Hongchun Shu^1,2, Yaqi Deng^1,2,*, Pulin Cao², Jun Dong², Hongjiang Rao², Zhiqian Bo²

Energy Engineering, Vol.119, No.2, pp. 523-538, 2022, DOI:10.32604/ee.2022.015570

Abstract The arc-suppression coil (ASC) in parallel low resistance (LR) multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm. If the fault disappears before LR is put into the system, it is judged as an instantaneous fault; while the fault does not disappear after LR is put into the system, it is judged as a permanent fault; the single-phase grounding fault (SLG) protection criterion based on zero-sequence power variation is proposed to identify the instantaneous-permanent fault. Firstly, the distribution characteristic of zero-sequence voltage… More >

Sumaira Azhar¹, Naveed Ahmed Azam^2,*, Umar Hayat¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4971-4988, 2022, DOI:10.32604/cmc.2022.023685

Abstract The demand for data security schemes has increased with the significant advancement in the field of computation and communication networks. We propose a novel three-step text encryption scheme that has provable security against computation attacks such as key attack and statistical attack. The proposed scheme is based on the Pell sequence and elliptic curves, where at the first step the plain text is diffused to get a meaningless plain text by applying a cyclic shift on the symbol set. In the second step, we hide the elements of the diffused plain text from the attackers. For this purpose, we use… More >

Ala Saleh Alluhaidan^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5949-5962, 2022, DOI:10.32604/cmc.2022.022028

Abstract Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the… More >

Liwei Wang^1,2,3, Jiankun Sun^1,2,3, Xiong Luo^1,2,3,*, Xi Yang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1003-1016, 2022, DOI:10.32604/cmes.2022.018492

Abstract With the development of information technology, malware threats to the industrial system have become an emergent issue, since various industrial infrastructures have been deeply integrated into our modern works and lives. To identify and classify new malware variants, different types of deep learning models have been widely explored recently. Generally, sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability. However, in current practical applications, an ample supply of data is absent in most specific industrial malware detection scenarios. Transfer learning as an effective approach can be used to alleviate the influence of the… More >

Sobia Pervaiz¹, Waqas Haider Bangyal², Adnan Ashraf³, Kashif Nisar^4,*, Muhammad Reazul Haque⁵, Ag. Asri Bin Ag. Ibrahim⁴, BS Chowdhry⁶, Waqas Rasheed⁷, Joel J. P. C. Rodrigues^8,9, Richard Etengu⁵, Danda B. Rawat¹⁰

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1427-1444, 2022, DOI:10.32604/iasc.2022.017304

Abstract In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary… More >

Siti Julia Rosli^1,2, Hasliza A Rahim^1,2,*, Khairul Najmy Abdul Rani^1,2, Ruzelita Ngadiran^2,3, Wan Azani Mustafa^3,4, Muzammil Jusoh^1,2, Mohd Najib Mohd Yasin^1,2, Thennarasan Sabapathy^1,2, Mohamedfareq Abdulmalek⁵, Wan Suryani Firuz Wan Ariffin², Ahmed Alkhayyat⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3533-3556, 2022, DOI:10.32604/cmc.2022.021719

Abstract The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains’ properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximum possible MF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm… More >

Jiaxin Shi¹, Lin Ye^1,2,*, Zhongwei Li³, Dongyang Zhan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 483-498, 2022, DOI:10.32604/cmes.2022.017467

Abstract With the rapid development of the Internet, a large number of private protocols emerge on the network. However, some of them are constructed by attackers to avoid being analyzed, posing a threat to computer network security. The blockchain uses the P2P protocol to implement various functions across the network. Furthermore, the P2P protocol format of blockchain may differ from the standard format specification, which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them. Therefore, the ability to distinguish different types of unknown network protocols is vital for network security. In this paper, we propose… More >

Chichao Zheng, Yazhong Wang, Yadan Wang^*, Qing He, Hu Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 397-413, 2022, DOI:10.32604/cmes.2022.016308

Abstract Virtual source (VS) imaging has been proposed to improve image resolution in medical ultrasound imaging. However, VS obtains a limited contrast due to the non-adaptive delay-and-sum (DAS) beamforming. To improve the image contrast and provide an enhanced resolution, adaptive weighting algorithms were applied in VS imaging. In this paper, we proposed an adjustable generalized coherence factor (aGCF) for the synthetic aperture sequential beamforming (SASB) of VS imaging to improve image quality. The value of aGCF is adjusted by a sequence intensity factor (SIF) that is defined as the ratio between the effective low resolution scan lines (LRLs) intensity and total… More >

S. Kalyani^*, K. Venkata Rao, A. Mary Sowjanya

Structural Durability & Health Monitoring, Vol.15, No.4, pp. 317-334, 2021, DOI:10.32604/sdhm.2021.016975

Abstract Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure. In the present work, the… More >

Wei Fang^1,2,*, Yupeng Chen¹, Qiongying Xue¹

Journal on Big Data, Vol.3, No.3, pp. 97-110, 2021, DOI:10.32604/jbd.2021.016993

Abstract In the past few years, deep learning has developed rapidly, and many researchers try to combine their subjects with deep learning. The algorithm based on Recurrent Neural Network (RNN) has been successfully applied in the fields of weather forecasting, stock forecasting, action recognition, etc. because of its excellent performance in processing Spatio-temporal sequence data. Among them, algorithms based on LSTM and GRU have developed most rapidly because of their good design. This paper reviews the RNN-based Spatiotemporal sequence prediction algorithm, introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction, and includes precipitation nowcasting… More >