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arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

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Analysis, circuit realization and controls of an autonomous Morse jerk oscillator

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SeMA Journal 2021年第3期78卷 415-433页

作者： Ainamon, Cyrille Tamba, Victor Kamdoum Pone, Justin Roger Mboupda Kingni, Sifeu Takougang Malwe, Hubert Boudoue Orou, Jean Bio Chabi Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi B.P. 613 Porto Novo Benin Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang PO Box 134 Bandjoun Cameroon Electrical Engineering Department of IUT-Fotso Victor of Bandjoun Research Unit of Automation and Applied Computer (RU-AIA) University of Dschang PO Box 134 Bandjoun Cameroon Department of Mechanical Petroleum and Gas Engineering Faculty of Mines and Petroleum Industries University of Maroua PO Box 46 Maroua Cameroon Department of Physics Faculty of Science University of Maroua PO Box 814 Maroua Cameroon

In this paper, an autonomous Morse jerk oscillator which is designed by converting an autonomous two-dimensional Morse oscillator to a jerk oscillator, is analysed. The stability of its unique equilibrium point reveals the existence of Hopf bifurcation. Periodic and chaotic oscillations, antimonotonicity, chaotic bubbles and coexisting attractors are generated in the proposed jerk oscillator. Then, this proposed jerk oscillator is implemented in PSIM software and realized in a printed circuit board to verify the numerical results. The experimental/PSIM results agree well with the numerical simulations. Moreover, it is possible to control partially or totally the amplitude of its signals by introducing two additional parameters in the rate-equations describing the proposed jerk oscillator. Furthermore based on the Routh–Hurwitz conditions and using a single linear feedback controller, the proposed jerk oscillator is controlled to its unique equilibrium point. Finally, the coexistence between periodic and chaotic attractors is destroyed and controlled to a desired trajectory thank to the linear augmentation method. © 2021, The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada.

关键词： Antimonotonicity Chaotic and coexisting attractors Control Electronic circuit realization Hopf bifurcation Morse jerk oscillator

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The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

arXiv

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arXiv 2024年

作者： Li, Boyang Ying, Xinyi Li, Ruojing Liu, Yongxian Shi, Yangsi Li, Miao Zhang, Xin Hu, Mingyuan Wu, Chenyang Zhang, Yukai Wei, Hui Tang, Dongli Zhao, Jian Jin, Lei Xiao, Chao Ling, Qiang Lin, Zaiping Sheng, Weidong Peng, Chenxu Ke, Wei He, Yuhang Yang, Huoren Liu, Lingjie Liu, Ziqi Shi, Zelin Liu, Yunpeng Yu, Chuang Zhao, Jinmiao Xiang, Heng Yang, Shan Li, You Wang, Guoqing Li, Tianyu Zhou, Minghang Xie, Dongyu Lan, Chenxi Qiao, Chaofan Gao, Yupeng Liu, Yongxu Feng, Wei Ma, Zhihao Chen, Deping Song, Xiaopeng Yang, Jiuping Qiu, Zhaobing Ni, Rixiang Lin, Ye Luo, Changhai Zheng, Shuyuan Huang, Baojin Zhou, Xiaoqi Guo, Qingshan Zhang, Xuyang Li, Xiuhong Wu, Dangxuan Ma, Jian Zeng, Haodong Wang, Luyao Fu, Qiang Dai, Yimian Kou, Renke Song, Jian Feng, Changfeng Xiao, Mengxuan Xiong, Zihao Huang, Hong Liu, Yingxu Zhao, Quanyi National University of Defense Technology China China Telecom Institute of AI China Northwestern Polytechnical University China Beijing University of Posts and Telecommunications China Zhejiang Supcon Information Co. Ltd. China School of Physics Xi’an Jiaotong University China College of Artificial Intelligence Faculty of Electronic and Information Engineering Xi’an Jiaotong University China School of Software Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China The Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Sichuan Zhongke Langxing Photoelectric Technology Co. Ltd. China Center for Future Media University of Electronic Science and Technology of China China Hangzhou Institute of Technology Xidian University China School of Electronic Engineering Xidian University China School of Mechanical Engineering and Automation Fuzhou University China Beijing Insitute of Technology Chongqing Innovation Center China Xinjiang University China Army Engineering University PLA Shijiazhuang Campus China Nanjing University of Science and Technology China Henan University of Technology China Chongqing University China

In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource. © 2024, CC BY-NC-SA.

