Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

“…Additionally, adequate lighting is crucial for detailed imaging in dark environments lacking ambient light. Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. …”
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Optimization of constant-speed control algorithm for high-speed EMUs by XIONG Yan, ZHOU Shangru, XU Qing, HUANG He

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Pareto Local Search Function for Optimal Placement of DG and Capacitors Banks in Distribution Systems by Abdolreza Sadighmanesh, Mehran Sabahi, Mahdi Zavvari

“…The particle swarm optimization method is applied to solving placement problems. …”
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On the issue of creating the optimal last stage of powerful steam turbines of nuclear power plants by T. V. Bogomolova, G. A. Ahmadzadeh

“…For this stage, perform multi-mode optimization to select the main characteristics of the stage operating with maximum efficiency according to a given model daily load schedule.METHODS. …”
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Optimization of Pearson Ⅲ Frequency Curve Based on R Software by HE Mei, ZHAO Huarong, YAO Yue, JIN Xin

“…The application results show that using R software for hydrological frequency analysis can quickly and efficiently calculate the sum of squares of deviation of different distribution parameters and frequency curves, improve the fitting accuracy and work efficiency, and reduce the workload of hydrological frequency calculation, so it is a new method for the optimal drawing of hydrological frequency curve.…”
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Robust Optimization for Time-Cost Tradeoff Problem in Construction Projects by Ming Li, Guangdong Wu

“…Based on multiobjective robust optimization method, a robust optimization model for time-cost tradeoff problem is developed. …”
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3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition by Binbin Wang, Tingli Su, Xuebo Jin, Jianlei Kong, Yuting Bai

“…Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. …”
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Predictive mathematical modeling of biomechanical behavior in all-on-4 implants design: effects of distal implant and occlusal load angulation using RSM based on FEA by Fahri Murat, Sema Nur Sevinç Gül, Abdullah Tahir Şensoy, Abdullah Tahir Şensoy, Abdullah Tahir Şensoy

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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

“…To select the optimal model, we apply fuzzy decision by opinion score method (FDOSM). …”
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An Entropy Optimizing RAS-Equivalent Algorithm for Iterative Matrix Balancing by Chlebus, Edward, Kasapu, Viswatej

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Optimal Operation of Integrated Electricity-Gas System Considering Uncertainty of Integrated Demand Response by Jinman LUO, Shanlong ZHAO, Youjun FENG, Zhuoxian LIU, Wenjie DONG, Yanhong YANG

“…Firstly, according to the implementation characteristics of energy price-based IDR project, different types of IDR uncertainty models are established using the fuzzy method and probability method comprehensively. …”
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Joint optimization of communication rates for multi-UAV relay systems by Chenghua Wen, Guifen Chen, Xinglong Gu, Wenzhe Wang

“…Finally, the proposed algorithm is compared under different optimization schemes and different optimization algorithms. …”
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Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite by Tian Bai, Yuanbo Zhang, Lin Kong, Hongrui Ao, Jisong Yu, Lei Zhang

“…To achieve the objectives of lower cost, reduced weight, minimized power consumption, and enhanced temperature stability, an optimized thermal design method tailored for satellites has been developed, with a particular focus on SAR antennas. …”
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Optimizing photovoltaic parameters with Monte Carlo and parallel resistance adjustment by Fatima Wardi, Mohamed Louzazni, Mohamed Hanine, Elhadi Baghaz, Sanjeevikumar Padmanaban

“…The extracted parameters using MCO are compared to contemporary research publications on metaheuristic optimization algorithms, iterative approaches, and analytical methods. …”
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Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. …”
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The analysis of optimization in music aesthetic education under artificial intelligence by Yixuan Peng

“…This paper is dedicated to investigating the feasibility and approach to optimizing the method of music aesthetic education through DL. …”
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Differential privacy budget optimization based on deep learning in IoT by Dan LUO, Ruzhi XU, Zhitao GUAN

“…In order to effectively process the massive data brought by the large-scale application of the internet of things (IoT), deep learning is widely used in IoT environment.However, in the training process of deep learning, there are security threats such as reasoning attacks and model reverse attacks, which can lead to the leakage of the original data input to the model.Applying differential privacy to protect the training process parameters of the deep model is an effective way to solve this problem.A differential privacy budget optimization method was proposed based on deep learning in IoT, which adaptively allocates different budgets according to the iterative change of parameters.In order to avoid the excessive noise, a regularization term was introduced to constrain the disturbance term.Preventing the neural network from over fitting also helps to learn the salient features of the model.Experiments show that this method can effectively enhance the generalization ability of the model.As the number of iterations increases, the accuracy of the model trained after adding noise is almost the same as that obtained by training using the original data, which not only achieves privacy protection, but also guarantees the availability, which means balance the privacy and availability.…”
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Differential privacy budget optimization based on deep learning in IoT by Dan LUO, Ruzhi XU, Zhitao GUAN

“…In order to effectively process the massive data brought by the large-scale application of the internet of things (IoT), deep learning is widely used in IoT environment.However, in the training process of deep learning, there are security threats such as reasoning attacks and model reverse attacks, which can lead to the leakage of the original data input to the model.Applying differential privacy to protect the training process parameters of the deep model is an effective way to solve this problem.A differential privacy budget optimization method was proposed based on deep learning in IoT, which adaptively allocates different budgets according to the iterative change of parameters.In order to avoid the excessive noise, a regularization term was introduced to constrain the disturbance term.Preventing the neural network from over fitting also helps to learn the salient features of the model.Experiments show that this method can effectively enhance the generalization ability of the model.As the number of iterations increases, the accuracy of the model trained after adding noise is almost the same as that obtained by training using the original data, which not only achieves privacy protection, but also guarantees the availability, which means balance the privacy and availability.…”
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Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO by Rongfu Luo, Zenghui Wang, Yanxia Sun

“…The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. …”
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