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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

Science China(Technological Sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Collaborative Optimization of Multi-regional Integrated Energy System Based on Improved Beluga Whale Optimization Algorithm

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Distributed Generation and Alternative Energy Journal 2024年第4期39卷 717-750页

作者： Liu, Ming School of Control and Computer Engineering North China Electric Power University Beijing102206 China

With the installed capacity of the renewable energy power generation is growing at a high speed, a large number of the renewable energy is connected to the grid, the energy problem has been solved to a large extent, but the ensuing problems can not be ignored, the first is the consumption of the renewable energy, which is followed by the stability problem of the power system, and a multi-regional integrated energy system (MRIES) is constructed to solve the problem. In view of the wind power uncertainty, the absorption treatment is carried out by the equipment layer and the optimization layer respectively. A hybrid energy storage system (HESS) is introduced in the equipment layer to suppress the influence of the wind power uncertainty on the system stability. A combination of the convolutional filtering algorithm, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm and the fuzzy control algorithm is introduced in the optimization layer to develop the charging and discharging strategy of the HESS. With the strategy, the impact of the electric power fluctuations on the power system during the optimization process can be reduced largely. Then, on the basis of fully considering the energy coupling in different equipments, a collaborative optimization model of the MRIES is constructed. And the integrated demand response model considering the time-of-use electric price is introduced in the load side. Finally, the improved Beluga optimization(IBWO) algorithm is developed to optimize the model. The optimization results show that the IBWO algorithm plays a good optimization effect both in the participation of energy supply equipments and the economy, plays a collaborative optimization role in the MRIES and ensures the stability of the whole power system. © 2024 River Publishers.

关键词： Hybrid power

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A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang LIU Xun CAO JiaBin LIU JinYu XU Yan YAN WeiXin School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation

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Load Frequency control Strategy of Interconnected Power System Based on Tube DMPC

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Distributed Generation and Alternative Energy Journal 2024年第3期39卷 635-658页

作者： Xinshan, Wang School of control and computer engineering North China Electric Power University Beijing102206 China

Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness. © 2024 River Publishers.

关键词： Electric power system interconnection

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PHOTOVOLTAIC POWER PREDICTION BASED ON IMPROVED PYRAFORMER

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第1期86卷 385-398页

作者： Li, Guodong Cai, Wenhao School of Control and Computer Engineering North China Electric Power University Beijing102206 China

The stochastic and high volatility of PV power generation poses a great challenge to the operational management of the electrical grid, while precise anticipation of PV power yield can reduce the impact of its uncertainty. Hence, a photovoltaic prediction model called PDGformer, based on an improved Pyraformer model, is proposed. The encoder of the PDGformer model employs a unique dual-branch structure, wherein the local branch captures the local information of the photovoltaic power sequence, enabling the simultaneous capture of temporal and dimensional dependencies of the photovoltaic data. Conversely, the global branch captures the global information of the photovoltaic power sequence. The model's decoder incorporates an Attention mechanism to effectively integrate both local and global information and generate the final prediction results. Additionally, an MSE reweighting framework is introduced to alleviate the interference caused by abrupt changes in predictions. This framework reduces the loss caused by mutations while increasing the loss for normal states. Employing actual photovoltaic data from a specific location for illustrative analysis, the experiments demonstrate the superior performance of the proposed model compared to others, such as Pyraformer, in effectively predicting photovoltaic output power. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Forecasting

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Active disturbance rejection control for COVID-19 outbreak based on SEIR model

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International Journal of Systems, control and Communications 2024年第3期15卷 292-311页

作者： Zhang, Haonan Tan, Wen Yu, Mei School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

Public health and social measures (PHSMs) standardise the non-pharmacological intervention (NPIs) policies that countries around the world have implemented to curb the spread of COVID-19, and may also serve as a guide for the implementation of future complex health policies in response to new pandemics of infectious diseases. In this paper we aim to use feedback control to help decision-makers establish PHSMs, which can effectively constrain the spread of COVID-19 pandemic with minimal financial loss. The time-dependent susceptible-exposed-infectious-removed (SEIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimisation. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model. © 2024 Inderscience Enterprises Ltd.

关键词： Disturbance rejection

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Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

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Intelligent Automation & Soft Computing 2023年第8期37卷 1963-1981页

作者： Zhizhong Liu Bin Zhou Lingqiang Meng Guangyu Huang School of Computer and Control Engineering Yantai UniversityYantai264005China

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment *** challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion ***,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level *** improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and *** we fuse the unimodal features into bimodal features using the bimodal attention fusion ***,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal ***,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level *** on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

关键词： Multimdoal sentiment analysis BiGRU attention mechanism features-level fusion hybrid fusion strategy

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Part Deviation Correction Method Based on Geometric Feature Recognition

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International Journal of Crowd Science 2023年第3期7卷 113-119页

作者： Zhang, Guoqing Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part’s surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications. © The author(s) 2023.

关键词： Hough transforms

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Modeling and Verification of Simulation-Oriented Digital Selves

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International Journal of Crowd Science 2023年第2期7卷 87-96页

作者： Wang, Zhaotong Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

Networked life has now become one of our major life forms. In social networks, each individual has its own attributes and certain functions, which makes the current network present characteristics that the previous network did not have. The existing research believes that the structure and attributes of individuals in a network are the same, and they are in a single network at the same time. However, individuals in any social network may be in different networks at the same time and thus exhibit different behaviors, and such individuals are called digital selves. In this paper, we propose a simulation-oriented modeling method for digital selves, which allows them to be in multiple networks at the same time and to have their own decision-making mechanisms. The model consists of six parts, namely, pattern, affecter, decider, executor, monitor, and connector. After the verification of three simulation experiments, namely coevolutions, ecological structure evolution of an e-commerce market, and multi-information coevolution spreading, the model can be well applied in various scenarios, which verifies its feasibility and applicability. © The author(s) 2023.

关键词： Decision making

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