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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Chung, Chi Yung Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Department of Electrical and Computer Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada

Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub (EH). However, it is usually challenged by the frequently changing and unpredictable uncertain parameters at different timescales. Under this scope, this paper investigates the coordinated energy management problem for day-ahead and intra-day conditions considering uncertainties of source-load and market prices concurrently. Note that the precise knowledge of distributions about uncertainties may be unaccessible before the decision-making in day-ahead phase. A two-stage chance-constrained model based on distributionally robust approach with ambiguous moment information is proposed to immunize scheduling strategies against the worst-case probability distributions. The first stage is dedicated to obtaining more energy arbitrage and operation flexibility by optimizing bidding strategies in day-ahead power, natural gas and carbon trading markets. The second stage focuses on the optimization of the worst-case expected operation cost. It provides a robust energy equipment and load scheduling strategy for the reference of subsequent intra-day arrangements. With respect to different variations of electrical and thermal components, an intra-day two-timescale coordination is implemented step by step. The energy scheduling is re-dispatched circularly to minimize the operation and penalty costs. The possible energy imbalance is also compensated by this way. As the energy management program is nonlinear, chance-constrained and multi-stage, some linearization and dual transformation techniques are designed to enhance tractability of the program. Experimental results show that the developed multi-level framework results in a carbon emission decrease of 37%, and reduces energy cost averagely 3% compared with corresponding contrasting cases. The obtained strategy validates a good tradeoff between robustness and optimality. Copyright © 2022, The Authors. All rights reser

关键词： Power markets

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Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

arXiv

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arXiv 2022年

作者： Li, Yuwei Yang, Bo Wu, Hao Han, Qiaoni Chen, Cailian Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Huawei Technologies Co. Ltd China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The Department of Automation Tianjin University Tianjin 300072 China

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks. © 2022, CC BY.

关键词： Fog computing

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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Middle East Conference on Biomedical Engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours image segmentation Charge carrier processes

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Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic System

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency Information technology

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Directed epileptic network from scalp and intracranial EEG of epileptic patients

Directed epileptic network from scalp and intracranial EEG o...

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IEEE Workshop on Machine Learning for Signal processing

作者： L. Amini C. Jutten S. Achard O. David H. Soltanian-Zadeh G.A. Hossein-Zadeh P. Kahane L. Minotti L. Vercueil Grenoble INP GIPSA Laboratory Grenoble France Grenoble INP GIPSA-LAB Grenoble Cedex France Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Tehran Iran CHUG Neurology Department GIN U836 INSERM Grenoble France Neurology Department CHUG Grenoble France Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA

We proposed recently the computation of epileptic connectivity graphs based on wavelet correlation coefficients between EEG signals. The suspected epileptiform electrodes are recognized using the clustering of the topological properties of the graph that can be useful for pre-surgical studies. Here, we present a method for comparing epileptic networks estimated from scalp and intracranial EEG (IEEG) in partial epilepsy patients. The results are presented for a patient with left temporal epilepsy. Good spatial correspondence between the IEEG and the scalp EEG epileptic graphs is obtained. These results are consistent with the patient's clinical diagnosis.

关键词： Epilepsy Scalp Electroencephalography Electrodes Surgery Time frequency analysis Indium phosphide Radio control Process control control systems

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Design and Analysis of Secure Routing Protocol for Wireless Sensor Networks

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IOP Conference Series: Materials Science and Engineering 2017年第1期242卷

作者： Jiong Wang Hua Zhang Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(MinJiang University) Fuzhou 350121 China

In recent years, with the development of science and technology and the progress of the times, China's wireless network technology has become increasingly prosperous and it plays an important role in social production and life. In this context, in order to further to enhance the stability of wireless network data transmission and security enhancements, the staff need to focus on routing security and carry out related work. Based on this, this paper analyzes the design of wireless sensor based on secure routing protocol.

关键词：

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Research on Infrared image processing Algorithm of Generator Carbon Brush Based on Adaptive Stochastic Resonance Array

Research on Infrared Image Processing Algorithm of Generator...

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Chinese Automation Congress (CAC)

作者： Shangbin Jiao Weichao Huang Bo Geng Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi ’an University of Technology Xi’an China Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’ hai China Xi'an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

The safe and stable operation of the generator set is related to the national economy and people's livelihood. As an important part of generator excitation system, carbon brush and slip ring temperature monitoring can effectively evaluate the state of the generator, which plays a vital role. Most of the current researches use infrared images to monitor carbon brush temperature, but there are often problems such as high noise and unclear focus. According to the requirements of infrared image display, this paper combines the method of bistable stochastic resonance to denoise the image, and on this basis, introduces the adaptive stochastic resonance array method to denoise the gray image. Experimental results show that compared with common image denoising methods and classic bistable stochastic resonance, the adaptive stochastic resonance array method has improved both visual effects and peak signal- to-noise ratio (PSNR), which further proves the good application of stochastic resonance in weak signal detection and extraction.

关键词： Temperature sensors Temperature measurement Brushes Stochastic resonance Adaptive arrays Generators Carbon

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China SYSTEMA Research Center European University Cyprus Nicosia Cyprus

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multistage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Impulsive control with time windows for hyperchaotic exponential synchronization

Impulsive control with time windows for hyperchaotic exponen...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Hongjuan Wu Xiang Hu Yuming Feng Zhengwen Tu Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing P. R. China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing P. R. China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

In actual situation, the input error of impulsive signal is inevitable and there are always some time delays when different systems communicate with each other. Then, considering the input error and time delays in the real world, this paper provides the criteria for the exponential lag synchronization of one kind of hyperchaotic system based on impulsive control with time windows, matrix inequalities, etc. Finally, the simulation results are displayed to illustrate the theoretical results.

关键词： Input error delays exponential lag synchronization hyperchaotic system impulsive control time windows Input Error Time windows control period Impulsiveness time delay delay

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Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method

arXiv

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arXiv 2024年

作者： Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Chen, keyan Wang, Zhengyi Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Geography University of Hong Kong Hong Kong The School of Oceanography Shanghai Jiao Tong University Shanghai200030 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

Downscaling (DS) of meteorological variables involves obtaining high-resolution states from low-resolution meteorological fields and is an important task in weather forecasting. Previous methods based on deep learning treat downscaling as a super-resolution task in computer vision and utilize high-resolution gridded meteorological fields as supervision to improve resolution at specific grid scales. However, this approach has struggled to align with the continuous distribution characteristics of meteorological fields, leading to an inherent systematic bias between the downscaled results and the actual observations at meteorological stations. In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Building on this foundation, we propose a new downscaling model based on hypernetwork architecture, namely HyperDS, which efficiently integrates different observational information into the model training, achieving continuous scale modeling of the meteorological field. Through extensive experiments, our proposed method outperforms other specially designed baseline models on multiple surface variables. Notably, the mean squared error (MSE) for wind speed and surface pressure improved by 67% and 19.5% compared to other methods. We will release the dataset and code subsequently. Copyright © 2024, The Authors. All rights reserved.

关键词： Weather forecasting

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