More elaborate use of the data, information and experience is rare. Using methods and techniques from artificial intelligence for experience reuse enables more informed actions based reducing accidents, mistakes and costs to mention some benefits. Building up and sharing experience is the key to “intelligent” monitoring and diagnostics acting as decision support. Intelligent Monitoring Agents are going beyond decision support since they also have communication skill and are able to make decisions on their own. Keywords: diagnostic systems, monitoring, artificial intelligence, agent based architecture.

  • 58.
    Hedelind, Mikael
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Milic, Milun
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Flexible Production Using Intelligent Algorithms for Controlling Dynamic Storage Areas for Industrial Robots2005Konferansepaper (Fagfellevurdert)
  • 59.
    Hedelind, Mikael
    et al.
    ABB Automation Technologies AB, Sweden.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Milic, Milun
    ABB Automation Technologies AB, Sweden.
    Intelligent Buffer Storage System: Enabling Fast and Flexible Assembling with Industrial Robots2006Inngår i: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Vol. 17, nr 4, s. 367-376Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Production cells usually require a continuous supply of parts to be assembled. Elaborate feeding mechanisms or a system of prepared pallets on which the parts have exact positions are expensive and if a variation of the product is to be produced, the feeding mechanism or pallets must be modified. Such solutions do not provide sufficient flexibility and increase production costs. Today's requirements for smaller series and customized orders have higher requirements on production cells.

    In this paper we show how flexible and adaptive production can be achieved, using methods and techniques from artificial intelligence by introducing an "autonomous" production cell, integrating and managing its own local buffer storage. The production cell is able to produce a number of variants of the product with no time delay between different configurations. The storage system, designated "Floating Storage", handles the local buffer storage and guides the industrial robots to use available floor-space as storage. The system also orders parts from the main storage as the buffer storage approaches depletion. The parts arrive to the cell in standard containers and a commercially available vision system is used to locate the material. The prototype has been introduced in an assembly line at ABB.

  • 60.
    Jackson, Mats
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Hedelind, Mikael
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Factory-in-a-box – solutions for availability and mobility of flexible production capacity2007Konferansepaper (Fagfellevurdert)
  • 61.
    Karlsson, Christer
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    HYBRID EARLY WARNING SYSTEMS2009Inngår i: COMADEM 2009 (in press), Spain, 2009Konferansepaper (Fagfellevurdert)
    Abstract [en]
    New tools are needed to reach high goals for uptime and availability in industrial processes. Early warning of developing faults is one part of the strategy to reach these goals. A single method rarely meets all requirements, but combining methods and techniques in a hybrid system offers advantages and can overcome limitations in the individual approaches. Methods considered are physical models, artificial neural networks, and case-based reasoning. The paper discusses the pros and cons, strengths and weaknesses of the three methods and three combinations of hybrid solutions in order to assist in select a suitable combination for a specific early warning challenge ahead.
  • 62.
    Komann, Marcus
    et al.
    Universitätsklinikum Jena.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Meiβner, Winfried
    Universitätsklinikum Jena.
    Exploiting the PAIN OUT registry with a clinical decision support system for acute pain management (extended abstract)2012Inngår i: Journal of critical care, ISSN 0883-9441, E-ISSN 1557-8615, Vol. 27, nr 3Artikkel i tidsskrift (Fagfellevurdert)
  • 63.
    Marling, Cindy
    et al.
    Ohio University, Athens, OH 45701, USA.
    Montani, Stefania
    Università del Piemonte Orientale.
    Bichindaritzc, Isabelle
    State University of New York at Oswego.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Synergistic case-based reasoning in medical domains2014Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, nr 2, s. 249-259Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents four synergistic systems that exemplify the approaches and benefits of case-based reasoning in medical domains. It then explores how these systems couple Artificial Intelligence (AI) research with medical research and practice, integrate multiple AI and computing methodologies, leverage small numbers of available cases, reason with time series data, and integrate numeric data with contextual and subjective information. The following systems are presented: (1) CARE-PARTNER, which supports the long-term follow-up care of stem-cell transplantation patients; (2) the 4 Diabetes Support System, which aids in managing patients with type 1 diabetes on insulin pump therapy; (3) Retrieval of HEmodialysis in NEphrological Disorders, which supports hemodialysis treatment of patients with end stage renal disease; and (4) the Mälardalen Stress System, which aids in the diagnosis and treatment of stress-related disorders.

