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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

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Identification of HDAC9 as a viable therapeutic target for the treatment of gastric cancer

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EXPERIMENTAL AND MOLECULAR MEDICINE 2019年第8期51卷 100-100页

作者： Xiong, Kai Zhang, Hejun Du, Yang Tian, Jie Ding, Shigang Peking Univ Dept Gastroenterol Hosp 3 Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Key Lab Helicobacter Pylori Infect & Uppe Beijing 100191 Peoples R China Univ Chinese Acad Sci Beijing 100080 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Beijing 100191 Peoples R China Xidian Univ Sch Life Sci & Technol Engn Res Ctr Mol & Neuro Imaging Minist Educ Xian 710126 Shaanxi Peoples R China

Histone deacetylase inhibitors (HDACis) are a new class of anticancer drugs confirmed to have good therapeutic effects against gastric cancer (GC) in preclinical experiments, but most HDACis are non-selective (pan-HDACis), with highly toxic side effects. Therefore, it is necessary to screen HDAC family members that play key roles in GC as therapeutic targets to reduce toxic side effects. In this study, we evaluated the targeting specificity of the HDACi suberoylanilide hydroxamic acid (SAHA) for GC via fluorescence molecular imaging (FMI). In vitro FMI results showed that SAHA had higher binding affinity for GC cells than for normal gastric cells. In vivo FMI of gastric tumor-bearing mice confirmed that SAHA can be enriched in GC tissues. However, there was also a high-concentration distribution in normal organs such as the stomach and lungs, suggesting potential side effects. In addition, we found that among the HDAC family members, HDAC9 was the most significantly upregulated in GC cells, and we verified this upregulation in GC tissues. Further experiments confirmed that knockdown of HDAC9 inhibits cell growth, reduces colony formation, and induces apoptosis and cell cycle arrest. These results suggest that HDAC9 has an oncogenic role in GC. Moreover, HDAC9 siRNA suppressed GC tumor growth and enhanced the antitumor efficacy of cisplatin in GC treatment by inhibiting the proliferation and inducing the apoptosis of GC cells in vitro and in vivo. Our findings suggest that the development of HDAC9-selective HDACis is a potential approach to improve the efficacy of chemotherapy and reduce systemic toxicity.

关键词： Cancer imaging Prognostic markers

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar systems

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SENSORS 2022年第24期22卷 9930-9930页

作者： Liu, Yuhang Shen, Yu Fan, Lili Tian, Yonglin Ai, Yunfeng Tian, Bin Liu, Zhongmin Wang, Fei-Yue 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 Beijing Inst Technol Sch Informat & Elect Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars;thus, digital twins' radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars.

关键词： parallel radars ACP method cyber-physical-social systems (CPSS) federated radars

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HackRL: Reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collaborative reasoning

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KNOWLEDGE-BASED systems 2021年 233卷 107498-107498页

作者： Yang, Linyao Wang, Xiao Dai, Yuxin Xin, Kejun Zheng, Xiaolong Ding, Weiping Zhang, Jun Wang, Fei-Yue 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 Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Nanjing Joinmap Data Res Inst Nanjing 211100 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Reasoning aiming at inferring implicit facts over knowledge graphs (KGs) is a critical and fundamental task for various intelligent knowledge-based services. With multiple distributed and complementary KGs, the effective and efficient capture and fusion of knowledge from different KGs is becoming an increasingly important topic, which has not been well studied. To fill this gap, we propose to explore cross-KG relation paths with the anchor links identified by entity alignment for the knowledge fusion and collaborative reasoning of multiple KGs. To address the heterogeneity of different KGs, this paper proposes a novel reasoning model named HackRL based on the reinforcement learning framework, which incorporates the long short-term memory and hierarchical graph attention in the policy network to infer indicative cross-KG relation paths from the history trajectory and the heterogeneous environment for predicting corresponding relations. Meanwhile, an entity alignment oriented representation learning method is utilized to embed different KGs into a unified vector space based on the anchor links to reduce the impact of distinct vector spaces, and two training mechanisms, action mask and retrain with sampled paths, are proposed to optimize the training process to learn more successful indicative paths. The proposed HackRL is validated on three cross-lingual datasets built from DBpedia on the link prediction and fact prediction tasks. Experimental results demonstrate that HackRL achieves better performance on most tasks than existing methods. This work provides an industrially-applicable framework for fusing distributed KGs to make better decisions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Knowledge fusion Knowledge reasoning Decision-making Hierarchical graph attention Reinforcement learning

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BlockNet: Beyond reliable spatial Digital Twins to Parallel Metaverse

