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Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

Integrating Informativeness, Representativeness and Diversit...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Liu, Ziang Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9781728169262

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria informativeness, representativeness, and diversity have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.

关键词： Active learning ridge regression greedy sampling

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Path planning and formation control for multi-agent system using ergodic exploration 18

Path planning and formation control for multi-agent system u...

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18th IEEE International Conference on Industrial Informatics (INDIN)

作者： Chen, Zhulei Xiao, Li Wang, Qi Wang, Zhuo Sun, Zhigang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781728149646

In this paper, a novel ergodic metric is used to plan paths of multi-agent system in formation based on the ergodic theory. In order to take the full advantages of the formation of multi-agent system in expanding the detection range, obtaining complete environmental information, reducing mutual blockage between agents and performing different types of tasks, we combine leader-follower method and artificial potential field method to control multiple agents and achieve ergodic exploration in formation. To avoid collision between agents and obstacles, a family of vector fields are designed around obstacles so that agents can bypass obstacles. Combining the vector field with the path planning of formation traversal, agents can traverse a given area with obstacles and avoid collide with obstacles.

关键词： ergodic theory multi-agent systems path planning obstacle avoidance formation control

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Event-Triggered Cooperative Tracking for Lipschitz-Type Multi-Agent Systems: An ARE-Based Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第1期69卷 119-123页

作者： Xian, Chengxin Zhao, Yu Dong, Xiwang Wen, Guanghui Zhang, Hai-Tao Northwestern Polytech Univ Res & Dev Inst Shenzhen 518057 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southeast Univ Dept Math Nanjing 210096 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Intelligent Control & State Key Lab Digital Mfg E Wuhan 430074 Peoples R China

This brief studies the event-triggered cooperative tracking problem for multiple autonomous agents described by Lipschitz-type nonlinear dynamics. A new kind of event-triggered cooperative tracking strategy is developed by using model-based local sampled state information and the state-dependent gain approach. Compared with some event-triggered cooperative tracking strategies in existing works, the dynamics of agents is extended to Lipschitz-type nonlinearity in this brief, which can better meet the practical engineering needs. Besides, the cooperative tracking algorithms overcome the infeasibility of linear matrix inequality (LMI) conditions, and the cooperative tracking problem can be addressed by only solving an algebraic Riccati equation (ARE), which is feasible if the system is stabilizable. Finally, a simulation example is presented to verify the theoretical analysis and the effectiveness of the control strategy.

关键词： Multi-agent systems Circuits and systems Nonlinear dynamical systems Riccati equations Heuristic algorithms Automation Topology Event-triggered cooperative tracking strategy Lipschitz-type nonlinear dynamics state-dependent gain approach

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Distributed second order sliding mode secondary frequency control for HVDC system

Distributed second order sliding mode secondary frequency co...

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Chinese Automation Congress (CAC)

作者： Dou, Yang-Ming Chi, Ming Liu, Zhi-Wei Huazhong Univ Sci & Technol Coll Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)9781728176871

This paper studies the secondary frequency control among non-synchronous AC areas interconnected by High Voltage Direct Current (HVDC). A distributed second order sliding mode control scheme is adopted to secondary frequency control of HVDC transmission systems to adjust the frequency of power grid to rated value, which solves the frequency disturbance caused by load power change of the DC power grid. And on this basis, the power generation in each region is reasonably distributed, so as to minimize the cost of power generation. Finally, the stability of the system is proved on an appropriate sliding manifold.

关键词： IIVDC frequency control sliding mode generation cost

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Generating Any Number of Initial Offset-Boosted Coexisting Chua's Double-Scroll Attractors via Piecewise-Nonlinear Memristor

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第7期69卷 7202-7212页

作者： Sen Zhang Li, Chunbiao Zheng, Jiahao Wang, Xiaoping Zeng, Zhigang Peng, Xuenan Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing 210044 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist China Wuhan 430074 Peoples R China Xiangtan Univ Sch Phys & Opotoelect Engn Xiangtan 411100 Peoples R China

Due to the natural nonlinearity and unique memory characteristics, memristors are promising candidates for the construction of multiscroll attractors having better application potential in the field of information encryption than the traditional double-scroll attractors. This article proposes a novel memristive multidouble-scroll Chua's system (MMDSCS) via coupling a nonideal flux-controlled memristor with multipiecewise-linear memductance function in Chua's system directly. Specially, any number of multidouble-scroll chaotic attractors can be generated through adjusting the internal parameters of the memristor conveniently and without changing the original system's nonlinearity. Moreover, the amount of double scrolls is also closely related to the strength of the memristive coupling. Another striking highlight is that infinite initial offset-boosted coexisting Chua's double-scroll attractors with the same shape are produced with the variation of the memristor initial conditions, indicating the emergence of an intriguing phenomenon of homogeneous extreme multistability. This unique property and its formation mechanism are investigated in detail using phase portraits, bifurcation diagrams, Lyapunov exponents, time series, and attraction basins. Furthermore, hardware experiments based on the field-programmable gate array are carried out to confirm the numerical simulations. Finally, an image encryption scheme is designed based on the memristor initial offset boosting dynamics from a perspective of engineering application. In comparison with the existing memristive Chua's systems, the proposed MMDSCS has many merits, such as multidouble-scroll attractors, memristor initial-controlled chaotic sequences with controllability, good robustness, and high security performance, which is more practical in applications involving information confidential communication.

