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Parameter optimization of hydro turbine governor PID controller by chaotic improved PSO algorithm

Energy Education Science and Technology Part A: Energy Science and Research 2014年第5期32卷 3949-3962页

作者： Chen, Gonggui Du, Yangwei Song, Peizhu Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Wireless Network and Networked Control of Ministry of Education Chongqing China Chongqing University of Posts and Telecommunications Research Center on Complex Power System Analysis and Control Chongqing China Hubei Minzu University Dept. of Electrical Engineering Enshi China Hubei Energy Group Qiyueshan Wind Power Co Ltd Lichuan China

For overcoming the disadvantages of easily falling into local extremum and easily losing species diversity in traditional particle swarm optimization (PSO) algorithm, a cross improved PSO (IPSO) algorithm is proposed. In addition, in order to enhance the exploitation capacity of IPSO algorithm, it also makes use of chaotic sequence features which is called chaotic IPSO (CIPSO) algorithm. In present paper, mathematical structure model of hydro turbine governor is established, and the integral of absolute value of error as the minimum objective function is adopted to select particles. In order to obtain satisfactory transient dynamic characteristics in process of tuning of PID parameters, the square of output signal after PID tuning and the rise time are proposed to integrate into the augmented objective function, and in order to minimize overshoot to the greatest extent, the penalty mechanism is concerned. These algorithms are performed for optimized designs of governor PID parameters on hydro turbine. Several specific working conditions are modeled and CIPSO algorithm results are compared with those in PSO and IPSO algorithms. It comes out that the CIPSO algorithm has made some improvements whether in convergent speed or convergent accuracy in comparing with other two algorithms. © Sila Science. All rights reserved.

关键词： Governors

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater networks and systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Uplink Performance of Cell-Free Extremely Large-Scale MIMO systems

arXiv

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arXiv 2023年

作者： Lei, Hao Wang, Zhe Xiao, Huahua Zhang, Jiayi Ai, Bo School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

In this paper, we investigate the uplink performance of cell-free (CF) extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, which is a promising technique for future wireless communications. More specifically, we consider the practical scenario with multiple base stations (BSs) and multiple user equipments (UEs). To this end, we derive exact achievable spectral efficiency (SE) expressions for any combining scheme. It is worth noting that we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio (MR) combining. Numerical results show that the SE performance of the CF XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is interesting that a smaller antenna spacing leads to a higher correlation level among patch antennas. Finally, we prove that increasing the number of UE antennas may decrease the SE performance with MR combining. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada 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

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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A Reputation Based network Security Isolation Method for Power systems

A Reputation Based Network Security Isolation Method for Pow...

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Smart Grid and Energy Engineering (SGEE), International Conference on

作者： Yingqi Huang Man Li Yang Cao Xiangbei Che Shiqin Zeng Siliang Suo Shen Zhen Power Supply Co. Ltd Shenzhen China China Southern Power Grid Dispatching and Control Center Guangzhou China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

In order to improve the security performance of power system networks, enhance data transmission security, and reduce latency, a reputation-based network security isolation method for power systems is proposed in this paper. In this method, the power system network reputation management model is first constructed, the design of a reputation mechanism is completed, and then the design of information privacy protection and shared information verification for the power system network are completed, and finally the progressiveness of the proposed method is verified by using the comparison method. The experimental results show that the proposed method has a data packet loss rate of less than 3.2% under different node numbers, a throughput of only 421 transactions/second, and a maximum transmission delay of only 44.8 ms, which is superior to the comparison method and has greater application value.

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A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel

A Ray-tracing and Deep Learning Fusion Super-resolution Mode...

