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SSRN 2023年

作者： Chunhui, Liang Chao, Ding Xiaoyang, Zuo Jinfa, Li Qing, Guo School of Electrical and Information Engineering Changchun Institute of Technology Changchun130025 China National local joint Engineering Research Center for Measurement and Control and Safe Operation Technology of Intelligent Distribution Network Changchun130103 China Jilin Institute of Chemical Technology Jilin132022 China

In order to solve the problem of wind and light abandonment in new energy power generation system, a scheduling strategy of wind, light and fire combined power generation system including concentrating solar power (CSP) station is proposed in this paper. The day-ahead scheduling model of the combined power generation system is established with the maximum net income and the maximum load tracking ability as the optimization objectives. The fixed scene method was used to simulate the source load uncertainty. The Latin Hypercube sampling method and synchronous back substitution method were used to generate and reduce the scene, and different typical scenes were selected according to the scene probability. The combined spiral motion control strategy is added to the grasshopper optimization algorithm (GOA) to solve the scheduling model, which enhances the optimization performance of the algorithm. The effectiveness and correctness of the proposed model and the improved algorithm are verified by simulation in IEEE-30 node system. The numerical results show that the optimal scheduling strategy of multi-energy system proposed in this paper has significant functions in calming load fluctuation and improving system absorption ability. © 2023, The Authors. All rights reserved.

关键词： Optimization

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Evaluation and Prognostics of the Higher Education Based on Neural Network and AHP-PLS Structural Equations

Evaluation and Prognostics of the Higher Education Based on ...

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2021国际计算机前沿大会

作者： Fanyueyang Zhang Xunbo Zhang Zirui Tang Xianhua Song School of Science Harbin University of Science and Technology School of Economics and Management Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology

As a subsystem of society, higher education is an inevitable choice to meet the political and economic development of a country. Research of the higher education evaluation system is of great significance to the development of society. In this paper, a backpropagation(BP) neural network model is established to predict the future development scale of higher education. The analytic hierarchy process(AHP) method and partial least squares regression(PLS) structural equations were used to verify the scientificity and feasibility of the model. BP neural network has strong nonlinear mapping capabilities, and it is capable of the prognostics. It performed well on issues with more complicated internal mechanisms. Through experimental simulations, it is found that the BP neural network model has a good fit when making predictions and the relative error is less than3%, which shows that the prediction results obtained with this model have high reliability.

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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Memristive Computing and Applications (ICMCA), IEEE International Conference on

作者： Ximei Zhao Hanyu Zhang Lijun Shao Lili Huang Jiao Li School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Collaborative Innovation Center of Memristive Computing Application(CICCA) Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality.

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INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMART ENVIRONMENT USING IOT NETWORK 1

INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMAR...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Parvathi, R. Kumar, T.Ch. Anil Nandhakumar, A. Arshad, Haqqani Singh, Ajith B. Department of Information Technology School of Computing Science Hindustan Institute of Technology and Science Padur Chennai India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Vadlamudi Dt. Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Dhaanish Ahmed Institute of Technology Chennai India Department of Mechanical Engineering Yanbu Industrial Collge Yanbu Western Region Saudi Arabia Department- Instrumentation and Control Engineering Sri Krishna College of Technology Coimbatore India

ISBN: (纸本)9781607685395

Internet of things (IoT) constitutes the combinations of sensors, controllers, actuators, connectivity. Tracking and navigating moving objects are crucial tasks for security as well as a secure and smart environment. By using the sensory data, location of objects can be identified then that sensory data will be transferred to controllers for the further processes. One pivot role of the system is to identify the trajectory and location of the object using the speed, velocity, acceleration, maps etc and this can be done by machine learning programs. Analyzing service required, prediction of the trajectory and real-world knowledge of maps or locations, To avoid the loop-carry dependency, service request parameters are dynamically changed to locate relevant virtual objects. In order to track moving items, this study provides an architecture based on IoT self-aware environment technology that facilitates the tracking and allocation of relevant virtual objects. © The Electrochemical Society

关键词： IoT loop-carry dependency machine learning object identification self-aware environment sensory data

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Remotely controllable Data Self-Destruction for Privacy-Preserving Sensitive Data Services

Remotely Controllable Data Self-Destruction for Privacy-Pres...