  • 64.
    Najib, Muhammad Sharfi
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Taib, M.N.
    Universiti Teknologi MARA, 40450, Selangor, Malaysia.
    Ali, N.A.M.
    Forest Research Institute Malaysia.
    Agarwood classification: A Case-Based Reasoning approach based on E-nose2012Inngår i: Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012, 2012, s. 120-126Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Using an array of sensors (E-nose) to classify Agarwood has proven to be successful and produced performance close to an expert level (90% of expert level performance) but it has proven difficult to eliminate misclassifications without over-fitting. In our effort to improve our result we explored a self-improving Case-Based Reasoning approach and reached 100% correct classification. Case-Based Reasoning is an approach that will learn from every new classified case and hence the risk for misclassification is reduced. Also when new cases have to be classified that have never occurred before the system will avoid misclassification (similarity measurement is low). The approach also enables indeterminism; in reality a sample may be both close to a good case and a bad case and need further exploration by experts. The approach also handles natural variants in the wood samples well; both low-quality and high-quality samples may spread considerably in the context of E-nose readings and there is no model available of low or high quality.

  • 65.
    Nilsson, Markus
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    A Case-Based Classification of Respiratory Sinus Arrhythmia2004Inngår i: Advances in Case-Based Reasoning, 2004, s. 673-685Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Respiratory Sinus Arrhythmia has until now been analysed manually by reviewing long time series of heart rate measurements. Patterns are identified in the analysis of the measurements. We propose a design for a classification system of Respiratory Sinus Arrhythmia by time series analysis of heart and respiration measurements. The classification uses Case-Based Reasoning and Rule-Based Reasoning in a Multi-Modal architecture. The system is in use as a research tool in psychophysiological medicine, and will be available as a decision support system for treatment personnel.

  • 66.
    Nilsson, Markus
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Olsson, Erik. M. G.
    von Schéele, Bo
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Clinical decision support for diagnosing stress related disorders by applying psychophysiological medical knowledge to an instance based learning system2006Inngår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 36, nr 2, s. 159-176Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objective: An important procedure in diagnosing stress-related disorders caused by dysfunction in the interaction of the heart with breathing, i.e., respiratory sinus arrhythmia (RSA), is to analyse the breathing first and then the heart rate. Analysing these measurements is a time-consuming task for the diagnosing clinician. A decision-support system in this area would reduce the analysis task of the clinician and enable him/her to give more attention to the patient. We have created a decision-support system which contains a signal classifier and a pattern identifier. The system performs an analysis of the physiological time series concerned which would otherwise be performed manually by the clinician. Methods: The signal-classifier, HR3Modul, classifies heart-rate patterns by analysing both cardio- and pulmonary signals, i.e., physiological time series. HR3Modul uses case-basedreasoning (CBR), using a wavelet-based method for retrieving features from the signals. The system searches for familiar shapes in the signals by comparing them with shapes already stored. We have applied a best fit scheme for handling signals of different lengths, as the length of a breath is highly dynamic. We also apply automatic weighting to the features to obtain a more autonomous system. The classified heart signals indicate if a patient may be suffering from a stress-related disorder and the nature of the disorder. These classified signals are thereafter sent to the second subsystem, the pattern-identifier. The pattern-identifier analyses the classified signals and searches for familiar patterns by identifying sequences in the classified signals. The identified sequences give clinicians a more complete analysis of the measurements, providing them with a better basis for diagnosis. Results and conclusion: We have shown that a case-based classifier with a wavelet feature extractor and automatic weighting is a viable option for building a decision-support system for the psychophysiological domain, as it is at par, or even outperforms other retrieval techniques and is less complex.

  • 67.
    Nilsson, Markus
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Sollenborn, Mikael
    Complex Measurement Classification in Medical Applications Using A Case-Based Approach2003Inngår i: Workshop proceedings of the fifth international conference on case-based reasoning, NTNU, Trondheim, Norway, 2003, s. 63-72Konferansepaper (Annet vitenskapelig)
  • 68.
    Olsson, Ella
    et al.
    Saab AB Aerosystems, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Case-based reasoning applied to geometric measurements for decision support in manufacturing2013Inngår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 4, nr 3, s. 223-230Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Measurements from products are continuously collected to allow adjustments in the production line to certify a feasible product quality. Case-based reasoning is a promising methodology for this type of quality assurance. It allows product measurements and its related adjustments to the production line to be stored as cases in a case-based reasoning system. The idea is to describe an event of adjustments based on deviations in geometric measurement points on a product and connect these measurements to their correlated adjustments done to the production line. Experience will implicitly be stored in each case in the form of uniquely weighted measurement points according to their positive influence on adjustments. Methods have been developed in order to find these positive correlations between measurements and adjustments by analysing a set of historical product measurement and their following adjustments. Each case saved in the case base will be “quality assured” according to this methods and only cases containing strong positive correlations will be used by the system. The correlations will be used to supply each case with its own set individual weights.

