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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 178-189页

作者： Fayaz, Muhammad Khan, Junaid Bilal, Muhammad Univ Cent Asia Sch Arts & Sci Dept Comp Sci Naryn 722900 Kyrgyzstan Chungnam Natl Univ Dept Environm & IT Engn Daejeon 34134 South Korea Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England

The reduction of energy usage and the enhancement of comfort indices within electric vehicles stand as key areas of focus for modern research. Numerous approaches have been devised to address these concerns, yet each of these models exhibit certain constraints;some effectively reduce energy usage yet do so at the cost of comfort, whereas others succeed in elevating comfort at the price of increased energy consumption. Our research introduces a comprehensive model that leverages optimization, machine learning, data smoothing, and control protocols to defeat these challenges. The purpose of the proposed model is two folds: firstly, to reduce energy use, and secondly, to enhance the comfort of the user. Our proposed model comprises three main modules: the smoothing, the optimization, and control modules. In the smoothing module, the alpha-beta filter, the simplest and most effective filter, has been used to remove noise and smooth the data. The optimization module is segmented into two additional modules: the FA-GA module and the support vector machine module. The purpose of the support vector machine in the optimization module is to make the system fully automatic and dynamic by setting user parameters in the objective function of the FA-GA module. Within the control module, Mamdani fuzzy logic is employed to deliver the appropriate energy to the respective actuators. The proposed technique is evaluated against several proven methods, with results suggesting that our approach achieves commendable results in comparison to similar algorithms.

关键词： Optimization Energy consumption Smoothing methods Genetic algorithms machine learning algorithms Electric vehicles Petroleum Energy user comfort optimization electric vehicles control algorithm machine learning

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Design and Fault Diagnosis of Induction Motor Using ML-Based algorithms for EV Application

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IEEE ACCESS 2023年 11卷 34186-34197页

作者： Aishwarya, M. Brisilla, R. M. Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 India

The need for alternate transportation is driven by the increased fossil fuel cost and the adverse effects of climatic change. Electric vehicles (EVs) are the best option as they have less carbon footprint and reduced dependency on fossil fuels. Prodigious efforts to enhance the efficiency of EVs resulted in the development of highly efficient three-phase induction motors. Difficulties in designing highly efficient induction motors (IM) with high torque and power factors hindered the success of EV applications. Hence, our aim is to diagnosis fault in the designed IM under variable load conditions. The proposed EV motor is designed for 415V, 50Hz, and 5HP output power rating using ANSYS RMxprt simulation software. A fault detection strategy is also implemented with various machine learning (ML) techniques like Support Vector machine (SVM), K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), Gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and Deep learning (DL) for both healthy and faulty conditions. Short Circuit (SC), High Resistance connection (HRC), and Open-Phase circuit (OPC) are considered as faulty states for this study. Motor performance with variable load for all the states healthy and faulty are evaluated through machine learning.

关键词： Induction motor electric vehicle motor design material ANSYS machine learning algorithms

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Resetting Weight Vectors in MOEA/D for Multiobjective Optimization Problems With Discontinuous Pareto Front

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9770-9783页

作者： Zhang, Chunjiang Gao, Liang Li, Xinyu Shen, Weiming Zhou, Jiajun Tan, Kay Chen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

关键词： Optimization Principal component analysis Evolutionary computation Clustering algorithms Heuristic algorithms machine learning algorithms machine learning DBSCAN multiobjective evolutionary algorithm based on decomposition (MOEA D) multiobjective evolutionary algorithm (MOEA) principal component analysis (PCA) weight vectors

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Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

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IEEE ACCESS 2020年 8卷 225692-225702页

作者： Ma, Chao Mohammadzadeh, Ardashir Turabieh, Hamza Mafarja, Majdi Band, Shahab S. Mosavi, Amir Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Univ Bonab Dept Elect Engn Fac Engn Bonab *** Iran Taif Univ Dept Informat Technol Coll Comp & Informat Technol At Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary Norwegian Univ Life Sci Sch Econ & Business N-1430 As Norway J Selye Univ Dept Informat Komarno 94501 Slovakia Oxford Brookes Univ Sch Built Environm Oxford OX3 0BP England

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.

