RESEARCH ON ACOUSTIC RADIATION CHARACTRRISTICS OF PERIODIC PERFORATED DAMPING SANDWICH PLATE by ZHOU JingDong, WEN Yao, CHEN Yuan, LIU MingYong, LIU Yang

“…Base on the principle of damping reduction vibration and reference the periodic boundary conditions,through the acoustic bridge vibration energy transfer, this paper established a double-layer plate with the opening damping model;Considering the frequency-dependent properties of viscoelastic,through the finite element and boundary element numerical algorithm of calculating the radiation sound power,analyzes the periodic perforated damped sandwich plate structure acoustic radiation characteristics. …”
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Mutation analysis of annual sediment discharge at Wu Long station in Wu Jiang River Basin from 1960 to 2016. by Peng Chen, Guangming Tan, Jinyun Deng, Quanxi Xu, Rouxin Tang

“…The mutation of average annual sediment discharge in Wu Jiang River Basin is caused by both climate change and human activity. Sediment reduction effect of the hydraulic engineerings built since 1990s climate is main and direct, and the climate change have secondary effect on sediment discharge change.…”
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Fault Diagnosis of Gearbox based on Multi-fractal and PSO-SVM by Li Sha, Pan Hongxia, Zhang Jundong, Zhao Weiwei

“…Aiming at the non-stationary and nonlinear of gearbox vibration signals,a fault diagnosis method based on the multi-fractal and particle swarm optimization support vector machine（PSO-SVM）is put forward.Firstly,the fractal filter with short-time fractal dimension as fuzzy control parameters is used to filtering noise reduction the gearbox vibration signals with bigger background noises.Secondly,the multi-fractal spectrum algorithm is applied to analyze the signal after filtering,the results show that the characteristic parameters:Δa（q）、f（a（q））maxand box dimensions Dbcan give a good presentation for gearbox working condition.Finally,the particle swarm optimization（PSO）is applied to optimize the parameters of support vector machine（SVM）.Taking the multi-fractal characteristic vectors as input parameters of PSO-SVM and SVM to recognize the fault types of the gearbox.The results show that SVM based on particle swarm optimization can improve the classification accuracy.Meanwhile,the validity of gearbox fault diagnosis based on muti-fractal and PSO-SVM is proved.…”
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立磨减速机弧齿锥齿轮副动态疲劳性能研究 by 张明

“…The 3Dcontact finite element model of the input spiral bevel gear pair of vertical mill reducer is built,the flexible-body dynamics simulation analysis of bevel gear pair is carried out by using the LS-DYNA software,and the transient contact force is obtained,the fatigue life of the bevel gear pair is performed by using maximum main stress algorithm in FE-SAFE based on fatigue load spectrum,the S-N curve of the bevel gear pair and stress results of the bevel gear pair under static load.Then,the influence of stress concentration,loads and residual stress on fatigue life of bevel gear pair are studied based on the results above.The results indicate that the life decreases of bevel gear pair with the increase of stress concentration and loads,the fatigue life shows sensitization to loads,the residual tensile stress leads to a reduction of bevel gear pair fatigue life,however,the residual press stress leads to a increase of bevel gear pair fatigue life.…”
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The Combination of Spectrum Subtraction and Cross-power Spectrum Phase Method for Time Delay Estimation by Feng BIN, Xu LEI

“…Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. …”
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RL-Based Vibration-Aware Path Planning for Mobile Robots’ Health and Safety by Sathian Pookkuttath, Braulio Felix Gomez, Mohan Rajesh Elara

“…Terrain roughness is classified into four levels using IMU sensor data, achieving average prediction accuracy of 97% with a 1D CNN model. A vibration cost map is created by assigning vibration costs to each predicted class on a 2D occupancy grid, incorporating obstacles, vibration-prone areas, and the robot’s pose for navigation. …”
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Machine learning approach for identifying and forecasting streamflow droughts in data limited basins of South Korea using threshold levels by Young-Ho Seo, Jang Hyun Sung, Byung-Sik Kim, Junehyeong Park

“…We utilized the XGBoost algorithm, integrating comprehensive meteorological data to enhance the accuracy and reliability of the drought predictions. …”
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A Novel Method Based on Particle Flow Filters for Stellar Gyroscope Parameter Estimations by Erol Duymaz