  • 69.
    Olsson, Erik
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    A CASE STUDY OF COMMUNICATION IN A DISTRIBUTED MULTI-AGENT SYSTEM IN A FACTORY PRODUCTION ENVIRONMENT2007Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A Distributed Multi-Agent System representing the behaviour of a machine maintenance procedure in a factory production environment is modelled using the BRIC language. The model provides an overview and simplification of the communication in the maintenance procedure. The model involves two distributed factory environments, each equipped with a Maintenance Agent and an Experience Sharing Agent. Maintenance agents can be seen as experts in interpreting local sensor data from the machine being observed. They have some basic domain knowledge about when to bring the findings to the attention of an agent, human or system. An agent is also autonomous and may have the trust to shut down a process. The maintenance agent will ask other agents or humans for assistance if bringing the macine ito working order is beyond the agent's ability. Necessary information about what maintenance actions to perform is provided by an Experience Sharing Agent which has the ability to identify past experience relevant for the current situation and thus beeing able to help the human to make a better and more informed decision avoiding previously, sometimes very costly mistakes.

  • 70.
    Olsson, Erik
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Agent-Based Monitoring using Case-Based Reasoning for Experience Reuse and Improved Quality2009Inngår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, Vol. 15, nr 2, s. 179-192Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose – The purpose with this paper is to propose an agent-based condition monitoringsystem for use in industrial applications. An intelligent maintenance agent is described that isable to autonomously perform necessary actions and/or aid a human in the decision makingprocess. An example is presented as a case-study from manufacturing of industrial robots.Design/methodology/approach – The paper is mainly based on a case-study performed at alarge multi-national company aiming to explore the usefulness of case-based experience reusein production.Findings – This paper presents a concept of case-based experience reuse in production. Amaintenance agent using a Case-Based Reasoning approach to collect, preserve and reuseavailable experience in the form of sound recordings exemplifies this concept. Sound fromnormal and faulty robot gearboxes are recorded during the production end test and stored in acase library together with their diagnosis results. Given an unclassified sound signal, relevantcases are retrieved to aid a human in the decision making process. The maintenance agentdemonstrated good performance by making right judgments in 91% of all the tests, which isbetter than an inexperienced technician.Originality/value – The main focus of this paper is to show how to perform efficientexperience reuse in modern production industry to improve quality of products. Twoapproaches are used: a case-study describing an example of experience reuse in productionusing a fault diagnosis system recognizing and diagnosing audible faults on industrial robotsand an efficient approach on how to package such a system using the agent paradigm and agent architecture.

  • 71.
    Olsson, Erik
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Andersson, Alf
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning Applied to Geometric Production Measurements2010Inngår i: 1st International Workshop and Congress on eMaintenance June 22-24, 2010 in Lulea, Sweden, Lulea, Sweden, 2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Measurements from products are continuously collected to allow adjustments in the production line to certify a feasible product quality. Case-Based Reasoning is a promising methodology for this type of quality assurance. It allows product measurements and its related adjustments to the production line to be stored as cases in a Case-Based Reasoning system. The idea is to describe an event of adjustments based on deviations in geometric measurement points on a product and connect these measurements to their correlated adjustments done to the production line. Experience will implicitly be stored in each case in the form of uniquely weighted measurement points according to their positive influence on adjustments. Methods have been developed in order to find these positive correlations between measurements and adjustments by analysing a set of historical product measurement and their following adjustments. Each case saved in the case base will be "quality assured" according to this methods and only cases containing strong positive correlations will be used by the system. The correlations will be used to supply each case with its own set individual weights.

