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In recent years, various control methods have been proposed for maximum power point tracking (MPPT) of photovoltaic (PV) power plants. Different MPPT methods for PV systems in the literature have been evaluated in terms of energy efficiency, energy conversion, dynamic performance and reliability in different environmental conditions. Among the various MPPT methods, the Artificial Neural Network (ANN) MPPT is one of the best methods due to its ability in noise rejection and no need for prior information of physical parameters. For implementing the ANN-based MPPT two input variables including temperature and irradiance and an output variable containing voltage of MPP are taken into account. In this paper, a hybrid shuffled frog leaping and pattern search (HSFL–PS) algorithm is used for optimizing ANN-based MPPT in a grid-tied PV system. The P&O approach is used for the tracking cycle procedure and starts a precise tracking scheme after training the ANN and specification of neuron weights. MATLAB/Simulink is utilized for simulation tests to confirm the performance of the offered MPPT method. The outcomes from simulation tests validate the improved performance of the recommended MPPT in comparison with the conventional methods with a fast response of 011 sec.
Mingxin Jiang; Mehrdad Ghahremani; Sajjad Dadfar; Hongbo Chi; Yahya N. Abdallah; Noritoshi Furukawa. A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system. Control Engineering Practice 2021, 114, 104880 .
AMA StyleMingxin Jiang, Mehrdad Ghahremani, Sajjad Dadfar, Hongbo Chi, Yahya N. Abdallah, Noritoshi Furukawa. A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system. Control Engineering Practice. 2021; 114 ():104880.
Chicago/Turabian StyleMingxin Jiang; Mehrdad Ghahremani; Sajjad Dadfar; Hongbo Chi; Yahya N. Abdallah; Noritoshi Furukawa. 2021. "A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system." Control Engineering Practice 114, no. : 104880.
The key criteria of the short-term hydrothermal scheduling (StHS) problem is to minimize the gross fuel cost for electricity production by scheduling the hydrothermal power generators considering the constraints related to power balance; the gross release of water, and storage limitations of the reservoir, and the operating limitations of the thermal generators and hydropower plants. For addressing the same problem, numerous algorithms were being used, and related studies exist in the literature; however, they possess limitations concerning the solution state and the number of iterations it takes to reach the solution state. Hence, this article proposes using an enhanced cuckoo search algorithm (CSA) called the rigid cuckoo search algorithm (RCSA), a modified version of the traditional CSA for solving the StHS problem. The proposed RCSA improves the solution state and decreases the iteration numbers related to the CSA with a modified Lévy flight. Here, the movement distances are divided into multiple possible steps, which has infinite diversity. The effectiveness of RCSA has been validated by considering the hydrothermal power system. The observed results reveal the superior performance of RCSA among all other compared algorithms that recently have been used for the StHS problem. It is also observed that the RCSA approach has achieved minimum gross costs than other techniques. Thus, the proposed RCSA proves to be a highly effective and convenient approach for addressing the StHS problems
Cui Zheyuan; Ali Hammid; Ali Kareem; Mingxin Jiang; Muamer Mohammed; Nallapaneni Kumar. A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem. Sustainability 2021, 13, 4277 .
AMA StyleCui Zheyuan, Ali Hammid, Ali Kareem, Mingxin Jiang, Muamer Mohammed, Nallapaneni Kumar. A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem. Sustainability. 2021; 13 (8):4277.
Chicago/Turabian StyleCui Zheyuan; Ali Hammid; Ali Kareem; Mingxin Jiang; Muamer Mohammed; Nallapaneni Kumar. 2021. "A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem." Sustainability 13, no. 8: 4277.
In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbines are used to predict the optimal gas-turbine operating parameters. The principle formulations of gas-turbine configurations with various operating conditions are introduced in detail. The effects of different parameters have been analyzed to select the optimum gas-turbine configuration. The adopted ANFIS model has five inputs, namely, isentropic turbine efficiency (Teff), isentropic compressor efficiency (Ceff), ambient temperature (T1), pressure ratio (rp), and turbine inlet temperature (TIT), as well as three outputs, fuel consumption, power output, and thermal efficiency. Both actual reported information, from Baiji Gas-Turbines of Iraq, and simulated data were utilized with the ANFIS model. The results show that, at an isentropic compressor efficiency of 100% and turbine inlet temperature of 1900 K, the peak thermal efficiency amounts to 63% and 375 MW of power resulted, which was the peak value of the power output. Furthermore, at an isentropic compressor efficiency of 100% and a pressure ratio of 30, a peak specific fuel consumption amount of 0.033 kg/kWh was obtained. The predicted results reveal that the proposed model determines the operating conditions that strongly influence the performance of the gas-turbine. In addition, the predicted results of the simulated regenerative gas-turbine (RGT) and ANFIS model were satisfactory compared to that of the foregoing Baiji Gas-Turbines.
