This page has only limited features, please log in for full access.
A Parallel and Compact version of the Sine Cosine Algorithm (PCSCA) is proposed in this article. Parallel method can effectively improve search ability and increase the diversity of solutions. We develop three communication strategies based on parallelism idea to serve different types of optimization function to achieve the best performance. Furthermore, compact method uses statistical distribution to represent the solutions, which can save memory space and energy of the digital device. To check the optimization effect of the proposed PCSCA algorithm, it is tested on the CEC2013 benchmark function set and compared to SCA, parallel compact Cuckoo Search (PCCS) algorithms. The empirical study demonstrates that PCSCA has improved by 50.1% and 5.6%, compared to SCA and PCCS, respectively. Finally, we apply PCSCA to optimize the position accuracy of sensor node deployed in 3D actual terrain. Experimental results show that PCSCA can achieve lower localization error via Time Difference of Arrival method.
Siqi Zhang; Fang Fan; Wei Li; Shu-Chuan Chu; Jeng-Shyang Pan. A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network. Telecommunication Systems 2021, 1 -11.
AMA StyleSiqi Zhang, Fang Fan, Wei Li, Shu-Chuan Chu, Jeng-Shyang Pan. A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network. Telecommunication Systems. 2021; ():1-11.
Chicago/Turabian StyleSiqi Zhang; Fang Fan; Wei Li; Shu-Chuan Chu; Jeng-Shyang Pan. 2021. "A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network." Telecommunication Systems , no. : 1-11.
Melanoma is a type of malignant skin cancer with high mortality, and its incidence is increasing rapidly in recent years. At present, the best treatment is surgical resection after early diagnosis. However, due to the high visual similarity between melanoma and benign melanocytic nevus, coupled with the scarcity and imbalance of data, traditional methods are difficult to achieve good recognition and detection results. Similarly, many machine learning methods have been applied to the task of skin disease detection and classification. However, the accuracy and sensitivity of the experiments are still not satisfactory. Therefore, this paper proposed a method to identify melanoma more efficiently and accurately. We implemented a Mixed Skin Lesion Picture Generate method based on Mask R-CNN (MSLP-MR) to solve the problem of data imbalance. Besides, we designed a melanoma detection framework of Mask-DenseNet+ based on MSLP-MR. This method used Mask R-CNN to introduce the method of mask segmentation, and combined with the idea of ensemble learning to integrate multiple classifiers for weighted prediction. Compared with the ablation experiments, the accuracy, sensitivity and AUC of the proposed network classification are improved by 2.56%, 29.33% and 0.0345. The experimental results on the ISIC dataset shown that the accuracy of the algorithm is 90.61%, the sensitivity reaches 78.00%, which is higher than the original methods; the specificity reaches 93.43%; and the AUC reaches 0.9502. The method is feasible and effective, and achieves the preliminary goal of melanoma detection. It is greatly improved the detection accuracy and reached the level of visual diagnosis of doctors.
Xingmei Cao; Jeng-Shyang Pan; Zhengdi Wang; Zhonghai Sun; Anwar Ul Haq; Wenyu Deng; Shuangyuan Yang. Application of generated mask method based on Mask R-CNN in classification and detection of melanoma. Computer Methods and Programs in Biomedicine 2021, 207, 106174 .
AMA StyleXingmei Cao, Jeng-Shyang Pan, Zhengdi Wang, Zhonghai Sun, Anwar Ul Haq, Wenyu Deng, Shuangyuan Yang. Application of generated mask method based on Mask R-CNN in classification and detection of melanoma. Computer Methods and Programs in Biomedicine. 2021; 207 ():106174.
Chicago/Turabian StyleXingmei Cao; Jeng-Shyang Pan; Zhengdi Wang; Zhonghai Sun; Anwar Ul Haq; Wenyu Deng; Shuangyuan Yang. 2021. "Application of generated mask method based on Mask R-CNN in classification and detection of melanoma." Computer Methods and Programs in Biomedicine 207, no. : 106174.
The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.
Jeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Hui-Qi Zhao; Gao-Yuan Liu. A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks. Security and Communication Networks 2021, 2021, 1 -15.
AMA StyleJeng-Shyang Pan, Fang Fan, Shu-Chuan Chu, Hui-Qi Zhao, Gao-Yuan Liu. A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks. Security and Communication Networks. 2021; 2021 ():1-15.
Chicago/Turabian StyleJeng-Shyang Pan; Fang Fan; Shu-Chuan Chu; Hui-Qi Zhao; Gao-Yuan Liu. 2021. "A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks." Security and Communication Networks 2021, no. : 1-15.
Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.
Huanyu Liu; Jiaqi Liu; Junbao Li; Jeng-Shyang Pan; Xiaqiong Yu. PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution. Journal of Healthcare Engineering 2021, 2021, 1 -14.
AMA StyleHuanyu Liu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan, Xiaqiong Yu. PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution. Journal of Healthcare Engineering. 2021; 2021 ():1-14.
Chicago/Turabian StyleHuanyu Liu; Jiaqi Liu; Junbao Li; Jeng-Shyang Pan; Xiaqiong Yu. 2021. "PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution." Journal of Healthcare Engineering 2021, no. : 1-14.
Due to the long-term operation of the transformer oil chromatographic online monitoring device, there will be chromatographic peak shifts and baseline jitter, which may cause chromatographic peak loss or misidentification. This study proposes a method for identifying chromatographic peaks of dissolved gas in transformer oil based on random forests. The peak position, peak height, peak width, and peak area were used as evaluation factors. While the evaluation factors subjected to factor analysis, public factors extracted and the random forest algorithm used for training as final. A random forest model based on factor analysis of the chromatographic peaks of dissolved gas in transformer oil was established. The results of a case study show that the chromatographic peak identification of the algorithm has high accuracy which can effectively avoid the misjudgment and missed detection of chromatographic peaks caused by peak position shift.
Hao-Min Chen; Cheng-Kuo Chang; Jeng-Shyang Pan; Jie Shan; Zhi-Jun Li. Chromatographic Peaks of Dissolved Gases in Transformer Oil Identification by Random Forest. Blockchain Technology and Innovations in Business Processes 2021, 60 -67.
AMA StyleHao-Min Chen, Cheng-Kuo Chang, Jeng-Shyang Pan, Jie Shan, Zhi-Jun Li. Chromatographic Peaks of Dissolved Gases in Transformer Oil Identification by Random Forest. Blockchain Technology and Innovations in Business Processes. 2021; ():60-67.
Chicago/Turabian StyleHao-Min Chen; Cheng-Kuo Chang; Jeng-Shyang Pan; Jie Shan; Zhi-Jun Li. 2021. "Chromatographic Peaks of Dissolved Gases in Transformer Oil Identification by Random Forest." Blockchain Technology and Innovations in Business Processes , no. : 60-67.
Aiming at the problems of chromatographic peak shift and baseline jitter after long-term operation of the transformer oil chromatography online monitoring device. This study proposes a chromatographic peak recognition algorithm for dissolved gas in transformer oil based on continuous Bayesian classification model. First, “peak position,” “peak height,” “peak width” and “peak area” used as characteristic attributes. Threshold screening used for data preprocessing to generate training set and test set. Then the maximum likelihood estimation used to calculate the prior probability and conditional probability of feature attributes in the training set. Finally, the Bayesian classifier used as the decision model for chromatographic peak identification. Experimental results show that the proposed algorithm improves the accuracy of transformer chromatographic peak identification. It can effectively solve the problem of back and forth movement of chromatographic peak position. Chromatographic peak shape contraction and expansion caused by chromatographic peak miss and misjudgment.
Jie Shan; Cheng-Kuo Chang; Haomin Chen; Jeng-Shyang Pan. Chromatographic Peak Identification Based on Bayesian Classification Model. Blockchain Technology and Innovations in Business Processes 2021, 188 -195.
