Wei Zhou¹, Xincong Jiang^2,*, Chuan Qin²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2069-2093, 2024, DOI:10.32604/cmes.2023.043248

Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict the code coverage of test… More >

Sheng Lan¹, Fei Yang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09077

Abstract Non-autoclaved concrete pipe piles are gaining attention as an environmentally friendly alternative to autoclaved concrete pipe piles. The purpose of this study was to investigate the changes in the impact resistance of a non-autoclaved concrete pipe pile with the addition of rubber. To this end, various volume fractions of rubber particles were used to replace the fine sand in the non-autoclaved pipe pile concrete (0%, 5%, 10% and 15%). Additionally, the axial impact resistance of rubber modified non-autoclaved concrete pipe pile was studied from the concrete materials and pipe pile components through quasi-static, dynamic compression and splitting tensile tests and… More >

Zhoujie Zhu¹, Gang Wang¹, Qingquan Liu¹, Guojun Wang², Rui Dong², Dayong Zhang^2,3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 625-639, 2024, DOI:10.32604/fdmp.2023.042128

Abstract Important challenges must be addressed to make wind turbines sustainable renewable energy sources. A typical problem concerns the design of the foundation. If the pile diameter is larger than that of the jacket platform, traditional mechanical models cannot be used. In this study, relying on the seabed soil data of an offshore wind farm, the m-method and the equivalent embedded method are used to address the single-pile wind turbine foundation problem for different pile diameters. An approach to determine the equivalent pile length is also proposed accordingly. The results provide evidence for the effectiveness and reliability of the model based… More >

Ziyang Jiang¹, Ziping Wang^1,*, Kan Feng¹, Yang Zhang², Rahim Gorgin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 557-569, 2023, DOI:10.32604/sdhm.2023.042393

Abstract With the advancement of computer and mathematical techniques, significant progress has been made in the 3D modeling of foundation piles. Existing methods include the 3D semi-analytical model for non-destructive low-strain integrity assessment of large-diameter thin-walled pipe piles and the 3D soil-pile dynamic interaction model. However, these methods have complex analysis procedures and substantial limitations. This paper introduces an innovative and streamlined 3D imaging technique tailored for the detection of pile damage. The approach harnesses the power of an eight-channel ring array transducer to capture internal reflection signals within foundation piles. The acquired signals are subsequently processed using the Hilbert-Huang Transform… More >

JIAO HU, JIALU MO, XIANGLIN CHENG^*

BIOCELL, Vol.47, No.8, pp. 1689-1701, 2023, DOI:10.32604/biocell.2023.029409

Abstract Epilepsy is one of the most common neurological diseases worldwide with a high prevalence and unknown pathogenesis. Further, its control is challenging. It is generally accepted that an imbalance between the excitatory and inhibitory properties of the central nervous system (CNS) leads to a large number of abnormally synchronized neuronal discharges in the brain. Transient receptor potential vanilloid protein type 1 (TRPV1) is a non-selective cation channel that contributes to the regulation of the nervous system and influences the excitability of the nervous system. This includes the release of neurotransmitters, action potential generation due to alterations in ion channels, synaptic… More >

Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1613-1633, 2023, DOI:10.32604/cmes.2023.027708

Abstract Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study. The new method is based on the maximum mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data. First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe. Second, in view of the deviation in the data distribution between the real source domain and the target domain, MMD is used to measure the distance between dierent data distributions. The objective… More >

Jiali Wang¹, Bing Li², Chengyu Qiu¹, Xinyun Zhang¹, Yuting Cheng¹, Peihua Wang¹, Ta Zhou³, Hong Ge², Yuanpeng Zhang^1,3,*, Jing Cai^3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457

Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source… More >

Srikanth Cherukuvada, R. Kayalvizhi^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207

Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep… More >

Jinrui Tang^*, Ganheng Ge, Jianchao Liu, Honghui Yang

Energy Engineering, Vol.120, No.5, pp. 1107-1132, 2023, DOI:10.32604/ee.2023.025666

Abstract Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term load forecasting (STLF) results for regional EV charging load are important to the scheduling plan of regional charging load, which can be derived to realize the optimal vehicle to grid benefit. In this paper, a regional-level EV ultra STLF method is proposed and discussed. The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles, and then constructed by our collected EV charging transaction data in the field. Secondly, these usage degrees… More >

Rong-Guey Chang, Tam-Van Hoang^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 999-1019, 2023, DOI:10.32604/csse.2023.034672

Abstract The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code, which often requires specific treatment for each platform. The problem becomes critical on embedded devices, where computational and memory resources are strictly constrained. Compilers play an essential role in deploying source code on a target device through the backend. In this work, a novel backend for the Open Neural Network Compiler (ONNC) is proposed, which exploits machine learning to optimize code for the ARM Cortex-M device. The backend requires minimal changes to Open Neural Network Exchange (ONNX) models. Several novel optimization… More >