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  • 1.
    Beckinghausen, Aubrey
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ivan, Jean-Paul A.
    Örebro Univ, Sch Sci & Technol, S-70182 Örebro, Sweden..
    Schwede, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Analysis of Influencing Characteristics of Biochars for Ammonium Adsorption2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 19, article id 9487Article in journal (Refereed)
    Abstract [en]

    This article summarizes and performs a systematic analysis using experimental results from recent research on ammonium recovery from aqueous sources using biochar. Numerous studies have focused on using different materials to produce biochar adsorbents, and many have attempted to draw conclusions about the physical or chemical characteristics that dominate the adsorption to infer the mechanism. However, to date, there has not been statistical analysis performed on a large set of adsorption data and physical/chemical characteristics of chars to be able to draw conclusions about ammonium adsorption mechanisms. From this analysis, it was found that consistency in experimental methods and characteristic measurement reporting is lacking, and therefore it is difficult to perform metadata analysis and draw conclusions about ammonium adsorption on biochar. Among the important factors influencing ammonia recovery proposed in literature, the meta-analysis only strongly supports the effect of BET surface area and NH4+ concentration, with weaker support for the importance of cation exchange capacity and pyrolysis temperature. This suggests that standard procedures for biochar production, experiments and analysis of physical and chemical characteristics are needed to usefully compare results across different studies. Examples of the present difficulty in identifying trends across studies are shown by comparing clusters in the data identified by the analysis. The ability to make such comparisons would provide clearer direction in how best to further improve the adsorption capacity of biochars.

  • 2.
    Bhatti, A.
    et al.
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, 24090, Pakistan.
    Arif, A.
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, 24090, Pakistan.
    Khalid, W.
    Computer Engineering Department, Bahria University, Islamabad, 44000, Pakistan.
    Khan, B.
    Department of Electrical and Computer Engineering, International Islamic University, Islamabad, 04436, Pakistan.
    Ali, A.
    Department of Software Engineering, Bahria University, Islamabad, 44000, Pakistan.
    Khalid, S.
    Computer Engineering Department, Bahria University, Islamabad, 44000, Pakistan.
    Rehman, Atiq Ur
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 1624Article in journal (Refereed)
    Abstract [en]

    Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine (SVM) classifier. We compare it with GoogLeNet and the residual network (ResNet) in terms of performance. Our proposed CNN gives an accuracy of 98.41% with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, we achieve an accuracy of 95.61% and 96.4% on ResNet. Moreover, we develop datasets for handwritten Urdu numbers and numbers of Pakistani currency to incorporate real-life problems. Our models achieve best accuracies as compared to previous models in the literature for optical character recognition (OCR).

  • 3.
    Degas, A.
    et al.
    Ecole Nationale de l’aviation Civile, Toulouse, France.
    Islam, Mir Riyanul
    Mälardalen University, School of Innovation, Design and Engineering.
    Hurter, C.
    Ecole Nationale de l’aviation Civile, Toulouse, France.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Rahman, Hamidur
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Poudel, M.
    Ecole Nationale de l’aviation Civile, Toulouse, France.
    Ruscio, D.
    Deep Blue s.r.l, Rome, Italy.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Rahman, Md Aquif
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bonelli, S.
    Deep Blue Srl, Via Manin 53, I-00185 Rome, Italy.
    Cartocci, G.
    Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
    Di Flumeri, G.
    Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
    Borghini, G.
    Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
    Babiloni, F.
    Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
    Aricó, P.
    Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
    A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, article id 1295Article, review/survey (Refereed)
    Abstract [en]

    Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

  • 4.
    Gore, Rahul Nandkumar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lisova, Elena
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network2021In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 24, article id 11985Article in journal (Refereed)
    Abstract [en]

    Recent advances in the industrial internet of things (IIoT) and cyber–physical systems drive Industry 4.0 and have led to remote monitoring and control applications that require factories to be connected to remote sites over wide area networks (WAN). The adequate performance of remote applications depends on the use of a clock synchronization scheme. Packet delay variations adversely impact the clock synchronization performance. This impact is significant in WAN as it comprises wired and wireless segments belonging to public and private networks, and such heterogeneity results in inconsistent delays. Highly accurate, hardware–based time synchronization solutions, global positioning system (GPS), and precision time protocol (PTP) are not preferred in WAN due to cost, environmental effects, hardware failure modes, and reliability issues. As a software–based network time protocol (NTP) overcomes these challenges but lacks accuracy, the authors propose a software–based clock synchronization method, called CoSiWiNeT, based on the random sample consensus (RANSAC) algorithm that uses an iterative technique to estimate a correct offset from observed noisy data. To evaluate the algorithm’s performance, measurements captured in a WAN deployed within two cities were used in the simulation. The results show that the performance of the new algorithm matches well with NTP and state–of–the–art methods in good network conditions; however, it outperforms them in degrading network scenarios.

