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Proposal for training a Cellular Neural Network using a Hybrid Artificial Bee Colony and Nelder-Mead Algorithms 17

Proposal for training a Cellular Neural Network using a Hybr...

引用

17th international conference on electrical engineering, computing science and automatic control, CCE 2020

作者： Morales-Romero, J.J. Reyes-Barranca, M.A. Flores-Nava, L.M. Espinosa-Garcia, E.R. CINVESTAV-IPN Department of Electrical Engineering Mexico City Mexico

ISBN: (纸本)9781728189871

In this work, we propose a methodology for training a Cellular Neural Network based on the Artificial Bee Colony Algorithm and the Nelder-Mead Algorithm the results of this proposal are compared with training using only the Artificial Bee Colony algorithm and with the Nelder-Mead algorithm, also with related works the Cellular Neural Network is directed and trained for image processing, and the selected tasks for the performance evaluation of the Cellular Neural Network are edge detection and noise remover. © 2020 IEEE.

关键词： Cellular neural networks

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A Mini-Mega Transformer for Image Fusion and Applications

A Mini-Mega Transformer for Image Fusion and Applications

引用

international conference on Image, Vision and computing (ICIVC)

作者： Shuwen He Xiaotian Zhang Yong Liu Wei Zhong North Automatic Control Technology Institute Shanxi China International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Technology Dalian University of Technology Dalian China

Multi-modal image fusion aims to integrate images from multiple sensors, generating visually pleasing images that encompass more comprehensive and complementary information. Existing approaches favor the use of convolutional neural networks and employ simple sequential training strategies to achieve high-quality fusion conducive to subsequent perception tasks. However, these measures often overlook the attention mechanism of long-distance pixel information and struggle to achieve stable and efficient collaborative training. To overcome these issues, this paper proposes a fusion network based on the transformer for infrared and visible image fusion and applications. Specifically, we design a cross-scale attention mechanism from both Mini and Mega perspectives to integrate features between different modalities. Additionally, we adopt the concept of bi-level optimization and propose a training strategy that fully associates fusion with perception tasks. Extensive experiments demonstrate the superiority of the proposed network and strategy. Not only does it produce fusion images with good visual effects, but it also improves the performance on perception tasks compared to state-of-the-art methods.

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Energy-and Spectral-Efficient Optimization in Cloud RAN based on Dueling Double Deep Q-Network

Energy-and Spectral-Efficient Optimization in Cloud RAN base...

引用

2021 IEEE international conference on automatic control and Intelligent Systems, I2CACIS 2021

作者： Iqbal, Amjad Tham, Mau-Luen Chang, Yoong Choon Lee Kong Chian Faculty of Engineering and Science Department of Electrical and Electronic Engineering Malaysia

ISBN: (纸本)9781665403436

Cloud radio access network (CRAN) has gained considerable attention for the upcoming cellular network that can offload the mobile data traffic and reduce energy consumption by deploying intelligent distributed multiple remote radio units (RRHs). However, it is still very challenging to achieve an optimal global strategy to maximize the performance of energy efficiency (EE) and spectral efficiency (SE) simultaneously due to non-convex and combinatorial features. Deep reinforcement learning (DRL)-based framework becomes an imperative solution to jointly maximize the EE-SE performance and guarantee the user quality of service (QoS) demands in downlink CRAN. Furthermore, in order to deal with the large state-action space problem, we leverage dueling double deep Q-network (D3QN) to achieve the nearly optimal control strategy. In the end, extensive simulation results demonstrate the effectiveness of the proposed D3QN method over the conventional-DRL methods. © 2021 IEEE.

关键词： Energy efficiency

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PITD3-based Autonomous Evasion Decision-making Method for Aircraft

PITD3-based Autonomous Evasion Decision-making Method for Ai...

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第43届中国控制会议

作者： Chang Liu Liqian Dou Ruilong Zhang Yifan Tang Mengyuan Ren Lun Zhang School of Electrical Engineering and Automation Beijing Aerospace Automatic Control Institute Tianjin Aerospace Institute of Computing Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Outstanding autonomous evasion decision-making capability has an important significance for ensuring flight safety and enhancing the autonomy of the aircraft. In this paper, a prediction information-based TD3(PITD3) evasion decision-making algorithm for aircraft is proposed, which can autonomously evade the interceptors' attack in an antagonistic environment. Firstly,an antagonistic environment is described according to environmental constraints and the dynamics of the aircraft and interceptor. Secondly, the autonomous decision-making process of the aircraft facing the threat target is modelled as a Markov decision process to describe the state information of the aircraft and interceptor. Finally, an autonomous evasion decision-making method based on the twin delayed deep deterministic policy gradient(TD3) is designed to find the optimal strategy, and the effectiveness of the proposed autonomous evasion decision-making method is demonstrated by simulation results and comparative experiments.

