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Tidare, J., Leon, M., Xiong, N. & Åstrand, E. (2019). Discriminating EEG spectral power related to mental imagery of closing and opening of hand. In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER): . Paper presented at 9th IEEE/EMBS International Conference on Neural Engineering (NER), MAR 20-23, 2019, San Francisco, CA (pp. 307-310). IEEE
Open this publication in new window or tab >>Discriminating EEG spectral power related to mental imagery of closing and opening of hand
2019 (English)In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), IEEE , 2019, p. 307-310Conference paper, Published paper (Refereed)
Abstract [en]

ElectroEncephaloGram (EEG) spectral power has been extensively used to classify Mental Imagery (MI) of movements involving different body parts. However, there is an increasing need to enable classification of MI of movements within the same limb. In this work, EEG spectral power was recorded in seven subjects while they performed MI of closing (grip) and opening (extension of fingers) the hand. The EEG data was analyzed and the feasibility of classifying MI of the two movements were investigated using two different classification algorithms, a linear regression and a Convolutional Neural Network (CNN). Results show that only the CNN is able to significantly classify MI of opening and closing of the hand with an average classification accuracy of 60.4%. This indicates the presence of higher-order non-linear discriminatory information and demonstrates the potential of using CNN in classifying MI of same-limb movements.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International IEEE EMBS Conference on Neural Engineering, ISSN 1948-3546
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-44330 (URN)000469933200077 ()2-s2.0-85066765799 (Scopus ID)978-1-5386-7921-0 (ISBN)
Conference
9th IEEE/EMBS International Conference on Neural Engineering (NER), MAR 20-23, 2019, San Francisco, CA
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-06-20Bibliographically approved
Zhou, Y., Li, S. & Xiong, N. (2019). Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning. Industrial & Engineering Chemistry Research, 58(9), 3782-3796
Open this publication in new window or tab >>Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning
2019 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 58, no 9, p. 3782-3796Article in journal (Refereed) Published
Abstract [en]

This paper proposes a boosting vine copula-based dependence description (BVCDD) method for multivariate and multimode process monitoring. The BVCDD aims to improve the standard vine copula-based dependence description (VCDD) method by establishing an ensemble of submodels from sample directions based on a boosting strategy. The generalized Bayesian inference-based probability (GBIP) index is introduced to assess the degrees of a VCDD model (submodel) to depict different samples, which means how likely an observation is under the probabilistic model for the system. Every sample is weighted individually according to the depiction degree. The weights are then used to choose a certain number of samples for each succeeding submodel. In this way, the samples with large error in the preceding model can be selected for training the next submodel. Moreover, the number of submodels as well as the number of training samples chosen for every submodel are determined adaptively in the ensemble learning process. The proposed BVCDD method can not only solve weak sample problems but also remove redundant information in samples. To examine the performance, empirical evaluations have been conducted to compare the BVCDD method with some other state-of-the-art methods in a numerical example, the Tennessee Eastman (TE) process, and an acetic acid dehydration process. The results show that the developed BVCDD models are superior to those obtained by the counterparts on weak samples in both accuracy and stability. 

Place, publisher, year, edition, pages
American Chemical Society, 2019
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-42943 (URN)10.1021/acs.iecr.8b04081 (DOI)000460996700022 ()2-s2.0-85062615381 (Scopus ID)
Available from: 2019-03-22 Created: 2019-03-22 Last updated: 2019-03-29Bibliographically approved
Leon, M. & Xiong, N. (2018). Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation. In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC): . Paper presented at IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) (pp. 103-109). IEEE
Open this publication in new window or tab >>Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 103-109Conference paper, Published paper (Refereed)
Abstract [en]

Differential evolution has many mutation strategies which are problem dependent. Some Adaptive Differential Evolution techniques have been proposed tackling this problem. But therein all individuals are treated equally without taking into account how good these solutions are. In this paper, a new method called Ranked-based Mutation Adaptation (RAM) is proposed, which takes into consideration the ranking of an individual in the whole population. This method will assign different probabilities of choosing different mutation strategies to different groups in which the population is divided. RAM has been integrated into several well-known adaptive differential evolution algorithms and its performance has been tested on the benchmark suit proposed in CEC2014. The experimental results shows the use of RAM can produce generally better quality solutions than the original adaptive algorithms.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Evolutionary Algorithm, Differential Evolution, Mutation strategy, Adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-41825 (URN)000451175500015 ()2-s2.0-85056286107 (Scopus ID)
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2019-01-04Bibliographically approved
Ramos, J., Leon, M. & Xiong, N. (2018). MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE. In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC): . Paper presented at IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) (pp. 1139-1146). IEEE
Open this publication in new window or tab >>MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 1139-1146Conference paper, Published paper (Refereed)
Abstract [en]