  • 5.
    Gore, Rahul Nandkumar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lisova, Elena
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Network Calculus Approach for Packet Delay Variation Analysis of Multi-Hop Wired Networks2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 21, article id 11207Article in journal (Refereed)
    Abstract [en]

    The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility of using the model parameters as a shaper of the background traffic. Thus, the analysis can be beneficial in providing QoS assurances for real-time applications.

  • 6.
    Islam, Mir Riyanul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, article id 1353Article, review/survey (Refereed)
    Abstract [en]

    Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.

  • 7.
    Khodadad, Davood
    et al.
    Department of Applied Physics and Electronics, Umeå Universitet, 90187 Umeå, Sweden.
    Tayebi, Behnam
    NYU Grossman School of Medicine, New York, NY 10016, USA.
    Saremi, Amin
    Department of Applied Physics and Electronics, Umeå Universitet, 90187 Umeå, Sweden.
    Paul, Satyam
    Gas Turbine and Transmissions Research Centre, University of Nottingham, Nottingham NG7 2RD, UK.
    Temperature Sensing in Space and Transparent Media: Advancements in Off-Axis Digital Holography and the Temperature Coefficient of Refractive Index2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 14, p. 8423-8423Article in journal (Refereed)
    Abstract [en]

    An off-axis digital holographic interferometry technique integrated with a Mach-Zehnder interferometer based setup is demonstrated for measuring the temperature and temperature profile of a transparent medium. This technique offers several advantages: it does not require precise optomechanical adjustments or accurate definition of the frequency carrier mask, making it simple and cost-effective. Additionally, high-quality optics are not necessary. The methodology relies on measuring the phase difference between two digitally reconstructed complex wave fields and utilizing the temperature coefficient of the refractive index. In this way, we presented an equation of the temperature as a function of phase changes and the temperature coefficient of refractive index. This approach simplifies the calculation process and avoids the burden of complicated mathematical inversions, such as the inverse Abel transformation. It also eliminates the need for additional work with the Lorentz-Lorentz equation and Gladstone-Dale relation and can be extend for 3D measurements.

  • 8.
    Lavassani, Mehrzad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Division of Industrial Systems, RISE-Research Institutes of Sweden, Sweden.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    From brown-field to future industrial networks, a case study2021In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 7, article id 3231Article in journal (Refereed)
    Abstract [en]

    The network infrastructures in the future industrial networks need to accommodate, manage and guarantee performance to meet the converged Internet technology (IT) and operational technology (OT) traffics requirements. The pace of IT-OT networks development has been slow despite their considered benefits in optimizing the performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for green-field configuration to lack of outlines for evolving from brown-fields to the converged network. Focusing on the brown-field, this study provides additional insight into a brown-field characteristic to set a baseline that enables the subsequent step development towards the future’s expected converged networks. The case study highlights differences between real-world network behavior and the common assumptions for analyzing the network traffic covered in the literature. Considering the unsatisfactory performance of the existing methods for characterization of brownfield traffic, a performance and dynamics mixture measurement is proposed. The proposed method takes both IT and OT traffic into consideration and reduces the complexity, and consequently improves the flexibility, of performance and configuration management of the brown-field.

  • 9.
    Lavassani, Mehrzad
    et al.
    Division of Industrial Systems, RISE—Research Institutes of Sweden, Sundsvall, Sweden.
    Åkerberg, Johan
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Modeling and Profiling of Aggregated Industrial Network Traffic2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 2, article id 667Article in journal (Refereed)
    Abstract [en]

    The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new tech-nologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles. 

  • 10.
    Mahmud, Mahmudul Hoque
    et al.
    Amer Int Univ Bangladesh, Dept Comp Sci, 408-1 Kuratoli, Dhaka 1229, Bangladesh..
    Nayan, Md Tanzirul Haque
    Amer Int Univ Bangladesh, Dept Comp Sci, 408-1 Kuratoli, Dhaka 1229, Bangladesh..
    Ashir, Dewan Md Nur Anjum
    Amer Int Univ Bangladesh, Dept Comp Sci, 408-1 Kuratoli, Dhaka 1229, Bangladesh..
    Kabir, Md Alamgir
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 22, article id 11694Article, review/survey (Refereed)
    Abstract [en]

    The Software Development Life Cycle (SDLC) includes the phases used to develop software. During the phases of the SDLC, unexpected risks might arise due to a lack of knowledge, control, and time. The consequences are severe if the risks are not addressed in the early phases of SDLC. This study aims to conduct a Systematic Literature Review (SLR) and acquire concise knowledge of Software Risk Prediction (SRP) from the published scientific articles from the year 2007 to 2022. Furthermore, we conducted a qualitative analysis of published articles on SRP. Some of the key findings include: (1) 16 articles are examined in this SLR to represent the outline of SRP; (2) Machine Learning (ML)-based detection models were extremely efficient and significant in terms of performance; (3) Very few research got excellent scores from quality analysis. As part of this SLR, we summarized and consolidated previously published SRP studies to discover the practices from prior research. This SLR will pave the way for further research in SRP and guide both researchers and practitioners.

