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Yingjun Wang^1,*, Xinqing Li¹, Kai Long², Peng Wei³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1-34, 2023, DOI:10.32604/cmes.2023.027603
Abstract Topology optimization (TO), a numerical technique to find the optimal material layout with a given design domain, has attracted interest from researchers in the field of structural optimization in recent years. For beginners, opensource codes are undoubtedly the best alternative to learning TO, which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method. In this paper, we present a summary of various open-source codes and related literature on TO methods, including solid isotropic material with penalization (SIMP), evolutionary method, level set method (LSM), moving morphable components/voids (MMC/MMV) methods, multiscale… More >

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Xiaoyan Jiang^1,2, Zuojin Hu¹, Shuihua Wang², Yudong Zhang^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 35-82, 2023, DOI:10.32604/cmes.2023.027676
Abstract Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage… More >

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Sufen Zhao^1,2, Rong Peng^1,*, Po Hu², Liansheng Tan²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781
Abstract Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various… More >

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Joseph Bamidele Awotunde¹, Sanjay Misra^2,*, Vikash Katta², Oluwafemi Charles Adebayo¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 131-154, 2023, DOI:10.32604/cmes.2023.026812
Abstract The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of many benefits like profit-making, which… More >

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Woo Chul Chung¹, Chungkuk Jin^2,*, MooHyun Kim³, Ju-young Hwang⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 155-174, 2023, DOI:10.32604/cmes.2023.026754
Abstract This paper presents dynamic-behavior comparisons and related forensic analyses of a submerged floating tunnel (SFT) between numerical simulation and physical experiment under regular and irregular waves. The experiments are conducted in the 3D wave tank with 1:33.3 scale, and the corresponding coupled time-domain simulation tool is devised for comparison. The entire SFT system consists of a long concrete tunnel and 12 tubular aluminum mooring lines. Two numerical simulation models, the Cummins equation with 3D potential theory including second-order wave-body interaction effects and the much simpler Morison-equation-based formula with the lumped-mass-based line model, are designed and compared. Forensic analyses for mooring-line… More >

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Zhifang Liao¹, Shuyuan Cao¹, Bin Li¹, Shengzong Liu^2,*, Yan Zhang³, Song Yu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 175-196, 2023, DOI:10.32604/cmes.2023.027287
Abstract GitHub repository recommendation is a research hotspot in the field of open-source software. The current problems with the repository recommendation system are the insufficient utilization of open-source community information and the fact that the scoring metrics used to calculate the matching degree between developers and repositories are developed manually and rely too much on human experience, leading to poor recommendation results. To address these problems, we design a questionnaire to investigate which repository information developers focus on and propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solve insufficient information utilization in open-source communities, we construct a Developer-Repository… More >

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Jianfang Qi¹, Yue Li^1,3, Haibin Jin¹, Jianying Feng¹, Dong Tian¹, Weisong Mu^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 197-232, 2023, DOI:10.32604/cmes.2023.026113
Abstract An appropriate optimal number of market segments (ONS) estimation is essential for an enterprise to achieve successful market segmentation, but at present, there is a serious lack of attention to this issue in market segmentation. In our study, an independent adaptive ONS estimation method BWCON-NSDK-means++ is proposed by integrating a new internal validity index (IVI) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions of the existing IVIs, we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two… More >

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Khalil Chebil^1,2, Skander Htiouech³, Mahdi Khemakhem^1,2,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 233-263, 2023, DOI:10.32604/cmes.2023.026476
Abstract Crowd management and analysis (CMA) systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles (UAVs) use. Crowd tracking using UAVs is among the most important services provided by a CMA. In this paper, we studied the periodic crowd-tracking (PCT) problem. It consists in using UAVs to follow-up crowds, during the life-cycle of an open crowded area (OCA). Two criteria were considered for this purpose. The first is related to the CMA initial investment, while the second is to guarantee the quality of service (QoS). The existing works focus on very specified assumptions that are highly… More >

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Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 265-298, 2023, DOI:10.32604/cmes.2023.027581
Abstract Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this… More >

