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7th IFIP International Conference on Intelligent Information Processing, IIP 2012

作者： Wang, Zhuxiao Peng, Hui Guo, Jing Zhang, Ying Wu, Kehe Xu, Huan Wang, Xiaofeng School of Control and Computer Engineering State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China Education Technology Center Beijing International Studies University Beijing 100024 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642328909

ADML is an architectural description language based on Dynamic Description Logic for defining and simulating the behavior of system architecture. ADML is being developed as a new formal language and/or conceptual model for representing the architectures of concurrent and distributed systems, both hardware and software. ADML embraces dynamic change as a fundamental consideration, supports a broad class of adaptive changes at the architectural level, and offers a uniform way to represent and reason about both static and dynamic aspects of systems. Because the ADML is based on the Dynamic Description Logic DDL( (D)), which can represent both dynamic semantics and static semantics under a unified logical framework, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL( (D)), and dynamic description logic algorithms and implementations can be used to provide reasoning services for ADML. In this article, we present the syntax of ADML, explain its underlying semantics using the Dynamic Description Logic DDL( (D)), and describe the core architecture description features of ADML. © 2012 IFIP International Federation for Information Processing.

关键词： Formal languages

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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A New Anomaly Detector of Flare Pilot Based on Siamese network

A New Anomaly Detector of Flare Pilot Based on Siamese Netwo...

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Chinese Automation Congress (CAC)

作者： Xiaotian Xie Ke Gu Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The flare pilot is the key to ensure that the flare system is ignited at any time. At present, domestic and foreign companies mainly use thermocouples, flame ionization detectors and other sensors to detect the working condition of the flare pilot. These electronic components have the hysteretic and vulnerable problems due to extreme heat, thermal shock and vibration, which may well further lead to the failure to ignite the flare gas with flare pilot in time and cause production accidents. In view of the above problems, this paper proposes an anomaly detector of flare pilot based on deep learning technology. First of all, we made an anomaly detection database for flare pilot, which was shot in a domestic petrochemical company. Then, we designed a specific siamese network based on the triplet loss function for learning the similarity between the samples, in order to apply to the curing scene of the flare pilot. Finally, it is determined whether the flare pilot is abnormal according to the similarity between the input picture and the positive and negative samples. Experimental results demonstrate that the proposed method can be effectively used for the anomaly detection of flare pilot.

关键词： Databases Anomaly detection Petrochemicals Feature extraction Employee welfare Detectors Combustion

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Hybrid brain/muscle-actuated control of an intelligent wheelchair

Hybrid brain/muscle-actuated control of an intelligent wheel...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhijun Li Shuangshuang Lei Chun-Yi Su Guanglin Li Key Lab of Autonomous System and Network Control Ministry of Education and College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Mechanical and Industrial Engineering Concordia University Montreal Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China

Brain-computer interface (BCI) controlled wheelchair robots can serve as powerful aids for severely disabled people in their daily life, especially to help them move voluntarily. In order to better understand human “thought”, owing to the development of the hybrid brain/muscle interface technique, in this paper, we present a real-time hybrid brain/muscle interface to control a wheelchair directly to keep the disables recovering several motion capabilities by using noninvasive motor imagery Electroencephalography (EEG) and Electromyography (EMG). The EMG and EEG signals from the users are extracted to control the motion of an intelligent wheelchair. Both signals processing consists of off-line training, online control evaluation, and real-time control. An algorithm called the common spatial patterns (CSP) is used in this human-robot system to extract the most discriminative spatial patterns pairs as features. The extensive experiments were conducted on the developed human-wheelchair systems to verify the proposed approaches.

关键词： Wheelchairs Electroencephalography Electromyography Training Robots Muscles Real-time systems

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An Architecture Dynamic Modeling Language for Self-Healing systems

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Procedia Engineering 2012年 29卷 3909-3913页

作者： Zhuxiao Wang Jing Guo Kehe Wu Hui He Fei Chen School of Control and Computer Engineering State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China

As modern software-based systems increase in complexity, recovery from malicious attacks and rectification of system faults become more difficult, labor-intensive, and error-prone. These factors have actuated research dealing with the concept of self-healing systems, which employ architectural models to monitor system behavior and use inputs obtaining therefore to adapt themselves to the run-time environment. Numerous architectural description languages (ADLs) have been developed, each providing complementary capabilities for architectural development and analysis. Unfortunately, few ADLs embrace dynamic change as a fundamental consideration and support a broad class of adaptive changes at the architectural level. The Architecture Dynamic Modeling Language (ADML) is being developed as a new formal language and/or conceptual model for representing dynamic software architectures. TheADML couple the static information provided by the system requirements and the dynamic knowledge provided by tactics, and offer a uniform way to represent and reason about both static and dynamic aspects of self-healing systems. Because the ADML is based on the Dynamic Description Logic DDL, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL.