  • 72.
    Olsson, Erik
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Bengtsson, Marcus
    Mälardalens högskola, Institutionen för innovation, design och produktutveckling.
    Fault Diagnosis of Industrial Robots using Acoustic Signals and Case-Based Reasoning2004Inngår i: Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science, vol 3155, 2004, s. 686-701Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence as deviations compared to a normal sound pro-file. This is the case in robot assembly, the selected application domain for the system. Manual diagnosis based on sound requires extensive experience, and the experience is often acquired through costly mistakes and reduced production efficiency or quality loss caused by missed faults. The acquired experience is also difficult to preserve and transfer, and often lost if personnel leave the task of testing and fault diagnosis. We propose a Case-Based Reasoning approach to collect and preserve experience. The solution enables fast experience transfer and leads to less experienced personnel required to make more reliable and informed testing. Sounds from normal and faulty equipment are recorded and stored in a case library together with a diagnosis. Addition of new validated sound profiles continuously improves the system’s performance. The system can preserve and transfer experience between technicians, reducing overall fault identification time and increases quality by reduced number of missed faults. The original sound recordings are stored in form of the extracted features to-gether with other experience, e.g. instructions, additional tests, advice, user feedback etc.

  • 73.
    Olsson, Erik
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Fault Diagnosis in Industry Using Sensor Readings and Case-Based Reasoning2004Inngår i: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Vol. 15, nr 1, s. 41-46Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fault diagnosis of industrial equipments becomes increasingly important for improving the quality of manufacturing and reducing the cost for product testing. Developing a fast and reliable diagnosis system presents a challenge issue in many complex industrial scenarios. The major difficulties therein arise from contaminated sensor readings caused by heavy background noise as well as the unavailability of experienced technicians for support. In this paper we propose a novel method for diagnosis of faults by means of case-based reasoning and signal processing. The received sensor signals are processed by wavelet analysis to filter out noise and at the same time to extract a group of related features that constitutes a reduced representation of the original signal. The derived feature vector is then forwarded to a classification component that uses case-based reasoning to recommend a fault class for the probe case. This recommendation is based on previously classified cases in a case library. Case-based diagnosis has attractive properties in that it enables reuse of past experiences whereas imposes no demand on the size of the case base. The proposed approach has been applied to fault diagnosis of industrial robots at ABB Robotics and the results of experiments are very promising.

  • 74.
    Olsson, Erik
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Hedelind, Mikael
    Mälardalens högskola, Institutionen för innovation, design och produktutveckling.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    EXPERIENCE REUSE BETWEEN MOBILE PRODUCTION MODULES - AN ENABLER FOR THE FACTORY-IN-A-BOX CONCEPT2007Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Today's increased demand for flexible and fast reconfiguration of production systems is seen as one of the key factors for survival by many branches, especially small and medium sized enterprises. To enable adaptable and flexible production, we propose an integrated experience reuse system assisting in setup, operation, maintenance and repair. We present three subsystems that facilitate experience reuse between different engineers and operators working with standardised production modules. It is composed of three separate software components enabling: a) easy programming and control of robot cells, b) monitoring and condition based maintenance, c) distributed experience reuse. The results presented in this paper have been developed within the Factory-in-a-Box project, the ExAct project and the Eken project.

  • 75.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based reasoning combined with statistics for diagnostics and prognosis2012Inngår i: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 364, nr 1, s. Article number: 012061-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

  • 76.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gillblad, D.
    SICS Swedish ICT, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Explaining probabilistic fault diagnosis and classification using case-based reasoning2014Inngår i: Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings, 2014, s. 360-374Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

  • 77.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gillblad, Daniel
    SICS Swedish ICT, Kista, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Case-Based Reasoning for Explaining Probabilistic Machine Learning2014Inngår i: International Journal of Computer Science & Information Technology (IJCSIT), ISSN 0975-4660, E-ISSN 0975-3826, Vol. 6, nr 2, s. 87-101Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

  • 78.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. SICS Swedish ICT, Sweden.
    Källström, Elisabeth
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Gillblad, Daniel
    SICS Swedish ICT, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindström, John
    Luleå University of Technology, Luleå, Sweden.
    Håkansson, Lars
    Blekinge Institute of Technology, Sweden.
    Lundin, Joakim
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Larsson, Jonas
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches2014Inngår i: Proceedings of the ICCBR 2014 Workshops, 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision mak- ing. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely in- tegrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions. 

  • 79.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. SICS Swedish ICT, Kista, Sweden.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Källström, Elisabeth
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Holst, Anders
    SICS Swedish ICT, Kista, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fault Diagnosis via Fusion of Information from a Case Stream2015Inngår i: Case-Based Reasoning Research and Development. Proceeding of the the 23th International Conference on Case-Based Reasoning (ICCBR-2015), 2015, s. 275-289Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of case-based reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction.

