Rapid inspection for shaft parts in the automotive field by Yanqiang Sun, Xinyu Lv, Tianbao Tao, Baoya Zhao

“…Concurrently, an intuitive and user-friendly interface measurement software has been developed. This machine enables the online rapid measurement of critical parameters, such as the total length of the workpiece, the outer diameters of multiple shafts, and the cross-bar distances of the gear shaft. …”
Get full text

Comprehensive performance assessment of an over-the-row olive harvester by Mohamed Ghonimy, Abdulaziz Alharbi, Mohamed M. Ibrahim

“…The assessment criteria included machine productivity, energy consumption, harvester efficiency (considering machine field efficiency, fruit removal percentage, and the percentage of fruits collected inside the machine box), fruit damage (assessed by bruising incidence, cut fruits, fruit firmness, and color index), and the olive trees damage. …”
Get full text

Heavy-Tailed Linear Regression and <i>K</i>-Means by Mario Sayde, Jihad Fahs, Ibrahim Abou-Faycal

“…Most standard machine learning algorithms are formulated with the implicit assumption that empirical data are “well-behaved”. …”
Get full text

An investigation on using measurement uncertainty as decision rule for statement of conformity by Gouda M. Mohamed, Riham S. Hegazy

“…Since the uncertainty value is equivalent to all parameters that may affect the performance of these machines, it is logical and accurate to use it as the basis for the classification. …”
Get full text

Formation of Bearings Parts Waviness in Centerless Mortise Grinding on Rigid Supports by Chalyj V., Moroz S., Tkachuk A., Zablotskyi V., Trokhymchuk I., Stelmakh O.

“…It was found that the formation of waviness depends on the position of the hodograph of the movement of the vector of the center of the part in the complex plane, which in turn depends on the geometric parameters of the rigid supports of the centerless grinder machine. …”
Get full text

Modeling and simulation of residual stress in metal cutting process: A review by Ruihu Zhou

“…Due to the complexity of analyzing residual stress, which involves numerous cutting parameters encompassing both mechanical and thermal stresses, various modeling and simulation methods, including analytical, numerical, and machine learning approaches have been summarized. …”
Get full text

A Genetic QoS-Aware Routing Protocol for the Smart Electricity Networks by Agustin Zaballos, David Vernet, Josep M. Selga

“…The proposed algorithm is a merger between a genetic algorithm (GA) and Ticket-Based Routing (TBR), which is an on-demand routing protocol for ad hoc networks that provide quality of service. A suitable parameterization of the GA parameters is needed in order to use this protocol in the coming Smart Grid networks. …”
Get full text

Knowledge based full aperture polishing by Schneckenburger Max, Shaikh Ayaan, Algac Ufuc, Mewis Jan

“…Understanding and controlling of the polishing process on conventional NC (Numerical Control) machines is an important step to optimize production, reduce machine time and increase production quality. …”
Get full text

Laptop power tuning based on system state prediction by Qihua Xiao, Zhihui Shan, Xueli Ban, Zhengcao Jin

“…The traditional method is to manually determine various power parameters, which is time-consuming and labor-intensive, and may not be able to achieve optimal machine performance. …”
Get full text

A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification by Mohanad Azeez Joodi, Muna Hadi Saleh, Dheyaa Jasim Kadhim

“…The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven classifiers. …”
Get full text

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients... by Syed Waseem Abbas Sherazi, Jang-Whan Bae, Jong Yun Lee

“…Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. …”
Get full text

Prediction for Tunnelling-Induced Ground Settlement in Multilayered Soils: An Improved Gradient Boosting Approach by Hongbin An, Yangyang Chen, Jingjie Lei, Hanbin Luo, Elton J. Chen

“…During the construction of a shield tunnel, it will disturb the surrounding ground and affect the use and structural safety of buildings around the tunnel. The geometric parameters of the tunnel, the operating parameters of the shield machine, and the geological parameters will affect the degree of disturbance. …”
Get full text

Comparison of the Pressure Support Mode of Anesthesic Respiratory and Resuscitation Ventilators by V. A. Pyzhov, K. N. Khrapov

“…Most of the anesthesia machines tested did not reach the target pressure within 500 ms, and by this parameter they differ significantly from intensive care respirators.…”
Get full text

Experimental investigation of wire EDM on squeeze cast Al6351/Gr/SiC hybrid metal matrix composites: A Taguchi–GRA-based optimization framework by Sneha H. Dhoria, K. Venkata Subbaiah, V. Durga Prasada Rao, Ramachandran Thulasiram, Karthikeyan A, Abhijit Bhowmik, A. Johnson Santhosh

“…This study aims to optimize process parameters in Wire Electrical Discharge Machining (WEDM) for a hybrid Metal Matrix Composite (MMC) composed of aluminum alloy (Al 6351) reinforced with 6% graphite (Gr) and 4% silicon carbide (SiC). …”
Get full text

CFET Beyond 3 nm: SRAM Reliability Under Design-Time and Run-Time Variability by Sufia Shahin, Swati Deshwal, Anirban Kar, Mahdi Benkhelifa, Yogesh S. Chauhan, Hussam Amrouch

Get full text

AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study by Tai-Han Lin, Hsing-Yi Chung, Ming-Jr Jian, Chih-Kai Chang, Hung-Hsin Lin, Chiung-Tzu Yen, Sheng-Hui Tang, Pin-Ching Pan, Cherng-Lih Perng, Feng-Yee Chang, Chien-Wen Chen, Hung-Sheng Shang

“…Even promising existing machine learning approaches are restricted by reliance on complex clinical factors that could delay results, underscoring the need for faster, simpler, and more reliable diagnostic strategies. …”
Get full text

Unveiling quantum phase transitions from traps in variational quantum algorithms by Chenfeng Cao, Filippo Maria Gambetta, Ashley Montanaro, Raul A. Santos

“…Achieving this requires both accessing good approximations to the ground state and identifying order parameters to distinguish different phases. Addressing these challenges, our work introduces a hybrid algorithm that combines quantum optimization with classical machine learning. …”
Get full text

A link prediction approach based on deep learning for opportunistic sensor network by Jian Shu, Qifan Chen, Linlan Liu, Lei Xu

“…A similarity index based on time parameters is proposed to describe similarities between nodes. …”
Get full text

Design and Motion Control of a Novel Weak-coupling Parallel Hip-joint Rehabilitation Mechanism by Xing Jiyuan, Xu Jilong, Liu Fucai

“…The human-machine forward kinematics model is established using the numerical method. …”
Get full text

A stochastic evaluation for non-stationary random noise over a long time period based on local statinarity and its application to actual acoustics environment by K. Nashimura, M. Ohta

“…For a non-stationarity with continuously slow temporal change, the noise evaluation method is derived by considering the temporal change of moment statistics or distribution parameters. On the other hand, for a non-stationarity with stepwise rapid variation like an ON/OFF operation of machine, the noise evaluation method is derived by considering in principle the occurring probability of each of locally stationary state based on the mutually exclusive property. …”
Get full text