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Because it is very expensive to collect a large number of labeled samples to train deep neural networks in certain fields, semi-supervised learning (SSL) researcher has become increasingly important in recent years. There are many consistency regularization-based methods for solving SSL tasks, such as the \(\Pi \) model and mean teacher. In this paper, we first show through an experiment that the traditional consistency-based methods exist the following two problems: (1) as the size of unlabeled samples increases, the accuracy of these methods increases very slowly, which means they cannot make full use of unlabeled samples. (2) When the number of labeled samples is vary small, the performance of these methods will be very low. Based on these two findings, we propose two methods, metric learning clustering (MLC) and auxiliary fake samples, to alleviate these problems. The proposed methods achieve state-of-the-art results on SSL benchmarks. The error rates are 10.20%, 38.44% and 4.24% for CIFAR-10 with 4000 labels, CIFAR-100 with 10,000 labels and SVHN with 1000 labels by using MLC. For MNIST, the auxiliary fake samples method shows great results in cases with the very few labels.
Wei Zhou; Cheng Lian; Zhigang Zeng; Bingrong Xu; Yixin Su. Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples. Neural Processing Letters 2021, 1 -17.
AMA StyleWei Zhou, Cheng Lian, Zhigang Zeng, Bingrong Xu, Yixin Su. Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples. Neural Processing Letters. 2021; ():1-17.
Chicago/Turabian StyleWei Zhou; Cheng Lian; Zhigang Zeng; Bingrong Xu; Yixin Su. 2021. "Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples." Neural Processing Letters , no. : 1-17.
With the advantages of flexible control ability, unmanned surface vehicle (USV) has been widely applied in civil and military fields. A number of researchers have been working on the development of intelligent path planning algorithms to plan a high-quality and collision-free path which is applied to guide USV through cluttered environments. The conventional algorithms may either have issues with trapping into a local optimal solution or face a slow convergence problem. This paper presents a novel multi-subpopulation bacterial foraging optimisation (MS-BFO) algorithm for USV path planning that enhances the searching performance, especially, in a complex environment. This method constructs the deletion and immigration strategies (DIS), which guarantees the elite optimised individual of each subpopulation to be inherited by others, thus to consequently lead to fast convergence speed. The experimental results show that the proposed method is able to suggest an optimised path within the shortest length of time, compared with other optimisation algorithms.
Yang Long; Yixin Su; Binghua Shi; Zheming Zuo; Jie Li. A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning. Intelligent Service Robotics 2021, 14, 303 -312.
AMA StyleYang Long, Yixin Su, Binghua Shi, Zheming Zuo, Jie Li. A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning. Intelligent Service Robotics. 2021; 14 (2):303-312.
Chicago/Turabian StyleYang Long; Yixin Su; Binghua Shi; Zheming Zuo; Jie Li. 2021. "A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning." Intelligent Service Robotics 14, no. 2: 303-312.
Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.
Yinghui Sun; Ming Fang; Yixin Su. AGV Path Planning based on Improved Dijkstra Algorithm. Journal of Physics: Conference Series 2021, 1746, 012052 .
AMA StyleYinghui Sun, Ming Fang, Yixin Su. AGV Path Planning based on Improved Dijkstra Algorithm. Journal of Physics: Conference Series. 2021; 1746 (1):012052.
Chicago/Turabian StyleYinghui Sun; Ming Fang; Yixin Su. 2021. "AGV Path Planning based on Improved Dijkstra Algorithm." Journal of Physics: Conference Series 1746, no. 1: 012052.
Interval prediction is an efficient approach to quantifying the uncertainties associated with landslide evolution. In this paper, a novel method, termed lower upper bound estimation (LUBE), of constructing prediction intervals (PIs) based on neural networks (NNs) is applied and extended to landslide displacement prediction. A random vector functional link network (RVFLN) is adopted as the NN used in the improved LUBE. A hybrid evolutionary algorithm, termed PSOGSA, that combines particle swarm optimization (PSO) and gravitational search algorithm (GSA) is utilized to train LUBE. The loss function of LUBE is redesigned by considering the quality of PI centre, which allows for a more comprehensive evaluation of PIs. The population initialization in the training process of LUBE is implemented by transferring the weights of a series of pre-trained RVFLNs. The performance of the improved LUBE method is validated by considering a comprehensive set of cases using seven benchmark datasets. In addition, a hybrid method that integrates ensemble empirical mode decomposition (EEMD) with the improved LUBE is proposed for the special case of landslide displacement prediction. Six real-world reservoir-induced landslides are considered to validate the capability and merit of the proposed hybrid method.
