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2024 12th International conference on Intelligent Computing and Wireless Optical Communications, ICWOC 2024

2024 12th International Conference on Intelligent Computing ...

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12th International conference on Intelligent Computing and Wireless Optical Communications, ICWOC 2024

ISBN: (纸本)9798350374377

The proceedings contain 22 papers. The topics discussed include: SDN-based optimized controller deployment strategy for satellite network;multi-UAV enabled maritime relay and edge-computing service migration;a TDoA-based single-LED VLP system assisted by circular photodiodes array receiver;research on parabolic cores and high refractive index ring assisted fibers;FUFQ: a heterogeneous-based full-amplitude quantum simulator;design of network topology control algorithm for multi-agent systems;aerial reconfigurable intelligent surface-assisted secrecy energy-efficient communication based on deep reinforcement learning;conceptual design to achieve stable communication performance in OWC systems with dimming control;and an improved label propagation algorithm for undirected weighted networks.

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Solar Photovoltaic Forecasting Using ANN network for Central and Southern Regions of Jordan

Solar Photovoltaic Forecasting Using ANN Network for Central...

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2023 IEEE Jordan International Joint conference on Electrical Engineering and Information Technology, JEEIT 2023

作者： Al-Rawashdeh, Rafi Alsarayreh, Mohammad Al-Odienat, Abdallah Mutah University Faculty of Engineering Mutah Jordan King Abdullah Ii School of Engineering Princess Sumaya University for Technology Department of Electrical Engineering Amman11941 Jordan Samra Electric Power Company Amman Jordan Amman Arab University Department of Renewable Energy Engineering Amman11953 Jordan Mutah University Faculty of Engineering Jordan

ISBN: (纸本)9798350324051

The use of renewable energy has increased during the last several decades. The most popular renewable energy source is photovoltaic (PV) technology, which uses solar radiation to create electricity. However, a number of variables, such as position, weather, etc., have an impact on the production of PV electricity. It is crucial to control the inherent changeability of PV plants as they expand and contribute significantly to the production of grid power. Predicting solar PV is therefore essential for ensuring efficient and dependable grid functioning. The forecasting model's inputs were historic PV power output data from two solar power installations in Jordan's central and southern regions. The prediction of PV power production in this research takes into account a stacked long short-term memory network (LSTM), a crucial part of the deep recurrent neural network. This model and Nonlinear Autoregressive NARX have been contrasted (Dynamic Neural network). The outcomes demonstrated equivalent, admirable performance for both the dynamic NARX ANN and the LSTM, with NARX being better. The dynamic ANN can be claimed to be superior to the deep neural network (DNN) for time-based performance modeling of PV systems with varying data. © 2023 IEEE.

关键词： Long short-term memory

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Distributed Optimal Data Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic networks 61

Distributed Optimal Data Allocation in Finite Time with Effi...

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IEEE 61st conference on Decision and control (CDC)

作者： Rikos, Apostolos, I Hadjicostis, Christoforos N. Johansson, Karl H. KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden Digital Futures SE-10044 Stockholm Sweden Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

ISBN: (纸本)9781665467612

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm, which operates over a dynamically changing network and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.

关键词： Optimal systems

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A Hybrid Autonomous control System Based on Adaptive Proportional and Model Predictive controllers for an Unmanned Helicopter 3rd

A Hybrid Autonomous Control System Based on Adaptive Proport...

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3rd International conference on Advances in Information and Communication Technology, ICTA 2024

作者： Le, Tri-Quang Hung Yen University of Technology and Education Hung Yen Viet Nam

ISBN: (纸本)9783031809422

In this paper, we propose integrating a learning-based online estimation with a hybrid control system that combines Model Predictive control (MPC) and an Adaptive Proportional controller (APC). This integration aims to improve the reliability and performance of autonomous flight control for unmanned helicopters. We use Subspace and Prediction Error Minimization methods to estimate discrete models of pitch, roll, yaw, and heave motions. The hybrid control system leverages MPC for robustness and performance, while APC adjusts control gains based on the squared error between the target attitude and the predicted output from the online estimator. The online estimator, implemented with a Radial Basis Function Neural network, uses a weighting matrix to reduce measurement errors and disturbances. Simulations in turbulent conditions show that the proposed hybrid system outperforms standalone MPC in both performance and robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radial basis function networks

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Research on Unsteady Aerodynamic Modeling Method Based on Deep Neural network

Research on Unsteady Aerodynamic Modeling Method Based on De...

