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SSRN 2024年

作者： Çeven, Süleyman Albayrak, Ahmet Bayır, Raif Department of Mechatronics Engineering The Institute of Graduate Studies Karabük University Karabük Turkey Department of Electronics and Automation Technology Düzce Vocational School Düzce University Düzce Turkey Department of Computer Engineering Faculty of Technology Düzce University Düzce Turkey Department of Mechatronics Engineering Faculty of Technology Karabük University Karabük Turkey

Road traffic fatalities and injuries remain a major global health and development challenge. According to the World Health Organization (WHO) global status report on road safety (2023), the number of road traffic fatalities has decreased slightly to 1.19 million per year, indicating that efforts to improve road safety are effective. Driver behaviour is known to be the main cause of traffic accidents. Drivers' behaviours such as steering, braking and acceleration are behaviours that have important effects on traffic. In this study, eco-safe driving behaviours of drivers were studied with the data obtained through in-vehicle sensor data of 16 drivers. In the experimental studies, vehicle parameters were obtained via On-board Diagnostics (OBD) interface on a driving route determined in Düzce province of Türkiye. In the literature, eco driving and safe driving have been studied separately. In this study, a new methodology is proposed to consider eco driving and safe driving together. The proposed methodology is developed with data labelling technique based on fuzzy logic approach. Through this process, drivers' eco-safe driving behaviours were classified into five different categories. After the eco-safe driving index was obtained with fuzzy logic, MLP (Multilayer Perceptron), Ensemble Learning and SVM (Support Vector Machine) were used to predict the driving behaviour of the drivers. In the experimental studies, both the accuracy of data labelling with fuzzy logic has been demonstrated and the performance of the proposed approach has been considered acceptable. © 2024, The Authors. All rights reserved.

关键词： Fuzzy logic

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Application of the Harmonic Analysis to Estimate the Gain of the Mathematical Model of the Aircraft Structure Strength Test Bench

Application of the Harmonic Analysis to Estimate the Gain of...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Maksim V. Trubin Valery D. Yurkevich Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Institute of Automation and Electrometry Siberian Branch Russian Academy of Sciences Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

The problem of estimating the amplification factor of the channel of force loading of the bench for strength tests of aircraft structures is considered. The solution of this problem may allow in the future automating the process of setting up regulators for electric hydraulic valves of the stand and thereby accelerate the process of carrying out fatigue and strength tests. In this paper, it is proposed to use the harmonic analysis of the signal from the load force sensor and the control signal at the input of the electrohydraulic distributor to estimate the gain of the force loading channel. In this paper, it has been shown that on the basis of this analysis an estimate of the gain of the power loading channel of the bench can be obtained. The results of application of the proposed approach on an experimental stand are presented. Finally, the proposed approach was compared with another approach based on the least squares method with sliding window.

关键词： Regulators Loading Force Channel estimation Life estimation Harmonic analysis Valves

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Ziyue Guan Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms.

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Xiangdong Xue Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms.

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Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Edge Computing Systems

Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Ed...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Meijia Wang Jing Bi Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

Current mobile edge computing (MEC) owns cloud resources at the network edge, which enables low-latency mobile services. In addition to fixed MEC servers, MEC proxy servers with certain mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs), and vehicles, have emerged as competitors in providing services. In this work, aiming at a task offloading problem of a UAV-assisted MEC system, a hybrid network environment with multiple mobile devices (MDs) and multiple UAVs is established. A constrained mixed integer nonlinear program of the UAV-assisted hybrid cloud-edge system is formulated. A novel hybrid metaheuristic algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) is presented to solve the program. Then, a task offloading and resource scheduling method is designed to intelligently minimize the total energy consumption of the hybrid system. Simulation results verify superiority of GSPSO over its three benchmark algorithms, thus demonstrating the proposed method significantly improves the energy efficiency of the UAV-enabled hybrid system.

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Design of Face Recognition Access Entrance Guard System with Mask Based on Embedded Development 2

Design of Face Recognition Access Entrance Guard System with...

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2021 2nd International Conference on computer Information and Big Data Applications, CIBDA 2021

作者： Ruiqin, Lin Wenan, Tan Zhenyu, Chen Qinqin Faculty of Environmental and Materials Engineering Shanghai Polytechnic University Shanghai201209 China Computer Engineering Shanghai Polytechnic University Shanghai201209 China Department of Measurement and Control and Automation Shanghai Polytechnic University Shanghai201209 China

This paper investigates all kinds of access entrance guard control systems at home and abroad and analyzes their advantages and disadvantages. COVID-19 epidemic has a serious impact on life, travel must wear a mask, so that the access entrance guard control system can not carry out face recognition under the mask. In view of this kind of situation, an embedded face recognition access entrance guard control system with mask based on EAIDK-310 development board is designed. The system can complete face recognition and body temperature measurement without contact, and drive the motor to open the gate. After testing, the system is suitable for deployment in companies, communities, campuses and other small application scenarios. It is convenient to travel and can reduce the impact of the epidemic. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system.

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Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Zhe Sun Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations.

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Analog Control Design for a Two Degrees of Freedom Bilateral Teleoperation System

Analog Control Design for a Two Degrees of Freedom Bilateral...

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Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Umair Ali Ahmad Hassan Jason Gu Khurram Karim Qureshi Muhammad Usman Asad Ghulam Abbas Umar Farooq Intelligent Systems Lab and Automation Facility (ISLAF) IEECE University of the Punjab Lahore Pakistan Department of Electrical and Computer Engineering Dalhousie University Canada Department of Electrical Engineering KFUPM Saudi Arabia Faculty of Open Learning Naval Fleet School Dalhousie University Canada Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

In this paper, we detail the process of designing and implementing a two-way teleoperation system in which the manipulators (master and slave) each have two degrees of freedom. Position-position architecture is implemented for both the joints on the master and slave manipulators. The controllers for both joints are implemented using operational amplifiers. The teleoperation system is tested for both free and contact motion. It is experimentally found that the joints of the slave manipulator follow the trajectory of the respective joints of the master manipulator. The slave manipulator also experiences force reflection whenever it comes into contact with the environment. The presented cost-effective setup can be used to introduce key concepts of haptics control technology to undergraduate students taking the course on linear control systems.

关键词： Operational amplifiers Technological innovation System performance 2-DOF Manipulators Main-secondary Reflection Haptic interfaces Trajectory Telerobotics

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