AMA StyleJie Shan, Cheng-Kuo Chang, Haomin Chen, Jeng-Shyang Pan. Chromatographic Peak Identification Based on Bayesian Classification Model. Blockchain Technology and Innovations in Business Processes. 2021; ():188-195.
Chicago/Turabian StyleJie Shan; Cheng-Kuo Chang; Haomin Chen; Jeng-Shyang Pan. 2021. "Chromatographic Peak Identification Based on Bayesian Classification Model." Blockchain Technology and Innovations in Business Processes , no. : 188-195.
The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.
Jeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence 2021, 1 -19.
AMA StyleJeng-Shyang Pan, Ai-Qing Tian, Shu-Chuan Chu, Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence. 2021; ():1-19.
Chicago/Turabian StyleJeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. 2021. "Improved binary pigeon-inspired optimization and its application for feature selection." Applied Intelligence , no. : 1-19.
Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
Huanyu Liu; Jiaqi Liu; Junbao Li; Jeng-Shyang Pan; Xiaqiong Yu. DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution. Journal of Healthcare Engineering 2021, 2021, 1 -9.
AMA StyleHuanyu Liu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan, Xiaqiong Yu. DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution. Journal of Healthcare Engineering. 2021; 2021 ():1-9.
Chicago/Turabian StyleHuanyu Liu; Jiaqi Liu; Junbao Li; Jeng-Shyang Pan; Xiaqiong Yu. 2021. "DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution." Journal of Healthcare Engineering 2021, no. : 1-9.
This paper studies the problem of intelligence optimization, a fundamental problem in analyzing the optimal solution in a wide spectrum of applications such as transportation and wireless sensor network (WSN). To achieve better optimization capability, we propose a multigroup Multistrategy Compact Sine Cosine Algorithm (MCSCA) by using the compact strategy and grouping strategy, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum. New evolution formulas are proposed for the intergroup communication strategy. Performance studies on the CEC2013 benchmark demonstrate the effectiveness of our new approach regarding convergence speed and accuracy. Finally, we apply MCSCA to solve the dispatch system of public transit vehicles. Experimental results show that MCSCA can achieve better optimization.
Minghui Zhu; Shu-Chuan Chu; Qingyong Yang; Wei Li; Jeng-Shyang Pan. Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles. Journal of Advanced Transportation 2021, 2021, 1 -16.
AMA StyleMinghui Zhu, Shu-Chuan Chu, Qingyong Yang, Wei Li, Jeng-Shyang Pan. Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles. Journal of Advanced Transportation. 2021; 2021 ():1-16.
Chicago/Turabian StyleMinghui Zhu; Shu-Chuan Chu; Qingyong Yang; Wei Li; Jeng-Shyang Pan. 2021. "Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles." Journal of Advanced Transportation 2021, no. : 1-16.
Inspired by migratory graying, Pan et al. proposed the fish migration optimization (FMO) algorithm. It integrates the models of migration and swim into the optimization process. This paper firstly proposes a binary version of FMO, called BFMO. In order to improve the search ability of BFMO, ABFMO is introduced to solve the problems of stagnation and falling into local traps. The transfer function is responsible for mapping the continuous search space to the binary space. It plays a critical factor in the binary meta-heuristics. This paper brings a new transfer function and compares it with the transfer functions used by BPSO, BGSA and BGWO. Experiments prove that the new transfer function has realized good results in the solving quality. Unit commitment (UC) is a NP-hard binary optimization problem. BFMO and ABFMO are tested with the IEEE benchmark systems consisting of various generating units with 24-h demand horizon. The effectivenesses of BFMO and ABFMO are compared with seven binary evolutionary algorithms. The simulation results and non-parametric tests verify that they achieve great performance.
Jeng-Shyang Pan; Pei Hu; Shu-Chuan Chu. Binary fish migration optimization for solving unit commitment. Energy 2021, 226, 120329 .
AMA StyleJeng-Shyang Pan, Pei Hu, Shu-Chuan Chu. Binary fish migration optimization for solving unit commitment. Energy. 2021; 226 ():120329.