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473

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Dongmin Wang^1,2,*, Gaoshuai Lin¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 299-318, 2023, DOI:10.32604/cmes.2023.027280
（This article belongs to this Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract There are five most widely used contact angle schemes in the pseudopotential lattice Boltzmann (LB) model for simulating the wetting phenomenon: The pseudopotential-based scheme (PB scheme), the improved virtual-density scheme (IVD scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the fluid layer density above the wall (MPB-C scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the weighted average density of surrounding fluid nodes (MPB-W scheme) and the geometric formulation scheme (GF scheme). But the numerical stability and accuracy of the schemes for wetting simulation remain unclear in the past. In… More >

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Ping-Huan Kuo^1,2, Ssu-Chi Chen¹, Chia-Ho Lee¹, Po-Chien Luan², Her-Terng Yau^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 319-343, 2023, DOI:10.32604/cmes.2023.026860
（This article belongs to this Special Issue: Computational Mechanics Assisted Modern Urban Planning and Infrastructure)
Abstract Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage, tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently, spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by the temperature rise of the Spindle from affecting the accuracy during the machining process, typically, the factory will warm up the machine before the manufacturing process. However, if there is no way to understand the tool spindle's thermal deformation, the machining quality will be greatly affected. In order to solve the above problem, this study aims to predict the thermal… More >

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Yonghao Zhang^1,3, Yongtang Wu², Tao Li¹, Hui Zhou^1,3, Yuling Chen^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 345-361, 2023, DOI:10.32604/cmes.2023.026920
（This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
Abstract The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a third party for key distribution… More >

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Yanjun Zhang¹, Yongqiang He², Jingbo Zhang¹, Yaru Zhao³, Zhihua Cui^1,*, Wensheng Zhang⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 363-383, 2023, DOI:10.32604/cmes.2023.025832
（This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
Abstract The video compression sensing method based on multi hypothesis has attracted extensive attention in the research of video codec with limited resources. However, the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task. To resolve this problem, this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimization method. It mainly includes the optimization of prediction blocks (OPBS), the selection of search windows and the use of neighborhood information. Specifically, the OPBS consists of two parts: the selection of blocks and the optimization of prediction blocks. We combine the high-quality optimization reconstruction of… More >

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Binwen Zhu¹, Qifang Luo^1,3,*, Yongquan Zhou^1,2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 385-413, 2023, DOI:10.32604/cmes.2023.026097
（This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract With the rapid development of communication technology, the problem of antenna array optimization plays a crucial role. Among many types of antennas, line antenna arrays (LAA) are the most commonly applied, but the side lobe level (SLL) reduction is still a challenging problem. In the radiation process of the linear antenna array, the high side lobe level will interfere with the intensity of the antenna target radiation direction. Many conventional methods are ineffective in obtaining the maximum side lobe level in synthesis, and this paper proposed a quantum equilibrium optimizer (QEO) algorithm for line antenna arrays. Firstly, the linear antenna… More >

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Li Yuan¹, Jianping Ji¹, Xuegong Liu¹, Tong Liu², Huiling Chen³, Deng Chen^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 415-454, 2023, DOI:10.32604/cmes.2023.026098
（This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called… More >

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Feezan Ahmad¹, Xiaowei Tang¹, Jilei Hu^2,*, Mahmood Ahmad^3,4, Behrouz Gordan⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 455-487, 2023, DOI:10.32604/cmes.2023.025993
（This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This paper’s reduced error pruning (REP) tree and random tree (RT) models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering. The data set of this study includes five parameters, namely slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The available data is split into two categories: training (75%) and test (25%) sets. The output of the RT and REP tree models is evaluated using performance measures including accuracy (Acc), Matthews correlation coefficient (Mcc), precision… More >

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Weiguang Zheng^1,2,3, Junzhu Zhang^1,2, Shanchao Wang^2,*, Gaoshan Feng², Xiaohong Xu², Qiuxiang Ma²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 489-508, 2023, DOI:10.32604/cmes.2023.025169
（This article belongs to this Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
Abstract The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehicle mass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variable slope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicle shifting strategy was formulated according to the identification results. The co-simulation results showed that, compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-time vehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had the following advantages, e.g., avoiding frequent shifting of vehicles… More >