Cheng Lian; Zhigang Zeng; Xiaoping Wang; Wei Yao; Yixin Su; Huiming Tang. Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization. Neural Networks 2020, 130, 286 -296.
AMA StyleCheng Lian, Zhigang Zeng, Xiaoping Wang, Wei Yao, Yixin Su, Huiming Tang. Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization. Neural Networks. 2020; 130 ():286-296.
Chicago/Turabian StyleCheng Lian; Zhigang Zeng; Xiaoping Wang; Wei Yao; Yixin Su; Huiming Tang. 2020. "Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization." Neural Networks 130, no. : 286-296.
Since the working situation of photovoltaic modules cannot be tracked in the real time and defective components cannot be located and controlled specifically, a remote monitoring system of photovoltaic modules based on wireless sensor networks is designed to improve the management efficiency of photovoltaic power plants. The monitoring data (current, voltage, and illumination, etc.) of photovoltaic modules are collected by the wireless sensor network nodes in real time, and then, there are orderly transmitted to the coordinator and server by the ZigBee communication protocol with the time synchronization algorithm. Time synchronization algorithm based on Gaussian delay model performs best in synchronization accuracy and energy-efficient reference broadcast synchronization algorithm is the most advantageous in synchronization energy consumption. Adaptive energy–efficient reference broadcasting synchronization algorithm is proposed with a balance between energy consumption and accuracy, which is applicable to large networks and thus conducive to the application of wireless sensor network in large-scale photovoltaic module monitoring. With the analysis and processing of the received data, the situation of the photovoltaic modules can be accordingly judged and thus the information management of photovoltaic power plant can be realized. For a photovoltaic power plant with a total power output of 100 kW and with 400 photovoltaic modules, experimental results show that the overall efficiency of the photovoltaic power plant can be increased by a wide margin.
Xianbo Sun; Yixin Su; Yong Huang; Jianjun Tan; Jinqiao Yi; Tao Hu; Li Zhu. Design and development of a wireless sensor network time synchronization system for photovoltaic module monitoring. International Journal of Distributed Sensor Networks 2020, 16, 1 .
AMA StyleXianbo Sun, Yixin Su, Yong Huang, Jianjun Tan, Jinqiao Yi, Tao Hu, Li Zhu. Design and development of a wireless sensor network time synchronization system for photovoltaic module monitoring. International Journal of Distributed Sensor Networks. 2020; 16 (5):1.
Chicago/Turabian StyleXianbo Sun; Yixin Su; Yong Huang; Jianjun Tan; Jinqiao Yi; Tao Hu; Li Zhu. 2020. "Design and development of a wireless sensor network time synchronization system for photovoltaic module monitoring." International Journal of Distributed Sensor Networks 16, no. 5: 1.
The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.
Yang Long; Zheming Zuo; Yixin Su; Jie Li; Huajun Zhang. An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles. Journal of Navigation 2020, 73, 1247 -1262.
AMA StyleYang Long, Zheming Zuo, Yixin Su, Jie Li, Huajun Zhang. An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles. Journal of Navigation. 2020; 73 (6):1247-1262.
Chicago/Turabian StyleYang Long; Zheming Zuo; Yixin Su; Jie Li; Huajun Zhang. 2020. "An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles." Journal of Navigation 73, no. 6: 1247-1262.
This paper proposes a path following control system for Unmanned Surface Vehicles (USVs) based on an improved integral Line-Of-Sight (LOS) guidance law. Unlike the conventional LOS guidance law, the look-ahead distance is designed as a function of the USV’s cruising speed and the cross tracking error to adapt to the different cruising speeds of USVs. Meanwhile, a reduced-order state observer is developed for online estimation of the time-varying sideslip angle caused by external disturbances such as wind, wave and current. Then, a heading controller is further designed using the dynamic surface control technique to track the desired heading angle. The guidance system and the reduced-order state observer subsystem are proved to be uniformly asymptotically stable and input-to-state stable respectively. The simulation results show that the path following control system designed in this paper can track the desired curved and straight line paths quickly and smoothly at different cruising speeds.
