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Socially Governed Energy Hub Trading Enabled by Blockchain-Based Transactions

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 8227-8242页

作者： Zhao, Pengfei Li, Shuangqi Cao, Zhidong Hu, Paul Jen-Hwa Gu, Chenghong Yan, Xiaohe Huo, Da Luo, Tianyi Wang, Zikang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England Univ Utah David Eccles Sch Business Salt Lake City UT 84112 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 100096 Peoples R China Cranfield Univ Sch Water Energy & Environm Bedford MK43 0AL England

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers' decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchainbased trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.

关键词： Blockchain energy hubs energy management integrated energy systems (IESs) peer-to-peer (P2P) trading social governance

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Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第6期10卷 2938-2948页

作者： Zeng, Xingjie Zhou, Tao Bao, Zhicheng Zhao, Hongwei Chen, Leiming Wang, Xiao Wang, Feiyue China Univ Petr East China Dept Coll Comp Sci & Technol Qingdao 266580 Peoples R China Anhui Univ Sch Artificial Intelligence e Hefei 266114 Peoples R China Qingdao Acad Intelligent Ind Qingdao 230031 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Federated learning enables multiple clients to learn a general model without sharing local data, and the federated learning system also improves information security and advances responsible artificial intelligence (AI). However, the data of different clients in the system are non-independently and identically distributed (IID), which results in weight divergence, especially for complex graph data extraction. This article proposes a novel feature-contrastive graph federated (FcgFed) learning approach to improve the robustness of the federated learning system in graph data. First, we design an architecture for FcgFed learning systems to analyze graph information. Furthermore, we present a graph federated learning method based on contrastive learning to alleviate the weight divergence in federated learning. The experiments in node classification and graph classification demonstrate that our method achieves better performance than model-contrastive federated learning (MOON) and federated average (FedAvg). We also test the adaptability of our method in image classification, and the results demonstrate that weight similarity evaluation works for other frameworks and tasks.

关键词： Federated learning Data models Artificial intelligence Graph neural networks Training Learning systems Servers graph neural networks responsible artificial intelligence (AI) weight divergence weight similarity evaluation

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Enhancement and Optimization of Underwater Images and Videos Mapping

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SENSORS 2023年第12期23卷 5708页

作者： Li, Chengda Dong, Xiang Wang, Yu Wang, Shuo Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel's transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G-B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

关键词： underwater image and video restoration transmission map optimizer saturation map color correction

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Application of Social Sensors in Natural Disasters Emergency management: A Review

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第6期10卷 3143-3158页

作者： Shi, Kaize Peng, Xueping Lu, Hao Zhu, Yifan Niu, Zhendong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst Sydney NSW 2007 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA

Natural disasters are public emergencies characterized by suddenness, universality, and nonconventionality. Realizing the early warning, monitoring, and intervention of natural disasters and their derivative social impacts is significant for reducing the disasters' damage and benefits the maintenance of social stability. Social sensors are ubiquitous sensors based on social network platforms. It uses the concepts and methods of physical space to mine social signals that integrate human perception and intelligence in cyberspace. Compared with traditional physical sensors, social sensors represent a crucial data acquisition channel in the emergency management of natural disasters and have the advantages of real time, comprehensive coverage, low cost, and flexible deployment. This article reviews the application of social sensors in natural disasters emergency management. We summarize the application functions of social sensors into three categories: natural disaster situation awareness and event detection, disaster information dissemination and communication, and disaster sentiment analysis and public opinion mining. Based on the above functions, this article analyzes the research status, data, technical methods, and application systems. Finally, this article proposes a research trend of applying social sensors in natural disaster emergency management according to the requirements of real scenarios.