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Optical super-resonances in dielectric microsphere particles

arXiv

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arXiv 2022年

作者： Wang, Zengbo Luk’yanchuk, Boris Wu, Baidong Yan, Bing Assel, Ahmetova Yaminsky, Igor Yu, Haibo Liu, Lianqing School of Computer Science and Electronic Engineering Bangor University Dean Street Gwynedd BangorLL57 1UT United Kingdom Faculty of Physics Lomonosov Moscow State University Moscow119991 Russia State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Extreme field localization and giant field enhancement are often achieved by using plasmonic nanostructures and metamaterials such as strongly coupled silver nanoparticles. Dielectric particles and structures can focus light beyond the diffraction limit (photonic nanojet effect), but with much weaker strengths. Recently, we showed that dielectric microspheres could support high-order Mie resonance modes (‘super-resonance modes’), that can generate similar level of electric field intensity enhancement as plasmonic structures on the order of 104- 107. In this work, we aim to further advance our understanding of the super-resonance modes. New results on the effects of size parameter and refractive index on optical super-resonances across a wide parameter range and with improved numerical accuracies is presented. The results suggest that the electric field intensity enhancement could reach a record high level of 109-1011 at specific conditions that surpass plasmonic enhancements. Moreover, super-resonance-enabled focusing by microsphere lens under different lighting sources (e.g., different color LEDs or lasers, halogen lamps) is investigated and compared for the first time. These results are important in understanding the super-resolution mechanism for microsphere nanoscopy and will find numerous potential applications in photonics. © 2022, CC BY.

关键词： Microspheres

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C²F²: Cross-Task Cross-Domain Feature Fusion for Semi-Supervised Change Detection

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IEEE Geoscience and Remote Sensing Letters 2024年 21卷

作者： Dongjie Zhang Yuting Hong Xiaojie Qiu Li Dong Diqun Yan Chengbin Peng Faculty of Electrical Engineering and Computer Science and the Key Laboratory of Mobile Network Application Technology of Zhejiang Province Ningbo University Ningbo China Zhejiang Cowain Automation Technology Company Ltd. Ningbo China

Semi-supervised learning for change detection (CD), which significantly reduces the labor costs associated with data annotation, has recently garnered substantial attention. In this study, we propose to enhance traditional semi-supervised learning frameworks by leveraging cross-task cross-domain (CTCD) models, which generate complementary features that differ from standard hidden features. The procedure is as follows. First, the standard features obtained from a traditional encoding–decoding structure are fused with attention-augmented complementary features. Second, a secondary decoder maps the fused heterogeneous features into the label space to obtain high-quality pseudo-labels, offering more precise guidance for semi-supervised learning on traditional structures. This approach improves pseudo-labels by leveraging the strength of CTCD models, including large pretrained models, to enhance the semi-supervised learning process of domain-specific and task-specific models. Experimental results on benchmark datasets demonstrate that our proposed approach surpasses state-of-the-art methods.

关键词： Standards Feature extraction Decoding Training Accuracy Semisupervised learning Supervised learning

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Kiruba Sandou D Sunad kumara A N Binod Kumar Choudhary Shashikala Gurpur Sarishma Natrayan L M. SivaramKumar Department of Robotics and Automation Manakula Vinayagar Institute of Technology Pondicherry India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Electrical and Electronics Engineering ARKA Jain University Jamshedpur Jharkhand India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of EEE Karpagam Academy of higher education Coimbatore Tamiinadu

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications.

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Research on Person Recognition Model of Domain Adaptive Learning

Research on Person Recognition Model of Domain Adaptive Lear...

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第32届中国控制与决策会议

作者： Ming-hai Jiao Wei-ming Duan Jue Wang Ben-dong Luo Chi Zhang Xiang-yu Sun School of Computer Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University College of Science Northeastern University School of Mechanical Engineering and Automation Northeastern University

The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.

关键词： Person recognition Deep self training Domain adaptive learning Loss function

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Enhanced simulation framework for realisation of mobility in 6LoWPAN wireless sensor networks 28

Enhanced simulation framework for realisation of mobility in...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Vasilev, Rusen Vasilev Haka, Aydan Mehmed Department of Computer Science and Engineering Faculty of Computer Sciences and Automation Technical University of Varna 1 Studentska str Varna9010 Bulgaria

ISBN: (纸本)9781728125749

The intense incursion of the Internet of Things (IoT) into all areas of modern life has led to a need for a more detailed study of these technologies and their mechanisms of work. It is necessary to study mechanisms in order to improve QoS, security, identifying shortest routes, mobility, etc. This paper proposes an enhanced simulation framework that implements an improved mechanism for prioritising traffic on 6LoWPAN networks and the realisation of micro-mobility. © 2019 IEEE.

关键词： Internet of things

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A parallel uncertain frequent itemset mining algorithm with spark 20

A parallel uncertain frequent itemset mining algorithm with ...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Ding, Jiaman Li, Haibin Wang, Yuanyuan Jia, Lianyin You, Jinguo Yang, Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781728126166

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. Third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a Parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability. © 2019 IEEE.

关键词： Data mining

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Spectral Features of EEG Signals for the Automated Recognition of Negative Emotional States 28

Spectral Features of EEG Signals for the Automated Recogniti...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Feradov, Firgan Nihatov Ganchev, Todor Dimitrov Department of Electronic Equipment and Microelectronics Faculty of Computer Science and Automation 1 Studentska str. Varna9010 Bulgaria Department of Computer Sciences and Engineering Faculty of Computer Science and Automation Technical University of Varna 1 Studentska str. Varna9010 Bulgaria

ISBN: (纸本)9781728125749

In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20-35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively. © 2019 IEEE.

关键词： Biomedical signal processing

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