Sinuo Zhang¹, Daicong Da², Yingjun Wang^1,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.2, pp. 1-2, 2023, DOI:10.32604/icces.2023.09085

Abstract Topology optimization (TO) designs involving multiple materials suffer either difficult interface modeling or finding physically meaningful transition domains with an accurate structural representation. Simple interpolation approaches are usually used in multi-material designs to represent the overlapped regions of different materials, which cannot solve either of these problems. In this paper, a moving morphable component (MMC)-based TO is developed to overcome this issue by leveraging the triply periodic minimal surfaces (TPMS). The TMPS-based architecture will serve as the infilling microstructure to accurately represent the overlapped domains of different materials. A TPMS function interpolation scheme is used… More >

Yuming He^1,*, Jie Chen¹, Yubo Jiao¹, Wei Wang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09061

Abstract In present study, a fast simulation algorithm based on linearization is used to simulate the flow parameters of the natural gas pipeline under transient operating conditions, analyze the impact of natural gas components on the transient operation, and conduct the tracking calculation of natural gas components [1- 3]. Under the condition that the simulation calculation accuracy is not affected, the first-order Taylor linearization expansion method is used to linearize the transient simulation model of natural gas pipeline, while the second-order implicit difference dispersion method is used to obtain the linearized discrete equations without initial value… More >

Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI:10.32604/cmc.2023.039683 - 30 August 2023

Abstract A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the… More >

Abid Mahboob¹, Muhammad Asif², Rana Muhammad Zulqarnain^3,*, Imran Siddique⁴, Hijaz Ahmad⁵, Sameh Askar⁶, Giovanni Pau⁷

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2547-2562, 2023, DOI:10.32604/csse.2023.040855 - 28 July 2023

Abstract The rapid advancement of data in web-based communication has created one of the biggest issues concerning the security of data carried over the internet from unauthorized access. To improve data security, modern cryptosystems use substitution-boxes. Nowadays, data privacy has become a key concern for consumers who transfer sensitive data from one place to another. To address these problems, many companies rely on cryptographic techniques to secure data from illegal activities and assaults. Among these cryptographic approaches, AES is a well-known algorithm that transforms plain text into cipher text by employing substitution box (S-box). The S-box… More >

Ruyi Luo, Yanyan Hu^*, Tingshu He^*, Xiaodong Ma, Yongdong Xu

Journal of Renewable Materials, Vol.11, No.9, pp. 3485-3500, 2023, DOI:10.32604/jrm.2023.027592 - 20 July 2023

Abstract The measures of steam curing and early-strengthening agents to promote the precast components to reach the target strength quickly can bring different degrees of damage to the concrete. Based on this, the new nanomaterial CSH-the hydration product of cement effectively solves these measures’ disadvantages, such as excessive energy consumption, thermal stress damage, and the introduction of external ions. In this paper, the effect of CSH on the early strength of precast fly ash concrete components was investigated in terms of setting time, workability, and mechanical properties and analyzed at the microscopic level using hydration temperature, More >

Muhammad Sajjad^1,*, Tariq Shah¹, Robinson Julian Serna²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5287-5305, 2023, DOI:10.32604/cmc.2023.035347 - 29 April 2023

Abstract In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing More >

Zibo Wang^1,3, Chaobin Huo², Yaofang Zhang^1,3, Shengtao Cheng^1,3, Yilu Chen^1,3, Xiaojie Wei⁵, Chao Li⁴, Bailing Wang^1,3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694 - 31 March 2023

Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific… More >

Tianyu He¹, Xiaolong Xu^1,*, Zhichen Hu¹, Qingzhan Zhao², Jianguo Dai², Fei Dai³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3657-3673, 2023, DOI:10.32604/iasc.2023.036831 - 15 March 2023

Abstract With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of… More >

P. Shanmuga Prabha^*, S. Magesh Kumar

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3101-3119, 2023, DOI:10.32604/iasc.2023.034885 - 15 March 2023

Abstract Today, securing devices connected to the internet is challenging as security threats are generated through various sources. The protection of cyber-physical systems from external attacks is a primary task. The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters. The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis (MEDA) through Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) for the extraction of unique parameters. The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network (R2CNN) and Gradient Boost Regression (GBR) to More >

Feisha Hu¹, Qi Wang^1,*, Haijian Shao^1,2, Shang Gao¹, Hualong Yu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732 - 09 March 2023

Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global More > Graphic Abstract