Chicago/Turabian StyleJeng-Shyang Pan; Pei Hu; Shu-Chuan Chu. 2021. "Binary fish migration optimization for solving unit commitment." Energy 226, no. : 120329.
The meta-heuristic evolutionary algorithm is widely used because of its excellent global optimization ability. However, its demand for a mass of evaluation times will lead to an increase in time complexity. Especially when the dimensions of actual problems are too high, the time cost for fitness evaluation is usually minutes, hours, or even days. To improve the above shortcomings and the ability to solve high-dimensional expensive problems, a Fuzzy Hierarchical Surrogate Assisted Probabilistic Particle Swarm Optimization is proposed in this paper. This algorithm first uses Fuzzy Surrogate-Assisted (FSA), Local surrogate-assisted (LSA), and Global surrogate-assisted (GSA) models to fit the fitness evaluation function individually. Secondly, a probabilistic particle swarm optimization is implemented to predict the trained model and update the samples. FSA mainly uses a Fuzzy Clustering algorithm that divides the archive DataBase (DB) into multiple sub-archives to model separately to accurately estimate the function landscape of the function in the partial search space. LSA is mainly designed to capture the local details of the fitness function around the current individual neighborhood and enhance the local optimal accuracy estimation. GSA will build an accurate global model in the entire search space. To verify the performance of our proposed algorithm in solving high-dimensional expensive problems, experiments on seven benchmark functions are conducted in 30D, 50D, and 100D. The final test results show that our proposed algorithm is more competitive than other most advanced algorithms.
Shu-Chuan Chu; Zhi-Gang Du; Yan-Jun Peng; Jeng-Shyang Pan. Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem. Knowledge-Based Systems 2021, 220, 106939 .
AMA StyleShu-Chuan Chu, Zhi-Gang Du, Yan-Jun Peng, Jeng-Shyang Pan. Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem. Knowledge-Based Systems. 2021; 220 ():106939.
Chicago/Turabian StyleShu-Chuan Chu; Zhi-Gang Du; Yan-Jun Peng; Jeng-Shyang Pan. 2021. "Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem." Knowledge-Based Systems 220, no. : 106939.
QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
Shu-Chuan Chu; Zhongjie Zhuang; Junbao Li; Jeng-Shyang Pan. A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm. Applied Sciences 2021, 11, 2251 .
AMA StyleShu-Chuan Chu, Zhongjie Zhuang, Junbao Li, Jeng-Shyang Pan. A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm. Applied Sciences. 2021; 11 (5):2251.
Chicago/Turabian StyleShu-Chuan Chu; Zhongjie Zhuang; Junbao Li; Jeng-Shyang Pan. 2021. "A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm." Applied Sciences 11, no. 5: 2251.
Cat swarm optimization (CSO) has been applied to a variety of fields because of the better capacity of searching for optimum and higher robustness. However, the poor convergency and larger memory consumption are still core defects, which restricts the efficiency of optimization to a larger extent. A new heuristic algorithm named Parallel Compact Cat Swarm Optimization (PCCSO) with three separate communication strategies and the concept of the compact are presented in this article. The advantage of PCCSO is not only reflected in enhancing the ability of local search, but also in saving the computer memory. The experimental results on CEC2013 benchmark functions demonstrate that the PCCSO is always superior to PSO, CSO, and improved CSO in getting convergent. Then, the PCCSO is applied to DV-Hop to effectively improve the localization accuracy of unknown nodes while also saving WSN memory. The experimental results based on PCCSO from the different number of sensor nodes also illustrate that the PCCSO-DV-Hop shows a lower localization error compared to other optimization algorithms based on DV-Hop.
Jianpo Li; Min Gao; Jeng-Shyang Pan; Shu-Chuan Chu. A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network. Wireless Networks 2021, 27, 2081 -2101.
AMA StyleJianpo Li, Min Gao, Jeng-Shyang Pan, Shu-Chuan Chu. A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network. Wireless Networks. 2021; 27 (3):2081-2101.
Chicago/Turabian StyleJianpo Li; Min Gao; Jeng-Shyang Pan; Shu-Chuan Chu. 2021. "A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network." Wireless Networks 27, no. 3: 2081-2101.
Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.
Jeng-Shyang Pan; Jie Shan; Shi-Guang Zheng; Shu-Chuan Chu; Cheng-Kuo Chang. Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm. Cluster Computing 2021, 1 -16.
AMA StyleJeng-Shyang Pan, Jie Shan, Shi-Guang Zheng, Shu-Chuan Chu, Cheng-Kuo Chang. Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm. Cluster Computing. 2021; ():1-16.
Chicago/Turabian StyleJeng-Shyang Pan; Jie Shan; Shi-Guang Zheng; Shu-Chuan Chu; Cheng-Kuo Chang. 2021. "Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm." Cluster Computing , no. : 1-16.
A multiserver environment can improve the efficiency of mobile network services more effectively than a single server in managing the increase in users. Because of the large number of users, the security of users’ personal information and communication information is more important in a multiserver environment. Recently, Wang et al. proposed a multiserver authentication scheme based on biometrics and proved the security of their scheme. However, we first demonstrate that their scheme is insecure against a known session-specific temporary information attacks, user impersonation attacks, and server impersonation attacks. To solve the security weakness, we propose an improved scheme based on Wang et al.’s scheme. The security of our improved scheme is also validated based on the formal security analysis, Burrows–Abadi–Needham (BAN) logic, ProVerif, and informal security analysis. Security and performance comparisons prove the security and efficiency of our scheme.
Tsu-Yang Wu; Lei Yang; Zhiyuan Lee; Chien-Ming Chen; Jeng-Shyang Pan; Sk Hafizul Islam. Improved ECC-Based Three-Factor Multiserver Authentication Scheme. Security and Communication Networks 2021, 2021, 1 -14.
AMA StyleTsu-Yang Wu, Lei Yang, Zhiyuan Lee, Chien-Ming Chen, Jeng-Shyang Pan, Sk Hafizul Islam. Improved ECC-Based Three-Factor Multiserver Authentication Scheme. Security and Communication Networks. 2021; 2021 ():1-14.
Chicago/Turabian StyleTsu-Yang Wu; Lei Yang; Zhiyuan Lee; Chien-Ming Chen; Jeng-Shyang Pan; Sk Hafizul Islam. 2021. "Improved ECC-Based Three-Factor Multiserver Authentication Scheme." Security and Communication Networks 2021, no. : 1-14.
Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.
Baoyong Guo; Zhongjie Zhuang; Jeng-Shyang Pan; Shu-Chuan Chu. Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm. IEEE Access 2021, 9, 8808 -8819.
AMA StyleBaoyong Guo, Zhongjie Zhuang, Jeng-Shyang Pan, Shu-Chuan Chu. Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm. IEEE Access. 2021; 9 (99):8808-8819.
Chicago/Turabian StyleBaoyong Guo; Zhongjie Zhuang; Jeng-Shyang Pan; Shu-Chuan Chu. 2021. "Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm." IEEE Access 9, no. 99: 8808-8819.
Visual cryptography (VC) has found numerous applications in privacy protection, online transaction security, and voting security, etc. To counteract potential cheating attacks, Lin et al. proposed flip visual cryptography in 2010, where a second secret image can be revealed by stacking one share with a flipped version of another share. The second secret image can be designed as an additional verification mechanism. However, Lin’s scheme produces meaningless shares and is only applicable to binary secret images. It is interesting to explore whether it is possible to extend the flip VC to having cover images (i.e., extended VC) and these cover images are color images. This problem is challenging since too many restricting conditions need to be met. In this paper, we designed a flip VC for gray-scale and color cover images based on constraint error diffusion. We show that it is possible to meet all the constraints simultaneously. Compared with existing schemes, our scheme enjoys the following features: Color cover images, no computation needed for decoding, and no interference from cover image on the recovered secret image.
Lu Wang; Bin Yan; Hong-Mei Yang; Jeng-Shyang Pan. Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images. Symmetry 2020, 13, 65 .
