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An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles' CAN Bus Using Machine Learning

IEEE ACCESS 2019年 7卷 127580-127592页

作者： Avatefipour, Omid Al-Sumaiti, Ameena Saad El-Sherbeeny, Ahmed M. Awwad, Emad Mahrous Elmeligy, Mohammed A. Mohamed, Mohamed A. Malik, Hafiz Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA Khalifa Univ Adv Power & Energy Ctr Elect Engn & Comp Sci Abu Dhabi 127788 U Arab Emirates King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Adv Mfg Inst Riyadh 11421 Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61519 Egypt

Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.

关键词： Electric vehicles controller area network (CAN Bus) anomaly detection one-class support vector machine optimization algorithm

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The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system

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SOLAR ENERGY 2019年第May期183卷 293-305页

作者： Gao, Wei Moayedi, Hossein Shahsavar, Amin Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Kermanshah Univ Technol Dept Mech Engn Kermanshah Iran

The main motivation of this study is to evaluate and compare the efficacy of three computational intelligence approaches, namely artificial neural network (ANN), genetic programming (GP), and adaptive neuro-fuzzy inference system (ANFIS) in predicting the energetic performance of a building integrated photovoltaic thermal (BIPVT) system. This system is capable of cooling PV panels by ventilation/exhaust air in winter/summer and generating electricity. A performance evaluation criterion (PEC) is defined in this study to examine the overall performance of the considered BIPVT system. Then, the mentioned methods are used to identify a relationship between the input and output parameters of the system. The parameter PEC is considered as the essential output of the system, while the input parameters are the length, width, and depth of the duct underneath the PV panels and air mass flow rate. To evaluate the accuracy of produced outputs, two statistical indices of R2 and RMSE are used. As a result, all models presented excellent performance where the ANN model could slightly perform better performance compared to GP and ANFIS. Finally, the equations belonging to ANN and GP models are derived, and the GP presents a more suitable formula, due to its simplicity of use, simplicity of concept, and robustness.

关键词： Building integrated photovoltaic/thermal (BIPVT) Genetic programming ANN ANFIS optimization algorithm Energetic performance

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An Accelerated Composite Gradient Method for Large-Scale Composite Objective Problems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第2期67卷 444-459页

作者： Florea, Mihai I. Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust Espoo Finland

Various signal processing applications can be expressed as large-scale optimization problems with a composite objective structure, where the Lipschitz constant of the smooth part gradient is either not known, or its local values may only be a fraction of the global value. The smooth part may be strongly convex as well. The algorithms capable of addressing this problem class in its entirety are black-box accelerated first-order methods, related to either Nesterov's Fast GradientMethod or the AcceleratedMultistep Gradient Scheme, which were developed and analyzed using the estimate sequence mathematical framework. In this paper, we develop the augmented estimate sequence framework, a relaxation of the estimate sequence. When the lower bounds incorporated in the augmented estimate functions are hyperplanes or parabolae, this framework generates a conceptually simple gap sequence. We use this gap sequence to construct the Accelerated Composite Gradient Method (ACGM), a versatile first-order scheme applicable to any composite problem. Moreover, ACGM is endowed with an efficient dynamic Lipschitz constant estimation (line-search) procedure. We also introduce the wall-clock time unit (WTU), a complexity measure applicable to all first-order methods that accounts for variations in per-iteration complexity and more consistently reflects the running time in practical applications. When analyzed using WTU, ACGM has the best provable convergence rate on the composite problem class, both in the strongly and non-strongly convex cases. Our simulation results confirm the theoretical findings and show the superior performance of our new method.

关键词： Acceleration composite objective estimate sequence first-order method large-scale optimization line-search optimization algorithm

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The study and application of a novel hybrid system for air quality early-warning

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APPLIED SOFT COMPUTING 2019年 74卷 729-746页

作者： Hao, Yan Tian, Chengshi Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Air quality forecasting Early-warning system Hybrid forecasting optimization algorithm

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Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 945-954页

作者： Gordan, Behrouz Koopialipoor, Mohammadreza Clementking, A. Tootoonchi, Hossein Mohamad, Edy Tonnizam Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Don Bosco Coll PG & Res Dept Comp Sci Yelagiri Hills India Univ Teknol Malaysia Fac Civil Engn Ctr Trop Geoengn GEOTROPIK Johor Baharu 81310 Malaysia