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2024 IEEE International conference on Unmanned systems, ICUS 2024

作者： Liu, Boya Shen, Yanjie Feng, Shuai Bu, Chen Tan, Hao AVIC Aerodynamics Research Institute Aviation Key Laboratory of Science and Technology on Low Speed and High Reynolds Number Aerodynamic Research Harbin China

ISBN: (纸本)9798350384185

The innovative design of the typical nonlinear unsteady aerodynamics of the aircraft during flight is the research focus of researchers in related fields in the industry. The algorithm model based on deep learning created for the high angle of attack range of innovative design aircraft is of great research value for aircraft flight simulation calculation and flight control law design. Aiming at the high-maneuverability aircraft with complex motion, the aerodynamic prediction model based on bidirectional-long short-term memory neural network is established by using the real data of large pitch oscillation obtained from wind tunnel test. Then, the transfer learning method is used to fine-tune the parameters and enhance the performance of the deep learning model. Finally, the effectiveness and generalization of the algorithm model are verified by the typical maneuver simulation actions ' tail dash ' and ' cobra ' test in wind tunnel test. The results show that the deep learning algorithm adopted in this paper can accurately predict the unsteady aerodynamic force of the standard model aircraft with small aspect ratio flying wing layout under different maneuvers, and the algorithm model enhanced by transfer learning technology has a good performance improvement on the accuracy of the algorithm. © 2024 IEEE.

关键词： Wind tunnels

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Neural-network-Based Adaptive Sliding Mode control Design for Nonlinear systems 23

Neural-Network-Based Adaptive Sliding Mode Control Design fo...

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23rd International conference on control, Automation and systems, ICCAS 2023

作者： Chu, Yundi Luo, Xujun Hou, Shixi College of Iot Engineering Hohai University Nanjing210098 China

ISBN: (纸本)9788993215274

In this article, for a series of nonlinear dynamic systems, a novel neuroadaptive controller which base on dual-loop recursive fuzzy neural network (DRFNN) is designed. Different from the traditional fuzzy neural network (FNN) in which parameters need to be preset and tested several times, owing to the existence of adaptive mechanism, the center vector and the base width in DRFNN do not need to be preset and debugged again and again, but are set randomly and they will eventually stabilize at the optimal values. In addition, thanks to the recursive structure, the DRFNN can store more useful information and process time varying signals effectively, consequently, the DRFNN possesses more accurate approximation ability and stronger robustness than fuzzy neural network. Then DRFNN and global sliding mode control are combined to design the controller, and the stability of the closed-loop system is proved strictly according to Lyapunov stability criterion. Furthermore, in order to verify the practicability and effectiveness of the proposed scheme, the APF is selected as the research object for simulation. The results demonstrate that the developed control scheme has superior performance. In the end, some comparisons among RFNN and the proposed DRFNN are conducted to suggest that the DRFNN possesses more superior properties. © 2023 ICROS.

关键词： Fuzzy neural networks

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Remaining Useful Life Prediction Method for Rolling Bearings Based on CBAM-CNN-BiLSTM 12

Remaining Useful Life Prediction Method for Rolling Bearings...

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IEEE 12th Data Driven control and Learning systems conference (DDCLS)

作者： Zhou, Honggen Ren, Xiaodie Sun, Li Li, Guochao Liu, Yinfei Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang 212000 Jiangsu Peoples R China Rocket Engn Univ Dept Control Engn Xian 710025 Peoples R China

ISBN: (纸本)9798350321050

Rolling bearings, as a rotating component, are of great importance to ensure the normal operation and smooth running of important equipment. Remaining useful life (RUL) prediction is a hot research topic in the engineering field, which is helpful to ensure the operational safety of system equipment and reduce maintenance cost. The topic of how to utilize the important feature information in the time-series data and the reasonable use of attention mechanism are addressed in this study with a CBAM-CNN-BiLSTM-based technique for estimating the remaining service life of rolling bearings. Firstly, multi-domain features of vibration signals are extracted from time domain, frequency domain and time-frequency domain, and the features are normalized to the maximum-minimum value. Then, a convolutional neural network incorporating a hybrid convolutional attention module is used to extract the important features;a bidirectional long- and short-term memory network is employed to obtain the before-and-after dependencies in the features. Next, the self-attention mechanism is introduced into the bidirectional long and short-term network to focus on more important deep features. Finally, the effectiveness of the method is verified by the XJTU-SY dataset. The comparative study shows that the proposed CBAM-CNN-BiLSTM model outperforms other state-of-the-art methods in RUL prediction and system prediction, with higher prediction accuracy and generalization performance.