  • 80.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Real Time Heart Rate Monitoring from Facial RGB Color Video using Webcam2016Inngår i: The 29th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2016, Malmö, Sweden, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Heart Rate (HR) is one of the most important Physiological parameter and a vital indicator of people’s physiological state and is therefore important to monitor. Monitoring of HR often involves high costs and complex application of sensors and sensor systems. Research progressing during last decade focuses more on noncontact based systems which are simple, low-cost and comfortable to use. Still most of the noncontact based systems are fit for lab environments in offline situation but needs to progress considerably before they can be applied in real time applications. This paper presents a real time HR monitoring method using a webcam of a laptop computer. The heart rate is obtained through facial skin color variation caused by blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR is subsequently quantified and compared to corresponding reference measurements. The obtained results show that there is a high degrees of agreement between the proposed experiments and reference measurements. This technology has significant potential for advancing personal health care and telemedicine. Further improvements of the proposed algorithm considering environmental illumination and movement can be very useful in many real time applications such as driver monitoring.

  • 81. Rothaug, J.
    et al.
    Zaslansky, R.
    Schwenkglenks, M.
    Komann, M.
    Allvin, R.
    Backström, R.
    Brill, S.
    Buchholz, I.
    Engel, C.
    Fletcher, D.
    Fodor, L.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Gerbershagen, H. J.
    Gordon, D. B.
    Konrad, C.
    Kopf, A.
    Leykin, Y.
    Pogatzki-Zahn, E.
    Puig, M.
    Rawal, N.
    Taylor, R. S.
    Ullrich, K.
    Volk, T.
    Yahiaoui-Doktor, M.
    Meissner, W.
    Patients' perception of postoperative pain management: Validation of the international pain outcomes (IPO) questionnaire2013Inngår i: Journal of Pain, ISSN 1526-5900, Vol. 14, nr 11, s. 1361-1370Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    PAIN OUT is a European Commission-funded project aiming at improving postoperative pain management. It combines a registry that can be useful for quality improvement and research using treatment and patient-reported outcome measures. The core of the project is a patient questionnaire - the International Pain Outcomes questionnaire - that comprises key patient-level outcomes of postoperative pain management, including pain intensity, physical and emotional functional interference, side effects, and perceptions of care. Its psychometric quality after translation and adaptation to European patients is the subject of this validation study. The questionnaire was administered to 9,727 patients in 10 languages in 8 European countries and Israel. Construct validity was assessed using factor analysis. Discriminant validity assessment used Mann-Whitney U tests to detect mean group differences between 2 surgical disciplines. Internal consistency reliability was calculated as Cronbach's alpha. Factor analysis resulted in a 3-factor structure explaining 53.6% of variance. Cronbach's alpha at overall scale level was high (.86), and for the 3 subscales was low, moderate, or high (range,.53-.89). Significant mean group differences between general and orthopedic surgery patients confirmed discriminant validity. The psychometric quality of the International Pain Outcomes questionnaire can be regarded as satisfactory. Perspective The International Pain Outcomes questionnaire provides an instrument for postoperative pain assessment and improvement of quality of care, which demonstrated good psychometric quality when translated into a variety of languages in a large European and Israeli patient population. This measure provides the basis for the first comprehensive postoperative pain registry in Europe and other countries. © 2013 by the American Pain Society.

  • 82.
    Sollenborn, Mikael
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Category-based filtering in Recommender systems for improved performance in dynamic domains2002Inngår i: Lecture Notes in Computer Science: vol. 2347, Springer, 2002, s. 436-439Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    In Recommender systems, collaborative filtering is the most commonly used technique. Although often successful, collaborative filtering encounters the latency problem in domains where items are frequently added, as the users have to review new items before they can be recommended. In this paper a novel approach to reduce the latency problem is proposed, based on category-based filtering and user stereotypes. © 2002 Springer-Verlag Berlin Heidelberg.