关键词： Sensors Emergency services Sentiment analysis Social networking (online) Sensor phenomena and characterization Intelligent sensors Earthquakes Decision support system emergency management parallel system social network analysis social sensor sudden natural disaster

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Autonomous Skill Learning of Water Polo Ball Heading for a Robotic Fish: Curriculum and Verification

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2023年第2期15卷 865-876页

作者： Zhang, Tiandong Wang, Rui Wang, Shuo Wang, Yu Cheng, Long Zheng, Gang Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Inria Lille Nord Europe F-59650 Villeneuve Dascq France Ctr Rech Informat Signalet & Automat Lille F-59650 Villeneuve Dascq France

This article concerns the autonomous skill learning of water polo ball heading for a robotic fish in highly dynamic aquatic environments via multistage serial-parallel curriculum learning (MSPCL). First, a radio-controlled robotic fish with hybrid fin propulsion is presented. Moreover, the water polo ball heading task is decomposed into two sequential subtasks: 1) preparation and 2) shooting. In order to cope with the multistage complex tasks, based on the easy-to-hard curriculum learning strategy, a novel MSPCL framework is proposed for the robotic fish to learn complex skills, which mainly includes curriculum scheduler, difficulty criterion, serial-parallel curriculum generation, and performance measure modules. Furthermore, under the MSPCL framework, soft actor-critic (SAC) algorithm is utilized for training the policy network. Therefore, the robotic fish can learn how to head the water polo ball via the proposed MSPCL method. Comparative simulations are carried out to verify the effectiveness of the MSPCL framework. Finally, the proposed MSPCL method is applied to a physical robotic fish named RoboDact. Swimming pool experiments of water polo ball heading of the RoboDact are conducted to demonstrate the validity and robustness of the proposed method.

关键词： Autonomous skill learning curriculum learning (CL) deep reinforcement learning (DRL) hybrid propulsion robotic fish

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Intelligent Target Visual Tracking and control Strategy for Open Frame Underwater Vehicles

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ROBOTICA 2021年第10期39卷 1791-1805页

作者： Sun, Chaoyu Wan, Zhaoliang Huang, Hai Zhang, Guocheng Bao, Xuan Li, Jiyong Sheng, Mingwei Yang, Xu Hosp Harbin Med Univ Harbin 150026 Peoples R China Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

关键词： Remotely autonomous underwater vehicle Kernelized correlation filter tracking Learning based fuzzy controller

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Two Birds With One Stone: Knowledge-Embedded Temporal Convolutional Transformer for Depression Detection and Emotion Recognition

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023年第4期14卷 2595-2613页

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410012 Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Depression is a critical problem in modern society that affects an estimated 350 million people worldwide, causing feelings of sadness and a lack of interest and pleasure. Emotional disorders are gaining interest and are closely entwined with depression, because one contributes to an understanding of the other. Despite the achievements in the two separate tasks of emotion recognition and depression detection, there has not been much prior effort to build a unified model that can connect these two tasks with different modalities, including multimedia (text, audio, and video) and unobtrusive physiological signals (e.g., electroencephalography). We propose a novel temporal convolutional transformer with knowledge embedding to address the joint task of depression detection and emotion recognition. This approach not only learns multimodal embeddings across domains via the temporal convolutional transformer but also exploits special-domain knowledge from medical knowledge graphs to improve the performance of detection and recognition. It is essential that the features learned by our method can be perceived as a priori and are suitable for increasing the performance of other related tasks. Our method illustrates the case of "two birds with one stone" in the sense that two or more tasks can be efficiently handled with our unique model, which captures effective features. Experimental results on ten real-world datasets show that the proposed approach significantly outperforms other state-of-the-art approaches. On the other hand, experiments in which our methodology is applied to other reasoning tasks show that our approach effectively supports model reasoning related to emotion and improves its performance.

