Simulation Optimization and Experiment on High-efficiency Cyclone Oil Separator for Air-conditioner by Zhou Wei, Pan Likui, Xiao Fangbin

“…In order to improve the separation efficiency and decrease the pressure loss, the cyclone oil separators used in air conditioner system were designed with optimization. …”
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Prevention of Mutation, Cancer, and Other Age-Associated Diseases by Optimizing Micronutrient Intake by Bruce N. Ames

“…I review three of our research efforts which suggest that optimizing micronutrient intake will in turn optimize metabolism, resulting in decreased DNA damage and less cancer as well as other degenerative diseases of aging. (1) Research on delay of the mitochondrial decay of aging, including release of mutagenic oxidants, by supplementing rats with lipoic acid and acetyl carnitine. (2) The triage theory, which posits that modest micronutrient deficiencies (common in much of the population) accelerate molecular aging, including DNA damage, mitochondrial decay, and supportive evidence for the theory, including an in-depth analysis of vitamin K that suggests the importance of achieving optimal micronutrient intake for longevity. (3) The finding that decreased enzyme binding constants (increased Km) for coenzymes (or substrates) can result from protein deformation and loss of function due to an age-related decline in membrane fluidity, or to polymorphisms or mutation. …”
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Mechanical product design and manufacturing system based on CNN and server optimization algorithm by Tian Lei, Zhao Ance

“…Overall, the accuracy of the research network is high, and the loss is small. The training accuracy and loss of the new activation function proposed in the study have always been superior to other functions. …”
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An Extensible Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network by Chuanjun Wang, Kehao Fei, Xinle Xu, Haoran Chen, Ke Hu, Shihe Xu, Jiayang Ma

“…By comparing the proposed method with several heuristic algorithms, it is found that the errors in RMSE, MAE, and MAPE criteria via AGBO-Pro are all about 2 times lower with a much faster and more stable convergence of the loss function. By further taking a linear transformation of the loss function, the AGBO-Pro model achieves a more robust performance with a much lower variance in repeat numerical calculations. …”
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An optimization method of multiscale storage tank target detection introducing an attention mechanism by Wenjia Sun, Chunchun Hu, Nianxue Luo, Qiansheng Zhao

“…Second, to improve the detection effect of small targets, the model adds a small target detection head and performs multiscale target detection. Finally, the EIOU loss function is employed instead of the CIOU loss function in the original model to improve the algorithm’s accuracy and speed. …”
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Enhancing outcomes in Langendorff-perfused rodent hearts through perfusion parameter optimization by Maya Bolger-Chen, Manuela Lopera Higuita, Casie A. Pendexter, Mohammadreza Mojoudi, Korkut Uygun, Shannon N. Tessier

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A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks by Ting Yang, Zhixian Lin, Bo Yuan

“…With betweenness calibration, BAEE algorithm expanded the minimum-cost edges to optimize the network topology. Two performance metrics-connectivity functions, robustness function R ( G ) and efficiency function E ( G ) , were utilized to evaluate the network capability of the robustness and invulnerability. …”
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Topology Optimization of Free Damping Treatments on Plates Using Level Set Method by Jian Pang, Weiguang Zheng, Liang Yang, Yuping Wan, Qibai Huang, Rongjiang Tang

“…The objective function is defined as a combination of several desired modal loss factors solved by the finite element-modal strain energy method. …”
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Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning by Yingjun Liu, Fuchun Liu, Renwei Huang

“…Reinforcement learning (RL) automatically learns complex decisions through trial and error, but it requires the design of precise reward functions combined with domain knowledge. For complex scenarios where the reward function cannot be achieved or is only with sparse rewards, we proposed a novel supervised optimal control framework based on trajectory imitation (TI) and reinforcement learning (RL) in this paper. …”
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Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Nonlinear Systems by Ashwin P. Dani, Shubhendu Bhasin

“…The parameter update law ensures that the parameters do not converge to <inline-formula><tex-math notation="LaTeX">$zero$</tex-math></inline-formula>, avoiding possible loss of stabilization. An infinite-horizon value function minimization objective is achieved by regulating the current states to the desired with near-optimal control efforts. …”
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Control optimization for permanent magnet synchronous generators in multi-source power systems by LI Lingyun, LIU Weizhi, WANG Yongxiang, LIU Yang

“…A novel approach was proposed that incorporates a Lagrangian function based on the principles of maximum torque and minimum loss to solve for the direct-axis and quadrature-axis currents, leading to the creation of a voltage stability control strategy that integrates vector control with efficiency optimization control. …”
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Optimal Design Method for Vessel-Bridge Collision Based on Life-Cycle Theory by Tao Fu, Kai Wang, Ye Zhou, Xiaoqian Ren, Zhixin Zhu, Yan Li, Yue Sun, Lingxiao Meng

“…Comprehensive consideration of vessel-bridge collision risk loss and structural performance in the design stage to achieve optimal cost over the life cycle is an important aspect of bridge design. …”
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Learning optimal image representations through noise injection for fine-grained search by Vidit Kumar, Vikas Tripathi, Bhaskar Pant, Manoj Diwakar, Prabhishek Singh, Anchit Bijalwan

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Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization by Sakshi Tiwari, Snigdha Bisht, Kanchan Sharma

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Optimizing RetinaNet anchors using differential evolution for improved object detection by Asaad Mohammed, Hosny M. Ibrahim, Nagwa M. Omar

“…Its success is largely due to the groundbreaking focal loss function, which adeptly addresses the issue of class imbalance prevalent in object detection tasks. …”
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Cooperative Operation Optimization for Integrated Energy Microgrid Groups Based on Federated Learning by Mingfeng XUE, Xiaobo MAO, Hao XIAO, Yibin ZHOU, Xiaowei PU, Wei PEI

“…Thirdly, according to the results of cloud federation learning, the packaging model of interaction characteristics of each integrated energy microgrid at the edge is modified and updated iteratively until the loss function converges. In this way the global collaborative optimization operation of the integrated energy microgrids can be achieved under privacy protection. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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Offshore wind farm layout optimization accounting for participation in secondary reserve markets by T.-H. Nguyen, J. Quick, P.-E. Réthoré, J.-F. Toubeau, E. De Jaeger, F. Vallée

“…<p>Wind farm layout optimization usually aims at maximizing annual energy production by placing wind turbines in a strategic way to avoid wake losses. …”
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Optimal Operation Method for Microgrid with Wind/PV/Diesel Generator/Battery and Desalination by Qingfeng Tang, Nian Liu, Jianhua Zhang

“…It first establishes the objective functions which include the life loss of storage batteries and the fuel cost of diesel generators. …”
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Optimizing 3D printing prosthesis technology for rehabilitation of oral cancer patients by N. Gopi Chander, Lakshita Sharma, Amrutha Manoj Kumar, Lakshmi Raj

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