Lili Wan; Yixin Su; Huajun Zhang; Binghua Shi; Mahmoud S. AbouOmar. An improved integral light-of-sight guidance law for path following of unmanned surface vehicles. Ocean Engineering 2020, 205, 107302 .
AMA StyleLili Wan, Yixin Su, Huajun Zhang, Binghua Shi, Mahmoud S. AbouOmar. An improved integral light-of-sight guidance law for path following of unmanned surface vehicles. Ocean Engineering. 2020; 205 ():107302.
Chicago/Turabian StyleLili Wan; Yixin Su; Huajun Zhang; Binghua Shi; Mahmoud S. AbouOmar. 2020. "An improved integral light-of-sight guidance law for path following of unmanned surface vehicles." Ocean Engineering 205, no. : 107302.
The application of lithium-ion (Li-ion) battery energy storage system (BESS) to achieve the dispatchability of a renewable power plant is examined. By taking into consideration the effects of battery cell degradation evaluated using electrochemical principles, a power flow model (PFM) of the BESS is developed specifically for use in system-level study. The PFM allows the long-term performance and lifetime of the battery be predicted as when the BESS is undertaking the power dispatch control task. Furthermore, a binary mode BESS control scheme is proposed to prevent the possible over-charge/over-discharge of the BESS due to the uncertain renewable input power. Analysis of the resulting new dispatch control scheme shows that a proposed adaptive BESS state of energy controller can guarantee the stability of the dispatch process. A particle swarm optimization algorithm is developed and is incorporated into a computational procedure for which the optimum battery capacity and power rating are determined, through minimizing the capital cost of the BESS plus the penalty cost of violating the dispatch power commitment. Results of numerical examples used to illustrate the proposed design approach show that in order to achieve hourly-constant power dispatchability of a 100-MW wind farm, the minimum-cost Li-ion BESS is rated 31-MW/22.6-MWh.
Yang Li; Mahinda Vilathgamuwa; San Shing Choi; Binyu Xiong; Jinrui Tang; Yixin Su; Yu Wang. Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability. Applied Energy 2019, 260, 114282 .
AMA StyleYang Li, Mahinda Vilathgamuwa, San Shing Choi, Binyu Xiong, Jinrui Tang, Yixin Su, Yu Wang. Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability. Applied Energy. 2019; 260 ():114282.
Chicago/Turabian StyleYang Li; Mahinda Vilathgamuwa; San Shing Choi; Binyu Xiong; Jinrui Tang; Yixin Su; Yu Wang. 2019. "Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability." Applied Energy 260, no. : 114282.
Gas insulated substations (GISs) are broadly used for transmission and distribution in electric power networks. Very fast transient overvoltage (VFTO) caused by SF6 discharge during switching operations in a GIS may threaten the insulation of electrical equipment. In this paper, a novel VFTO suppression method with great prospects in engineering, called the spiral tube damping busbar, is proposed. The suppressing mechanism of the new method is analyzed. The structure and the design characteristics of the damping busbar are introduced as well. Moreover, a calculation method for the self-inductance of the damping busbar at high frequency is presented. According to the structural characteristics of the damping busbar, the inductance effect on suppressing VFTO is analyzed. A further improvement in damping VFTO is investigated by designing a spiral litz coil connected in series with the busbar, which increases the damping effect. The simulation results show that the improved damping busbar has a significant suppressing effect on the amplitude and the frequency of VFTO.
Reem A. Almenweer; Yi-Xin Su; Wu Xixiu; Su Yixin. Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS. Applied Sciences 2019, 9, 5076 .
AMA StyleReem A. Almenweer, Yi-Xin Su, Wu Xixiu, Su Yixin. Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS. Applied Sciences. 2019; 9 (23):5076.
Chicago/Turabian StyleReem A. Almenweer; Yi-Xin Su; Wu Xixiu; Su Yixin. 2019. "Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS." Applied Sciences 9, no. 23: 5076.
