Article
Automation & Control Systems
Ying Zhang, Tao You, Jinchao Chen, Chenglie Du, Zhaoyang Ai, Xiaobo Qu
Summary: This article proposes a safe and energy-saving decision-making framework for autonomous vehicles to enforce safety first principle by considering both driving safety and energy efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Alessandra Duz, Alex Gimondi, Matteo Corno, Sergio M. Savaresi
Summary: This paper proposes a computationally efficient global speed planner that includes comfort as one of the main objectives for self-driving vehicles. The planner considers energy consumption, trip time, and user-specified constraints to optimize a cost function. The algorithm is tested on a realistic case study, and the trade-off between energy consumption and comfort is quantified.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Automation & Control Systems
Berkay Turan, Mahnoosh Alizadeh
Summary: This article investigates the impacts of competition in autonomous mobility-on-demand systems. It first determines the optimal strategies and prices in monopoly and duopoly markets, and then studies the benefits of introducing competition. The article derives theoretical bounds and examines the effects of consumer loyalty on various metrics. Finally, it quantifies the efficacy of different pricing and routing policies using real-life data.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Correction
Engineering, Civil
F. Camara
Summary: The article was originally intended to appear in the September issue but was delayed and published in the October issue.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Thanh-Anh Huynh, Po-Hsun Chen, Min-Fu Hsieh
Summary: This paper proposes a novel traction unit for electric vehicles that combines different types of motors to improve traction performance and energy efficiency while reducing cost. The characteristics of three different traction units are analyzed and compared, and the operational characteristics of each motor to achieve a wide constant power speed range are investigated. The evaluation using the Urban Dynamometer Driving Schedule shows that the proposed traction unit has higher efficiency compared to other configurations. The advantages and drawbacks of the proposed unit are also investigated, and experiments are conducted to validate the analysis.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Kihan Kwon, Jongseok Lee, Seungjae Min
Summary: In this article, a series hybrid powertrain is proposed for tracked vehicles to reduce energy consumption, and an equivalent inertia model is developed to optimize key design parameters of traction systems. Through multiobjective optimization, a Pareto front is obtained, demonstrating balanced relationships between design objectives and showing improvements in energy efficiency and driving performance compared to initial HEV designs.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Robotics
Tong Li, Lu Zhang, Sikang Liu, Shaojie Shen
Summary: This letter presents a novel planning framework, MARC (Multipolicy And Risk-aware Contingency) planning, that enhances the multipolicy-based pipelines in automated vehicles by addressing the challenges of generating safe and non-conservative behaviors in dense, dynamic environments. The framework achieves efficient decision-making and human-like driving maneuvers by considering multiple possible futures and user-defined risk tolerance levels. Experimental results demonstrate superior performance compared to other strong baselines in various environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Junliang Zhu, Zhigang Wu, Chongchen Chen, Entong Su
Summary: This paper explores the charging behavior of electric vehicle groups in the autonomous driving-shared travel mode and establishes a multi-agent simulation model that accurately simulates real-life scenarios and analyzes charging load curves and influencing factors.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Engineering, Civil
Wei Ming Dan Chia, Sye Loong Keoh, Cindy Goh, Christopher Johnson
Summary: This paper highlights the importance of Risk Assessment (RA) in ensuring the safety of Autonomous Driving Systems (ADS) in urban cities. It acknowledges the maturity of ADS technologies and the need for safe deployment on public roads. However, it also identifies the complexity of ADS compared to traditional vehicles and the limitations of existing standards in validating functional safety. The paper provides a comparison of existing RA methodologies and recommends potential solutions for AV RA to meet ISO 26262 and ISO/PAS 21448 standards.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Angelo Coppola, Dario Giuseppe Lui, Alberto Petrillo, Stefania Santini