关键词： Architecture Description Languages Knowledge Representation and Reasoning Software Architecture Dynamic Description Logics Self-Healing systems Dynamic Adaptation

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PID parameters optimization research for hydro turbine governor by an improved fuzzy particle swarm optimization algorithm

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Open Electrical and Electronic Engineering Journal 2016年 10卷 101-117页

作者： Chen, Gonggui Yangwei, Du Yanyan, Guo Shanwai, Huang Lilan, Liu Key Laboratory of Industrial Wireless Network and Networked Control of Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Research Center on Complex Power System Analysis and Control Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Locomotive and Vehicle Engineering Wuhan Railway Vocational College of Technology Wuhan430205 China

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the standard particle swarm optimization (PSO) algorithm can speed up the convergence speed and improve convergence precision with combination of the fuzzy control thought and the crossover thought in genetic algorithm (GA). The fuzzy control is employed to get better dynamics of balance between global and local search capabilities, and the crossover operator is introduced to enhance the diversity of particles. Two different types of WTRS systems are built and analyzed in the simulation experiments. Furthermore, the sum of regulating time and another number that is the integral of sum for absolute value of system error and the squared governor output signal is considered as the fitness function of this algorithm. The simulation experiments for parameter optimization problem of WTRS system are carried out to confirm the validity and superiority of the proposed IFPSO, as compared to standard PSO, Ziegler Nichols (ZN) algorithm and fuzzy PID algorithm in terms of parameter optimization accuracy and convergence speed. The simulation results reveal that IFPSO significantly improves the dynamic performance of system under all of the running conditions. © Gonggui et al.

关键词： Fuzzy control

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Development of a Residential Space-Heating Demand Prediction Method under Uncertainly

Development of a Residential Space-Heating Demand Prediction...

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2015 2nd International Conference on Green Materials and Environmental Engineering (GMEE2015)

作者： Dianzheng Fu Zeyu Zheng Yiming Tong Yang Fu Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Network Control System Chinese Academy of Sciences

ISBN: (纸本)9781510819054

in this paper, a prediction method was advanced for residential space heating demand under the fuzz environment. This developed method improved the traditional heat load duration curve method via the quantification expression technique for the fuzzy heating durations. The results indicate that the higher the system possibility is, the narrower the interval of the heat demand prediction result becomes, in which the decision maker can adjust the practical heat provisions of the heat sources. Besides, the opposite heat provision variation trend of the main heat source and peak-shaving heat source can be obtained with the rising thermalization coefficient. Compared with the traditional heat load duration curve method, it is helpful for the relative multi-heat-source centralized heating system managers by providing a potential tradeoff analysis between the system possibility and the selectable heat provision ranges of different heat sources.

关键词： heat demand prediction uncertainty heating system fuzzy set

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and Automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms networked control systems Reinforcement learning Real-time systems Sensors

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Swin Transformer-Based CSI Feedback for Massive MIMO

arXiv

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arXiv 2024年

作者： Cheng, Jiaming Chen, Wei Xu, Jialong Guo, Yiran Li, Lun Ai, Bo State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System Key Laboratory of Railway Industry of Broadband Mobile Information Communications China State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518055 China ZTE Corporation Shenzhen518055 China

For massive multiple-input multiple-output systems in the frequency division duplex (FDD) mode, accurate downlink channel state information (CSI) is required at the base station (BS). However, the increasing number of transmit antennas aggravates the feedback overhead of CSI. Recently, deep learning (DL) has shown considerable potential to reduce CSI feedback overhead. In this paper, we propose a Swin Transformer-based autoencoder network called SwinCFNet for the CSI feedback task. In particular, the proposed method can effectively capture the long-range dependence information of CSI. Moreover, we explore the impact of the number of Swin Transformer blocks and the dimension of feature channels on the performance of SwinCFNet. Experimental results show that SwinCFNet significantly outperforms other DL-based methods with comparable model sizes, especially for the outdoor scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Frequency Perception network for Camouflaged Object Detection

arXiv

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arXiv 2023年

作者： Cong, Runmin Sun, Mengyao Zhang, Sanyi Zhou, Xiaofei Zhang, Wei Zhao, Yao School of Control Science and Engineering Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong University Jinan Shandong China School of Automation Hangzhou Dianzi University Zhejiang Hangzhou China Institute of Information Science Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However, the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively. The code will be released at https://***/rmcong/FPNet_ACMMM23. © 2023, CC BY-NC-SA.

关键词： Frequency domain analysis

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