  • 11.
    Mehboob, Fozia
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Fattouh, Anas
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Sahoo, Smruti
    Alstom, S-72136 Vasteras, Sweden..
    Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors2024In: Applied Sciences, E-ISSN 2076-3417, Vol. 14, no 11, article id 4455Article in journal (Refereed)
    Abstract [en]

    Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor's thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

  • 12.
    Mubeen, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lisova, Elena
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Feljan, Aneta Vulgarakis
    Ericsson Res, Kista, Sweden..
    Timing Predictability and Security in Safety-Critical Industrial Cyber-Physical Systems: A Position Paper2020In: Applied Sciences, E-ISSN 2076-3417, Vol. 10, no 9, article id 3125Article in journal (Refereed)
    Abstract [en]

    Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems.

  • 13.
    Rehman, Atiq Ur
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Department of Electrical and Computer Engineering, Pak Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur, 22621, Pakistan.
    Belhaouari, Samir Brahim
    Hamad Bin Khalifa University, Qatar.
    Kabir, Md Alamgir
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Khan, Adnan
    Hamad Bin Khalifa University, Qatar.
    On the Use of Deep Learning for Video Classification2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 2007Article in journal (Refereed)
    Abstract [en]

    The video classification task has gained significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition of the importance of the video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are several existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers do not include the recent state-of-art works, and they also have some limitations. To provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the datasets used. To make the review self-contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided. The critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.

  • 14.
    Sheuly, Sharmin Sultana
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 13, article id 6512Article in journal (Refereed)
    Abstract [en]

    The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

  • 15.
    Tilwari, Valmik
    et al.
    Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur, Malaysia..
    Dimyati, Kaharudin
    Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur, Malaysia..
    Hindia, M. H. D. Nour
    Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia..
    Fattouh, Anas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Amiri, Iraj Sadegh
    on Duc Thang Univ, Ho Chi Minh City, Vietnam.
    Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm2019In: Applied Sciences, E-ISSN 2076-3417, Vol. 9, no 8, article id 1582Article in journal (Refereed)
    Abstract [en]

    To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

  • 16.
    Xin, Tao
    et al.
    School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; Beijing Key Laboratory of Track Engineering, Beijing Jiaotong University, Beijing 100044, China.
    Yang, Yi
    School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.
    Zheng, Xiaoli
    Beijing Municipal Institute of City Planning and Design, Beijing 100045, China.
    Lin, Jing
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, Sweden.
    Wang, Sen
    School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; Frontiers Science Center for Smart High-Speed Railway System, Beijing 100044, China.
    Wang, Pengsong
    School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Railway Traffic Safety, Beijing 100044, China.
    Time Series Recovery Using Adjacent Channel Data Based on LSTM: A Case Study of Subway Vibrations2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 22, p. 11497-11497Article in journal (Refereed)
    Abstract [en]

    Multi-sensor technology has been widely applied in the condition monitoring of rail transit. In practice, the data of some channels in the high channel counts are often abnormal or lost due to the abnormality and damage of the sensors, thus resulting in a large amount of data waste. A method for the data recovery of lost channels by using adjacent channel data is proposed to solve this problem. Based on the LSTM network algorithm, a data recovery model is established based on the “sequence-to-sequence” regression analysis of adjacent channel data. Taking the measured vibration data of a subway as an example, the network is trained with multi-channel measured data to recover the lost channel data of time-series characteristics. The results show that this multi-channel data recovery model is feasible, and the accuracy is up to 98%. This method can also further reduce the number of channels that need to be collected.

  • 17.
    Åkerberg, Johan
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkesson, J.F.
    Westermo Network Technologies AB, Västerås, Sweden.
    Gade, J.
    ABB AB Corporate Research, Västerås, Sweden.
    Vahabi, Maryam
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. ABB AB Corporate Research, Västerås, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lavassani, Mehrzad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Division of Industrial Systems, RISE-Research Institutes of Sweden, Sundsvall, Sweden.
    Gore, Rahul Nandkumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindh, T.
    Iggesund Paperboard, Iggesund, Sweden.
    Jiang, X.
    ABB AB Corporate Research, Västerås, Sweden.
    Future industrial networks in process automation: Goals, challenges, and future directions2021In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 8, article id 3345Article in journal (Refereed)
    Abstract [en]

    There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented.

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