关键词： Artificial neural networks Differential equations Convergence Licenses machine learning algorithms Information technology Statistical analysis machine learning artificial intelligence fuzzy systems Lyapunov stability learning algorithm multi-pantograph differential equations

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Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第11期45卷 13235-13249页

作者： Cui, Sen Pan, Weishen Zhang, Changshui Wang, Fei Tsinghua Univ Dept Automat BNRist Beijing 100190 Peoples R China Cornell Univ Dept Populat Hlth Sci Weill Cornell Med Ithaca NY 14850 USA

Recently, with the applications of algorithms in various risky scenarios, algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this article, we focus on the bipartite ranking scenario, where the instances come from either the positive or negative class and the goal is to learn a ranking function that ranks positive instances higher than negative ones. We are interested in whether the learned ranking function can cause systematic disparity across different protected groups defined by sensitive attributes. While there could be a trade-off between fairness and performance, we propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking and maintaining the algorithm classification performance. In particular, we optimize a weighted sum of the utility as identifying an optimal warping path across different protected groups and solve it through a dynamic programming process. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. In addition to binary groups, xOrder can be applied to multiple protected groups. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories. xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics. From the visualization of the calibrated ranking scores, xOrder mitigates the score distribution shifts of different groups compared with baselines. Moreover, additional analytical results verify that xOrder achieves a robust performance when faced with fewer samples and a bigger difference between training and testing ranking score distributions.

关键词： Measurement Heuristic algorithms Training Prediction algorithms machine learning algorithms Calibration Source coding Fairness bipartite ranking model-agnostic post-processing

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Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

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IEEE SENSORS JOURNAL 2021年第16期21卷 18214-18221页

作者： Liaqat, Sidrah Dashtipour, Kia Shah, Syed Aziz Rizwan, Ali Alotaibi, Abdullah Alhumaidi Althobaiti, Turke Arshad, Kamran Assaleh, Khaled Ramzan, Naeem Univ West Scotland Sch Engn & Comp Paisley PA1 2BE Renfrew Scotland Edinburgh Napier Univ Sch Comp Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Coventry Univ Res Ctr Intelligent Healthcare Coventry CV1 5FB W Midlands England Qatar Mobil Innovat Ctr Qatar Sci & Technol Pk Doha Qatar Taif Univ Coll Ranyah Dept Sci & Technol At Taif 21944 Saudi Arabia Northern Border Univ Fac Sci Ar Ar 91431 Saudi Arabia Ajman Univ Coll Engn & IT Ajman U Arab Emirates

Ambient assisted living is good way to look after ageing population that enables us to detect human's activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms.

关键词： Sensors Legged locomotion Feature extraction machine learning algorithms machine learning Support vector machines Senior citizens Posture detection ensemble algorithm deep learning machine learning ubiquitous devices

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Optimization Attribute Reduction With Fuzzy Rough Sets Based on Algorithm Stability

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 2052-2062页

作者： Gao, Yue Chen, Degang Wang, Hui Shi, Ruifeng North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Math & Phys Beijing 102206 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5BN North Ireland

Fuzzy rough sets (FRSs) theory is an important granular computing method to deal with incomplete information systems, and the attribute reduction is a basic key issue in FRSs. In this article, we construct a novel framework for selecting the optimal reduct of FRSs with theoretical guarantees by considering the influence of granule size and incorporating the stability theory in machine learning. First, a granule-based soft-margin support vector machine (GSSVM) algorithm is proposed for classification tasks by introducing the $\lambda$-conditional entropy into the hinge loss function, which takes the impact of granule size on data loss into account. Then, according to stability theory, the generalization error bound of the GSSVM algorithm is derived as a theoretical guarantee for selecting the optimal reduct. Finally, an optimization attribute reduction algorithm (RDROAR) based on the relative discernibility relation is presented by removing the attributes with low importance in a reduct while ensuring the generalization ability of GSSVM. Numerical experiments prove the effectiveness of the improved algorithm as well as verify the rationality and effectiveness of the optimal reduct.