关键词： Multimodal depression detection multimodal emotion recognition transformer knowledge embedding joint learning

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A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第3期10卷 869-877页

作者： Liang, Jing Tang, Ying Hare, Ryan Wu, Ben Wang, Fei-Yue Squirrel AI Learning Yixue Educ Inc Highland Pk NJ 08904 USA Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Qingdao Acad Intelligent Ind Inst Smart Educ Qingdao 266000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Qingdao 266000 Peoples R China

Serious games (SGs) are a practice of growing importance due to their high potential as an educational tool for augmented learning. However, little effort has been devoted to address student learning optimization in an SG from a systematic point of view. This article tackles this challenge by developing a learning-embedded attribute Petri net (LAPN) model to represent game flow and student learning decision-makings. The dynamics of learner behaviors in game are then addressed through the incorporation of learning mechanisms (i.e., reinforcement learning (RL) and random forest classification) into the Petri net model for knowledge reasoning and learning. Finally, an algorithm based on LAPN is proposed, aiming to guide learners to achieve a faster and better solution to problem-solving in game. The benefit of the proposed model and algorithm is then demonstrated in the SG Gridlock.

关键词： Games Task analysis Problem-solving Petri nets Random forests Training Radio frequency Learning optimization Petri nets (PNs) serious game (SG)

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Smart Mining With Autonomous Driving in Industry 5.0: Architectures, Platforms, Operating systems, Foundation Models, and Applications

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4383-4393页

作者： Chen, Long Li, Yuchen Silamu, Wushour Li, Qingquan Ge, Shirong Wang, Fei-Yue Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China WAYTOUS Inc Beijing 100083 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China BNU HKBU United Int Coll Fac Sci & Technol Zhuhai 519087 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

The increasing importance of mineral resources in contemporary society is becoming more prominent, playing an indispensable and crucial role in the global economy. These resources not only provide essential raw materials for the global economic system but also play an irreplaceable role in supporting the development of modern industry, technology, and infrastructure. With the rapid development of intelligent technologies such as Industry 5.0 and advanced Large Language Models (LLMs), the mining industry is facing unprecedented opportunities and challenges. The development of smart mines has become a crucial direction for industry progress. This article aims to explore the strategic requirements for the development of smart mines by combining advanced products or technologies such as Chat-GPT (one of the successful applications of LLMs), digital twins, and scenario engineering. We propose a comprehensive architecture consisting of three different levels, the mining industrial Internet of Things (IoT) platform, mining operating systems, and foundation models. The systems and models empower the mining equipment for transportation. The architecture delivers a comprehensive solution that aligns perfectly with the demands of Industry 5.0. The application and validation outcomes of this intelligent solution showcase a noteworthy enhancement in mining efficiency and a reduction in safety risks, thereby laying a sturdy groundwork for the advent of Mining 5.0.

关键词： Digital twins Fifth Industrial Revolution Industries Task analysis Production Ontologies Biological system modeling Mining 5.0 smart mining autonomous driving industry 5.0 architectures mining transportation trucks

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Learning Hierarchical Graph Convolutional Neural Network for Object Navigation 31st

Learning Hierarchical Graph Convolutional Neural Network for...

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Xu, Tao Yang, Xu Zheng, Suiwu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Huizhou Zhongke Adv Mfg Ltd Co Huizhou Peoples R China

ISBN: (纸本)9783031159312;9783031159305

The goal of object navigation is to navigate an agent to a target object using visual input. Without GPS and the map, one challenge of this task is how to locate the target object in the unseen environment, especially when the target object is not in the field of view. Previous works use relation graphs to encode the concurrence relationships among all the object categories, but these relation graphs are usually too flat for the agent to locate the target object efficiently. In this paper, a Hierarchical Graph Convolutional Neural Network (HGCNN) is proposed to encode the object relationships in a hierarchical manner. Specifically, the HGCNN consists of two graph convolution blocks and a graph pooling block, which constructs the hierarchical relation graph by learning an area-level graph from the object-level graph. Consequently, the HGCNN based framework enables the agent to locate the target object efficiently in the unseen environment. The proposed model is evaluated in the AI2-iTHOR environment, and the performance of object navigation shows a significant improvement.

关键词： Object navigation Hierarchical relation graph AI2-iTHOR

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