Summary: This work proposes a novel Eco-Driving Control Architecture to address the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon. The architecture consists of a Nonlinear Model Predictive Control strategy and a distributed exponentially-stable robust PID-like protocol to achieve precise leader-tracking and energy-saving control.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Naman Patel, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
Summary: This study introduces an automated, physically-realizable, dynamic adversarial attack aimed at compromising an end-to-end trained DNN controlled autonomous vehicle. The attack, initiated by a billboard displaying videos, leads the vehicle to track an adversary customized trajectory, showing high effectiveness.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Haoxuan Dong, Weichao Zhuang, Boli Chen, Guodong Yin, Yan Wang
Summary: This study introduces an enhanced eco-approach control strategy to improve energy efficiency at signalized intersections by predicting the movement of vehicle queues. Through a hierarchical framework and numerical simulations, it is shown that the EEAC strategy can effectively enhance energy utilization efficiency.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Zijun Liu, Shuo Cheng, Xuewu Ji, Liang Li, Lingtao Wei
Summary: The paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire system to improve tracking accuracy and dynamic stability for autonomous vehicles. The architecture is robust against different types of disturbances in the path tracking process through hierarchical decoupling.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Sung-Woo Hwang, Jun-Yeol Ryu, Jun-Woo Chin, Soo-Hwan Park, Dae-Kee Kim, Myung-Seop Lim
Summary: This paper proposes a fast and accurate coupled analysis method to improve the accuracy of motor characteristics prediction by considering the temperature change of motor. The method utilizes electromagnetic finite element analysis (FEA) to calculate motor circuit parameters, and consists of two stages to exclude the time-consuming FEA from repetitive process, aiming at improving both accuracy and speed.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Review
Computer Science, Information Systems
Matthew Liam De Klerk, Akshay Kumar Saha
Summary: Efficient optimization of the traction motor control system is crucial for improving the overall powertrain efficiency of electric vehicles. Commonly applied control techniques such as direct torque control and indirect field oriented control allow for advanced control over the induction and synchronous motors used in most electric vehicles. Efforts are being made to reduce ripple and improve parameter insensitivity, with recent advancements achieved through optimal selection of stator flux and DC link voltage values. Research into improving efficiency is ongoing to enhance extended vehicle range.
Article
Computer Science, Software Engineering
Jinchao Chen, Pengcheng Han, Yifan Liu, Xiaoyan Du
Summary: This article focuses on the scheduling problem of independent tasks in a cloud environment with heterogeneous and distributed resources. An exact formulation based on linear programming is presented to find the optimal allocation schemes for tasks. Inspired by the differential evolution method, a population-based approach is proposed to minimize the total time cost. Experimental results demonstrate the effectiveness and convergence of the proposed approach.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Software Engineering
Yifan Liu, Chenglie Du, Jinchao Chen, Xiaoyan Du
Summary: This article focuses on the energy-conscious task scheduling problem in distributed heterogeneous computing systems and proposes an efficient approach to optimize the priorities and processor allocation of tasks, aiming to minimize system energy consumption and overall makespan. Experimental results demonstrate that the proposed approach outperforms in terms of energy-saving and makespan reducing.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jinchao Chen, Fuyuan Ling, Ying Zhang, Tao You, Yifan Liu, Xiaoyan Du
Summary: This study focuses on the coverage path planning problem of heterogeneous UAVs. By building models and proposing an algorithm, it achieves good enough path planning and efficient coverage of multiple separated regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Hardware & Architecture
Jinchao Chen, Yu He, Ying Zhang, Pengcheng Han, Chenglie Du
Summary: Heterogeneous multiprocessor platform provides strong computation capability and cost reduction in large-scale systems, but also brings a complex multi-task scheduling problem, especially for dependent tasks with energy consumption constraints. This work focuses on the energy-aware scheduling problem in heterogeneous multiprocessor systems, and proposes an algorithm to minimize the schedule length of tasks while meeting dependence and energy requirements.
