Bin Chen¹, Ziyang Li¹, Shipeng Li¹, Qingzhou Zhao¹, Xingdou Liu^2,*

Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374

Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion… More >

Kranthi Alluri, S. Gopikrishnan^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621

Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each… More >

XIAOYAN LIU^a,*, XU CHEN^a, HONGXING ZHANG^b, CHANGJUN ZHANG^a, SHIYUAN YANG^a, GUANGQUAN LI^a

Journal of Polymer Materials, Vol.36, No.2, pp. 121-132, 2019, DOI:10.32381/JPM.2019.36.02.2

Abstract In this work, three in situ impact polypropylene copolymer(IPC) samples were prepared through Ziegler-Natta catalyst only changing the feed composition (ethylene to ethylene and propylene molar ratio, C2/C2+C3) in gas-phase polymerization reactor. Polymer (IPC) were characterical by solvent classification, gel permeation chromatography (GPC), differential scanning calorimetry (DSC), successive self-nucleation and annealing (SSA), nuclear magnetic resonance(13C-NMR) and scanning electron microscopy(SEM). The mechanical properties of IPC samples were tested.The results indicate that with similar ethylene content, the feed composition which determines the content and structure of EPR and EbP component in IPC, further impacts the rubber phase size and distribution in IPC,… More >

GOBI NALLATHAMBI, BHARGAVI RAM THIMMIAH^*

Journal of Polymer Materials, Vol.37, No.3-4, pp. 189-200, 2020, DOI:10.32381/JPM.2020.37.3-4.6

Abstract The aim of this study is to optimize the process parameters of alkali treated Saharan aloe vera cactus fibres using of Box-behnken experimental design. The Saharan aloe vera cactus fibres were treated with different concentration of NaOH, soaking time and temperature which affect the properties of fibres and plays main role in removal of lignin, hemicellulose, pectin and wax content. The chemical composition of untreated and treated fibres was analyzed by standard methods. XRD result shows the improvement in the crystallinity index of fibres due to alkali treatment. ATR-FTIR analysis shows that hemicellulose and lignin were decreased in all alkali… More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships. Firstly, a dataset for… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Shadi Moqbel^1,*, Habib Al-Ghoul², Abd Al-Majeed Al-Ghzawi³, Rami Mukbel⁴

Journal of Renewable Materials, Vol.12, No.2, pp. 349-367, 2024, DOI:10.32604/jrm.2023.046369

Abstract The composition of paper cups creates a challenge for the recycling industry, as the paperboard–plastic film composite is hard to separate. Therefore, paper cups are sent to landfills or waste incinerators. This study explores the combined use of red worms (Eisenia fetida) and Greater wax moth (Galleria mellonella) in the biodegradation of paper cups. The study investigates the conditions and combinations that promote using Eisenia fetida and Galleria mellonella for degrading paper cups. The study considered the influence of environmental temperature, the presence of food waste, varying the number of Eisenia fetida worms, and the presence of a Galleria mellonella… More > Graphic Abstract

Hongshang Xu¹, Bei Dong^1,2,*, Xiaochang Liu¹, Xiaojun Wu^1,2

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 185-202, 2023, DOI:10.32604/iasc.2023.041177

Abstract Deep neural networks often outperform classical machine learning algorithms in solving real-world problems. However, designing better networks usually requires domain expertise and consumes significant time and computing resources. Moreover, when the task changes, the original network architecture becomes outdated and requires redesigning. Thus, Neural Architecture Search (NAS) has gained attention as an effective approach to automatically generate optimal network architectures. Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity. A myriad of research has revealed that network performance and structural complexity are often positively correlated. Nevertheless, complex network structures will bring enormous computing resources. To cope… More >

Haolun Zhang¹, Mengna Yang¹, Jie Wei², Yufeng Nie^2,*

Digital Engineering and Digital Twin, Vol.2, pp. 49-77, 2024, DOI:10.32604/dedt.2023.044180

Abstract This paper mainly considers the formulation and theoretical analysis of the reduced-order numerical method constructed by proper orthogonal decomposition (POD) for nonlocal diffusion problems with a finite range of nonlocal interactions. We first set up the classical finite element discretization for nonlocal diffusion equations and briefly explain the difference between nonlocal and partial differential equations (PDEs). Nonlocal models have to handle double integrals when using finite element methods (FEMs), which causes the generation of algebraic systems to be more challenging and time-consuming, and discrete systems have less sparsity than those for PDEs. So we establish a reduced-order model (ROM) for… More >

Xiaoyi Wang¹, Xinyue Chang², Wenxuan Wang^1,*, Zijie Qiao³, Feng Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1775-1796, 2024, DOI:10.32604/cmes.2023.043913

Abstract The objective of reliability-based design optimization (RBDO) is to minimize the optimization objective while satisfying the corresponding reliability requirements. However, the nested loop characteristic reduces the efficiency of RBDO algorithm, which hinders their application to high-dimensional engineering problems. To address these issues, this paper proposes an efficient decoupled RBDO method combining high dimensional model representation (HDMR) and the weight-point estimation method (WPEM). First, we decouple the RBDO model using HDMR and WPEM. Second, Lagrange interpolation is used to approximate a univariate function. Finally, based on the results of the first two steps, the original nested loop reliability optimization model is… More >