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Wu Song¹, Yayuan Tang^2,3,*, Wenxue Tan¹, Sheng Ren¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 509-526, 2023, DOI:10.32604/cmes.2023.026933
（This article belongs to this Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
Abstract In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior (human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligent standing human detection (ISHD) method based on an improved single shot multibox detector to detect the target of standing human posture in the scene frame of exam room video surveillance at a specific examination stage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posture feature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the training… More >

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Zeyu Wu¹, Bo Sun^1,2, Qiang Feng^2,*, Zili Wang¹, Junlin Pan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124
（This article belongs to this Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative… More >

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Yang Zhao¹, Jingmin An¹, Hao Li¹, Saru Kumari^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 555-575, 2023, DOI:10.32604/cmes.2023.026808
（This article belongs to this Special Issue: Information Security Practice and Experience: Advances and Challenges)
Abstract The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment of VSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTAS-ET). The scheme combines fault-tolerant and aggregate signcryption, which not only makes up for… More >

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Yuhao Wang, Yuying Li, Yanbin Sun, Yu Jiang^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 577-605, 2023, DOI:10.32604/cmes.2023.026791
（This article belongs to this Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
Abstract To identify industrial control equipment is often a key step in network mapping, categorizing network resources, and attack defense. For example, if vulnerable equipment or devices can be discovered in advance and the attack path can be cut off, security threats can be effectively avoided and the stable operation of the Internet can be ensured. The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability. This paper proposes an industrial control device identification method based on PCA-Adaboost, which integrates rule matching and machine learning. We first build a rule base from network data… More >

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Jian Kang¹, Wanshu Fan¹, Yijing Li², Rui Liu¹, Dongsheng Zhou^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 607-629, 2023, DOI:10.32604/cmes.2023.024420
（This article belongs to this Special Issue: Recent Advances in Virtual Reality)
Abstract With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression… More >

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Lingli Zhang¹, Yadong Wu^1,*, Qikai Chu², Pan Li², Guijuan Wang^3,4, Weihan Zhang¹, Yu Qiu¹, Yi Li¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 631-645, 2023, DOI:10.32604/cmes.2023.027179
（This article belongs to this Special Issue: Recent Advances in Virtual Reality)
Abstract Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small. It does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetic sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension (BERT-wwm-ext) and Enhanced representation through knowledge integration (ERNIE)… More >

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Yanzhao Zhou, Binghao Liu, Yiran Liu, Jianbin Jiao^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 647-668, 2023, DOI:10.32604/cmes.2023.026745
（This article belongs to this Special Issue: Deep Learning for Marine and Underwater Environment: Theory, Method, and Applications)
Abstract Deep Convolution Neural Networks (DCNNs) can capture discriminative features from large datasets. However, how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world, e.g., classifying newly discovered fish species, remains an open problem. We address an even more challenging and realistic setting of this problem where new class samples are insufficient, i.e., Few-Shot Class-Incremental Learning (FSCIL). Current FSCIL methods augment the training data to alleviate the overfitting of novel classes. By contrast, we propose Filter Bank Networks (FBNs) that augment the learnable filters to capture fine-detailed features for adapting… More >

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Siyu Lu¹, Jun Yang¹, Bo Yang¹, Zhengtong Yin², Mingzhe Liu^3,*, Lirong Yin⁴, Wenfeng Zheng^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 669-685, 2023, DOI:10.32604/cmes.2023.027417
（This article belongs to this Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract With the help of surgical navigation system, doctors can operate on patients more intuitively and accurately. The positioning accuracy and real-time performance of surgical instruments are very important to the whole system. In this paper, we analyze and design the detection algorithm of surgical instrument location mark, and estimate the posture of surgical instrument. In addition, we optimized the pose by remapping. Finally, the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments. The final result shows a high accuracy. More >

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Zhifang Liao¹, Bolin Zhang¹, Xuechun Huang¹, Song Yu^1,*, Yan Zhang²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 687-704, 2023, DOI:10.32604/cmes.2023.027466
（This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor,… More >

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Muwei Jian^1,2,#,*, Ronghua Wu^1,#, Hongyu Chen¹, Lanqi Fu³, Chengdong Yang¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net. Then, image… More >

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You Lu^1,2,#,*, Linqian Cui^1,2,#,*, Yunzhe Wang^1,2, Jiacheng Sun^1,2, Lanhui Liu³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on… More >