JOURNAL OF SYSTEMS ARCHITECTURE
(2022)
Article
Engineering, Civil
Jinchao Chen, Chenglie Du, Ying Zhang, Pengcheng Han, Wei Wei
Summary: Unmanned aerial vehicles (UAVs) are widely utilized in civilian and military applications for their high autonomy and strong adaptability. This paper addresses the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions by proposing an exact formulation based on mixed integer linear programming and a clustering-based algorithm inspired from density-based clustering methods to achieve optimal flight paths and efficient coverage tasks. Experiments demonstrating the efficiency and effectiveness of the proposed approach with randomly generated regions are conducted.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chaodong Fan, Jiawei Wang, Laurence T. Yang, Leyi Xiao, Zhaoyang Ai
Summary: In this paper, a reference vector-guided with dominance co-evolutionary multi-objective algorithm is proposed to solve constrained large-scale multi-objective problems. The algorithm utilizes a reference vector to guide several sub-populations and constructs a new environmental selection strategy using angle penalty distance with dominance relationship. It also applies a co-evolutionary constraint handling technology to efficiently span the infeasible region. Experimental results demonstrate the effectiveness of the algorithm in constrained large-scale multi-objective optimization.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Li Zeng, Hongzhong Tang, Wei Wang, Mingjian Xie, Zhaoyang Ai, Lei Chen, Yongjun Wu
Summary: In this paper, a multi-resolution attention and multi-scale convolution network (MAMC-Net) is proposed for the automatic tumor segmentation of WSI. The network utilizes multi-resolution images to generate a wider range feature information and richer details, and employs a multi-scale convolution module to aggregate semantic information and high-resolution details. Additionally, a fully connected Conditional Random Field (CRF) is adopted to splice overlapping maps and improve segmentation accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Zhe Zhang, Yaonan Wang, Jing Zhang, Hui Zhang, Zhaoyang Ai, Kan Liu, Feng Liu
Summary: This article examines a new approach of combining the vector Lyapunov function with M-matrix to settle the asymptotic stabilization control of fractional-order memristor-based neural networks system with large delays. New stability and stabilization criteria are deduced, with strong generality and universality. The method has lower conservativeness, fewer constraints, and overcomes the difficulty in dealing with systems owning large delays.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Leyi Xiao, Chaodong Fan, Zhaoyang Ai, Jie Lin
Summary: This paper proposes a hierarchical locally informed gravitational search algorithm (HLIGSA), which achieves better optimization performance by designing a hierarchical topology and adaptively adjusting the agents' gravitational constant.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Biology
Yining Zhang, Tao Yan, Wei Mo, Bin Song, Yuehua Zhang, Fenghao Geng, Zhimin Hu, Daojiang Yu, Shuyu Zhang
Summary: This study investigated the metabolism of bile acids (BAs) and their derivatives during radiogenic skin injury (RSI). The results showed significant differences in 12 BA metabolites during the progression of RSI. Single-cell RNA sequencing (scRNA-Seq) analysis indicated alterations in genes involved in the 7a-hydroxylation process of BA synthesis. Deoxycholic acid (DCA) was found to promote wound healing and attenuate epidermal hyperplasia in a rat model of radiogenic skin damage. These findings suggest the potential of DCA as a therapeutic agent for RSI treatment.
INTERNATIONAL JOURNAL OF RADIATION BIOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Jinchao Chen, Pengcheng Han, Ying Zhang, Tao You, Pengyi Zheng
Summary: In this paper, the energy consumption-constrained scheduling problem of workflows in heterogeneous multi-processor embedded systems is studied. The workflows and energy consumption of processors are modeled, and the problem is formulated as an optimization one. A novel energy difference coefficient-based scheduling algorithm is proposed to produce an approximately optimal allocation of processors, frequencies, and start times for each task while satisfying the data dependency and energy limitation constraints. Experiments on both randomly-generated and real-world workflows are conducted to verify the reliability and efficiency of the proposed approach.
JOURNAL OF SYSTEMS ARCHITECTURE
(2023)
Article
Computer Science, Interdisciplinary Applications
Jinchao Chen, Haoran Zhang, Ruimeng He, Chenglie Du, Jie Cui, Xiaoying Sun
Summary: In recent years, the use of invested resources in experiments for embedded applications has decreased due to the widespread use of simulation technologies. However, the efficiency of simulations can be affected by expensive and hard-to-obtain devices. To improve the efficiency and effectiveness of system development and implementation, it is urgent and significant to build a universal simulation platform for embedded applications on general-purpose operating systems. This paper designs and implements a real-time simulation platform using virtualization technology, allowing correct and efficient debugging and testing of embedded applications on general-purpose operating systems. The proposed simulation platform consists of four layers, enabling dynamic debugging and testing without the need for physical devices. Experimental results demonstrate that the proposed simulation platform meets the real-time and high reliability requirements of embedded applications.
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL
(2023)
Article
Engineering, Electrical & Electronic
Zhao-Hua Liu, Liang Chen, Hua-Liang Wei, Ying Zhang, Lei Chen, Ming-Yang Lv
Summary: This article proposes a novel coarse-to-fine bilevel adversarial domain adaptation approach for bearings fault diagnosis. This approach aligns the distribution of different domains and considers the fine-grained information of the same fault categories, resulting in improved diagnostic performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Yingjie Zhang, Ming Li, Ying Zhang, Zuolei Hu, Qingshuai Sun, Biliang Lu
Summary: This paper proposes a method based on enhanced adaptive unscented Kalman filter for vehicle state estimation under unknown noise conditions. By designing an adaptive exponential attenuation factor and considering the influence of the latest data, the accuracy of vehicle state variable estimation is improved.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