This paper develops a decision-making model to assist the improvement of the carrying capacity of ship locks by combing fuzzy logic, the analytic hierarchy process (AHP) method, and the technique for order preference by similarity to an ideal solution (TOPSIS). A three-level hierarchical structure is constructed to identify the key factors influencing the carrying capacity of ship locks from the aspects of ship locks, vessels, environment, and administration. On this basis, a series of targeted strategies have been put forward to improve the carrying capacity of ship locks, and the TOPSIS method is applied to rank these strategies in terms of their performance. A case study of the five-stage dual-track ship lock of the Three Gorges Dam in China has been conducted to demonstrate the feasibility and rationality of the proposed model, and correlation analysis is conducted to verify the identified influencing factors in order to eliminate potential bias which may be generated from using AHP. The results obtained from the proposed methods are consistent with the real-life situation to a certain extent, indicating that the proposed method can provide a useful reference for improving the carrying capacity of ship locks.
Shaoyue Shi; Danhong Zhang; Yixin Su; Chengpeng Wan; Mingyang Zhang; Cong Liu. A Fuzzy-Based Decision-Making Model for Improving the Carrying Capacity of Ship Locks: A Three Gorges Dam Case. Journal of Marine Science and Engineering 2019, 7, 244 .
AMA StyleShaoyue Shi, Danhong Zhang, Yixin Su, Chengpeng Wan, Mingyang Zhang, Cong Liu. A Fuzzy-Based Decision-Making Model for Improving the Carrying Capacity of Ship Locks: A Three Gorges Dam Case. Journal of Marine Science and Engineering. 2019; 7 (8):244.
Chicago/Turabian StyleShaoyue Shi; Danhong Zhang; Yixin Su; Chengpeng Wan; Mingyang Zhang; Cong Liu. 2019. "A Fuzzy-Based Decision-Making Model for Improving the Carrying Capacity of Ship Locks: A Three Gorges Dam Case." Journal of Marine Science and Engineering 7, no. 8: 244.
To avoid collision accidents and achieve independent recovery in the cluttered marine environment, this work proposes an intelligent path planning system for a waterjet-propelled unmanned surface vehicle (USV). Unlike the existing works on USV navigation systems, our study focuses on the real-time, smoothness and seaworthiness properties of the path in practical clutter environments. A hybrid A* algorithm with motion primitive constraints is proposed to generate an initial reference path. According to different types of dynamic obstacles, the International Regulations for Preventing Collisions at Sea (COLREGs) rules are used, and a local threat map based on the Apollonius circle is constructed to avoid these obstacles. Together with the motion characteristics of the waterjet-propelled USV, the Reeds-Shepp curve is used to calculate the autonomous recovery path. The performance of the proposed intelligent navigation system is verified in a practical marine environment. The experimental results show that our hybrid A* algorithm outperforms the conventional A* algorithm, and it is easy for the waterjet-propelled USV to follow the final continuous curvature path. It was demonstrated that our method can effectively avoid various static and dynamic obstacles in real time and thus help achieve autonomous recovery.
Binghua Shi; Yixin Su; Chen Wang; Lili Wan; Yi Luo. Study on intelligent collision avoidance and recovery path planning system for the waterjet-propelled unmanned surface vehicle. Ocean Engineering 2019, 182, 489 -498.
AMA StyleBinghua Shi, Yixin Su, Chen Wang, Lili Wan, Yi Luo. Study on intelligent collision avoidance and recovery path planning system for the waterjet-propelled unmanned surface vehicle. Ocean Engineering. 2019; 182 ():489-498.
Chicago/Turabian StyleBinghua Shi; Yixin Su; Chen Wang; Lili Wan; Yi Luo. 2019. "Study on intelligent collision avoidance and recovery path planning system for the waterjet-propelled unmanned surface vehicle." Ocean Engineering 182, no. : 489-498.
The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.
Mahmoud S. Abouomar; Hua-Jun Zhang; Yi-Xin Su. Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm. Energies 2019, 12, 1435 .
AMA StyleMahmoud S. Abouomar, Hua-Jun Zhang, Yi-Xin Su. Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm. Energies. 2019; 12 (8):1435.