  • 83.
    Tomasic, Ivan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, A.
    Chalmers University, Gothenburg, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Mixed-Effect Models for the Analysis and Optimization of Sheet-Metal Assembly Processes2017Inngår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, nr 5, s. 2194-2202, artikkel-id 7857795Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Assembly processes can be affected by various parameters, which is revealed by the measured geometrical characteristics (GCs) of the assembled parts deviating from the nominal values. Here, we propose a mixed-effect model (MEM) application for the purposes of analyzing variations in assembly cells, as well as for screening the input variables and characterization. MEMs make it possible to take into account statistical dependencies that originate from repeated measurements on the same assembly. The desirability functions approach was used to describe how to find corrective or control actions based on the fitted MEM. Objectives: To examine the usefulness of the MEM between the positions of the in-going parts as the input controllable variables and the measured GCs as the outputs. Methods: The data from 34 car frontal cross members (each measured three times) were experimentally collected in a laboratory environment by intentionally changing the positions of the in-going parts, assembling the parts, and subsequently measuring their GCs. A single MEM that completely describes the assembly process was fitted between the GCs and the positions of the in-going parts. Results: We present a modeling technique that can be used to establish which measured GCs are influenced by which controllable variables, and how this occurs. The fitted MEM shows evidence that the variability of some GCs changes over time. The natural variation in the system (i.e., unmodeled variations) is about two times larger than the variation between the assembled cross members. We also present two cases that demonstrate how to use the fitted MEM desirability functions to find corrective or control actions. Conclusion: MEMs are very useful tools for analyzing the assembly processes for car-body parts, which are nonlinear processes with multiple inputs and multiple correlated outputs. MEMs can potentially be applied in numerous industrial processes, since modern manufacturing plants measure all important process variables, which is the sole prerequisite for MEMs applications. 

  • 84.
    Tomasic, Ivan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Erdem, Ilker
    Chalmers University of Technology, Sweden.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process2016Inngår i: Swedish Production Symposium 2016 SPS 2016, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The assembly quality is affected by various factors within which fixture variations are the most important. For that reason significant research on fixture variations has already been done. In this work we propose a linear mixed models (LMMs) application for the purpose of analyzing sources of variation in the fixture Objective: To estimate the strength of influences of different sources of variation on the control and assembly fixtures. The variables considered are: time, operator, default pin positions, shifts from the default pin positions . Methods: The data was collected through assembly and measurement for repeatability and experimental corrective actions. We use LMMs to model the relation between features measured on the assembled parts and the input variables of interest. The LMMs allow taking into account the correlation of observations contained in the dataset. We also use graphical data presentation methods to explore the data. Results: The expected results are the strengths of influences of the individual variables considered, and the pairwise interactions of between the variables, on the assembled parts variations.

  • 85.
    Tomasic, Ivan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Erdem, Ilker
    Chalmers University of Technology, Sweden.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Input-output Mapping and Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Processes2016Inngår i: Swedish Production Symposium 2016 SPS 2016, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The assembly quality is affected by various factors within which fixture variations are the most important. For that reason an extensive research on fixture variations has already been done. In this work we propose a linear models (LMs) application for the purpose of analyzing sources of variation in the fixture as well as establishing a model between positions of ingoing parts and measured geometrical characteristics of the assemblies. Objectives: (1) To estimate the strengths of different sources of variation on the assembled parts. (2) Estimate a regression model between the positions of ingoing parts as inputs (that are defined by positions of pins holding them), and measured geometrical characteristics as outputs, that can be used to determine which measured characteristics are influenced by which input variable. Methods: The data was experimentally collected in a laboratory environment by intentionally changing positions of ingoing part, assembling the parts and subsequently measuring their geometrical characteristics. We use liner model to establish the relation between geometrical characteristics measured on the assembled parts, and the input variables of interest. Results: Presented is a modeling technique that can be used to establish which measured geometrical characteristics are influenced by input variables (i.e.pins’ positions) of interest. The natural variation in the system (i.e. not modeled variation) is quite high. The time passed between measurements has a significant influence on measured values.

  • 86.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A novel framework for case-based decision analysis2008Inngår i: TENTH SCANDINAVIAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2008, s. 141-148Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Case-based reasoning (CBR) and decision analysis have been two separate research areas aiming to solve problems from different perspectives. CBR is powerful to offer solutions to problems by reusing previous experiences, while decision theory exhibits its strength in dealing with uncertain, nondeterministic situations subject to likelihoods, risks, and probable consequences. In this paper, we present a novel framework of integrating CBR and decision analysis for the purpose of case-based decision analysis. CBR is employed as a methodology to reason from previous cases for building a decision model given the current situation, while decision theory is applied to the decision model learnt from previous cases to identify the most promising, Secured, and rational choices. In such a way, we take advantage of both the ability of CBR to learn without domain knowledge and the strength of decision theory to analyze under uncertainty. 