关键词： PITD3 Autonomous Evasion Decision-Making Deep Reinforcement Learning Decision Network Aircraft

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IoT Based automatic Traffic Light And Speed Breaker System

IoT Based Automatic Traffic Light And Speed Breaker System

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Devraj Gautam Sandeep Bhatia P Neha Bharat Bhushan Naib Electronics and Communication Engineering Department ADGITM New Delhi India School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

The IoT-based automatic traffic light and speed breaker system is designed to enhance traffic management and road safety. It utilizes sensors and devices connected to a NodeMCU Esp8266 to detect vehicle speed and control the speed breaker. Barrier gate connected to servo motors adjust according to the traffic light, optimizing traffic flow. An emergency vehicle detection system detects sirens of emergency vehicle, turning the traffic light green and lowering barrier gate automatically. The system is connected to the cayenne IoT platform, enabling alert notification emails during the detected value is more than threshold value. Power is supplied through a solar panel during the day, a battery at night, and piezoelectric sensors will be put below the zebra crossing to generate power. And the direct supply of this generated power will be given to the street lights and etc. During adverse weather conditions as the solar panel will not be perform well. Thus, the power generated by piezoelectric sensors can be use here, so that this system can work properly at any cost. While maintenance is necessary, this work demonstrates the potential of IoT-based solutions to address real-world traffic issues and enhance road safety.

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Sliding Mode control of a Quadrotor with Suspended Payload: A Differential Flatness Approach 17

Sliding Mode Control of a Quadrotor with Suspended Payload: ...

引用

17th international conference on electrical engineering, computing science and automatic control, CCE 2020

作者： Martinez-Vasquez, A.H. Castro-Linares, Rafael Rodriguez-Mata, A.E CINVESTAV-IPN Mechatronics Section Departament of Electrical Engineering Mexico City Mexico Nacional de Mexico Instituto Tecnologico de Culiacan CONACYT-Tecnologico Culiacan Sinaloa Mexico

ISBN: (纸本)9781728189871

This article addresses the problem of transporting a slung load with a cable with a quadrotor unmanned aerial vehicle UAV in the frame x-z. The proposed solution introduces a nonlinear model system and a robust sliding mode control approach for a trajectory tracking control of the quadrotor with minimum slung load swing. A differential flatness approach is introduced for parametrization of the horizontal dynamics of the system and a sliding mode approach is proposed to control the system by means of thrust force and torque due to the motors of the quadrotor. Numerical results are presented to evaluate the performance of the control strategy. © 2020 IEEE.

关键词： Sliding mode control

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Traffic Sign Detection using Deep Learning Techniques in Autonomous Vehicles

Traffic Sign Detection using Deep Learning Techniques in Aut...

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2021 IEEE international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2021

作者： Juyal, Amit Sharma, Sachin Matta, Priya School of Computing Graphic Era Hill University Graphic Era Deemed to Be University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665435215

Autonomous vehicle is an emerging topic for both researchers and the automobile industry as companies are still struggling to make fully functional autonomous vehicles. Driving a safe vehicle in a real world depends on different conditions, such as distance from other vehicles, pedestrians, animals, speed-breakers, traffic signals and other unpredictable dynamic environments. Autonomous vehicle can decrease vehicle crashes because software installed in the vehicle instructs the control system of the autonomous vehicle rather than human, and Software makes less error compare to human beings. Automated Traffic Sign Detection and Recognition (ATSDR) is an important task for a safe driving by an autonomous vehicle. Many researchers have used various deep learning-based models for in real-Time ATSDR. Here in the present review, we have studied various deep learning models used for in real-Time ATSDR. Our study suggested that YOLO and SSD can detect the traffic sign in real time and are superior models for ATSDR as compared to other deep learning methods as CNN, R-CNN, Fast R-CNN and Faster RCNN. © 2021 IEEE.

