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29th International Scientific Conference Electronics, ET 2020

作者： Marinova, Ginka Todorova, Maya Technical University of Varna Faculty of Faculty of Computer Sciences and Automation Department of Computer Science and Engineering Department of Software and Internet Technologies 1 Studentska str Varna9010 Bulgaria

ISBN: (纸本)9781728174266

The subject of analysis and research is data in the field of medical oncology through machine learning methods. The types of classification in machine learning are presented, and three methods are described-K-Nearest Neighbors, Naive Bayes Classifier, Support Vector Machines. Two classification tasks in the field of medicine have been formulated, implemented, and researched. Classifiers are created and trained with the methods KNN, SVM, and NBC. The solved classification tasks aim to support patients and clinical psycho-oncologists. © 2020 IEEE.

关键词： Support vector machines

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A loading path planning method for co-loading in automatic harvesting system

A loading path planning method for co-loading in automatic h...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Xiaoxiong Wang Zhuo Wang Xiaoping Bai Zhikang Ge Jian Wang Zhihong Man Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

In this paper, a path planning method is developed for co-loading in master-slave automatic harvesting. It is shown that, the adjustment of the unloading port position relative to the grain tank can be performed jointly by the longitudinal relative position between the vehicles and the angle of the unloading arm. Then the loading path based on the Hamiltonian circuit is designed to guide the motion of the unloading port during the loading process. By improving the loading uniformity, the loading path proposed can maximize the capacity utilization of grain tank which can reduce the round-trip frequency of the transport vehicle and improve the energy and time efficiency of the harvesting operation. Simulation results are provided to validate the excellent performance of presented path planning method.

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

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Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

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A Robust Adaptive Terminal Sliding Mode Control for Lane-keeping in Road Vehicles

A Robust Adaptive Terminal Sliding Mode Control for Lane-kee...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Zhikang Ge Zhuo Wang Xiaoping Bai Xiaoxiong Wang Zhihong Man Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

In this paper, a novel adaptive terminal sliding-mode control (ATSMC) method is developed for road vehicles with uncertain dynamics. It is shown that the designed adaptive laws can recursively update the controller parameters based on the estimation values of the unknown parameter bound in the Lyapunov sense, and then the lateral position error can be driven to zero in a finite time. In comparison to the conventional sliding-mode-based vehicle lateral control method, the proposed ATSMC not only ensures fast error convergence, but also eliminates the requirement for known bounded information in controller design. Simulation results are presented to demonstrate the good performance of the proposed ATSMC scheme.

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Machine learning in materials genome initiative:A review

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Journal of Materials science & Technology 2020年第22期54卷 113-122页

作者： Yingli Liu Chen Niu Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and TechnologyKunming650500China Light Alloy Research Institute Central South UniversityChangsha410083China Chinese Academy of Engineering Beijing100088China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China Chengdu MatAi Technology Co. LtdChengdu610041China

Discovering new materials with excellent performance is a hot issue in the materials genome *** experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

关键词： Materials genome initiative(MGI) Materials database Machine learning Materials properties prediction Materials design and discovery

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Choosing a Suitable Requirement Prioritization Method: A Survey

arXiv

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arXiv 2024年

作者： Alhenawi, Esraa Awawdeh, Shatha Khurma, Ruba Abu García-Arenas, Maribel Castillo, Pedro A. Hudaib, Amjad Faculty of Information Technology Zarqa University Zarqa Jordan Applied Science Private University Amman Jordan MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain King Abdullah II School for Information Technology The University of Jordan Amman Jordan

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and within budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk, and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey, we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class, where each class is divided into two subclasses. We depend in our classification on the way the value of ranking is given to the requirement, either explicitly as a specific value in the case of the exact prioritization techniques class, or implicitly in the case of the Relative prioritization technique class. An overview of fifteen different requirements prioritization techniques are presented and organized according to the proposed classification criteria’s. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weaknesses. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selecting the most suitable technique to prioritize requirements based on their project characteristics (number of requirements, time, cost, and accuracy). © 2024, CC BY.

关键词： Requirements engineering

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Pupillometry examinations of the human eye with the eye diagnostic device PEP-2000-First results

Pupillometry examinations of the human eye with the eye diag...

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作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-SchmerbachStr. 19 Chemnitz09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Tu Ilmenau Ilmenau Germany

Pupillometry forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the pupillometry measurements. A study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the minimal pupil diameter is 40 % higher than the literature values. The main reason for the differences is the low light intensity of 15 cd/m2. However, the maximum pupil diameter is within the range of the researched values. The results of the pupillary reaction measurements show that the values obtained (amplitude, contraction time and peak time) are within the range of literature values. The latency time of 690 ms is 40 % too high. The reason for this could be the moderate pupil detection rate of 50-70 %. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further work will look at improving pupil detection rates. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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Research on Magnetic Anisotropy in Amorphous Materials with Soft Magnetic Properties

Research on Magnetic Anisotropy in Amorphous Materials with ...

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作者： Stambula, S. Jeż, K. Major, M. Said, R.M. Vizureanu, P. Nabialek, M. Jeż, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Częstochowa42-200 Poland Faculty of Civil Engineering Czestochowa University of Technology Akademicka 3 Częstochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Taman Muhibbah Perlis Arau02600 Malaysia Faculty of Material Science and Engineering Gheorghe Asachi Technical University 64 Dumitru Mangeron Blvd Iasi700050 Romania Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Częstochowa42-200 Poland

During the magnetization process, the increase in magnetization is blocked by the increase in the magnetic field strength. The reason for these magnetic delays may be the magnetic anisotropy of the tested material. In crystalline materials, this phenomenon is quite well described and is related to the parameters of the crystal lattice. For amorphous alloys, which do not have long-range atomic order, this phenomenon has been much less studied. The paper presents the results of studies on the primary magnetization curves for two different alloys showing soft magnetic properties measured in three different directions: parallel to the casting direction, perpendicular to this direction, and perpendicular to the alloy surface. As indicated by the research, in amorphous materials, one can distinguish the direction of diffIcult magnetization and two easier ones, which means that the phenomenon of magnetic anisotropy occurs in them. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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