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SSRN 2022年

作者： Shen, Yindong Li, Yuanyuan School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education China

Vehicle scheduling plays a profound role in public transit planning. Vehicle scheduling problem (VSP) addresses the task of assigning vehicles to cover timetabled trips to minimize the fleet-size as well as operating costs. The fixed cost of acquiring and maintaining vehicles is much more expensive than the operating cost. It is of great theoretical and practical significance to construct the vehicle schedules with the exact minimum fleet-size (EMF). The theorem that the EMF is equal to the maximum incompatible degree of the timetabled trips is first proposed and proved. Based on the theorem and time compatibility between any two trips, this paper theoretically examines vehicle scheduling with EMF. Specifically, construction procedures of schedules with EMF are provided for VSP in a specific case as well as the general case. Furthermore, sensitivity analysis of the EMF and the schedule with EMF are carried out. The EMF is possible to decrease by 1 if a trip is deleted or the start/end time of a trip is changed in the timetable. © 2022, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Short-Term Hybrid Prognostics of Fuel Cells: A Comparative and Improvement Study

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SSRN 2024年

作者： Sheng, Chuang Fu, Jun Zu, YanMin Deng, ZhongHua Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China School of Electronic Information Engineering Jingchu University of Technology Jingmen448000 China

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the service life of the stack, which led to the creation of a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. The integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), is explored to investigate their effect on improving the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behaviour. The simulated annealing (SA) algorithm is utilized to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two types of prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Different hybrid methods are evaluated under general conditions and further detailed optimization. The proposed hybrid framework is verified by the data obtained from the experimental platform. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other prognostic methods. © 2024, The Authors. All rights reserved.

关键词： Simulated annealing

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Cross-Modal De-Deviation for Enhancing Few-Shot Classification

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SSRN 2023年

作者： Pan, Mei-Hong Shen, Hong-Bin School of Electronic Information and Electrical Engineering Shanghai Jiaotong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achieve this, we propose a cross-modal de-deviation framework that leverages class semantic information to provide robust prior knowledge for the samples. This framework begins with a visual-to-semantic autoencoder trained on the labeled samples to predict semantic features for the unlabeled samples. Then, we devise a binary linear programming model to incorporate the initial prototypes with the cluster centers of the unlabeled samples. To avoid mismatch between the cluster centers and the initial prototypes, we conduct the label assignment process in the semantic space using the class ground truth semantic features as the reference points, with the cluster centers transformed into semantic representations. Moreover, we model a linear classifier with the concatenation of the refined prototypes and the class ground truth semantic features serving as the initial weights. Then we propose a novel optimization strategy based on the alternating least squares (ALS) model. From the ALS model, we can derive two closed-form solutions regarding to the features and weights, facilitating alternative optimization of them. Extensive experiments conducted on three standard benchmarks demonstrate the competitive advantages of our CMDD method over the state-of-the-art few-shot classification methods, confirming its effectiveness in reducing deviation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Front-end Replication Dynamic Window (FRDW) for Online Motor imagery Classification

arXiv

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arXiv 2024年

作者： Chen, Xinru An, Jiayu Wu, Huanyu Li, Siyang Liu, Bin Wu, Dongrui The 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 The Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Beijing Fenghuo Wanjia Technology Co. Ltd. Beijing100026 China

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed;front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022. © 2024, CC BY.

关键词： Brain computer interface

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Multi-objective capacity configuration optimization of a grid-connected SOFC/LB microgrid

Multi-objective capacity configuration optimization of a gri...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Beibei Wang Zishan Yin Jingxuan Peng Shiwei Zeng Zhonghua Deng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan China Wuhan Huamao Automation Co. Ltd Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

The solid Oxide Fuel Cell (SOFC) technique with electric to gas technique is an investment-worthy way to enhance the consumption of renewable energy in microgrids. Moreover, SOFC’s high fuel adaptability and efficient cogeneration are ideal for stationary power generation. However, due to SOFC’s slow load response and high cost, the capacity configuration designation of microgrid with SOFC system is significant. To enhance energy utilization and reduce cost, in this paper, a microgrid connected with an upper grid, containing SOFC, wind turbine (WT), photovoltaics (PV), electrolyzer, a hydrogen storage system (HSS), and lithium battery (LB), is built. Considering the dynamic characteristics of SOFC’s load response, transient and steady-stage’s energy management strategies assisted by LB are proposed. Capacity configuration parameters of microgrid’s components are selected as decision variables and minimal life cycle cost (LCC) and minimal curtailment rate of wind and PV are selected as objective functions. Using proposed strategies to process microgrids, the NSGA-II algorithm is used to optimize. An actual case is studied and appropriate capacity configuration is solved, with 2112 120W PVs,315 500W WTs, 5 10kW • h LBs,5 SOFCs,98.73kW electrolyzer and 38.94kg *** reaches $4.268 × 10 5 and wind &PV’s curtailment rate reaches 5.71%. The result’s rationality is proved by analyzing the detailed cost and operation.