关键词： Deep learning

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Safety Analysis of automatic Crane Trolley Running System Based on STAMP/STPA

Safety Analysis of Automatic Crane Trolley Running System Ba...

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2021 international conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Zhang, Wenbo Meng, Xiangkun Wang, Jianyuan Li, Tieshan Shan, Qihe Teng, Fei Dalian Maritime University College of Navigation Dalian China Dalian Maritime University Marine Electrical Engineering College Dalian China University of Electronic Science and Technology College of Automation Engineering Chengdu China

ISBN: (纸本)9781665402453

automatic crane is a complex system affected by the external environment and the internal components of the system, information fusion, software and hardware combination, and man-machine integration. The improvement of its automation and informatization proposes various challenges in the accident model construction and safety analysis. However, the safety analysis methods based on fault types consider that the occurrence of accidents is linear and ignore the correlation among components of the system. This paper adopts the system-theoretic accident model and process (STAMP) and system-theoretic process analysis (STPA) mode is to implement safety analysis of the automatic crane trolley running system (ACTRs). The paper starts from the identification of system-level losses and hazards, clarifies the function and internal logical control relationships of the system's components, and then finds potential unsafe control actions (UCAs) and loss scenarios during the trolley running. The results show that the control requirements for the regular operation of the trolley running system can be analyzed in detail. Therefore, the STAMP/STPA can apply to the safety investigation of automatic cranes. © 2021 IEEE.

关键词： Accidents

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control of a smart electric wheelchair based on EEG signal and graphical user interface for disabled people

Control of a smart electric wheelchair based on EEG signal a...

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2021 international conference on System science and engineering, ICSSE 2021

作者： Ngo, Ba-Viet Nguyen, Thanh-Hai Tran, Dang-Khoa Vo, Duc-Dung Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh Viet Nam

ISBN: (纸本)9781665448482

A Brain-Computer Interface (BCI) can help disabled people to control an electric wheelchair in an indoor environment. However, using the BCI requires a continuously concentrated effort and this can make them tired. This paper proposes a semi-automatic control method for a smart electric wheelchair based on EEG signal and graphical user interface to provide safe and convenient mobility for disabled people. In particular, EEG signals obtained from an Emotiv device through eye movements are digitized with a sampling frequency of 128Hz. Patterns of an EEG signal are identified to use a peak detection algorithm and the width of the signal pattern. In addition, a graphical user interface designed for on-screen display can support disabled people to select the desired destination from a list of predefined locations to reach by an eye movement for controlling the electric wheelchair. For smooth, safety, and prediction, the wheelchair is designed to move on the path towards virtual orbit and the user can stop the wheelchair in case of appearing danger. Experiments in an obstacle-free environment have been demonstrated to illustrate the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Brain computer interface

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Exploring Advanced Edge Techniques in AI to Develop High-Performance Information Retrieval Systems

Exploring Advanced Edge Techniques in AI to Develop High-Per...

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international conference on Power Energy, Environment and Intelligent control (PEEIC)

作者： Jambi Ratna Raja Kumar Dipannita Mondal G. Ravivarman Latha B Gitanjali B. Yadav Shivangi Dwivedi Department of Computer Engineering Genba Sopanrao Moze College of Engineering Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y. Patil College Of Engineering And Innovation Talegaon Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Commerce Mangalayatan University Jabalpur India

Information retrieval (IR) is an essential aspect of modern-day generation, especially with the fast increase and expansion of the net and its related technologies. IR aims to broaden systems that can correctly and appropriately retrieve applicable statistics from considerable data, assisting customers in their search and records accumulating wishes. AI has performed a tremendous function in advancing IR techniques, making systems more innovative and efficient in retrieving data. However, with the ever-growing extent of facts on the net and the want for real-time admission to statistics, there may be a growing desire for side computing in IR systems. Area computing refers to the processing and garage of records at the brink of the community instead of sending them to a critical area for processing. This method gives numerous advantages, including reduced latency, stepped-forward information privacy and safety, and efficient use of community bandwidth. In recent years, researchers have explored superior side strategies in AI to broaden high-overall performance IR systems. These techniques include aspect caching, facet gadget mastering, and edge-based statistics filtering, Part caching entails storing frequently accessed data at the community's edge, reducing the need to retrieve information from a critical server. This technique can improve IR systems' performance by reducing latency and community congestion.

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