Wei Chen^1,*, Xiaogang Wei^2,*, Hongxu Zhang², Wenpeng He²

Journal on Artificial Intelligence, Vol.6, pp. 225-240, 2024, DOI:10.32604/jai.2024.052366

Abstract The Head-Related Transfer Function (HRTF) describes the effects of sound reflection and scattering caused by the environment and the human body when sound signals are transmitted from a source to the human ear. It contains a significant amount of auditory cue information used for sound localization. Consequently, HRTF renders 3D audio accurately in numerous immersive multimedia applications. Because HRTF is high-dimensional, complex, and nonlinear, it is a relatively large and intricate dataset, typically consisting of hundreds of thousands of samples. Storing HRTF requires a significant amount of storage space in practical applications. Based on this, More >

Yu Zhou¹, Yun Zhang¹, Guowei Li¹, Hang Yang¹, Wei Zhang¹, Ting Lyu², Yueqiang Xu^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1809-1829, 2024, DOI:10.32604/cmc.2024.050975

Abstract In current research on task offloading and resource scheduling in vehicular networks, vehicles are commonly assumed to maintain constant speed or relatively stationary states, and the impact of speed variations on task offloading is often overlooked. It is frequently assumed that vehicles can be accurately modeled during actual motion processes. However, in vehicular dynamic environments, both the tasks generated by the vehicles and the vehicles’ surroundings are constantly changing, making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios. Taking into account the actual dynamic vehicular scenarios, this paper considers the real-time… More >

Mingyuan Li^1,*, Yuanli Gu¹, Qingqiao Geng², Hongru Yu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765

Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >

Samar M. Alqhtani¹, Toufique A. Soomro^2,*, Faisal Bin Ubaid³, Ahmed Ali⁴, Muhammad Irfan⁵, Abdullah A. Asiri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep… More >

Chun Mao¹, Ji He^1,*, Xuefeng Wen¹, Yangzhou Xiang², Jihong Feng¹, Yingge Shu¹

Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 487-502, 2024, DOI:10.32604/phyton.2024.048246

Abstract The contents of carbon (C), nitrogen (N), and phosphorus (P) in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea, such as tea polyphenols, amino acids, and caffeine. However, few studies have quantified the effects of these factors on the main quality components of tea. The study aimed to explore the interactions of C, N, and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method. The results indicated that (1) The contents of C, N, and P in… More >

Shuai Chen¹, Yinwei Ma², Zhongshu Wang², Zongmei Xu³, Song Zhang¹, Jianle Li¹, Hao Xu¹, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method More > Graphic Abstract

Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related More >

Lifeng Liu¹, Menghe Shi², Jianhui Wang³, Wendong Wang^2,*, Yuliang Su², Xinyu Zhuang²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 595-607, 2024, DOI:10.32604/fdmp.2023.041962

Abstract Ultra-low permeability reservoirs are characterized by small pore throats and poor physical properties, which are at the root of well-known problems related to injection and production. In this study, a gas injection flooding approach is analyzed in the framework of numerical simulations. In particular, the sequence and timing of fracture channeling and the related impact on production are considered for horizontal wells with different fracture morphologies. Useful data and information are provided about the regulation of gas channeling and possible strategies to delay gas channeling and optimize the gas injection volume and fracture parameters. It More >

Xiaoyu Zhang^1,2, Huizhong Zeng², Shaohui Zhang², Yan Zhang^3,*, Mi Xiao⁴, Liping Liu², Hao Zhou^2,*, Hongyou Chai², Liang Gao⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 201-220, 2024, DOI:10.32604/cmes.2023.029389

Abstract Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting from the sandwich effect. Such structures can be fabricated by metallic additive manufacturing technique, such as selective laser melting (SLM). However, the maximum dimensions of actual structures are usually in a sub-meter scale, which results in restrictions on their appliance in aerospace and other fields. In this work, a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness More >