Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…It further investigates the relationship between turbulent coherent structures and the intensity of particle movement, clarifying the mechanism through which turbulent coherent structures influence sediment transport.MethodsThe optimization algorithm developed in this study aims to maximize the detection of moving particles, providing more accurate data to support understanding sediment transport patterns at the particle scale and their association with turbulent coherent structures. …”
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Collection tree-oriented mesh routing optimization for extending the lifetime of wireless sensor networks by Li-Yong Yuan, Fei-Long Lin

“…Experimental simulations show that the proposed collection tree-oriented mesh routing optimization strategy can extend the network lifetime by more than 20%.…”
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Optimal Contract Choice in Sustainable Tourism Supply Chain When Environmental Consumers Exist by Fei Du, Dejun Zou, Jianping Hu

“…Our analysis reveals that cost sharing and revenue sharing contracts are more effective in motivating the hotel to implement environmental effort than reselling contract. …”
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Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer by Pei Hu, Yibo Han, Zheng Zhang

“…The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. …”
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Signaling Effects in AI Streamers: Optimal Separation Strategy Under Different Market Conditions by Ying Yu, Yunpeng Yang

“…We build an analytical model and compare scenarios where the acceptance level is either exogenously given or endogenously determined, highlighting the implications for firms’ optimal separation strategy. Our findings suggest that in markets with moderate information asymmetry, using both price and acceptance level as joint signals can be more profitable for high-quality firms. …”
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Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning by Yi ZHOU, Xiaoyong MA, Fuxiao GAO, Wei LI, Nan CHENG, Ning LU

“…Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.…”
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Deep Learning Strategies for Intraday Optimal Carbon Options Trading with Price Impact Considerations by Qianhui Lai, Qiang Yang

“…This paper solves the optimal trading problem of carbon options with a deep learning approach. …”
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A novel multi-objective coverage optimization memetic algorithm for directional sensor networks by Aimin Wang, Yingnan Gao, Jing Wu, Geng Sun, Wenjuan Jia

“…Experimental results show that the proposed algorithm can prolong the network lifetime more effectively than similar heuristic algorithms in other studies.…”
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An efficient deep learning approach with frequency and channel optimization for underwater acoustic target recognition by Di Zeng, Shefeng Yan, Jirui Yang, Xianli Pan

“…Ablation studies confirm the contribution of each component, and comparative results demonstrate that FCResNet5 offers a more efficient alternative to existing models without compromising performance.…”
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Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning by Yi ZHOU, Xiaoyong MA, Fuxiao GAO, Wei LI, Nan CHENG, Ning LU

“…Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.…”
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Torsional Vibration Characteristics Analysis and Parameter Optimization of Parallel Hybrid Electric Vehicle Powertrain by Zhiyong Wen, Baoyu Guo, Hongfei Nie, Lin Chen, Shaofei Liu, Zhengfeng Yan

“…The diversity of vibration excitation sources and working modes in hybrid electric vehicles makes the problem of torsional vibration in the powertrain more prominent. To address the intricate torsional vibration issues in hybrid electric vehicles, this study analyzes the vibration characteristics of the powertrain and optimizes the parameters of the torsional damper. …”
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Quality of service optimization algorithm based on deep reinforcement learning in software defined network by Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU

“…Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.…”
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Optimal treatment strategies in comorbid patients with hypertension and dyslipidemia: the role of fixed-dose combinations by A. A. Sokolova, D. A. Napalkov

“…Of course, these algorithms are not mandatory, and in a specific clinical situation there may be any deviations even from standard treatment regimens as follows: an alternative decision of a practitioner, intolerance to therapy, special indications, and much more. However, differentiated approach to fixed-dose combinations is extremely useful in cases where it is necessary to quickly make the right decision to prescribe optimal therapy for hypertension and dyslipidemia in lack of time to comprehensively weigh all the pros and cons underlying any clinical guidelines.…”
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Combining Global Features and Local Interoperability Optimization Method for Extracting and Connecting Fine Rivers by Jian Xu, Xianjun Gao, Zaiai Wang, Guozhong Li, Hualong Luan, Xuejun Cheng, Shiming Yao, Lihua Wang, Sunan Shi, Xiao Xiao, Xudong Xie

“…To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. …”
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WSN clustering routing algorithm based on Cuckoo Search algorithm optimized K-means by Kailei ZHU, Aijing SUN

“…In order to extend the lifetime of wireless sensor network (WSN), a clustering routing algorithm for WSN based on Cuckoo Search (CS) algorithm optimized K-means was presented.In the clustering stage, the initial cluster centers were selected by CS algorithm, which make the clustering results of the K-means algorithm more uniform to balance node energy consumption.The remaining energy of the node, the distance from the center of the cluster were comprehensively considered in the cluster election, and the weight according to the remaining energy of the node was dynamically adjusted.In the data communication stage, in order to further balance the load of the cluster head, the remaining energy of the relay node and its load, and the cluster head routing energy consumption were comprehensively considered, CS algorithm was combined to plan routing for the cluster head.The simulation results show that the proposed algorithm is better than LEACH-K, LEACH-improve and DTK-means in terms of energy consumption balance.With the death of the first node as the life cycle of the network, the network lifespan was increased by 173%, 21%, and 6% respectively.The proposed algorithm effectively extending the network life cycle.…”
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Quadrotor Robust Fractional-Order Control Based on a Recent Bonobo Optimization Algorithm by Faycal Ziani, Aissa Benhammou, Mohamed Amine Hartani, Hegazy Rezk

“…The five fractional parameters for each engine are also improved using the Bonobo Optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). …”
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A comprehensive review of AI and machine learning techniques in antenna design optimization and measurement by Pradnya A. Gajbhiye, Satya P. Singh, Madan Kumar Sharma

“…The review concludes that AI/ML approaches have the capacity to transform antenna design by offering quicker and more precise solutions to complex problems. However, since the field is still in its early stages, continuous research and development are necessary to address these challenges and fully capitalize on the capabilities of AI/ML in optimizing antenna design.…”
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Evaluation of the Geomechanical Properties of Bashkirian Carbonate Rocks at the Tatarstan Republic for Optimization of Hydraulic Fracturing by Ziganshin Eduard, Kolchugin Anton, Dautov Ayrat, Nurieva E.

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Optimizing the Synthesis of Novel Calcium Carbonate/Cobalt Oxide Nanocomposite With Highest Antifungal Activity by Nima Fallahnia, Mohammad Salmani Mobarakeh, Hasti Sarabikia, Mohsen Safaei

“…The results indicated that the synthesized nanocomposite in optimal conditions (20 mg/mL of calcium carbonate, 3 mg/mL of cobalt oxide, and 90 min of stirring time) could inhibit the growth of C. albicans by more than 74%. …”
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Research on optimization of indoor positioning batch ranging system based on DS-TWR algorithm by Hongwei SUN, Xuehong CAO, Liangbao JIAO, Lin MENG, Ziheng LIU, Feng YUAN

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