Dear Editor,This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle(UAV)-assisted multiple-input multiple-output(MIMO)communication system.The particle swarm opt...Dear Editor,This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle(UAV)-assisted multiple-input multiple-output(MIMO)communication system.The particle swarm optimization(PSO)algorithm is used to achieve optimal beamforming and power allocation for this system.Additionally,sensitive particle(SP)and parameter adaptive adjustment are introduced into the traditional PSO algorithm,aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position.A reinforcement learning(RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters,which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission.Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach.展开更多
Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of S...Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.展开更多
针对复杂地理实体建模中,多模态点云数据融合处理耗时、精度偏差较大等问题,本文以南通大剧院异形建筑三维建模为例,提出一种多模态控制点辅助约束的最近点迭代算法(Multimodal Control Point Assistant-Iterative Closest Point)对点...针对复杂地理实体建模中,多模态点云数据融合处理耗时、精度偏差较大等问题,本文以南通大剧院异形建筑三维建模为例,提出一种多模态控制点辅助约束的最近点迭代算法(Multimodal Control Point Assistant-Iterative Closest Point)对点云数据进行配准,该算法能够实现复杂地理实体多模态点云数据的精确配准。通过实验对比,结果表明,与传统ICP算法、SAC-IA算法、NDT算法相比,本文算法不但能够快速实现全局收敛,而且可以满足城市级三维建模精度要求。展开更多
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligen...Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.展开更多
基金supported by the National Natural Science Foundation of China(62173251,62203113the“Zhishan”Scholars Programs of Southeast University,and the Fundamental Research Funds for the Central Universities(2242023K30034).
文摘Dear Editor,This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle(UAV)-assisted multiple-input multiple-output(MIMO)communication system.The particle swarm optimization(PSO)algorithm is used to achieve optimal beamforming and power allocation for this system.Additionally,sensitive particle(SP)and parameter adaptive adjustment are introduced into the traditional PSO algorithm,aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position.A reinforcement learning(RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters,which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission.Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach.
文摘Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.
文摘针对复杂地理实体建模中,多模态点云数据融合处理耗时、精度偏差较大等问题,本文以南通大剧院异形建筑三维建模为例,提出一种多模态控制点辅助约束的最近点迭代算法(Multimodal Control Point Assistant-Iterative Closest Point)对点云数据进行配准,该算法能够实现复杂地理实体多模态点云数据的精确配准。通过实验对比,结果表明,与传统ICP算法、SAC-IA算法、NDT算法相比,本文算法不但能够快速实现全局收敛,而且可以满足城市级三维建模精度要求。
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
文摘Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.