JADE is an state-of-the-art adaptive differential evolution algorithm which implements "DE/current-to-pbest" as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Differential evolution, multi-population, coarse-grained parallel, population topology, adaptive parameter, global optimization, evolutionary optimization
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-41826 (URN)000451175500146 ()2-s2.0-85056280928 (Scopus ID)
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2019-01-04Bibliographically approved
Leon, M. & Xiong, N. (2017). Alopex-based mutation strategy in Differential Evolution. In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings: . Paper presented at 2017 IEEE Congress on Evolutionary Computation, CEC 2017; Donostia-San Sebastian; Spain; 5 June 2017 through 8 June 2017; Category numberCFP17ICE-ART; Code 129053 (pp. 1978-1984). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Alopex-based mutation strategy in Differential Evolution
2017 (English)In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 1978-1984Conference paper, Published paper (Refereed)
Abstract [en]

Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
Alopex, Differential Evolution, Evolutionary Algorithm, Mutation strategy
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-36297 (URN)10.1109/CEC.2017.7969543 (DOI)000426929700256 ()2-s2.0-85027852098 (Scopus ID)9781509046010 (ISBN)
Conference
2017 IEEE Congress on Evolutionary Computation, CEC 2017; Donostia-San Sebastian; Spain; 5 June 2017 through 8 June 2017; Category numberCFP17ICE-ART; Code 129053
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2018-03-29Bibliographically approved
He, F. & Xiong, N. (2017). Big Data Stream Learning Based on Hybridized Kalman Filter and Backpropagation Through Time Method. In: Liu, Y Zhao, L Cai, G Xiao, G Li, KL Wang, L (Ed.), 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD): . Paper presented at 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, PEOPLES R CHINA (pp. 2886-2891). IEEE
Open this publication in new window or tab >>Big Data Stream Learning Based on Hybridized Kalman Filter and Backpropagation Through Time Method
2017 (English)In: 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) / [ed] Liu, Y Zhao, L Cai, G Xiao, G Li, KL Wang, L, IEEE , 2017, p. 2886-2891Conference paper, Published paper (Refereed)
Abstract [en]

Most real-time control systems are often accompanied with various changes such as variations of working load and changes of the environment. Hence it is necessary to perform real-time process modeling so that the model can adjust itself in runtime to maintain high accuracy of states under control. This paper considers process model represented as a deep recurrent neural network. We propose a new hybridized learning method for online updating the weights of such recurrent neural networks by exploiting both fast convergence of Kalman filter and stable search of the Backpropagation through time algorithm. Several experiments were made to show that the proposed learning method has fast convergence, high accuracy and good adaptivity. It can not only achieve high modeling accuracy for a static process but also quickly adapt to changes of characteristics in a time -varying process.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
control system, real-time process modeling, deep learning, deep recurrent neural network, Backpropagation through time, Kalman filter
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-40289 (URN)10.1109/FSKD.2017.8393239 (DOI)000437355302144 ()2-s2.0-85050237781 (Scopus ID)978-1-5386-2165-3 (ISBN)
Conference
13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, PEOPLES R CHINA
Available from: 2018-07-26 Created: 2018-07-26 Last updated: 2019-06-25Bibliographically approved
Zhang, X., Lindberg, T., Xiong, N., Vyatkin, V. & Mousavi, A. (2017). Cooling Energy Consumption Investigation of Data Center IT Room with Vertical Placed Server. Paper presented at 8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016. Energy Procedia, 105, 2047-2052
Open this publication in new window or tab >>Cooling Energy Consumption Investigation of Data Center IT Room with Vertical Placed Server
Show others...
2017 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 2047-2052Article in journal (Refereed) Published
Abstract [en]