An important task of geotechnical engineering is a suitable design of safety factor (SF) of retaining wall under both static and dynamic conditions. This paper presents the advantages of both prediction and optimization of retaining wall SF through artificial neural network (ANN) and artificial bee colony (ABC), respectively. These techniques were selected because of their capability in predicting and optimizing science and engineering problems. To gain purpose of this research, a comprehensive database consisted of 2880 datasets of wall height, wall width, wall mass, soil mass and internal angle of friction as input parameters and SF of retaining wall as output was prepared. In fact, SF is considered as a function of the mentioned parameters. At the first step of modeling, several ANN models were constructed and the best one among them was selected. The coefficient of determination (R-2) value of 0.998 for both training and testing datasets was obtained for the best ANN model which indicates an excellent accuracy level in predicting SF values. In the next step of modeling, the results of selected ANN model were used as an input for the optimization technique of ABC. In general, 11 models of ABC optimization with different strategies were built. As a result, by decreasing wall height value from 10m to 8m and 5.628m and using almost constant values for the other input parameters, SF values were obtained as 2.142 and 5.628, respectively. Results of (8.003, 0.794, 0.667, 1800 and 2800) and (5.628, 0.763, 0.660, 1735 and 2679) were obtained for wall height, wall width, internal friction angle, soil mass and wall mass of the best models with 2.142 and 5.628 SF values, respectively.

关键词： Retaining wall Safety factor ANN ABC optimization algorithm

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Railroad infrastructure 4.0: Development and application of an automatic ballast support condition assessment system

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TRANSPORTATION GEOTECHNICS 2019年 19卷 19-34页

作者： Qian, Yu Dersch, Marcus S. Gao, Zhengboyang Edwards, J. Riley Univ South Carolina Dept Civil & Environm Engn 300 Main St Columbia SC 29208 USA Univ Illinois Dept Civil & Environm Engn Rail Transportat & Engn Ctr RailTEC 205 N Mathews Ave Urbana IL 61801 USA Saiful Bouquet Struct Engineers Inc 155 N Lake Ave 600 Pasadena CA 91101 USA

Industry 4.0, or the fourth industrial revolution, is the current trend of automation and data exchange in manufacturing technologies. Even though Industry 4.0 emerged as a manufacturing initiative, the idea of bridging digital and physical systems can be expanded beyond the manufacturing industry. "Railroad Infrastructure 4.0" is thus proposed in this study to revolutionize the maintenance operations of the railroad industry. In North America, most primary freight and passenger rail corridors are constructed using ballasted track. One of the primary maintenance activities is to ensure the ballast is performing adequately. Being able to monitor the ballast condition and conduct tamping operations (e.g. maintenance activities) at optimal intervals can increase the safety and efficiency of railroad operations. Previously, techniques such as ground penetrating radar (GPR) and Matrix Based Tactile Surface Sensors (MBTSS) have been used to assess the condition of ballast, but these investigative tools lack the capability of automatically and continuously monitoring the track system. Researchers at the University of Illinois at Urbana-Champaign and the University of South Carolina have developed a non-intrusive method as a key component of Railroad Infrastructure 4.0 to continuously quantify ballast pressure distribution (i.e. ballast condition) under the sleeper (also known as a "crosstie"). This method innovatively uses the bending moment profile across the concrete sleeper, and the approximated rail seat loads as inputs, to back-calculate the ballast support condition through the use of ana* optimization algorithm. The ballast support condition assessment system that was developed has been validated for accuracy from extensive laboratory experimentation. The laboratory validated system was deployed in the field on a Class I heavy axle load (HAL) freight railroad in the United States to continuously monitor the ballast pressure distribution beneath concrete sleepers in re

关键词： Industry 4.0 Automation Cyber-physical systems Big data Railway track structure Concrete sleeper Ballast optimization algorithm Laboratory experimentation Field application Real-time monitoring

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Anisotropic Vector Hysteresis Simulation of Soft Magnetic Composite Materials Based on a Hybrid algorithm of PSO-Powell

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MATERIALS 2020年第14期13卷 3138页