关键词： rolling bearings lifetime prediction convolutional neural network bidirectional long and short-term memory network self-attention mechanism

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control Method of Dynamic Clustering network in Wireless Sensor network 5

Control Method of Dynamic Clustering Network in Wireless Sen...

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5th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2023

作者： Liu, Hailin Zhang, Chengguang Hu, Bingfu Weifang Engineering Vocational College Department of Information Engineering Shandong Province Qingzhou China

ISBN: (纸本)9798350333442

To solve the routing holes in wireless sensor networks caused by high energy consumption of cluster head nodes and shortage of spectrum, a dynamic clustering based method for wireless sensor network networking control is presented. In the early stage of cluster networking, the communication efficiency index is constructed by collecting the signal-to-noise ratio and energy consumption of wireless sensors (nodes), and the cluster head node and working frequency are selected. To avoid the routing holes caused by cluster head node death, periodically check the communication efficiency index of the nodes to dynamically adjust the cluster head node and working frequency. A smooth transition between old and new cluster head nodes is achieved by substituting nodes. This control method ensures that a wireless sensor with the best performance always has a stable communication with the external terminal as the cluster head node in the wireless sensor network, and improves the performance of the entire wireless sensor network. © 2023 IEEE.

关键词： Sensor nodes

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Model Predictive control: Trade-offs between performance and Computation time 8

Model Predictive Control: Trade-offs between Performance and...

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8th conference on Advances in control and Optimization of Dynamical systems (ACODS)

作者： Khimani, Deepti Mate, Sammyak Bhartiya, Sharad VES Inst Technol Dept Automat & Robot Mumbai Maharashtra India Indian Inst Bombay Dept Chem Engn Mumbai Maharashtra India

Model predictive control (MPC) uses a model of the system as a proxy to obtain multi-step predictions of the state or output variables. Thus MPC performance significantly depends on the quality of system approximation by the model. While a linear model provides a reasonable approximation of a nonlinear system over a small operating region and only requires online solution of a quadratic program (QP), the multi-step predictions can be grossly inadequate if the system trajectories cover a large operating range and the MPC performance may become unacceptable. Here, nonlinear models are necessary to provide accurate predictions. However, the online computation requires solution of a nonlinear program (NLP) instead of a QP. This trade-off between a linear and nonlinear system can be solved by use of multiple linear models, where the online optimization problem takes form of a QP but allows superior predictions than linear models. In this work, we explore the trade-off for a significantly nonlinear system namely, a continuous stirred tank reactor, by using a feed forward neural network (FFNN) as the nonlinear model for the multi-step predictions. It is seen that while the FFNN provides reasonable approximations the computation times to solve the online optimization problems are significantly higher. This trade-off motivates our future research directions of approximating the FFNN by a quasi-linear parameter varying system as a new approach to the trade-off.

关键词： Predictive control of nonlinear systems Neural networks quasi-linear parameter varying systems Multiple linear models

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Dynamic performance Evaluation of Air-to-Air Missiles Based on Deep Learning

Dynamic Performance Evaluation of Air-to-Air Missiles Based ...

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International conference on Autonomous Unmanned systems (ICAUS)

作者： Bai, Xiaomeng Meng, Yuanjie Bi, Peng Han, Zhiguo Xu, Cheng Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China Beijing Electromech Engn Inst Beijing 100074 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Inst Shanghai Electromech Engn Shanghai 201109 Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

Modern air combat is increasingly complex. In the face of manoeuvring target with high velocity, how to use the limited power and manoeuvrability of air-to-air missile to achieve effective interception is an urgent consideration. To address the problems in predicting whether the missile can hit the target and the velocity when the missile hits the target, this paper adopts a dynamic performance evaluation model by building a classification neural network and a regression neural network. The offline computation is performed through the attack and defense confrontation simulations, and the model is trained according to the generated experimental dataset. Simulation results show that the classification network can effectively determine whether a missile can hit the target. Furthermore, the regression network can predict the residual velocity of the missile when it hits the target. The accuracy of the classification neural network can reach more than 96%. The coefficient of determination (R-2 score) of the velocity prediction network is 0.996727, and 99.3% of the predicted velocities have an error within +/- 20 m/s, meeting the precision requirement in actual combat.

关键词： Air-to-air missile Deep neural network Dynamic performance evaluation Velocity prediction

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