Friction Monitoring in Kaplan Turbines by Lars-Johan Sandström, Kim Berglund, Pär Marklund, Gregory F. Simmons

“…The study shows that the model approach works for both turbines. However, the parameter optimization must be performed separately for each turbine.…”
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Prediction of water inrush from coal seam floor based on machine learning with small sample data by Chenxi LI, Haifeng LU

“…In this paper, by using the nearest neighbor algorithm (KNN) and the combination algorithm of gradient boosting decision tree (GBDT) and logistic regression (LR), a water inrush prediction model was established based on the sample data of six indexes, including water pressure, mining height, water-barrier thickness, fault drop, coal seam inclination, and fault distance from the working face. …”
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Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights by Filippo Genuario, Giuseppe Santoro, Michele Giliberti, Stefania Bello, Elvira Zazzera, Donato Impedovo

“…The proposed work compares both shallow learning algorithms, such as decision trees, random forests, Naïve Bayes, logistic regression, XGBoost, and support vector machines, and deep learning algorithms, such as DNNs, CNNs, and LSTM, whose approach is relatively new in the literature. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…This study proposes a newly integrated intelligent model of mixing entropy- and congestion degree-based particle swarm optimization support vector regression (MC-PSO-SVR) to predict the yield stress, plastic viscosity, fluidity and uniaxial compressive strength (UCS) of fresh gangue backfill slurry (FGBS). …”
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APPLICATION OF MULTIHOLE PRESSURE PROBE FOR RESEARCH OF COOLANT VELOCITY PROFILE IN NUCLEAR REACTOR FUEL ASSEMBLIES by S. M. Dmitriev, A. A. Dobrov, M. A. Legchanov, A. E. Khrobostov

“…Design of intensifiers affects both the coolant mixing and the hydraulic resistance. The aim of this work is to develop a methodology of measuring coolant local velocity in the fuel assembly models with different mixing grids. …”
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Regional Funds of Scientific and Innovation Activity Support: Institutional Aspect by T. N. Topoleva

“…Shaping innovation economy in Russia stipulates passing-over from traditional partial models to system-integrated models of development accompanied by building institutional vector of succession of federal and regional institutions. …”
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Research on Flexible Job Shop Scheduling Method for Agricultural Equipment Considering Multi-Resource Constraints by Zhangliang Wei, Zipeng Yu, Renzhong Niu, Qilong Zhao, Zhigang Li

“…An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. …”
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Approach to design of distributed multi-agent system for processing sound information of the environment by U. A. Vishniakou, B. H. Shaya

“…The amplitude and effect of sound waves vary considerably in continuous space-time measurements. Modeling different types of sounds and their spatiotemporal effects becomes important for assessing the sound situation both in working spaces and in recreation areas. …”
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Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction by Chaoyu Shi, Bohao Chen, Yao Shi, Jun Zha

“…In order to improve the surface roughness of bearing ring grinding under multiple working conditions, a prediction model of bearing ring surface roughness based on feature extraction was proposed. …”
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MACROECONOMIC POLICIES AND THEIR IMPACT ON THE AGRICULTURAL SECTOR AND ECONOMIC GROWTH IN SELECTED ARAB COUNTRIES FOR THE PERIOD 1990-2020 by Eman Alzubaidi, Walid Sultan Albajari

“…Estimated from the first model on the economic growth of the second stage v Using the two-stage method, in order to reach more accurate results, and one of the most important conclusions reached by the research is that all economic variables are stable at the first level and difference, so the ARDL autoregressive vector model was chosen to estimate the relationship between the long and short term variables. …”
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Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets by Abbas Saffari, Seyed Hamid Zahiri, Navid Khozein Ghanad

“…In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro- Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving sonar targets. …”
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Enhancing Fault Diagnosis: A Hybrid Framework Integrating Improved SABO with VMD and Transformer–TELM by Jingzong Yang, Xuefeng Li, Min Mao

“…Compared with traditional machine learning techniques such as kernel extreme learning machine (KELM), extreme learning machine (ELM), support vector machine (SVM), and Softmax, this method significantly outperforms other models in terms of accuracy, recall, and F1 score.…”
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What Can DKIST/DL-NIRSP Tell Us about Quiet-Sun Magnetism? by Jiayi Liu, Xudong Sun, Peter W. Schuck, Sarah A. Jaeggli

“…To this end, we synthesize high-resolution, high-cadence Stokes profiles of the Fe i 630 nm lines using a realistic magnetohydrodynamic simulation, degrade them to emulate the DKIST/DL-NIRSP observations, and subsequently infer the vector magnetic and velocity fields. For the assessment, we first verify that a widely used flow tracking algorithm, the Differential Affine Velocity Estimator for Vector Magnetograms, works well for estimating the large-scale (>200 km) photospheric velocity fields with these high-resolution data. …”
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Enhancing cardiac disease detection via a fusion of machine learning and medical imaging by Tao Yu, KeYue Chen

“…Abstract Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that integrates machine learning techniques with medical image analysis to improve the identification of cardiovascular diseases. …”
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Weather sensing with structured light by Zhaozhong Chen, Ultan Daly, Aleksandr Boldin, Martin P. J. Lavery

“…This technique can be used reliably with Support Vector Machine regression models to measure temperature variations of 0.49 °C and wind speed variations of 0.029 ms−1 over a 36 m experimental turbulent free-space channel with controllable and verifiable temperature and wind speed with a short 3 s measurement. …”
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COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers by Syed Ali Jafar Zaidi, Indranath Chatterjee, Samir Brahim Belhaouari

“…Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. …”
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Design of Microstrip antenna by Integrating Octagonal Patch Configuration for UWB Application by S. Patil, A. Naik

“… This work proposes gain enhancement of the microstrip antenna to use in applications operated in UWB spectrum. …”
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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). …”
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Automating tephra fall building damage assessment using deep learning by E. Tennant, S. F. Jenkins, V. Miller, R. Robertson, B. Wen, S.-H. Yun, B. Taisne

“…We used these labelled data to train convolutional neural networks (CNNs) for (1) building localisation (average precision equals 0.728) and (2) damage classification into two levels of granularity: no damage vs. damage (<span class="inline-formula"><i>F</i>₁</span> score <span class="inline-formula">=</span> 0.809) and moderate damage vs. major damage (<span class="inline-formula"><i>F</i>₁</span> score <span class="inline-formula">=</span> 0.838) (1 is the maximum obtainable for both metrics). The trained models were incorporated into a pipeline along with all the necessary image processing steps to generate spatial data (a georeferenced vector with damage state attributes) for rapid tephra fall building damage mapping. …”
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混合驱动凸轮连杆机械压力机的分析与仿真 by 李竞洋, 葛正浩, 苏鹏刚, 蒋萌

“…The significance of applying hybrid cam-linkage mechanism to mechanical press is expounded.This new type of mechanistic configuration for the hybrid cam-linkage mechanical press is proposed and its kinematic principle is analyzed.Using complex number vector method,the inverse kinematic analysis of this hybrid mechanism is carried out and the actual operating function that can meet the demand of processing techniques is worked out.Based on Pro/Mechanism,The mechanism motion simulation is done.The results show that the hybrid cam-linkage mechanical press can realize multiple motion specification and satisfy multi-technique request from slider velocity characteristics in the course of process.According to the structure analysis and simulation for hybrid seven-bar mechanism,the conclusion that compared to the hybrid seven-bar press,the hybrid cam-linkage press has the merits of easy link lengths restriction and good motion output is obtained.At last,the dynamic simulation model is designed,and the power distribution of the constant speed motor and the servo-motor is analyzed.…”
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