关键词： Memristors Couplings Chaotic communication Encryption Boosting Shape Mathematical model Field-programmable gate array (FPGA) homogeneous multistability image encryption initial offset boosting memristor multidouble-scroll attractor

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INSPIRATION: A reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Wang, Hao Sun, Shixin Chang, Laibin Li, Huanyu Zhang, Wenwen Frery, Alejandro C. Ren, Peng China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Victoria Univ Wellington Sch Math & Stat Wellington 6140 New Zealand Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710126 Peoples R China

This paper delves into enhancing underwater images to improve human observation of underwater scenes. To this end, a reinforcement learning -based human visual perception -driven image enhancement paradigm for underwater scenes is proposed, referred to as INSPIRATION. This paradigm is so named because it constructs rewards for guiding model training by incorporating metrics inspired by human visual perception, eliminating the need for reference images. It overcomes the limitations of deep models, which often result in poor performance in dynamic and diverse underwater scenes due to the scarcity of underwater images with enhancement references. Specifically, a residual -enhancement network, composed of residual modules and a channel -attention module, is utilized to extract features as a state. An extensible collection encompassing diverse image enhancement algorithms is utilized to provide an image enhancement algorithm as an action. A multi -non -reference human visual perception metric increment is utilized as a reward, and proximal policy optimization (PPO) is utilized to conduct reinforcement learning. This paradigm treats underwater images as sole inputs during both training and implementation, learning and organizing a sequence of image enhancement algorithms to explicitly achieve a step -wise image enhancement process, aligning the enhanced images with human visual perception. Extensive qualitative and quantitative evaluations conclusively demonstrate that our paradigm outperforms nine state-of-the-art underwater image enhancement methods in terms of visual quality and achieves better performance on five underwater image quality assessment measures across three underwater image datasets. Additionally, the potential advantages of our paradigm as a preprocessing step in other underwater computer vision applications are demonstrated. The code has been released publicly for reproducibility and evaluations at https://***/wanghaoupc/uie_inspiration.

关键词： Underwater scene image enhancement Human visual perception Reinforcement learning

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A Multi-scale Network for Semantic Segmentation of 3D Point Clouds

A Multi-scale Network for Semantic Segmentation of 3D Point ...

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Chinese Automation Congress (CAC)

作者： He, Ying Xiao, Li Jiang, Yong Sun, Zhigang Wang, Zhuo Peng, Gang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781728176871

Semantic segmentation is a fundamental operation in scene analysis. In this paper, an effective multiscale network for 3D point cloud semantic segmentation was introduced. By using a multiscale local feature extraction module which composed of four feature extractors of different scales in parallel, the generalizability of network for complex structures is enhanced effectively. To adaptively learn important feature channels, an attention mechanism is designed. Combining multiple features through skip connection, the network can preferably assign the semantic label for every point by exploiting global and local features. Experiments on 3D dataset (S3DIS) verify that our network is able to learn local region features, and the results are superior or comparable to the state-of-the-art.

关键词： multiscale network 3D semantic segmentation channel attention module

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T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

arXiv

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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Game-based peer-to-peer energy sharing management for a community of energy buildings

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 123卷 106204-106204页

作者： Cui, Shichang Xiao, Jiang-Wen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Energy building techniques have gained significant attention for the potential to achieve local energy sustainability. Different from the research on individual energy management for zero energy buildings requiring sufficiently large renewable energy systems due to the mismatch of energy loads and generations, this paper investigates the coordinated energy management for a community of energy buildings with peer-to-peer energy sharing to promote community energy efficiency. Specifically, the energy buildings have controllable heating, ventilation and air conditioning (HVAC) units and renewable energy installations. In the coordinated energy management, in a peer-to-peer mode, the buildings determine their energy sharing profiles and related payments within the community while the sharing energy balance and payment balance are required to be satisfied. Since the buildings are self-interested, the problem with peer-to-peer energy sharing is modeled as a noncooperative game with global constraints. Then the existence of the game equilibrium is illustrated, and a distributed algorithm is proposed to seek the game equilibrium. A 6-building case and a 30-building case are carried out to verify the energy efficiency, economic benefits, and the scalability of the proposed peer-to-peer energy sharing management model.

关键词： Energy building Energy sharing Energy economics Noncooperative game Distributed optimization

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SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

SSRN

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SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

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