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Chien-Ming Chen¹, Zhenzhou Zhang¹, Jimmy Ming-Tai Wu¹, Kuruva Lakshmanna^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three… More >

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Yalong Yang^1,2,3, Wenjing Xu^1,2,3, Yinfeng Zhu⁴, Liangliang Su^1,2,3,*, Gongquan Zhang^1,2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 761-773, 2023, DOI:10.32604/cmes.2023.027010
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance… More >

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Weiqiang Tan^1,*, Quanquan Zhou¹, Weijie Tan², Longcheng Yang³, Chunguo Li⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 775-787, 2023, DOI:10.32604/cmes.2023.027427
（This article belongs to this Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
Abstract In this paper, we investigate the end-to-end performance of intelligent reflecting surface (IRS)-assisted wireless communication systems. We consider a system in which an IRS is deployed on a uniform planar array (UPA) configuration, including a large number of reflecting elements, where the transmitters and receivers are only equipped with a single antenna. Our objective is to analytically obtain the achievable ergodic rate, outage probability, and bit error rate (BER) of the system. Furthermore, to maximize the system’s signal-to-noise ratio (SNR), we design the phase shift of each reflecting element and derive the optimal reflection phase of the IRS based on… More >

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Zhiping Lu¹, Xinwei Yue^2,*, Shuo Chen², Weiguo Ma¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 789-812, 2023, DOI:10.32604/cmes.2023.027663
（This article belongs to this Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
Abstract Active intelligent reflecting surface (IRS) is a novel and promising technology that is able to overcome the multiplicative fading introduced by passive IRS. In this paper, we consider the application of active IRS to non-orthogonal multiple access (NOMA) networks, where the incident signals are amplified actively through integrating amplifier to reflecting elements. More specifically, the performance of active/passive IRS-NOMA networks is investigated over large and small-scale fading channels. Aiming to characterize the performance of active IRS-NOMA networks, the exact and asymptotic expressions of outage probability for a couple of users, i.e., near-end user and far-end user are derived by exploiting… More >

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Shayla Islam¹, Anil Kumar Budati^1,*, Mohammad Kamrul Hasan², Saoucene Mahfoudh³, Syed Bilal Hussian Shah³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 813-825, 2023, DOI:10.32604/cmes.2023.027595
（This article belongs to this Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
Abstract In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing… More >

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M. B. Almatrafi, Abdulghani Alharbi^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 827-841, 2023, DOI:10.32604/cmes.2023.027344
（This article belongs to this Special Issue: Integration of Geometric Modeling and Numerical Simulation)
Abstract The extraction of traveling wave solutions for nonlinear evolution equations is a challenge in various mathematics, physics, and engineering disciplines. This article intends to analyze several traveling wave solutions for the modified regularized long-wave (MRLW) equation using several approaches, namely, the generalized algebraic method, the Jacobian elliptic functions technique, and the improved Q-expansion strategy. We successfully obtain analytical solutions consisting of rational, trigonometric, and hyperbolic structures. The adaptive moving mesh technique is applied to approximate the numerical solution of the proposed equation. The adaptive moving mesh method evenly distributes the points on the high error areas. This method perfectly and… More >

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Na Lei¹, Ping Zhang², Xiaopeng Zheng^3,*, Yiming Zhu³, Zhongxuan Luo³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 843-857, 2023, DOI:10.32604/cmes.2023.027296
（This article belongs to this Special Issue: Integration of Geometric Modeling and Numerical Simulation)
Abstract We propose a new method to generate surface quadrilateral mesh by calculating a globally defined parameterization with feature constraints. In the field of quadrilateral generation with features, the cross field methods are well-known because of their superior performance in feature preservation. The methods based on metrics are popular due to their sound theoretical basis, especially the Ricci flow algorithm. The cross field methods’ major part, the Poisson equation, is challenging to solve in three dimensions directly. When it comes to cases with a large number of elements, the computational costs are expensive while the methods based on metrics are on… More >

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Yigang Wang¹, Hong Li¹, Wanbin Pan^1,*, Weijuan Cao¹, Jie Miao¹, Xiaofei Ai¹, Enya Shen²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 859-878, 2023, DOI:10.32604/cmes.2023.027320
（This article belongs to this Special Issue: Integration of Geometric Modeling and Numerical Simulation)
Abstract Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First, as the geometry of an… More >