关键词： Photovoltaic systems Renewable energy sources Costs Power demand Fuel cells Microgrids Solids

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Containment control Protocol Design for PDE Agents Subject to External Disturbances

Containment Control Protocol Design for PDE Agents Subject t...

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Chinese control Conference (CCC)

作者： Yining Chen School of Artificial Intelligence Tiangong University Tianjin P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples.

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SMTC-CL: Continuous Learning via Selective Multi-Task Coordination for Adaptive Signal Classification

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Hao, Xiaoyang Yang, Shuyuan Liu, Ruoyu Feng, Zhixi Peng, Tongqing Huang, Bincheng Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education School of Artificial Intelligence Xi'an710071 China Avic Xi'an Flight Automatic Control Research Institute China MeiTuan China China Electronics Technology Group Corporation Institute of Information Science and Technology China

Continuously adaptive signal classification in complex electromagnetic environments is a desired property of realistic intelligent systems. However, the limitation of most existing signal processing tasks and methods lies in their assumption of fixed categories and invariant distributions. In contrast, humans naturally acquire knowledge continuously from data streams, enhancing their capabilities through cross-task knowledge integration and learning from the past. Inspired by this, we propose a continuous learning (CL) method based on selective multi-task coordination (SMTC) to deal with ever-changing task categories and distributions in practical electromagnetic environments (denoted as SMTC-CL). Firstly, we propose a signal task distribution comparison (TDC) method that leverages intra-domain density and inter-domain divergence. TDC selectively activates multiple old tasks to collaboration with the current incremental new task. Secondly, we propose the SMTC method. By revisiting potential collaborative tasks from the past using TDC and formulating different selection strategies based on the context, continuous learners can achieve mutual benefits between past and current tasks in SMTC learning. SMTC can partially alleviate catastrophic forgetting and enable continuous learners to adapt to the changing electromagnetic environment. Based on the signal automatic modulation classification (AMC) and real-world specific emitter identification (SEI) datasets, extensive experimental results validate the effectiveness of our proposed method on a variety of CL scenarios, including varying signal task categories, distributions, and their combinations. © 2015 IEEE.

关键词： Contrastive Learning

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Ultrasound Imaging Based on Mean and Standard Deviation

Ultrasound Imaging Based on Mean and Standard Deviation

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International Conference on Computational Science and Computational intelligence (CSCI)

作者： Cuijuan Lou Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Dept. of Artificial Intelligence Henan University of Technology Zhengzhou China

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Resolution and contrast are two important indicators for evaluating the quality of ultrasound imaging. In order to improve both indicators of ultrasound imaging, the ultrasound imaging method based on mean and standard deviation are proposed. Through simulation results of multiple scattering points at different depths, the proposed method achieves a lateral resolution of 50.50% compared to delay and sum (DAS) and 36.17% compared to square-neighborhood robust signal mean-to-standard-deviation factor imaging (SN-RSMSF). Through the simulated anechoic and hyperechoic regions results, the contrast improvement of the proposed method can reach up to 0.36dB, compared to DAS and SN-RSMSF.

关键词： In vivo Ultrasonic imaging image resolution Scientific computing Simulation Imaging Scattering

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Jianqing Lin Huachao Zhu Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy.

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Impact of Rail Transit Systems on Transport Accessibility under Road Congestion

Impact of Rail Transit Systems on Transport Accessibility un...

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2022世界交通运输大会(WTC2022)

作者： Bo Zhan Hui Zhang Min Ouyang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Lab.for Image Processing and Intelligent Control Huazhong University of Science and TechnologyMinistry of Education

Investment inrail transit systems is widely advocated in large,high-density cities on the basis of its purported role in reducing road congestion,but the impact of rail transit systems on congestion remains a *** paper provides a method for evaluating the impact of rail transit systems on road congestion in terms of transport accessibility,and applies the method to 43 Chinese cities that have opened rail transit *** show that the rail transit systems can reduce road congestion in most cities,but their performance largely depends on the congestion speed and the rail transit network ***,the structural properties analysis of rail transit networks in two typical cities(Dalian and Changchun)indicates that rail transit lines with longer length,smaller detour factor,and larger population coverage rate tend to perform better in congestion *** findings in this paper provide guidance for city authorities regarding current and future investment in rail transit systems.

关键词： Rail transit system Congestion reduction Accessibility Rail transit network size Congestion speed Structural properties

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