  • 87.
    Xiong, Ning
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Building similarity metrics reflecting utility in case-based reasoning2006Inngår i: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 17, nr 4, s. 407-416Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fundamental to case-based reasoning is the idea that similar problems have similar solutions. The meaning of the concept of "similarity" can vary in different situations and remains an issue. Since we want to identify and retrieve truly useful or relevant cases for problem solving, the metrics of similarity must be defined suitably to reflect the utility of cases for solving a particular target problem. A framework for utility-oriented similarity modeling is developed in this paper. The main idea is to exploit a case library to obtain adequate samples of utility from pairs of cases. The task of similarity modeling then becomes the customization of the parameters in a similarity metric to minimize the discrepancy between the assessed similarity values and the utility scores desired. A new structure for similarity metrics is introduced which enables the encoding of single feature impacts and more competent approximation of case utility. Preliminary experimental results have shown that the proposed approach can be used for learning with a surprisingly small case base without the risk of over-fitting and that it yields stable system performance with variations in the threshold selected for case retrieval.

  • 88.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    CBR supports decision analysis with uncertainty2009Inngår i: CASE-BASED REASONING RESEARCH AND DEVELOPMENT, PROCEEDINGS, Springer, 2009, s. 358-373Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    This paper proposes a novel approach to case-based decision analysis supported by case-based reasoning (CBR). The strength of CBR is utilized for building a situation dependent decision model without complete domain knowledge. This is achieved by deriving states probabilities and general utility estimates from the case library and the subset of cases retrieved in a situation described in query. In particular, the derivation of state probabilities is realized through an information fusion process which comprises evidence (case) combination using the Dempster-Shafer theory and Bayesian probabilistic reasoning. Subsequently decision theory is applied to the decision model learnt from previous cases to identify the most promising, secured, and rational choices. In such a way we take advantage of both the strength of CBR to learn without domain knowledge and the ability of decision theory to analyze under uncertainty. We have also studied the issue of imprecise representations of utility in individual cases and explained how fuzzy decision analysis can be conducted when case specific utilities are assigned with fuzzy data.

  • 89.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Combined feature selection and similarity modelling in case-based reasoning using hierarchical memeticalgorithm2010Inngår i: Proc. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010, s. article number: 5586421-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.

  • 90.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Concise case indexing of time series in health care by means of key sequence discovery2008Inngår i: Applied intelligence (Boston), ISSN 0924-669X, E-ISSN 1573-7497, Vol. 28, nr 3, s. 247-260Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Coping with time series cases is becoming an important issue in applications of case based reasoning in medical cares. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly valuable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Four alternative ways to develop case indexes based on key sequences are suggested and discussed in detail. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval. Preliminary experiment results have revealed that such case indexes utilizing key sequence information result in substantial performance improvement for the underlying case-based reasoning system.

  • 91.
    Xiong, Ning
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Construction of Fuzzy Knowledge Bases Incorporating Feature Selection2006Inngår i: Soft Computing, ISSN 1432-7643, Vol. 10, nr 9, s. 796-804Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Constructing concise fuzzy rule bases from databases containing many features present an important yet challenging goal in the current researches of fuzzy rule-based systems. Utilization of all available attributes is not realistic due to the "curse of dimensionality" with respect to the rule number as well as the overwhelming computational costs. This paper proposes a general framework to treat this issue, which is composed of feature selection as the first stage and fuzzy modeling as the second stage. Feature selection serves to identify significant attributes to be employed as inputs of the fuzzy system. The choice of key features for inclusion is equivalent to the problem of searching for hypotheses that can be numerically assessed by means of case-based reasoning. In fuzzy modeling, the genetic algorithm is applied to explore general premise structure and optimize fuzzy set membership functions at the same time. Finally, the merits of this work have been demonstrated by the experiment results on a real data set.

  • 92.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Integrating Case-Based Inference and Approximate Reasoning for Decision Making under Uncertainty2009Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a novel approach to decision analysis with uncertainty based on integrated case-based inference and approximate reasoning. The strength of case-based inference is utilized for building a situation dependent decision model without complete domain knowledge. This is achieved by deriving states probabilities and general utility estimates from the subset of retrieved cases and the case library given a situation in query. In particular, the derivation of state probabilities is realized through an approximate reasoning process which comprises evidence (case) combination using the Dempster-Shafer theory and Bayesian probabilistic computation. The decision model learnt from previous cases is further exploited using decision theory to identify the most promising, secured, and rational choices. We have also studied the issue of imprecise representations of utility in individual cases and explained how fuzzy decision analysis can be conducted when case specific utilities are assigned with fuzzy data.

