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Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies

Procedia computer science 2021年 192卷 3922-3931页

作者： Beata Zielosko Urszula Stańczyk University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms.

关键词： Discretisation Decision rule Rule length Support Attribute Rough Sets

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Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon's Adamaoua region

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Intelligence-Based Medicine 2025年 11卷 100220-100220页

作者： Naroum, Esaie Maka, Ebenezer Maka Abboubakar, Hamadjam Dayang, Paul Bamana, Appolinaire Batoure Garga, Benjamin Daouda, Hassana Daouda Bakouri, Mohsen Khan, Ilyas University of Ngaoundéré Department of Mathematics and computer Science Faculty of Science P.O. Box 455 Ngaoundéré Cameroon University of Douala National Higher Polytechnic School Department of Computer Engineering and Telecommunications P.O. Box 2701 Douala Cameroon University of Ngaoundéré University Institute of Technology Department of Computer Engineering P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré Laboratory of Analysis Simulation and Test (LASE) P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré School of Geology and Mining Engineering Department Applied Mathematics and Computer Science P.O. Box 115 Meiganga Cameroon University of Ngaoundéré ENSAI Department of Electrical Electronic and Automatic Engineering P.O. Box 455 Ngaoundéré Cameroon Majmaah University College of Applied Medical Science Department of Medical Equipment Technology Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia

The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031. © 2025 The Authors

关键词： Adamaoua-Cameroon Deep learning Machine learning Malaria Metric evaluation Time-series forecasting

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Distributed formation control of networked mechanical systems

arXiv

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

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Performance Investigation of Instantaneous Reactive Power control for 5 Level-Neutral Point Clamped Grid-Connected Photovoltaic Inverter

Performance Investigation of Instantaneous Reactive Power Co...

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Electrical engineering and automatic control (ICEEAC), International Conference on

作者： Oumaymah Elamri Alla Eddine Toubal Maamar Charaf Chermite Moulay Rachid Douiri Mostefa Kermadi Saad Mekhilef LAMIGEP multidisciplinary research laboratory École Marocaine des Sciences de l'Ingénieur (EMSI) of Marrakech Marrakech Morocco Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara of Boumerdes Boumerdes Algeria Physics Department Control and Computer Science for Intelligent Systems and Green Energy Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco School of Engineering University of Leicester Leicester United Kingdom Power Electronics and Renewable Energy Research Laboratory (PEARL) Kuala Lumpur Malaysia Swinburne University of Technology Hawthorn Vic Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The utilization of solar energy in grid-connected photovoltaic installations has become increasingly significant. This article introduces a photovoltaic system seamlessly integrated with a three-phase network. The system employing a five-level Neutral Point Clamped (NPC) voltage source inverter is designed for a single-step connection to enhance power quality in the distribution network. The control technique employed for the efficient functioning of the photovoltaic system is based on the notion of instantaneous reactive power. Notably, this strategy is straightforward, operates the system throughout the day, and eliminates the need for Phase-Locked Loop (PLL) usage. The Pulse-Width Modulated (PWM) Current controller sends switching signals to the NPC multilevel inverter, which does a great job of dynamic performance under steady-state and transient situations. The proportional-integral controller regulates the voltage of the Uninterruptible Power Supply DC bus capacitors. The proposed system undergoes comprehensive simulation validation under various irradiation levels.

关键词： Photovoltaic systems Reactive power Voltage source inverters Switches Solar energy Steady-state Transient analysis

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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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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CCMS Model: A novel approach to digitalization level assessment for manufacturing companies 16

CCMS Model: A novel approach to digitalization level assessm...