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Lemei Da¹, Hai Liang^1,*, Yong Ding^1,2, Yujue Wang¹, Changsong Yang¹, Huiyong Wang³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 879-902, 2023, DOI:10.32604/cmes.2023.026309
（This article belongs to this Special Issue: Emerging Trends on Blockchain: Architecture and Dapp Ecosystem)
Abstract The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario. Each UAV cluster has an… More >

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Guangxia Xu^1,*, Lei Liu², Jingnan Dong³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 903-922, 2023, DOI:10.32604/cmes.2023.026627
（This article belongs to this Special Issue: Emerging Trends on Blockchain: Architecture and Dapp Ecosystem)
Abstract In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts… More >

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Shujing Sun^1,3, Jiale Wu², Jian Yao¹, Yang Cheng⁴, Xin Zhang¹, Zhihua Lu³, Pengjiang Qian^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 923-938, 2023, DOI:10.32604/cmes.2023.027356
（This article belongs to this Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
Abstract Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the… More >

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M. C. Shanmukha¹, Sokjoon Lee^2,*, A. Usha³, K. C. Shilpa⁴, Muhammad Azeem⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 939-964, 2023, DOI:10.32604/cmes.2023.027254
（This article belongs to this Special Issue: Resolvability Parameters and their Applications)
Abstract Graph theory plays a significant role in the applications of chemistry, pharmacy, communication, maps, and aeronautical fields. The molecules of chemical compounds are modelled as a graph to study the properties of the compounds. The geometric structure of the compound relates to a few physical properties such as boiling point, enthalpy, π-electron energy, molecular weight. The article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and graphenylene. The topological index is an invariant of a molecular graph associated with the chemical structure, which shows the correlation… More >

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Adnan Malik¹, Muhammad Ali¹, Faisal S. Alsubaei², Nisar Ahmed^3,*, Harish Kumar⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 965-999, 2023, DOI:10.32604/cmes.2023.022493
（This article belongs to this Special Issue: Computational Intelligence Techniques for Securing Systems and Networks beyond 5G)
Abstract The security of digital images transmitted via the Internet or other public media is of the utmost importance. Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities. This study provides an approach to color image encryption that could find practical use in various contexts. The proposed method, which combines four chaotic systems, employs singular value decomposition and a chaotic sequence, making it both secure and compression-friendly. The unified average change intensity, the number of pixels’ change rate, information entropy analysis, correlation coefficient analysis,… More >

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Yin Li¹, Yuanjia Song¹, Zhengwei Yang², Haijun Jiang³, Bowen Liu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1001-1018, 2023, DOI:10.32604/cmes.2023.027072
（This article belongs to this Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
Abstract The temperature field in laser line scanning thermography is investigated comprehensively in this work, including analytical calculation and experiment. Firstly, the principle of laser line scanning thermography is analyzed. On this basis, a physical laser line scanning model is proposed. Afterwards, based on Fourier transform (FT) and segregation variable method (SVM), the heat conduction differential equation in laser line scanning thermography is derived in detail. The temperature field of the composite-based coatings model with defects is simulated numerically. The results show that the laser line scanning thermography can effectively detect the defects in the model. The correctness of the analytical… More >

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Milovan Kovač¹, Snežana Tadić¹, Mladen Krstić^1,*, Violeta Roso²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1019-1046, 2023, DOI:10.32604/cmes.2023.027909
（This article belongs to this Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
Abstract Overcoming the global sustainability challenges of logistics requires applying solutions that minimize the negative effects of logistics activities. The most efficient way of doing so is through intermodal transportation (IT). Current IT systems rely mostly on road, rail, and sea transport, not inland waterway transport. Developing dry port (DP) terminals has been proven as a sustainable means of promoting and utilizing IT in the hinterland of seaport container terminals. Conventional DP systems consolidate container flows from/to seaports and integrate road and rail transportation modes in the hinterland which improves the sustainability of the whole logistics system. In this article, to… More >

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Yan Zeng^1,2,3, Siyuan Teng¹, Tian Xiang^4,*, Jilin Zhang^1,2,3, Yuankai Mu⁵, Yongjian Ren^1,2,3,*, Jian Wan^1,2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226
（This article belongs to this Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the global model with the selected… More >

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