Chao Deng; Ahmed N. Abdalla; Thamir K. Ibrahim; Mingxin Jiang; Ahmed T. Al-Sammarraie; Jun Wu. Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines. Advances in High Energy Physics 2020, 2020, 1 -17.
AMA StyleChao Deng, Ahmed N. Abdalla, Thamir K. Ibrahim, Mingxin Jiang, Ahmed T. Al-Sammarraie, Jun Wu. Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines. Advances in High Energy Physics. 2020; 2020 ():1-17.
Chicago/Turabian StyleChao Deng; Ahmed N. Abdalla; Thamir K. Ibrahim; Mingxin Jiang; Ahmed T. Al-Sammarraie; Jun Wu. 2020. "Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines." Advances in High Energy Physics 2020, no. : 1-17.
Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.
Ming-Xin Jiang; Xian-Xian Luo; Tao Hai; Hai-Yan Wang; Song Yang; Ahmed N. Abdalla. Visual Object Tracking in RGB-D Data via Genetic Feature Learning. Complexity 2019, 2019, 1 -8.
AMA StyleMing-Xin Jiang, Xian-Xian Luo, Tao Hai, Hai-Yan Wang, Song Yang, Ahmed N. Abdalla. Visual Object Tracking in RGB-D Data via Genetic Feature Learning. Complexity. 2019; 2019 ():1-8.
Chicago/Turabian StyleMing-Xin Jiang; Xian-Xian Luo; Tao Hai; Hai-Yan Wang; Song Yang; Ahmed N. Abdalla. 2019. "Visual Object Tracking in RGB-D Data via Genetic Feature Learning." Complexity 2019, no. : 1-8.
Mingxin Jiang; Tao Hai; Zhi-Geng Pan; Haiyan Wang; Yinjie Jia; Chao Deng; Yinshan Yu; Jingsong Shan. Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker. IEEE Access 2019, 7, 32400 -32407.
AMA StyleMingxin Jiang, Tao Hai, Zhi-Geng Pan, Haiyan Wang, Yinjie Jia, Chao Deng, Yinshan Yu, Jingsong Shan. Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker. IEEE Access. 2019; 7 ():32400-32407.
Chicago/Turabian StyleMingxin Jiang; Tao Hai; Zhi-Geng Pan; Haiyan Wang; Yinjie Jia; Chao Deng; Yinshan Yu; Jingsong Shan. 2019. "Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker." IEEE Access 7, no. : 32400-32407.
Visual tracking is still a challenging task due to occlusion, appearance changes, complex motion, etc. We propose a novel RGB-D tracker based on multimodal deep feature fusion (MMDFF) in this paper. MMDFF model consists of four deep Convolutional Neural Networks (CNNs): Motion-specific CNN, RGB- specific CNN, Depth-specific CNN, and RGB-Depth correlated CNN. The depth image is encoded into three channels which are sent into depth-specific CNN to extract deep depth features. The optical flow image is calculated for every frame and then is fed to motion-specific CNN to learn deep motion features. Deep RGB, depth, and motion information can be effectively fused at multiple layers via MMDFF model. Finally, multimodal fusion deep features are sent into the C-COT tracker to obtain the tracking result. For evaluation, experiments are conducted on two recent large-scale RGB-D datasets and results demonstrate that our proposed RGB-D tracking method achieves better performance than other state-of-art RGB-D trackers.
Ming-Xin Jiang; Chao Deng; Ming-Min Zhang; Jing-Song Shan; Haiyan Zhang. Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking. Complexity 2018, 2018, 1 -8.
AMA StyleMing-Xin Jiang, Chao Deng, Ming-Min Zhang, Jing-Song Shan, Haiyan Zhang. Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking. Complexity. 2018; 2018 ():1-8.
Chicago/Turabian StyleMing-Xin Jiang; Chao Deng; Ming-Min Zhang; Jing-Song Shan; Haiyan Zhang. 2018. "Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking." Complexity 2018, no. : 1-8.
Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened.
Ming-Xin Jiang; Chao Deng; Zhi-Geng Pan; Lan-Fang Wang; Xing Sun. Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning. Complexity 2018, 2018, 1 -12.
AMA StyleMing-Xin Jiang, Chao Deng, Zhi-Geng Pan, Lan-Fang Wang, Xing Sun. Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning. Complexity. 2018; 2018 ():1-12.
Chicago/Turabian StyleMing-Xin Jiang; Chao Deng; Zhi-Geng Pan; Lan-Fang Wang; Xing Sun. 2018. "Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning." Complexity 2018, no. : 1-12.
Person re-identification (ReID), aiming to identify people among multiple camera views, has attracted an increasing attention due to the potential of application in surveillance security. Large variations in subjects’ postures, view angles, and illuminating conditions as well as non-ideal human detection significantly increase the difficulty of person ReID. Learning a robust metric for measuring the similarity between different person images is another under-addressed problem. In this paper, following the recent success of part-based models, in order to generate a discriminative and robust feature representation, we first propose to learn global and weighted local body-part features from pedestrian images. Then, in the training phase, angular loss and part-level classification loss are employed jointly as a similarity measure to train the network, which significantly improves the robustness of the resultant network against feature variance. Experimental results on several benchmark data sets demonstrate that our method outperforms the state-of-the-art methods.
Yuanyuan Wang; Zhijian Wang; Wenjing Jia; Xiangjian He; Mingxin Jiang. Joint Learning of Body and Part Representation for Person Re-Identification. IEEE Access 2018, 6, 44199 -44210.
AMA StyleYuanyuan Wang, Zhijian Wang, Wenjing Jia, Xiangjian He, Mingxin Jiang. Joint Learning of Body and Part Representation for Person Re-Identification. IEEE Access. 2018; 6 ():44199-44210.
Chicago/Turabian StyleYuanyuan Wang; Zhijian Wang; Wenjing Jia; Xiangjian He; Mingxin Jiang. 2018. "Joint Learning of Body and Part Representation for Person Re-Identification." IEEE Access 6, no. : 44199-44210.
For compressive tracking (CT) algorithm, it is vulnerable to the occlusion, when tracking targets. An improved CT algorithm based on target division and feature point matching is proposed in this paper, which can determine different target tracking states by the method of target division. When the target is in normal tracking or partial occlusion, the target is located accurately by the sub-block with the highest discrimination degree. In this scenario, the classifier only updates the unblocked sub regions in order to avoid the error of updating the occlusion information. When the target is completely occluded or lost in some frames, ORB feature matching is used to re-locate the target. Experimental results show that our proposed CT algorithm can improve the robustness of the algorithm and reduces the drift problem.
Wenhao Wang; Mingxin Jiang; Yunyang Yan; Xiaobing Chen; Wendong Zhao. Improved CT algorithm based on target block division and feature points matching. EURASIP Journal on Image and Video Processing 2018, 2018, 60 .
AMA StyleWenhao Wang, Mingxin Jiang, Yunyang Yan, Xiaobing Chen, Wendong Zhao. Improved CT algorithm based on target block division and feature points matching. EURASIP Journal on Image and Video Processing. 2018; 2018 (1):60.
Chicago/Turabian StyleWenhao Wang; Mingxin Jiang; Yunyang Yan; Xiaobing Chen; Wendong Zhao. 2018. "Improved CT algorithm based on target block division and feature points matching." EURASIP Journal on Image and Video Processing 2018, no. 1: 60.
Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier. Finally, the object tracking results over RGB-D data are obtained using aBayesian maximum a posteriori (MAP) probability estimation algorithm. The experimental results show that the proposed method has strong robustness to abnormal changes (e.g., occlusion, rotation, illumination change, etc.). The algorithm can steadily track multiple targets and has higher accuracy.
Mingxin Jiang; Zhigeng Pan; Zhenzhou Tang. Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors. Sensors 2017, 17, 121 .
AMA StyleMingxin Jiang, Zhigeng Pan, Zhenzhou Tang. Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors. Sensors. 2017; 17 (1):121.
Chicago/Turabian StyleMingxin Jiang; Zhigeng Pan; Zhenzhou Tang. 2017. "Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors." Sensors 17, no. 1: 121.