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EDITORIAL

Wei-Chiang Hong^1,*, Yi Liang²
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 763-766, 2023, DOI:10.32604/cmes.2022.023022
（This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract This article has no abstract. More >

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Tingshun Li¹, Jiaohui Xu^1,*, Zesan Liu², Dadi Wang², Wen Tan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 767-782, 2023, DOI:10.32604/cmes.2022.020702
（This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are labeled, normalized, and balanced. The… More >

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Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Yu-Chieh Lin¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128
（This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized… More >

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EDITORIAL

S. A. Edalatpanah^1,*, Florentin Smarandache²
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 817-819, 2023, DOI:10.32604/cmes.2022.024060
（This article belongs to this Special Issue: Advances in Neutrosophic and Plithogenic Sets for Engineering and Sciences: Theory, Models, and Applications (ANPSESTMA))
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Yiming Zhang^1,*, Zhiran Gao¹, Xueya Wang¹, Qi Liu²
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 821-833, 2023, DOI:10.32604/cmes.2022.022088
Abstract A large amount of data can partly assure good fitting quality for the trained neural networks. When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice, numerical simulations can provide a large amount of controlled high quality data. Once the neural networks are trained by such data, they can be used for predicting the properties/responses of the engineering objects instantly, saving the further computing efforts of simulation tools. Correspondingly, a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks… More >

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Mohammad Sadegh Barkhordari¹, Danial Jahed Armaghani^2,*, Panagiotis G. Asteris³
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840
（This article belongs to this Special Issue: Soft Computing Techniques in Materials Science and Engineering)
Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are… More >

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Huiwen Xue¹, Jiwei Wen^1,*, Akshya Kumar Swain¹, Xiaoli Luan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127
（This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore,… More >

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Wentao Liu, Junxia Ma, Weili Xiong^*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565
（This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance. Furthermore, to improve the performance of… More >

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Xin Wang, Zhilin Zhu, Zhen Hua^*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287
（This article belongs to this Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)
Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial… More >

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Passent El-kafrawy^1,2, Maie Aboghazalah^2,*, Abdelmoty M. Ahmed³, Hanaa Torkey⁴, Ayman El-Sayed⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713
（This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.… More >

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Ahmed M. Elshenhab^1,2,*, Xingtao Wang¹, Fatemah Mofarreh³, Omar Bazighifan^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 927-940, 2023, DOI:10.32604/cmes.2022.021512
（This article belongs to this Special Issue: Advanced Numerical Methods for Fractional Differential Equations)
Abstract We study nonhomogeneous systems of linear conformable fractional differential equations with pure delay. By using new conformable delayed matrix functions and the method of variation, we obtain a representation of their solutions. As an application, we derive a finite time stability result using the representation of solutions and a norm estimation of the conformable delayed matrix functions. The obtained results are new, and they extend and improve some existing ones. Finally, an example is presented to illustrate the validity of our theoretical results. More >

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Kamal Shah^1,2, Hafsa Naz², Thabet Abdeljawad^1,3,*, Aziz Khan¹, Manar A. Alqudah⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 941-955, 2023, DOI:10.32604/cmes.2022.021483
（This article belongs to this Special Issue: Advanced Numerical Methods for Fractional Differential Equations)
Abstract In this manuscript, an algorithm for the computation of numerical solutions to some variable order fractional differential equations (FDEs) subject to the boundary and initial conditions is developed. We use shifted Legendre polynomials for the required numerical algorithm to develop some operational matrices. Further, operational matrices are constructed using variable order differentiation and integration. We are finding the operational matrices of variable order differentiation and integration by omitting the discretization of data. With the help of aforesaid matrices, considered FDEs are converted to algebraic equations of Sylvester type. Finally, the algebraic equations we get are solved with the help of… More >

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Peng Zhao¹, Yongxin Zhang¹, Qiaozhi Hua^2,*, Haipeng Li³, Zheng Wen⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 957-979, 2023, DOI:10.32604/cmes.2022.021783
（This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
Abstract Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side… More >