As energy consumption by cooling data center IT equipment can be over 40% of total energy consumption, efficient cooling for large data centers is essential for reducing operation costs. Modern data centers are complex systems involving IT facilities, power system, cooling and ventilation systems. In our previous work, literature study was made to investigate available data center energy consumption models; and energy consumption models for data center IT room with distributed air flow control were developed. In this paper, the models are further extended and developed to cover the combined distributed air flow control and vertical placed servers in raised floor ventilation system. Simulation of the three types of ventilation systems with Even load, Idle server and Uneven load scenarios showed that significant cooling energy consumed by a traditional ventilation system can be saved by applying the proposed new concept and method. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2017
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-35997 (URN)10.1016/j.egypro.2017.03.581 (DOI)000404967902024 ()2-s2.0-85020733801 (Scopus ID)
Conference
8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2018-07-25Bibliographically approved
Mustafic, F., Herera, F., Xiong, N. & Gallego, S. (2017). MapReduce distributed highly random fuzzy forest for noisy big data. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017: . Paper presented at 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 29 Jul 2017, Guilin, China (pp. 560-567).
Open this publication in new window or tab >>MapReduce distributed highly random fuzzy forest for noisy big data
2017 (English)In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 2017, p. 560-567Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays the amounts of data available to us have the ever larger growth trend. On the other hand such data often contain noise. We call them noisy Big Data. There is an increasing need for learning methods that can handle such noisy Big Data for classification tasks. In this paper we propose a highly random fuzzy forest algorithm for learning an ensemble of fuzzy decision trees from a big data set contaminated with attribute noise. We also present the distributed version of the proposed learning algorithm implemented in the MapReduce framework. Experiment results have demonstrated that the proposed algorithm is faster and more accurate than the state-of-the-art approach particularly in the presence of attribute noise. 

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37074 (URN)10.1109/FSKD.2017.8393331 (DOI)000437355300089 ()2-s2.0-85050191333 (Scopus ID)978-1-5386-2165-3 (ISBN)
Conference
13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 29 Jul 2017, Guilin, China
Projects
ADAPTER: Adaptive Learning and Information Fusion for Online Classification Based on Evolving Big Data Streams
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2019-06-25Bibliographically approved
Leon, M. & Xiong, N. (2016). A new differential evolution algorithm with Alopex-based local search. In: Lecture Notes in Computer Science, Volume 9692: . Paper presented at 15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016; Zakopane; Poland; 12 June 2016 through 16 June 2016 (pp. 420-431).
Open this publication in new window or tab >>A new differential evolution algorithm with Alopex-based local search
2016 (English)In: Lecture Notes in Computer Science, Volume 9692, 2016, p. 420-431Conference paper, Published paper (Refereed)
Abstract [en]

Differential evolution (DE), as a class of biologically inspired and meta-heuristic techniques, has attained increasing popularity in solving many real world optimization problems. However, DE is not always successful. It can easily get stuck in a local optimum or an undesired stagnation condition. This paper proposes a new DE algorithm Differential Evolution with Alopex-Based Local Search (DEALS), for enhancing DE performance. Alopex uses local correlations between changes in individual parameters and changes in function values to estimate the gradient of the landscape. It also contains the idea of simulated annealing that uses temperature to control the probability of move directions during the search process. The results from experiments demonstrate that the use of Alopex as local search in DE brings substantial performance improvement over the standard DE algorithm. The proposed DEALS algorithm has also been shown to be strongly competitive (best rank) against several other DE variants with local search. 

Keywords
Alopex, Differential evolution, Local search, Memetic algorithm, Optimization, Algorithms, Artificial intelligence, Evolutionary algorithms, Heuristic methods, Local search (optimization), Simulated annealing, Soft computing, Biologically inspired, Differential evolution algorithms, Memetic algorithms, Meta-heuristic techniques, Real-world optimization
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-32383 (URN)10.1007/978-3-319-39378-0_37 (DOI)000389514800037 ()2-s2.0-84976613386 (Scopus ID)978-3-319-39377-3 (ISBN)
Conference
15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016; Zakopane; Poland; 12 June 2016 through 16 June 2016
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2018-01-10Bibliographically approved
Leon, M. & Xiong, N. (2016). Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters. Journal of Artificial Intelligence and Soft Computing Research, 6(2), 103-118
Open this publication in new window or tab >>Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
2016 (English)In: Journal of Artificial Intelligence and Soft Computing Research, ISSN 2449-6499, Vol. 6, no 2, p. 103-118Article in journal (Refereed) Published
Abstract [en]

Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

Keywords
Differential evolution, Optimization, Parameter adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-34771 (URN)10.1515/jaiscr-2016-0009 (DOI)000408865800004 ()2-s2.0-85009725987 (Scopus ID)
Available from: 2017-02-08 Created: 2017-02-02 Last updated: 2018-07-26Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-9857-4317

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