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IEEE International Conference on High Performance Computing and communications (HPCC)

作者： Chengxin Zhou Xianbin Xue Khaled Riad Yan Zhu William Cheng-Chung Chu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North Navigation Control Technology Co. Ltd. Beijing China College of Computer Science & Information Technology King Faisal University Saudi Arabia School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China Department of Computer Science Tunghai University Taichung Taiwan

This paper address a comprehensive solution to address data privacy and security challenges in data science, providing secure self-destruction of sensitive data, confidentiality through encryption, and flexible control over message destruction. Based on outsourcing cloud data services, we introduce a cloud retrograde storage (CRS) system for privacy-preserving self-destruction of sensitive data. The CRS is based on a frequently colliding hash table and recycle pool, and supports remote time, count, and range control. Hereby, we provide a remotely controllable mechanism for the prompt destruction of inactive messages against digital forensics without reducing the availability of services. The method used in the CRS involves storing messages in a recycle pool, where they are automatically erased at the end of their validity period or after being viewed by all recipients. The system also incorporates encryption and decryption processes to ensure confidentiality. The result of performance evaluation shows that the CRS effectively handles the destruction of messages and provides secure data removal from storage pools. The experiments demonstrate the efficiency and practicality of the CRS for large-scale data services.

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Junjie Wu Haixiao Li Yuanguo Zhou Le Cao Yan Wang College of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications.

关键词： Location awareness Accuracy Ground penetrating radar Transmitting antennas Stacking Radar imaging Vectors Reflection Clutter Ultra wideband technology

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

arXiv

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arXiv 2024年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf The School of Control Science and Engineering Shandong University Shandong Jinan China The School of software Shandong University Shandong Jinan China School of Computer Science and Technology Shandong University China The School of Communication and Information Engineering Shanghai University Shanghai China The School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This presents a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.02 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectively), and visually. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Capacity Configuration Optimization of Wind-Solar Combined Power Generation System Based on Improved Grasshopper Algorithm

SSRN

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SSRN 2023年

In this paper, a wind-solar combined power generation system is proposed in order to solve the absorption problem of new energy power generation. Based on the existing installed capacity of local wind power, a concentrating solar power (CSP) station and its energy storage system are configured, and a two-layer capacity optimization allocation method considering the incentive user response is proposed. Firstly, a two-layer capacity optimization model considering incentive user response is established. Secondly, grasshopper optimization algorithm based on embedded spiral motion control strategy was used to solve the upper configuration model, and the lower scheduling model was solved by Cplex solver. Finally, three typical scenarios are set up for simulation, and the wind power, CSP and energy storage configuration capacity are respectively given in different scenarios. The simulation results show that the addition of a CSP station can effectively improve the absorption capacity of local wind power generation system and reduce the amount of wind abandonment. The capacity optimization allocation method proposed in this paper can effectively alleviate the load peak demand, improve the optimization allocation model of wind-solar combined power generation system, make the configuration results more reasonable, and improve the economy of the system. © 2023, The Authors. All rights reserved.

关键词： Wind power

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Pruning CNN based on Combinational Filter Deletion

Pruning CNN based on Combinational Filter Deletion

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Annual Conference of Industrial Electronics Society

作者： Wenli Huang Xiujie Wang Zhihong Zhao Li Su Shuai Sui Jinjun Wang School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Science and Technology on Communication Networks Laboratory Shijiazhuang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Ningbo University of Technology Ningbo China Liaoning University of Technology Jinzhou China Institute of Artificial Intelligence and Robotic Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques.

关键词： measurement Industrial electronics Filters Neural networks Diversity reception Excavation

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TransTracking for UAV: An Autonomous Real-time Target Tracking System for UAV via Transformer Tracking

TransTracking for UAV: An Autonomous Real-time Target Tracki...

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2021 International Conference on Intelligent technology and Embedded Systems, ICITES 2021

作者： Sun, Xiaolou Xie, Fei Wang, Qi Yao, Yuncong Wang, Hao Wang, Wei Yang, Wankou School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Nanjing University of Information Science and Technology School of Automation Nanjing211800 China

ISBN: (纸本)9781665427555

Most of the existing Siamese-type trackers usually adopt pre-defined anchor boxes or anchor-free schemes to accurately estimate the bounding box of targets. Unfortunately, they suffer from complicated hand-designed components and tedious post-processings. It is not easy to adjust parameters for unique scenes in real applications. So, we propose a new scheme by formulating visual tracking as a direct set prediction problem to alleviate this issue. The main component is a transformer attached to the Siamese-type feature extraction networks. Thus, our new framework can be summarized as Siamese Network with Transformers (SiamTFR). With a fixed small set of learned object queries, we force the final set of predictions via bipartite matching, significantly reducing hyper-parameters associated with the candidate boxes. Due to the unique predictions of this framework, we significantly ease the heavy burden of hyper-parameters search of post-processings in visual tracking. Extensive experiments on visual tracking benchmarks, including GOT-10K, demonstrate that SiamTFR achieves competitive performance and runs at 50 FPS. Specifically, SiamTFR outperforms leading anchor-based tracker SiamRPN++ in the GOT-10K benchmark, confirming its effectiveness and efficiency. Furthermore, SiamTFR is deployed on the embedded device in which the algorithm can be run at 30FPS or 54FPS with TensorRT meeting the real-time requirements. In addition, we design the complete tracking system demo that can work in the real road to narrow the gap between the academic models and industrial deployments. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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