Estimation of Current RMS for DC Link Capacitor of S-PMSM Drive System by ZHANG Zhigang, CHANG Jiamian, ZHANG Pengcheng

“…The Cotes method eliminates numerous integration calculations, thus improving calculation accuracy. The proposed technique simplifies the tedious calculation process of traditional algorithms and guarantees high calculation accuracy, providing guidance for optimizing the selection of DC link capacitors and the design of life monitoring controllers. …”
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Online Attentive Kernel-Based Off-Policy Temporal Difference Learning by Shangdong Yang, Shuaiqiang Zhang, Xingguo Chen

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An Efficient Design of DCT Approximation Based on Quantum Dot Cellular Automata (QCA) Technology by Ismail Gassoumi, Lamjed Touil, Bouraoui Ouni, Abdellatif Mtibaa

“…Optimization for power is one of the most important design objectives in modern digital image processing applications. …”
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A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Nowadays, spatio-temporal series prediction algorithms are emerging, but their prediction accuracy requires improvement. …”
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Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping by Oluwatobi Emmanuel Dare, Kennedy Okokpujie, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi, Raymond Jules Kala, Emmanuel Owolabi, Udeme Christopher Ukpong

“…The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. The achieved root mean square error (RMSE) is 0.1145dBm and MAE is 0.0820dBm, which shows the deviation between the ground truth and the generated REM. …”
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Edge-Fog Computing-Based Blockchain for Networked Microgrid Frequency Support by Ying-Yi Hong, Francisco I. Alano, Yih-der Lee, Chia-Yu Han

“…The parameters and hyperparameters of the LSTM-MFPC are optimized using the Bayesian Adaptive Direct Search (BADS) algorithm. …”
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Intracardiac abscess in the clinical course of infective endocarditis complicated by acute heart failure by Собіров Барно Бобір огли

“…Objective: to determine the optimal diagnostic and treatment algorithm for patients with infective endocarditis complicated by acute heart failure (AHF) and intracardiac abscess. …”
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Intelligent Data Reduction for IoT: A Context-Driven Framework by Laercio Pioli, Douglas D. J. De Macedo, Daniel G. Costa, Mario A. R. Dantas

“…With these predictions based on existing datasets, a selector algorithm module is adopted to identify the most suitable data reduction approach for specific IoT applications. …”
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Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems by Li-li Zhang, Qi Zhao, Li Wang, Ling-yu Zhang

“…Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. …”
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…Second, we develop a Context-Aware Adaptive Threshold Optimization (CA-ATO) algorithm that dynamically determines optimal thresholds by integrating multi-scale contextual information and uncertainty estimates, substantially improving boundary delineation accuracy and fine structure preservation. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…Models that integrate all formation-related data yielded the lowest root mean square error (RMSE) of 2.928% for capacity estimation and 16 cycles for RUL prediction, highlighting the significant role of surface-level physical features in improving accuracy. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. …”
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Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg by Xu Wang, Hongjie Liu, Pengfei Wang, Long Gao, Xin Yang

“…The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). …”
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A New Approach to ORB Acceleration Using a Modern Low-Power Microcontroller by Jorge Aráez, Santiago Real, Alvaro Araujo

“…This work also allows for future optimizations that will improve the results of this paper.…”
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MC64-ClustalWP2: a highly-parallel hybrid strategy to align multiple sequences in many-core architectures. by David Díaz, Francisco J Esteban, Pilar Hernández, Juan Antonio Caballero, Antonio Guevara, Gabriel Dorado, Sergio Gálvez

“…The new parallelization approach has focused into the most time-consuming stages of this algorithm. In particular, the so-called progressive alignment has drastically improved the performance, due to a fine-grained approach where the forward and backward loops were unrolled and parallelized. …”
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The Mode of Constructing Safe Trajectories of Motion of the Unmanned Aerial Vehicle while Monitoring Power Lines Considering the Influence of their Electromagnetic Fields by Shabanova A.R., Tolstoy I.M., Lebedev I.V.

“…This leads to poorer image processing performance and causes additional errors. To improve evaluation of power line characteristics, the algorithm of aerial imaging is proposed, which in-cludes vehicle position adjustment relative to wire for two imaging settings. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…Particularly, an EfficientNetB0 convolutional neural networks achieved the most impressive results, demonstrating a high sensitivity of 98.85, a specificity of 99.22, and a Matthews correlation coefficient of 0.98. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…The model is built to anticipate the gas turbine's Energy Yield (EY) output, optimize energy production efficiency, improve maintenance schedules, and enable operational decision-making within the power plant. …”
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Precise GNSS Positioning with Time-differenced Carrier Phases at Variable Sampling Rates by S. Guo, H. Yang, Y. Gao

“…Most Global Navigation Satellite System (GNSS) receivers typically have a sampling rate at 1Hz. …”
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A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder by Hoyeon Lee, Jaehong Yu

“…In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. …”
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