关键词： Stability criteria Uncertainty Rough sets Optimization Numerical stability Measurement uncertainty machine learning algorithms Attribute reduction fuzzy rough sets granule relative discernibility relation stability theory

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Improved characterization of multi-stage voltage dips based on the space phasor model

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 154卷 319-328页

作者： Bagheri, Azam Bollen, Math H. J. Gu, Irene Y. H. Lulea Univ Technol S-93187 Skelleftea Sweden Chalmers Univ Technol S-41296 Gothenburg Sweden

This paper proposes a method for characterizing voltage dips based on the space phasor model of the three phase-to-neutral voltages, instead of the individual voltages. This has several advantages. Using a K-means clustering algorithm, a multi-stage dip is separated into its individual event segments directly instead of first detecting the transition segments. The logistic regression algorithm fits the best single segment characteristics to every individual segment, instead of extreme values being used for this, as in earlier methods. The method is validated by applying it to synthetic and measured dips. It, can be generalized for application to both single- and multi-stage dips. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Electric power distribution Electric power transmission Power quality Voltage dips machine learning algorithms Clustering algorithms Logistic regression

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A Feasibility Study on Evasion Attacks Against NLP-Based Macro Malware Detection algorithms

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IEEE ACCESS 2023年 11卷 138336-138346页

作者： Mimura, Mamoru Yamamoto, Risa Natl Def Acad Japan Yokosuka Kanagawa 2398686 Japan Japan Ground Self Def Force Shinju Ku Tokyo 1628801 Japan

machine learning-based models for malware detection have gained prominence in order to detect obfuscated malware. These models extract malicious features and endeavor to classify samples as either malware or benign entities. Conversely, these benign features can be employed to imitate benign samples. With respect to Android applications, numerous researchers have assessed the hazard and tackled the problem. This evasive technique can be extended to other malicious scripts, such as macro malware. In this paper, we investigate the potential for evasive attacks against natural language processing (NLP)-based macro malware detection algorithms. We assess three language models as methods for feature extraction: Bag of Words, Latent Semantic Analysis, and Paragraph Vector. Our experimental result demonstrates that the detection rate declines to 2 percent when benign features are inserted into actual macro malware. This approach is effective even against advanced language models.

关键词： Feature extraction Malware Classification algorithms Source coding machine learning algorithms Semantics Natural language processing Macro malware machine learning evasion attack LSA paragraph vector

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Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第1期63卷 19-31页

作者： Perez, Noel Perez Guevara Lopez, Miguel A. Silva, Augusto Ramos, Isabel Inst Mech Engn & Ind Management INEGI P-4200465 Porto Portugal Campus Univ Santiago Inst Elect & Telemat Engn Aveiro P-3810193 Aveiro Portugal Ctr Hosp Sao Joao Fac Med P-4200319 Porto Portugal

Objective: This work addresses the theoretical description and experimental evaluation of a new feature selection method (named uFilter). The uFilter improves the Mann-Whitney U-test for reducing dimensionality and ranking features in binary classification problems. Also, it presented a practical uFilter application on breast cancer computer-aided diagnosis (CADx). Materials and methods: A total of 720 datasets (ranked subsets of features) were formed by the application of the chi-square (CHI2) discretization, information-gain (IG), one-rule (1Rule), Relief, uFilter and its theoretical basis method (named U-test). Each produced dataset was used for training feed-forward backpropagation neural network, support vector machine, linear discriminant analysis and naive Bayes machine learning algorithms to produce classification scores for further statistical comparisons. Results: A head-to-head comparison based on the mean of area under receiver operating characteristics curve scores against the U-test method showed that the uFilter method significantly outperformed the U-test method for almost all classification schemes (p < 0.05);it was superior in 50%;tied in a 37.5% and lost in a 12.5% of the 24 comparative scenarios. Also, the performance of the uFilter method, when compared with CHI2 discretization, IC, 1Rule and Relief methods, was superior or at least statistically similar on the explored datasets while requiring less number of features. Conclusions: The experimental results indicated that uFilter method statistically outperformed the U-test method and it demonstrated similar, but not superior, performance than traditional feature selection methods (CHI2 discretization, IG, 1Rule and Relief). The uFilter method revealed competitive and appealing cost-effectiveness results on selecting relevant features, as a support tool for breast cancer CADx methods especially in unbalanced datasets contexts. Finally, the redundancy analysis as a complementary step to the uFilte

关键词： Feature selection methods Mann-Whitney U-test uFilter method machine learning algorithms Redundancy analysis Breast cancer CADx

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