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16th European Conference on Management Leadership and Governance, ECMLG 2020

作者： Nick, Gábor Szaller, Ádám Várgedo, Tamás Institute for Computer Science and Control Budapest Hungary EPIC InnoLabs Ltd. Budapest Hungary Department of Manufacturing Science and Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781912764761

Today, a significant number of different models and methods are available to assess the readiness of industrial companies concerning Industry 4.0 implementation. In this respect, the SMEs are in the most vulnerable position. Thus, there is a need for a critical review and analysis of the existing models' fit while recognizing the specific requirements of this type of enterprise. The results of a study that has become a point of reference (Mittal 2018) show that only a limited number of the smart manufacturing and Industry 4.0 roadmaps, maturity models, frameworks, and readiness assessments available today reflect at least partly the specific requirements and challenges of SMEs. In this paper, an attempt is made to introduce a solution to mitigate this gap, that considers cultural and location-specific, ecosystem-related restrictions or influences (macro-level view) and particular company-level aspects (micro-level view), as well. The CCMS (Company CoMpaSs) model developed by EPIC InnoLabs is based on an online survey and considers both business and technological views. The answers given by the respondent company are weighted, and intervention points are selected from a predefined set of eight elements per dimension, on the basis of analyzing the responses (coming from different areas). The output of the model (besides the statistical evaluation of the results like comparisons to averages derived from metadata) is a prioritized list of intervention points that shows the most important fields of action for the respondent on an interactive dashboard. The applied weightings are determined by the model creators and result from the inherent structure of the model. The questions and the weights can also be changed as the used technologies evolve. CCMS provides a quick (meaning that the respondent gets prompt results), low-risk, online maturity assessment for manufacturing SMEs, which helps decision-makers to determine target maturity levels in different fields. © ECIAIR 20

关键词： Industry 4.0

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The Use of Various Neurostimulators in Therapy of Neurodegenerative Disorders – A Survey

The Use of Various Neurostimulators in Therapy of Neurodegen...

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Progress in Applied Electrical engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Agata Gajos Mateusz Pawłowski Witold Libionka Władysław Piotr Duch Bartosz Pańczyszak Monika Stomal-Słowińska Michał Podpora Waldemar Bauer Adam Sudoł Mariusz Pelc Jarosław Zygarlicki Piotr Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Department of Neurosurgery "Vital Medic" Hospital Kluczbork Poland Department of Extrapyramidal Diseases Medical University of Łódź Łódź Poland Faculty of Health Sciences Wroclaw Medical University Wrocław Poland Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Faculty of Natural Sciences and Technology University of Opole Opole Poland School Of Computing and Mathematical Sciences University of Greenwich London United Kindgom Center for Technology Transfer Opole University of Technology Opole Poland

ISBN: (数字)9781665455152

ISBN: (纸本)9781665455169

The use of neurostimulators in the treatment and therapy of neurodegenerative diseases is an alternative to pharmacological treatment. Unfortunately, most of these disorders are incurable, however, the use of neurostimulators can delay the onset of symptoms, the course of the disease and significantly improve the quality of life. Treatment with the use of neurostimulators also allows you to avoid side effects which usually accompany pharmacotherapy, or in order to replace it when prescribed medication is not working properly or not working at all. This paper focuses mainly on vagus nerve and deep brain stimulation.

关键词： Electrical engineering Deep brain stimulation Medical treatment Delays Diseases

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Hard hat wearing detection based on head keypoint localization

arXiv

引用

arXiv 2021年

作者： Wójcik, Bartosz Zarski, Mateusz Ksiązek, Kamil Miszczak, Jaroslaw A. Skibniewski, Miroslaw J. Faculty of Civil Engineering Silesian University of Technology Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Civil & Environmental Engineering University of Maryland College Park United States

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems. However, still there is no reliable way to establish the relationship between supervised construction workers and their basic personal protective equipment, like hard hats. To address this problem, a novel deep learning method combining object detection, keypoint localization, and simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the above-mentioned approaches, with class specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that the conjunction of deep learning methods with humanly-interpretable rule-based algorithm is better suited for detection of hard hat non-wearers. Code is available at barwojcik/hard-hats. © 2021, CC BY.

关键词： Object detection

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