Chicago/Turabian StyleMahmoud S. Abouomar; Hua-Jun Zhang; Yi-Xin Su. 2019. "Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm." Energies 12, no. 8: 1435.
Variable-speed operation of a dish-Stirling (DS) concentrated solar-thermal power generating system can achieve higher energy conversion efficiency compared to the conventional fixed-speed operation system. However, tuning of the controllers for the existing control schemes is cumbersome due to the presence of a large number of control parameters. This paper proposes a new control system design approach for the doubly-fed induction generator (DFIG)-based DS system to achieve maximum power point tracking and constant receiver temperature regulation. Based on a developed thermo-electro-pneumatic model, a coordinated torque and mean pressure control scheme is proposed. Through steady-state analysis, the optimal torque is calculated using the measured insolation and it serves as the tracking reference for direct torque control of the DFIG. To minimize the tracking error due to temperature variation and the compressor loss of the hydrogen supply system, four optimal control parameters are determined using particle swarm optimization (PSO). Model-order reduction and the process of the pre-examination of system stability are incorporated into the PSO algorithm, and it effectively reduces the search effort for the best solution to achieve maximum power point tracking and maintain the temperature around the set-point. The results from computational simulations are presented to show the efficacy of the proposed scheme in supplying the grid system with smoothened maximum power generation as the solar irradiance varies.
Yang Li; Binyu Xiong; Yixin Su; Jinrui Tang; Zhiwen Leng. Particle Swarm Optimization-Based Power and Temperature Control Scheme for Grid-Connected DFIG-Based Dish-Stirling Solar-Thermal System. Energies 2019, 12, 1300 .
AMA StyleYang Li, Binyu Xiong, Yixin Su, Jinrui Tang, Zhiwen Leng. Particle Swarm Optimization-Based Power and Temperature Control Scheme for Grid-Connected DFIG-Based Dish-Stirling Solar-Thermal System. Energies. 2019; 12 (7):1300.
Chicago/Turabian StyleYang Li; Binyu Xiong; Yixin Su; Jinrui Tang; Zhiwen Leng. 2019. "Particle Swarm Optimization-Based Power and Temperature Control Scheme for Grid-Connected DFIG-Based Dish-Stirling Solar-Thermal System." Energies 12, no. 7: 1300.
A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.
Lili Wan; Yixin Su; Huajun Zhang; Yongchuan Tang; Binghua Shi. Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle. International Journal of Advanced Robotic Systems 2019, 16, 1 .
AMA StyleLili Wan, Yixin Su, Huajun Zhang, Yongchuan Tang, Binghua Shi. Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle. International Journal of Advanced Robotic Systems. 2019; 16 (2):1.
Chicago/Turabian StyleLili Wan; Yixin Su; Huajun Zhang; Yongchuan Tang; Binghua Shi. 2019. "Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle." International Journal of Advanced Robotic Systems 16, no. 2: 1.
The obstacles modeling is a fundamental and significant issue for path planning and automatic navigation of Unmanned Surface Vehicle (USV). In this study, we propose a novel obstacles modeling method based on high resolution satellite images. It involves two main steps: extraction of obstacle features and construction of convex hulls. To extract the obstacle features, a series of operations such as sea-land segmentation, obstacles details enhancement, and morphological transformations are applied. Furthermore, an efficient algorithm is proposed to mask the obstacles into convex hulls, which mainly includes the cluster analysis of obstacles area and the determination rules of edge points. Experimental results demonstrate that the models achieved by the proposed method and the manual have high similarity. As an application, the model is used to find the optimal path for USV. The study shows that the obstacles modeling method is feasible, and it can be applied to USV path planning.
Binghua Shi; Yixin Su; Huajun Zhang; Jiawen Liu; Lili Wan. Obstacles modeling method in cluttered environments using satellite images and its application to path planning for USV. International Journal of Naval Architecture and Ocean Engineering 2019, 11, 202 -210.
AMA StyleBinghua Shi, Yixin Su, Huajun Zhang, Jiawen Liu, Lili Wan. Obstacles modeling method in cluttered environments using satellite images and its application to path planning for USV. International Journal of Naval Architecture and Ocean Engineering. 2019; 11 (1):202-210.