AMA StyleLu Wang, Bin Yan, Hong-Mei Yang, Jeng-Shyang Pan. Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images. Symmetry. 2020; 13 (1):65.
Chicago/Turabian StyleLu Wang; Bin Yan; Hong-Mei Yang; Jeng-Shyang Pan. 2020. "Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images." Symmetry 13, no. 1: 65.
A recently modern stochastic optimization algorithm has been developed by observing the life of slime mold physarum polycephalum in nature. The algorithm is called the slime mold algorithm (SMA) with an excellent exploratory capacity and exploitation inclination. Still, slipping into optimal local is easy to happen and slowly converges speed while dealing with complicated problems. This article proposes a new process of improving SMA (namely ISMA) by adapting the weight coefficient and cooperating the reverse learning strategy in the expression of agents updating locations to enhance the algorithm’s optimization performance. Many selected benchmark functions and the optimal operation of cascade reservoirs are applied to evaluate the performance of the proposed algorithm. Comparisons of the proposed approach’s results with the various algorithms under the case situations show that the recommended solution produces better performance than the different competing algorithms.
Trong-The Nguyen; Hong-Jiang Wang; Thi-Kien Dao; Jeng-Shyang Pan; Jian-Hua Liu; ShaoWei Weng. An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations. IEEE Access 2020, 8, 226754 -226772.
AMA StyleTrong-The Nguyen, Hong-Jiang Wang, Thi-Kien Dao, Jeng-Shyang Pan, Jian-Hua Liu, ShaoWei Weng. An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations. IEEE Access. 2020; 8 ():226754-226772.
Chicago/Turabian StyleTrong-The Nguyen; Hong-Jiang Wang; Thi-Kien Dao; Jeng-Shyang Pan; Jian-Hua Liu; ShaoWei Weng. 2020. "An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations." IEEE Access 8, no. : 226754-226772.
Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems. However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved. Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence. In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm. As we all know, the transfer function is very important in the binarization of continuous optimization algorithms. Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm. Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development. In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm. Finally, we apply the algorithm to feature selection in the ten data sets of UCI. The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.
Zhi-Gang Du; Jeng-Shyang Pan; Shu-Chuan Chu; Yi-Jui Chiu. Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection. IEEE Access 2020, 8, 225730 -225744.
AMA StyleZhi-Gang Du, Jeng-Shyang Pan, Shu-Chuan Chu, Yi-Jui Chiu. Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection. IEEE Access. 2020; 8 (99):225730-225744.
Chicago/Turabian StyleZhi-Gang Du; Jeng-Shyang Pan; Shu-Chuan Chu; Yi-Jui Chiu. 2020. "Improved Binary Symbiotic Organism Search Algorithm With Transfer Functions for Feature Selection." IEEE Access 8, no. 99: 225730-225744.
Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this study, an efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching–learning-based optimization (TLBO) and differential evolution (DE). The TLBO is focused on global exploration and the DE is concentrated on local exploitation. These two algorithms are carried out alternately when no better candidate solution can be found. Meanwhile, a new prescreening criterion based on the best and top collection information is introduced to choose promising candidates for real function evaluations. Besides, two evolution control (i.e., the generation-based and individual-based) strategies and a top-ranked restart strategy are integrated in the SAHO. Moreover, a local RBF surrogate which does not need too many training samples is employed to model the landscapes of the target function. Sixteen benchmark functions and the tension/compression spring design problem are adopted to compare the proposed SAHO with other state-of-the-art approaches. Extensive comparison results demonstrate that the proposed SAHO has superior performance for solving expensive optimization problems.
Jeng-Shyang Pan; Nengxian Liu; Shu-Chuan Chu; Taotao Lai. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences 2020, 561, 304 -325.
AMA StyleJeng-Shyang Pan, Nengxian Liu, Shu-Chuan Chu, Taotao Lai. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences. 2020; 561 ():304-325.
Chicago/Turabian StyleJeng-Shyang Pan; Nengxian Liu; Shu-Chuan Chu; Taotao Lai. 2020. "An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems." Information Sciences 561, no. : 304-325.