作者： Zhao, Xiaojun Xu, Huawei Du, Zhenbin Li, Yongjian Liu, Lanrong Zhao, Zhigang North China Elect Power Univ Dept Elect & Elect Engn Baoding 071003 Peoples R China Hebei Prov Key Lab Electromagnet & Struct Perform Baoding 071056 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China

To simulate the anisotropic hysteresis characteristics of soft magnetic composite (SMC) materials accurately, an improved vector hysteresis model was proposed and utilized to adjust the shape of hysteresis curves by introducing two parameters. These two parameters are correlated with the amplitude of the vector Everett function and the projection of magnetic flux density along different directions. An experimental platform was built to measure the two-dimensional (2-D) magnetic properties of the SMC material under rotational magnetizations. The scalar and vector Everett functions were constructed by the measured limiting hysteresis loops. A hybrid optimization strategy based on the particle swarm optimization (PSO) and Powell technique was proposed to identify the parameters of the improved model efficiently and precisely, which significantly improved the local optimization ability of the PSO algorithm. The simulated results strongly agree with the measured ones, and thus the effectiveness of the improved vector model and the parameter identification method proposed in this paper was verified.

关键词： SMC material vector Preisach hysteresis model rotational magnetization anisotropy parameter identification optimization algorithm

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An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年第0期205卷 112443-000页

作者： Li, Shuijia Gu, Qiong Gong, Wenyin Ning, Bin Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models.

关键词： Parameter extraction Adaptive differential evolution Photovoltaic models optimization algorithm

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Saddle Point of Orbital Pursuit-Evasion Game Under J2-Perturbed Dynamics

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2020年第9期43卷 1733-1739页

作者： Li, Zhen-yu Zhu, Hai Yang, Zhen Luo, Ya-zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

In this study, an indirect method, named the combined shooting and collocation method (CSCM), is proposed to solve the accurate saddle point of the three-dimensional orbital pursuit–evasion (PE) game under J2-perturbed dynamics. With the analytical conditions for optimality used for both players, the CSCM can compute an accurate saddle-point solution. By decomposing the original two-point boundary-value problem (TPBVP) P into two layers and progressively handling the constraints, the developed CSCM can robustly find a saddle-point solution for a variety of orbital PE scenarios, even under J2-perturbed dynamics. The computation effort of the proposed method is below 50 iterations in practice.

关键词： Lagrange Multipliers Earth Pontryagin's Maximum Principle Exhaust Velocity Particle Swarm optimization Argument of Latitude Nonlinear Dynamics Orbital Elements optimization algorithm Boundary Value Problems

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optimization Design and Performance Analysis of Vehicle Powertrain Mounting System

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Chinese Journal of Mechanical Engineering 2018年第2期31卷 76-88页

作者： Han Zhou Hui Liu Pu Gao Chang-Le Xiang School of Mechanical Engineering Beijing Institute of Technology National Key Lab of Vehicular Transmission Beijing Institute of Technology

The design strategies for powertrain mounting systems play an important role in the reduction of vehicular vibration and noise. As stiffness and damping elements connecting the transmission system and vehicle body, the rubber mount exhibits better vibration isolation performance than the rigid connection. This paper presents a complete design process of the mounting system, including the vibration decoupling, vibration simulation analysis, topology optimization, and experimental verification. Based on the 6?degrees?of?freedom vibration coupling model of the powertrain mounting system, ana* optimization algorithm is used to extract the best design parameters of each mount, thus rendering the mounting system fully decoupled and the natural frequency well configured, and the optimal parameters are used to design the mounting system. Subsequently, vibration simulation analysis is applied to the mounting system, considering both transmission and road excitations. According to the results of finite element analysis, the topological structure of the metal frame of the front mount is optimized to improve the strength and dynamic characteristics of the mounting system. Finally, the vibration bench test is used to verify the availability of the optimization design with the analysis of acceleration response and vibration transmissibility of the mounting system. The results show that the vibration isolation performance of the mounting system can be improved effectively using the vibration optimal decoupling method, and the structural modification of the metal frame can well promote the dynamic characteristics of the mounting system.

关键词： Mounting system optimization algorithm Vibration simulation analysis Topological structure Acceleration response Vibration transmissibility

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