Transformer Fault Diagnosis Based on Knowledge Distillation and Residual Convolutional Neural Networks by Haikun Shang, Yanlei Wei, Shen Zhang

“…Subsequently, the Sparrow Optimization Algorithm (SSA) is applied to optimize the hyperparameters of the ResNet50 model, which is trained on DGA data as the teacher model. …”
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RL-MAB-Based Resource Allocation for Efficient Bandwidth Utilization in Industrial IoT Networks by Dongyeong Seo, Kidong Nam, Kwansoo Jung

“…This approach improves the performance of industrial networks while maintaining compatibility with existing industrial network systems, supporting reliability and real-time processing. …”
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Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

“…By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.…”
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Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques by Daniel Amigo, David Sánchez Pedroche, Jesús García, José Manuel Molina, Jekaterina Trofimova, Emmanuel Voet, Benoît Van Bogaert

“…We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved invalid height detection process using LOWESS and sliding window analysis, (3) a protection mechanism against simultaneous measurements at the Kalman filter level, and (4) an optimized approach for smooth overshoot correction during segment transitions. …”
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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…In this study, a VIV dataset of a cylindrical body with different degrees of freedom is used to compare the performance of the PINN and three PINN optimization algorithms. The findings suggest that, compared to a standard PINN, the AW-PINN lowers the mean squared error (MSE) on the test set by 50%, significantly improving the prediction accuracy. …”
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Toward a unified gait freeze index: a standardized benchmark for clinical and regulatory evaluations by Alessandro Schaer, Henrik Maurenbrecher, Carlo Mangiante, Roman Sobkuliak, Kathrin Müsch, Paula Sanchez Lopez, Eduardo Martin Moraud, Eduardo Martin Moraud, Olgac Ergeneman, George Chatzipirpiridis

“…Our method demonstrates improved performance compared to existing approaches while effectively mitigating the risk of divergent outcomes, which could otherwise lead to unforeseen and potentially hazardous consequences in real-world applications. …”
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Quantum circuit unoptimization by Yusei Mori, Hideaki Hakoshima, Kyohei Sudo, Toshio Mori, Kosuke Mitarai, Keisuke Fujii

“…In this paper, we define and construct a quantum algorithmic primitive called quantum circuit unoptimization, which makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence, i.e., the inverse operation of circuit optimization. …”
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DynBlock: dynamic data encryption with Toffoli gate for IoT by Mubasher Haq, Ijaz Ali Shoukat, Alamgir Naushad, Mohsin Raza Jafri, Moid Sandhu, Abd Ullah Khan, Hyundong Shin

“…It is found that DynBlock significantly improves energy efficiency and throughput compared to its predecessors. …”
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Energy Efficiency in Measurement and Image Reconstruction Processes in Electrical Impedance Tomography by Barbara Stefaniak, Tomasz Rymarczyk, Dariusz Wójcik, Marta Cholewa-Wiktor, Tomasz Cieplak, Zbigniew Orzeł, Janusz Gudowski, Ewa Golec, Michał Oleszek, Marcin Kowalski

“…Special attention is given to minimizing energy consumption during electromagnetic measurements and data processing, significantly improving the system’s overall performance. Research studies confirm the device’s high energy efficiency while maintaining the accuracy of the classification of lung disease using the LightGBM algorithm. …”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…The integration of these advanced algorithms accurately identified defects and categorized them, thus optimizing manufacturing processes. …”
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Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response by Elissaios Sarmas, Afroditi Fragkiadaki, Vangelis Marinakis

“…Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. …”
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Adaptive robust self‐scheduling for a wind‐based GenCo equipped with power‐to‐gas system and gas turbine to participate in electricity and natural gas markets by Mojtaba Fereydani, Mohammad Amin Latify

“…A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. …”
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Research on intelligent energy management strategies for connected range-extended electric vehicles based on multi-source information by Xuewen Zhai, Hanwu Liu, Wencai Sun, Zihang Su

“…Abstract Reliance solely on vehicle-specific information, while neglecting multi-source information such as traffic flow and traffic light status, results in difficulties in optimizing energy allocation based on complex road conditions. …”
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Power-Yeoh: A Yeoh-Type Hyperelastic Model with Invariant I₂ for Rubber-like Materials by Subraya Krishna Bhat, Keerthan A.

“…Also, the interesting phenomenon of thin balloon expansion is investigated by applying the model to relevant experimental data on elastomeric balloons available in the literature. A genetic algorithm-based least squares optimization routine is carried out to determine the material constants while applying the reported experimental data. …”
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An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek, Olena Chernyk

“…An explicit synthesis algorithm constructs new test cases by selecting API calls from a learned policy encoded in a Q-table. …”
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Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform by Di Liu, Jianfeng Sun, Wei Lu, Sining Li, Xin Zhou

“…This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. …”
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Multi-Underwater Target Interception Strategy Based on Deep Reinforcement Learning by Wenhao GAN, Yunfei PENG, Lei QIAO

“…Next, the multi-agent proximal policy optimization algorithm was used to construct a scalable state and action space and design a compound reward function, enhancing interception efficiency and cooperation of AUVs. …”
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3D evolutionarily designed metamaterials for scattering maximization by Dmitry Dobrykh, Konstantin Grotov, Anna Mikhailovskaya, Dmytro Vovchuk, Vladyslav Tkach, Mykola Khobzei, Anton Kharchevskii, Aviel Glam, Pavel Ginzburg

“…The resonance cascading is engineered with the aid of multi-objective optimization, implemented on top of a genetic algorithm, operating in a large search space, encompassing over 100 independent variables. …”
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Dynamic externalities of basic medical insurance compensation, health capital accumulation, and economic growth by Xinyue Yuan, Xujin Yang

“…A gradually declining medical insurance compensation rate not only improves population health and boosts economic output but also provides a viable strategy for labor market optimization and health system sustainability. …”
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Myocarditis Diagnosis Using Semi-Supervised Generative Adversarial Network and Differential Evolution by Haifeng Gui, Na Zhang

“…To minimize reliance on hyperparameters, the Random Key method is employed, optimized using the DE algorithm. The efficacy of the model is demonstrated on the Z-Alizadeh Sani myocarditis dataset, with further validation achieved through experiments on the EMIDEC dataset, assessing transfer learning (TL) effects. …”
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