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Jintao Liu¹, Juan Zhao¹, Xiaowei Shen^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 981-1003, 2023, DOI:10.32604/cmes.2022.021641
（This article belongs to this Special Issue: Recent Advance of the Isogeometric Boundary Element Method and its Applications)
Abstract In this work, an acoustic topology optimization method for structural surface design covered by porous materials is proposed. The analysis of acoustic problems is performed using the isogeometric boundary element method. Taking the element density of porous materials as the design variable, the volume of porous materials as the constraint, and the minimum sound pressure or maximum scattered sound power as the design goal, the topology optimization is carried out by solid isotropic material with penalization (SIMP) method. To get a limpid 0–1 distribution, a smoothing Heaviside-like function is proposed. To obtain the gradient value of the objective function, a… More >

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Hanhan Zhang¹, Zhihui Xu², Hong Qian¹, Xiaoyan Su^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1005-1019, 2023, DOI:10.32604/cmes.2022.021898
（This article belongs to this Special Issue: Computer-Aided Structural Integrity and Safety Assessment)
Abstract Failure mode and effects analysis (FMEA) is a widely used safety assessment method in many fields. Z-number was previously applied in FMEA since it can take both possibility and reliability of information into consideration. However, the use of fuzzy weighted mean to integrate Z-valuations may have some drawbacks and is not suitable for some situations. In this paper, an improved method is proposed based on Z-numbers and the graded mean integration representation (GMIR) to deal with the uncertain information in FMEA. First, Z-numbers are constructed based on the evaluations of risk factors O, S, D for each failure mode by… More >

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Yu Song¹, Xuejun Chen¹, Baoping Zou^2,*, Jundong Mu³, Rusheng Hu⁴, Siqi Cheng⁵, Shengli Zhao³
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1021-1039, 2023, DOI:10.32604/cmes.2022.021164
（This article belongs to this Special Issue: Mechanical Reliability of Advanced Materials and Structures for Harsh Applications)
Abstract During subway operation, various factors will cause long-term land subsidence, such as the vibration subsidence of foundation soil caused by train vibration load, incomplete consolidation deformation of foundation soil during tunnel construction, dense buildings and structures in the vicinity of the tunnel, and changes in water level in the stratum where the tunnel is located. The monitoring of long-term land subsidence during subway operation in high-density urban areas differs from that in low-density urban construction areas. The former is the gathering point of the entire urban population. There are many complex buildings around the project, busy road traffic, high pedestrian… More >

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Guoshuai Sun^1,2,3, Xiuru Tang¹, Shuping Wan^2,*, Jiao Feng⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1041-1067, 2023, DOI:10.32604/cmes.2022.021981
（This article belongs to this Special Issue: Extension, Modeling and Applications of Fuzzy Set Theory in Engineering and Science)
Abstract China has been promoting the renovation of old residential communities vigorously. Due to the financial pressure of the government and the sustainability of the renovation of old residential communities, public-private partnerships (PPP) have already gained attention. The selection of social capital is key to improving the efficiency of the PPP model in renovating old residential communities. In order to determine the influencing factors of social capital selection in the renovation of old residential communities, this paper aims to find an effective approach and analyze these factors. In this paper, a fuzzy decision-making and trial evaluation laboratory (fuzzy-DEMATEL) technique is extended… More >

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Bingzheng Wu¹, Peizhong Liu¹, Huiling Wu², Shunlan Liu², Shaozheng He², Guorong Lv^2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1069-1089, 2023, DOI:10.32604/cmes.2022.020870
（This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract Congenital heart defect, accounting for about 30% of congenital defects, is the most common one. Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns. In Fetal and Neonatal Cardiology, medical imaging technology (2D ultrasonic, MRI) has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis. It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane (FHUSP) manually. Compared with manual identification, automatic identification through artificial intelligence can save a lot of time, ensure the efficiency of diagnosis, and improve the… More >

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466

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Xindai An¹, Di Wu^1,2,*, Xiangwen Xie¹, Kefeng Song¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670
（This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural… More >

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Noor Zanib¹, Munira Batool¹, Saleem Riaz², Farkhanda Afzal³, Sufian Munawar⁴, Ibtisam Daqqa⁵, Najma Saleem^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1105-1136, 2023, DOI:10.32604/cmes.2022.021676
（This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
Abstract This paper presents a comprehensive study that includes the sizing and power flow by series and parallel inverters in a distributed generation system (DGs) that integrates the system of hybrid wind photovoltaic with a unified power quality conditioner (UPQC). In addition to supplying active power to the utility grid, the system of hybrid wind photovoltaic functions as a UPQC, compensating reactive power and suppressing the harmonic load currents. Additionally, the load is supplied with harmonic-free, balanced and regulated output voltages. Since PVWind-UPQC is established on a dual compensation scheme, the series inverter works like a sinusoidal current source, while the… More >