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PATTERNS 2022年第5期3卷 100468页

作者： Lv, Zhihan Qiao, Liang Li, Yuxi Yuan, Yong Wang, Fei-Yue Uppsala Univ Fac Arts Dept Game Design S-62167 Visby Sweden Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

The development of Digital Twins has enabled them to be widely applied to various fields represented by intelligent manufacturing. A Metaverse, which is parallel to the physical world, needs mature and secure Digital Twins technology in addition to Parallel Intelligence to enable it to evolve autonomously. We propose that Blockchain combined with other areas does not simultaneously require all of the basic elements. We extract the immutable characteristics of Blockchain and propose a secure multidimensional data storage solution called BlockNet that can ensure the security of the digital mapping process of the Internet of Things, thereby improving the data reliability of Digital Twins. Additionally, to address some of the challenges faced by multiscale spatial data processing, we propose a nonmutagenic multidimensional Hash Geocoding method, allowing unique indexing of multidimensional information and avoiding information loss due to data dimensionality reduction while improving the efficiency of information retrieval and facilitating the implementation of the Metaverse through spatial Digital Twins based on these two studies.

关键词： Digital Twins Parallel Metaverse Geocode Blockchain

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A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 843267页

作者： Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Chongqing Univ Sch Automation Chongqing Peoples R China Wuhan Second Ship Design & Res Inst Sci & Technol Thermal Energy & Power Lab Wuhan Peoples R China Beihang Univ Sch Automation Sci & Elect Engn Beijing Peoples R China

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

关键词： multifingered hand visual-motor control multi-mode fusion hand structural evolution learning-based manipulation

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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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A Method of SMS Spam Filtering Based on AdaBoost Algorithm

A Method of SMS Spam Filtering Based on AdaBoost Algorithm

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World Congress on Intelligent control and Automation

作者： Xipeng Zhang Gang Xiong Yuexiang Hu Fenghua Zhu Xisong Dong Timo R. Nyberg State Key Laboratory of Management and Control for Complex Systems Institute of Automation of Chinese Academy of Science Beijing CO 100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan CO 523000 China Hainan Zhongke Flower Ocean Cloud Commerce Technology Co. Ltd Haikou CO570311 Hainan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing CO 100190 China Qingdao Academy of Intelligent Industries Qingdao CO 266061 China Department of Industrial Engineering and Management Aalto University Aalto CO FI-00076 Finland

ISBN: (纸本)9781467384155

Short message is one of the most common communication media for mobile subscribers, so major mobile operators are devoted to improve their Short Message Service (SMS). However, the annoying and undesired messages, also named message spam or simply spam, not only worsen the users' experience, but also cause their complaints on SMS. In this paper, we present a novel Chinese SMS spam filtering framework based on AdaBoost algorithm to provide accurate and effective short messages classification. Three content-based weak filters are introduced to boost the performance of final classification decision. Results from Receiver Operating Characteristics (ROC) analysis prove the proposed method has such advantages as higher efficiency and fewer parameters over those established SMS spam filtering methods. The application of the proposed method is expected to block the most spam for mobile subscribers and improve the service quality of SMS. With simple data processing and few training parameters, the proposed method can be applied into the practice of short text classification.

关键词： Filtering Mobile communication Training Unsolicited electronic mail Classification algorithms Text categorization Bayes methods

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Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Weiwei Nie, Xiangli Zhang, Bo Sun, Xian Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci AMSS State Key Lab Sci & Engn Comp LSEC Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci AMSS Inst Appl Math Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol NIST Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Image scene classification aiming to assign specific semantic labels for each image is vitally important for the applications of remote sensing (RS) data. In real world, since the observation environment is open and dynamic, RS images are collected sequentially and the numbers of images and classes grow rapidly over time. Most existing scene classification methods are offline learning algorithms, which are inefficient and unscalable for this scenario. In this article, an incremental learning with open-set recognition (ILOSR) framework is proposed for RS image scene classification in the open and dynamic environment, which can identify the unknown classes from a stream of data and learn these new classes incrementally. Specifically, a controllable convex hull-based exemplar selection strategy is designed to address the catastrophic forgetting issue in incremental learning, which can reduce training time and memory footprint effectively. In addition, a new loss function based on prototype learning and uncertainty measurement is proposed for OSR to enhance the interclass discrimination and intraclass compactness of the learned deep features. Experimental results on real RS datasets demonstrate that the proposed method can not only outperform the state-of-the-art approaches on offline classification, incremental learning, and OSR problem separately but also achieve better and more stable performance in the experiments for ILOSR.

关键词： Task analysis Feature extraction Data models Computational modeling Learning systems Training Support vector machines Deep learning incremental learning open-set recognition (OSR) remote sensing (RS) image scene classification

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