  • 93.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Towards a probabilistic method for longitudinal monitoring in health care2016Inngår i: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, s. 30-35Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The advances in IoT and wearable sensors enable long term monitoring, which promotes earlier and more reliable diagnosis in health care. This position paper proposes a probabilistic method to address the challenges in handling longitudinal sensor signals that are subject to stochastic uncertainty in health monitoring. We first explain how a longitudinal signal can be transformed into a Markov model represented as a matrix of conditional probabilities. Further, discussions are made on how the derived models of signals can be utilized for anomaly detection and classification for medical diagnosis.

  • 94.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

  • 95.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based reasoning supports fault diagnosis using sensor information2013Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 16, nr 4, s. 25-30Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

  • 96.
    Zaslansky, R.
    et al.
    Friedrich-Schiller University Hospital, Jena, German.
    Rothaug, J.
    Friedrich-Schiller University Hospital, Jena, German.
    Chapman, R. C.
    University of Utah, Salt Lake City, UT, United States .
    Backström, R.
    University Hospital Örebro, Örebro, Sweden .
    Brill, S.
    Sourasky Medical Center, Tel-Aviv, Israel .
    Engel, C.
    University of Leipzig, Leipzig, Germany.
    Fletcher, D.
    Raymond Poincaré Hospital, Garches, France.
    Fodor, L.
    Cluj University Hospital, Cluj, Romania .
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gordon, D.
    Harborview Medical Center, Seattle, WA, United States.
    Komann, M.
    Friedrich-Schiller University Hospital, Jena, German.
    Konrad, C.
    Kantonsspital, Lucerne, Switzerland.
    Kopf, A.
    Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany .
    Leykin, Y.
    University of Trieste and Udine, Pordenone, Italy .
    Pogatzki-Zahn, E.
    University Hospital Muenster, Muenster, Germany.
    Puig, M.
    IMIM-Hospital Del Mar-Universitat Autònoma de Barcelona, Barcelona, Spain.
    Rawal, N.
    University Hospital Örebro, Örebro, Sweden.
    Schwenkglenks, M.
    University of Basel, Basel, Switzerland .
    Taylor, R. S.
    University of Exeter, Exeter, United Kingdom .
    Ullrich, K.
    University of London, London, United Kingdom.
    Volk, T.
    Saarland University Hospital, Homburg, Germany.
    Yahiaoui-Doktor, M.
    University of Leipzig, Leipzig, Germany .
    Meissner, W.
    Friedrich-Schiller University Hospital, Jena, German.
    PAIN OUT: An international acute pain registry supporting clinicians in decision making and in quality improvement activities2014Inngår i: Journal of Evaluation In Clinical Practice, ISSN 1356-1294, E-ISSN 1365-2753, Vol. 20, nr 6, s. 1090-1098Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Rationale, aims and objectives Management of post-operative pain is unsatisfactory worldwide. An estimated 240 million patients undergo surgery each year. Forty to 60% of these patients report clinically significant pain. Discrepancy exists between availability of evidence-based medicine (EBM)-derived knowledge about management of perioperative pain and increased implementation of related practices versus lack of improvement in patient-reported outcomes (PROs). We aimed to assist health care providers to optimize perioperative pain management by developing and validating a medical registry that measures variability in care, identifies best pain management practices and assists clinicians in decision making. Methods PAIN OUT was established from 2009 to 2012 with funding from the European Commission. It now continues as a self-sustaining, not-for-profit project, targeting health care professionals caring for patients undergoing surgery. Results The growing registry includes data from 40 898 patients, 60 hospitals and 17 countries. Collaborators upload data (demographics, clinical, PROs) from patients undergoing surgery in their hospital/ward into an Internet-based portal. Two modules make use of the data: (1) online, immediate feedback and benchmarking compares PROs across sites while offline analysis permits in-depth analysis; and (2) the case-based clinical decision support system offers practice-based treatment recommendations for individual patients; it is available now as a prototype. The Electronic Knowledge Library provides succinct summaries on perioperative pain management, supporting knowledge transfer and application of EBM. Conclusion PAIN OUT, a large, growing international registry, allows use of 'real-life' data related to management of perioperative pain. Ultimately, comparative analysis through audit, feedback and benchmarking will improve quality of care.

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