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Hongliang Wang¹, Jiahua Hu¹, Danhuang Dong¹, Cenfeng Wang¹, Feixia Tang², Yizheng Wang¹, Changsen Feng^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1137-1158, 2023, DOI:10.32604/cmes.2022.021965
（This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
Abstract With the rapid and wide deployment of renewable energy, the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance. The output power of renewable energy is uncertain, and thus flexible regulation for the power balance is highly demanded. Considering the multi-timescale output characteristics of renewable energy, a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper. Through the multi-timescale decomposition algorithm on the basis of mathematical morphology, the multi-timescale components are separated to determine the flexibility requirements… More >

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Sue Wang¹, Xintao Luo^1,*, Saleem Riaz², Haider Zaman³, Chaohong Zhou¹, Pengfei Hao¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1159-1176, 2023, DOI:10.32604/cmes.2022.021850
（This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
Abstract Due to attractive features, including high efficiency, low device stress, and ability to boost voltage, a Vienna rectifier is commonly employed as a battery charger in an electric vehicle (EV). However, the 6k ± 1 harmonics in the acside current of the Vienna rectifier deteriorate the THD of the ac current, thus lowering the power factor. Therefore, the current closed-loop for suppressing 6k ± 1 harmonics is essential to meet the desired total harmonic distortion (THD). Fast repetitive control (FRC) is generally adopted; however, the deviation of power grid frequency causes delay link in the six frequency fast repetitive control… More >

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Bida Zhang^1,*, Qiang Yan¹, Hairui Zhang², Lin Zhang³
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1177-1194, 2023, DOI:10.32604/cmes.2022.022089
（This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)
Abstract The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem. Considering the differences between these two modes of power replenishment, we constructed a joint location-planning model to minimize construction and operation costs, user costs, and user satisfaction-related penalty costs. We designed an improved genetic algorithm that changes the crossover rate using the fitness value, memorizes, and transfers excellent genes. In addition, the present model addresses the problem of “premature convergence” in conventional genetic algorithms. A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels… More >

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Yani Feng¹, Kejun Tang², Lianxing He³, Pingqiang Zhou¹, Qifeng Liao^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1195-1218, 2023, DOI:10.32604/cmes.2022.021636
（This article belongs to this Special Issue: Numerical Methods in Engineering Analysis, Data Analysis and Artificial Intelligence)
Abstract This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, compared with existing methods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projection with bounded variance, and we show that… More >

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Linchuan Liu¹, Jian Wu², Zhongwei Hu², Xiaochao Jin^1,*, Pin Lu¹, Tao Zhang², Xueling Fan^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1219-1236, 2023, DOI:10.32604/cmes.2022.022221
（This article belongs to this Special Issue: Recent Trends in Thermal Barrier Coatings for Turbine Blades: Theory, Simulation, and Experiment)
Abstract A virtual wall thickness method is developed to simulate the temperature field of turbine blades with thermal barrier coatings (TBCs), to simplify the modeling process and improve the calculation efficiency. The results show that the virtual wall thickness method can improve the mesh quality by 20%, reduce the number of meshes by 76.7% and save the calculation time by 35.5%, compared with the traditional real wall thickness method. The average calculation error of the two methods is between 0.21% and 0.93%. Furthermore, the temperature at the blade leading edge is the highest and the average temperature of the blade pressure… More >

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Qiansheng Fang, Haojie Wang, Chenlei Xie^*, Jie Chen
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1237-1250, 2023, DOI:10.32604/cmes.2022.021131
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a leak location method for water… More >