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European Conference on Antennas and Propagation, EuCAP

作者： Zhao Zhang Danping He Xiping Wang Ke Guan Zhangdui Zhong Jianwu Dou State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing China Frontiers Science Center for Smart High-Speed Railway System Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China ZTE Corporation Shenzhen China

Mobile channel modeling has always been the core part for design, deployment and optimization of communication system, especially in 5G and beyond era. Deterministic channel modeling could precisely achieve mobile channel description, however with defects of equipment and time consuming. In this paper, we proposed a novel super resolution (SR) model for cluster characteristics prediction. The model is based on deep neural networks with residual connection. A series of simulations at 3.5 GHz are conducted by a three-dimensional ray tracing (RT) simulator in diverse scenarios. Cluster characteristics are extracted and corresponding data sets are constructed to train the model. Experiments demonstrate that the proposed SR approach could achieve better power and cluster location prediction performance than traditional interpolation method and the root mean square error (RMSE) drops by 51% and 78% relatively. Channel impulse response (CIR) is reconstructed based on cluster characteristics, which could match well with the multi-path component (MPC). The proposed method can be used to efficiently and accurately generate big data of mobile channel, which significantly reduces the computation time of RT-only.

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Measurement and Modeling of Vehicular Millimeter Wave Radar Signal in Expressway Environment

Measurement and Modeling of Vehicular Millimeter Wave Radar ...

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European Conference on Antennas and Propagation, EuCAP

作者： Hongyu Duan Danping He Ke Guan Zhangdui Zhong Yihua Ma State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China ZTE Corporation Shenzhen China

With the gradual development of autonomous driving technology, the cost of traditional road test is becoming higher and higher. The mainstream approach of autonomous driving test has been transferred to the accurate and efficient simulation test system based on typical environment. Millimeter wave (mmWave) radar is an important part of autonomous driving and integrated sensor system. This means that the foundation of the future autonomous driving environment simulation system needs the real mmWave vehicular radar signal propagation mechanism. Therefore, the research of radio wave propagation characteristics in vehicular mmWave radar frequency band is the basis of mmWave radar simulation. In this paper, the vehicular mmWave radar signal in the expressway environment is measured, and ray-tracing (RT) simulation is conducted according to the measurement configuration. The electromagnetic (EM) parameters is calibrated through the Radar Cross section (RCS). The signal propagation characteristics are researched, and a hybrid channel model is established with RT, which effectively increases the simulation efficiency and serves for future autonomous vehicle driving test.

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Optimization Method of Shared Energy Storage Configuration on Source-Grid-Load Side of Power system

Optimization Method of Shared Energy Storage Configuration o...

引用

International Conference on Power system Technology (POWERCON)

作者： Cuiping Li Yang Liu Tianxun Zhang Zhiqiang Zhao Haojun Liu Xingxu Zhu Zhi Li Chenyu An Junhui Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China Beijing Technology and Business University Beijing China State Grid Hami Power Supply Company Hami City Xinjiang Uygur Autonomous Region China Hami China

With the rise of the application of sharing economy in various fields of power system, As a typical application of shared economy in the field of energy storage, the optimal allocation of shared energy storage on the source-network-load side has been a great topic. The problem dealt with in this paper is the configuration result of the source-grid-load energy storage system under the same control strategy. This paper designs an optimization method for the source-network-load side configuration of generalized shared energy storage in regional power grid: Firstly, according to the extensional usage scenario and demand of energy storage, the alternative set of shared energy storage configuration is constructed. Secondly, based on the profit model of shared energy storage, a power system source-network-load optimization scheduling strategy is proposed. Finally, a numerical example is carried out to prove the feasibility and efficacy of the suggested method.

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Implicit dual-domain convolutional network for robust color image compression artifact reduction

arXiv

引用

arXiv 2018年

作者： Zheng, Bolun Chen, Yaowu Tian, Xiang Zhou, Fan Liu, Xuesong Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Embedded System Engineering Research Center Ministry of Education of China Zhejiang University Hangzhou310027 China Institute of Advanced Digital Technology and Instrument Zhejiang University Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Hangzhou310027 China

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they are unable to handle color images as the compression processes for gray scale and color images are different. Moreover, these methods train a specific model for each compression quality, and they require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with a pixel position labeling map and quantization tables as inputs. We proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN;the implicit dual-domain translation allows the IDCN to handle color images with discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was also developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with little trade-off against performance;further, it demonstrates great potential for practical applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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