“…However, “the particle flow filter structure” is used for the prediction and calibration of gyroscope error parameters for the first time in the literature in this study. …”
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Parking Backbone: Toward Efficient Overlay Routing in VANETs by Jinqi Zhu, Ming Liu, Yonggang Wen, Chunmei Ma, Bin Liu

“…Secondly, to a specific vehicle, a daily mobility model is established, to determine its location through a corresponding location prediction algorithm. Finally, a novel message delivery scheme is designed to efficiently transmit messages to destination vehicles through the proposed virtual overlay network. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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Modélisation pluie-débit en région tropicale humide : application des réseaux de neurones sur quatre stations hydrométriques du Bandama Blanc (Bada, Marabadiassa, Tortiya et Bou) s... by Yao Blaise Koffi

“…To achieve this goal, two Multilayer Perceptrons trained with the backpropagation algorithm of error have been built. The first model was used only in simulation and the second in simulation and prediction. …”
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Identification of an HLA-A2-restricted epitope peptide derived from hypoxia-inducible protein 2 (HIG2). by Sachiko Yoshimura, Takuya Tsunoda, Ryuji Osawa, Makiko Harada, Tomohisa Watanabe, Tetsuro Hikichi, Masahiro Katsuda, Motoki Miyazawa, Masaji Tani, Makoto Iwahashi, Kazuyoshi Takeda, Toyomasa Katagiri, Yusuke Nakamura, Hiroki Yamaue

“…Among several candidate peptides predicted by the HLA-binding prediction algorithm, HIG2-9-4 peptide (VLNLYLLGV) was able to effectively induce peptide-specific cytotoxic T lymphocytes (CTLs). …”
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LSTM-Based State-of-Charge Estimation and User Interface Development for Lithium-Ion Battery Management by Abdellah Benallal, Nawal Cheggaga, Amine Hebib, Adrian Ilinca

“…The Hyperband optimization algorithm accelerates model training and enhances adaptability to varying operating conditions, making it scalable for diverse battery applications. …”
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An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province by Relebogile Langa, Michael Nthabiseng Moeti, Thabiso Maubane

“…A machine learning precipitation prediction algorithm optimizes water usage. The paper also describes a system with multiple sensors that detect soil parameters, and automatically irrigate land based on soil moisture by switching the motor on/off. …”
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DMRIntTk: Integrating different DMR sets based on density peak clustering. by Wenjin Zhang, Wenlong Jie, Wanxin Cui, Guihua Duan, You Zou, Xiaoqing Peng

“…However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis.…”
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Enhancing Flow Direction in Geothermal Fields Using Sentinel-1 Data for Sustainability Water Management by Utama Widya, Anjasmara Ira Mutiara, Handayani Hepi Hapsari, Indriani Rista Fitri

“…This study develops a flow direction prediction model using Sentinel-1 satellite imagery during rainy and dry seasons through the Random Forest machine learning algorithm. …”
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Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining by Jingping Cui, Wei Kuang, Kai Geng, Pihua Jiao

“…Furthermore, Apriori correlation analysis is performed on the transformer load rate and upper oil layer, winding temperature, and fusion indices of gas components by support and confidence levels to achieve a predictive assessment of the transformer state. Finally, the validity of the algorithm is verified by applying actual data from a power system monitoring platform. …”
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A Novel Vision Sensing System for Tomato Quality Detection by Satyam Srivastava, Sachin Boyat, Shashikant Sadistap

“…Zigbee module has been interfaced with developed system for wireless transmission from host system to PC based server for further processing. Algorithm development consists of three major steps, preprocessing steps like noise rejection, segmentation and scaling, classification and recognition, and automatic disease detection and classification. …”
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A Cell Component-Related Prognostic Signature for Head and Neck Squamous Cell Carcinoma Based on the Tumor Microenvironment by Siyu Li, Yajun Gu, Junguo Wang, Dengbin Ma, Xiaoyun Qian, Xia Gao

“…The tumor microenvironment (TME) is composed of numerous noncancerous cells that contribute to tumorigenesis and prediction of therapeutic effects. In this study, we aimed to develop a cell component-related prognostic model based on TME. …”
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Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma by Shiyue He, Wenzhong Peng, Xinyue Hu, Yong Chen

“…Based on six selected cuproptosis-related genes a predictive prognosis model was established and CRRS displayed a good prediction accuracy. …”
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