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Hao Chen¹, Jie Xu¹, Yibo Hu¹, Fuzhou Niu^1,*, Zhiyan Li², Dan Wang², Guizhong Fu¹, Chuanxin Li^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1251-1262, 2023, DOI:10.32604/cmes.2022.022279
（This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract Aiming at the in situ and mobile observation of urban environmental air pollution, a portable instrument using ultraviolet spectrum retrieval algorithm was developed based on the basis of Differential Optical Absorption Spectroscopy (DOAS) and multiple-pass cell technique. Typical trace gas pollutants, NH₃, SO₂, and NO₂, were explored using their optical spectral characteristics in deep ultraviolet wavelength range from 210 to 215 nm. The gas concentration was retrieved by Lambert-Beer's law and nonlinear least square method. With an optimized optical alignment, the detection limits of NH₃, SO₂, NO₂ were estimated to be 2.2, 2.3, and 36.2 ppb, respectively. The system was… More >

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Qi Zhou^1,2, Jinghua Li^1,3, Ruipu Dong^1,*, Qinghua Zhou^3,*, Boxin Yang³
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1263-1281, 2023, DOI:10.32604/cmes.2022.020744
（This article belongs to this Special Issue: Computer Modeling in Ocean Engineering Structure and Mechanical Equipment)
Abstract Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes and multiple resource constraints. Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems (RCPSPs). To solve RCPSP problems in offshore engineering construction more rapidly, a hybrid genetic algorithm was established. To solve the defects of genetic algorithms, which easily fall into the local optimal solution, a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation. Then, an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions, reduce the computation time and… More >

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Jianjun Yao^*, Chen Qian, Yikun Zhang, Geyang Yu
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1283-1303, 2023, DOI:10.32604/cmes.2022.021328
（This article belongs to this Special Issue: Computer Modeling in Ocean Engineering Structure and Mechanical Equipment)
Abstract The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space, low power consumption, and excellent flexibility. However, the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining. In the process of ship construction, the performance of the parts’ protective coating needs to be machined to meet the Performance Standard of Protective Coatings (PSPC). The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle, greatly reducing machining quality and efficiency. There have been some studies on… More >

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Jun Wu, Penghui Fan, Yingxin Sun, Weifeng Gui^*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1305-1321, 2023, DOI:10.32604/cmes.2022.020919
（This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
Abstract Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and… More >

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Wenbin Wu¹, Guanjun Liu^1,*, Kaiyi Liang², Hui Zhou²
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1323-1335, 2023, DOI:10.32604/cmes.2022.020428
（This article belongs to this Special Issue: Deep Learning based Computational Methods for Abnormality Detection in Human Medical Images)
Abstract Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the… More >

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Jihong Yan^1,2, Weihan Ni^1,*, Jianshu Zhai², Haiyang Dong¹
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1337-1351, 2023, DOI:10.32604/cmes.2022.021567
（This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
Abstract Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then… More >

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Hao Han, Wei Wang^*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494
（This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

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Umair Khan^1,2, Aurang Zaib³, Anuar Ishak¹, Iskandar Waini⁴, El-Sayed M. Sherif⁵, Dumitru Baleanu^6,7,8,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1371-1392, 2023, DOI:10.32604/cmes.2022.020911
（This article belongs to this Special Issue: Advanced Computational Methods in Fluid Mechanics and Heat Transfer)
Abstract The utilization of solar energy is essential to all living things since the beginning of time. In addition to being a constant source of energy, solar energy (SE) can also be used to generate heat and electricity. Recent technology enables to convert the solar energy into electricity by using thermal solar heat. Solar energy is perhaps the most easily accessible and plentiful source of sustainable energy. Copper-based nanofluid has been considered as a method to improve solar collector performance by absorbing incoming solar energy directly. The goal of this research is to explore theoretically the Agrawal axisymmetric flow induced by… More >

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Francesco Tornabene^*, Matteo Viscoti, Rossana Dimitri
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1393-1468, 2023, DOI:10.32604/cmes.2022.022237
（This article belongs to this Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures)
Abstract In the present manuscript, a Layer-Wise (LW) generalized model is proposed for the linear static analysis of doublycurved shells constrained with general boundary conditions under the influence of concentrated and surface loads. The unknown field variable is modelled employing polynomials of various orders, each of them defined within each layer of the structure. As a particular case of the LW model, an Equivalent Single Layer (ESL) formulation is derived too. Different approaches are outlined for the assessment of external forces, as well as for non-conventional constraints. The doubly-curved shell is composed by superimposed generally anisotropic laminae, each of them characterized… More >

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