Chicago/Turabian StyleBinghua Shi; Yixin Su; Huajun Zhang; Jiawen Liu; Lili Wan. 2019. "Obstacles modeling method in cluttered environments using satellite images and its application to path planning for USV." International Journal of Naval Architecture and Ocean Engineering 11, no. 1: 202-210.
Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.
Lili Wan; Yixin Su; Huajun Zhang; Yongchuan Tang; Binghua Shi. Neural adaptive sliding mode controller for unmanned surface vehicle steering system. Advances in Mechanical Engineering 2018, 10, 1 .
AMA StyleLili Wan, Yixin Su, Huajun Zhang, Yongchuan Tang, Binghua Shi. Neural adaptive sliding mode controller for unmanned surface vehicle steering system. Advances in Mechanical Engineering. 2018; 10 (9):1.
Chicago/Turabian StyleLili Wan; Yixin Su; Huajun Zhang; Yongchuan Tang; Binghua Shi. 2018. "Neural adaptive sliding mode controller for unmanned surface vehicle steering system." Advances in Mechanical Engineering 10, no. 9: 1.
Gang Shen; Yixin Su; Danhong Zhang; Huajun Zhang; Binyu Xiong; Mingwu Zhang. Secure and Fine-grained Electricity Consumption Aggregation Scheme for Smart Grid. KSII Transactions on Internet and Information Systems 2018, 12, 1553 -1571.
AMA StyleGang Shen, Yixin Su, Danhong Zhang, Huajun Zhang, Binyu Xiong, Mingwu Zhang. Secure and Fine-grained Electricity Consumption Aggregation Scheme for Smart Grid. KSII Transactions on Internet and Information Systems. 2018; 12 (4):1553-1571.
Chicago/Turabian StyleGang Shen; Yixin Su; Danhong Zhang; Huajun Zhang; Binyu Xiong; Mingwu Zhang. 2018. "Secure and Fine-grained Electricity Consumption Aggregation Scheme for Smart Grid." KSII Transactions on Internet and Information Systems 12, no. 4: 1553-1571.
A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.
Rui Chi; Yi-Xin Su; Dan-Hong Zhang; Xue-Xin Chi; Hua-Jun Zhang. A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications 2017, 31, 653 -670.
AMA StyleRui Chi, Yi-Xin Su, Dan-Hong Zhang, Xue-Xin Chi, Hua-Jun Zhang. A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications. 2017; 31 (S1):653-670.
Chicago/Turabian StyleRui Chi; Yi-Xin Su; Dan-Hong Zhang; Xue-Xin Chi; Hua-Jun Zhang. 2017. "A hybridization of cuckoo search and particle swarm optimization for solving optimization problems." Neural Computing and Applications 31, no. S1: 653-670.
A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing method and is applied to dynamically adjust the percentage of use of PSO and DE. In addition, the mutation operation of DE is improved, to satisfy that the proposed algorithm has different mutation operation in different searching stage. As a result, the capability of the local searching is enhanced and the prematurity of the population is restrained. The effectiveness of the proposed method has been validated through comprehensive tests using benchmark test functions. The numerical results obtained by this algorithm are compared with those obtained by the improved non-dominated sorting genetic algorithm (NSGA-II) and the other algorithms mentioned in the literature. The results show the effectiveness of the proposed MOPSDE algorithm.
Yi-Xin Su; Rui Chi. Multi-objective particle swarm-differential evolution algorithm. Neural Computing and Applications 2015, 28, 407 -418.
AMA StyleYi-Xin Su, Rui Chi. Multi-objective particle swarm-differential evolution algorithm. Neural Computing and Applications. 2015; 28 (2):407-418.
Chicago/Turabian StyleYi-Xin Su; Rui Chi. 2015. "Multi-objective particle swarm-differential evolution algorithm." Neural Computing and Applications 28, no. 2: 407-418.
Jiawen Liu; Yixin Su. Research on Image Processing Based Component Localization Techniques. Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering 2015, 1 .
AMA StyleJiawen Liu, Yixin Su. Research on Image Processing Based Component Localization Techniques. Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering. 2015; ():1.
Chicago/Turabian StyleJiawen Liu; Yixin Su. 2015. "Research on Image Processing Based Component Localization Techniques." Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering , no. : 1.