With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a c...With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.展开更多
Since precise self-position estimation is required for autonomous flight of aerial robots, there has been some studies on self-position estimation of indoor aerial robots. In this study, we tackle the self-position es...Since precise self-position estimation is required for autonomous flight of aerial robots, there has been some studies on self-position estimation of indoor aerial robots. In this study, we tackle the self-position estimation problem by mounting a small downward-facing camera on the chassis of an aerial robot. We obtain robot position by sensing the features on the indoor floor.In this work, we used the vertex points(tile corners) where four tiles on a typical tiled floor connected, as an existing feature of the floor. Furthermore, a small lightweight microcontroller is mounted on the robot to perform image processing for the onboard camera. A lightweight image processing algorithm is developed. So, the real-time image processing could be performed by the microcontroller alone which leads to conduct on-board real time tile corner detection. Furthermore, same microcontroller performs control value calculation for flight commanding. The flight commands are implemented based on the detected tile corner information. The above mentioned all devices are mounted on an actual machine, and the effectiveness of the system was investigated.展开更多
In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration pr...In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.展开更多
Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and...Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and uncertainty of the simulations of soil N cycle of winter wheat-summer maize rotation system in the North China Plain(NCP)to the parameters were analyzed.First,the N module in the Vegetation Interface Processes(VIP)model was expanded to capture the dynamics of soil N cycle calibrated with field measurements in three ecological stations from 2000 to 2015.Second,the Morris and Sobol algorithms were adopted to identify the sensitive parameters that impact soil nitrate stock,denitrification rate,and ammonia volatilization rate.Finally,the shuffled complex evolution developed at the University of Arizona(SCE-UA)algorithm was used to optimize the selected sensitive parameters to improve prediction accuracy.The results showed that the sensitive parameters related to soil nitrate stock included the potential nitrification rate,Michaelis constant,microbial C/N ratio,and slow humus C/N ratio,the sensitive parameters related to denitrification rate were the potential denitrification rate,Michaelis constant,and N2 O production rate,and the sensitive parameters related to ammonia volatilization rate included the coefficient of ammonia volatilization exchange and potential nitrification rate.Based on the optimized parameters,prediction efficiency was notably increased with the highest coefficient of determination being approximately 0.8.Moreover,the average relative interval length at the 95% confidence level for soil nitrate stock,denitrification rate,and ammonia volatilization rate were 11.92,0.008,and 4.26,respectively,and the percentages of coverage of the measured values in the 95% confidence interval were 68%,86%,and 92%,respectively.By identifying sensitive parameters related to soil N,the expanded VIP model optimized by the SCE-UA algorithm can effectively simulate the dynamics of soil nitrate stock,denitrification rate,and ammonia volatilization rate in the NCP.展开更多
Emerging new technologies are reshaping healthcare.At the cutting edge of this transformation stands artificial intelligence,which has presented the potential to significantly enhance healthcare outcomes.As a pivotal ...Emerging new technologies are reshaping healthcare.At the cutting edge of this transformation stands artificial intelligence,which has presented the potential to significantly enhance healthcare outcomes.As a pivotal branch of artificial intelligence,machine learning(ML)involves the development of intelligent algorithms with self-improvement through experience.In recent years,ML has been shown to be instrumental in tackling complex challenges in numerous medical domains,that is,disease diagnosis,1 medical device development,2 and biological networks.3 As we will discuss below,there are opportunities for moving the field forward by integrating pediatric neurorehabilitation with novel ML approaches.展开更多
基金express their gratitude to the Higher Institution Centre of Excellence (HICoE) fund under the project code (JPT.S(BPKI)2000/016/018/015JId.4(21)/2022002HICOE)Universiti Tenaga Nasional (UNITEN) for funding the research through the (J510050002–IC–6 BOLDREFRESH2025)Akaun Amanah Industri Bekalan Elektrik (AAIBE) Chair of Renewable Energy grant,and NEC Energy Transition Grant (202203003ETG)。
文摘With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.
基金supported by Branding Research Fund by Shibaura Institute of Technology(SIT)。
文摘Since precise self-position estimation is required for autonomous flight of aerial robots, there has been some studies on self-position estimation of indoor aerial robots. In this study, we tackle the self-position estimation problem by mounting a small downward-facing camera on the chassis of an aerial robot. We obtain robot position by sensing the features on the indoor floor.In this work, we used the vertex points(tile corners) where four tiles on a typical tiled floor connected, as an existing feature of the floor. Furthermore, a small lightweight microcontroller is mounted on the robot to perform image processing for the onboard camera. A lightweight image processing algorithm is developed. So, the real-time image processing could be performed by the microcontroller alone which leads to conduct on-board real time tile corner detection. Furthermore, same microcontroller performs control value calculation for flight commanding. The flight commands are implemented based on the detected tile corner information. The above mentioned all devices are mounted on an actual machine, and the effectiveness of the system was investigated.
基金supported by The Training Plan of Young Backbone Teachers in Colleges and Universities of Henan Province(2018GGJS175:Research on Intelligent Power Management System).
文摘In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.
基金financially supported by the National Natural Science Foundations of China(Nos.41790424 and 41471026)。
文摘Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and uncertainty of the simulations of soil N cycle of winter wheat-summer maize rotation system in the North China Plain(NCP)to the parameters were analyzed.First,the N module in the Vegetation Interface Processes(VIP)model was expanded to capture the dynamics of soil N cycle calibrated with field measurements in three ecological stations from 2000 to 2015.Second,the Morris and Sobol algorithms were adopted to identify the sensitive parameters that impact soil nitrate stock,denitrification rate,and ammonia volatilization rate.Finally,the shuffled complex evolution developed at the University of Arizona(SCE-UA)algorithm was used to optimize the selected sensitive parameters to improve prediction accuracy.The results showed that the sensitive parameters related to soil nitrate stock included the potential nitrification rate,Michaelis constant,microbial C/N ratio,and slow humus C/N ratio,the sensitive parameters related to denitrification rate were the potential denitrification rate,Michaelis constant,and N2 O production rate,and the sensitive parameters related to ammonia volatilization rate included the coefficient of ammonia volatilization exchange and potential nitrification rate.Based on the optimized parameters,prediction efficiency was notably increased with the highest coefficient of determination being approximately 0.8.Moreover,the average relative interval length at the 95% confidence level for soil nitrate stock,denitrification rate,and ammonia volatilization rate were 11.92,0.008,and 4.26,respectively,and the percentages of coverage of the measured values in the 95% confidence interval were 68%,86%,and 92%,respectively.By identifying sensitive parameters related to soil N,the expanded VIP model optimized by the SCE-UA algorithm can effectively simulate the dynamics of soil nitrate stock,denitrification rate,and ammonia volatilization rate in the NCP.
基金supported by the Featured Clinical Technique of Guangzhou(2023C-TS59)the Natural Science Foundation of Guangdong Province(2021A1515012543)the Scientific and Technological Planning Project of Guangzhou City(2024A03J01274)
文摘Emerging new technologies are reshaping healthcare.At the cutting edge of this transformation stands artificial intelligence,which has presented the potential to significantly enhance healthcare outcomes.As a pivotal branch of artificial intelligence,machine learning(ML)involves the development of intelligent algorithms with self-improvement through experience.In recent years,ML has been shown to be instrumental in tackling complex challenges in numerous medical domains,that is,disease diagnosis,1 medical device development,2 and biological networks.3 As we will discuss below,there are opportunities for moving the field forward by integrating pediatric neurorehabilitation with novel ML approaches.
