Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonl...Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.展开更多
在“双碳”目标背景下,为有效降低虚拟电厂(virtual power plant,VPP)系统中的碳排量及源、荷不确定性对系统经济优化调度的影响,并进一步挖掘电转气(power-to-gas,P2G)过程中氢能的高品位利用,将富氧燃烧碳捕集技术引入虚拟电厂中,建...在“双碳”目标背景下,为有效降低虚拟电厂(virtual power plant,VPP)系统中的碳排量及源、荷不确定性对系统经济优化调度的影响,并进一步挖掘电转气(power-to-gas,P2G)过程中氢能的高品位利用,将富氧燃烧碳捕集技术引入虚拟电厂中,建立了考虑信息间隙决策理论(information gap decision theory,IGDT)和富氧燃烧碳捕集技术的虚拟电厂优化调度。首先,分析富氧燃烧碳捕集机组运行原理与能流特性,并建立数学模型;其次,构建富氧燃烧碳捕集机组与P2G协同运行框架,搭建细化两阶段的P2G和热电比可调的氢燃料电池模型,实现P2G过程中氢能高品位利用,减少能量的梯级损耗;然后,引入奖惩阶梯碳交易机制,以系统总运行成本最小为目标建立VPP优化调度模型;接着,利用IGDT构建风险规避和机会寻求策略下的优化调度模型,并调用GUROBI商业求解器进行求解。最后,通过设置不同的方案进行算例验证,结果表明文中所提的方案在考虑富氧燃烧碳捕集技术和源、荷不确定性下能满足VPP低碳经济运行。展开更多
Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. T...Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network(CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.展开更多
As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc...As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc.)of multi-actual trainees may be obscured when they perform the collaborative interactive operation.To address this issue,motion data compensation method based on the additional feature information provided by the electromagnetic spatial position tracking equipment is proposed in this paper.The main working principle and detailed realization process of the proposed method are introduced step by step,and the practical implementation is presented to illustrate its validity and efficiency.The results show that the missing capture data and motion information of relevant obscured markers on arms can be retrieved with the proposed method,which can avoid the simulation motions of corresponding virtual operators being interrupted and deformed during the collaborative interactive operation process performed by multiactual trainees with optical human motion capture system in a limited capture range.展开更多
Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that ad...Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.展开更多
针对风光出力的不确定性容易对虚拟电厂调度产生影响的问题,提出了基于信息间隙决策理论(information gap decision theory,IGDT)的新型虚拟电厂优化调度模型。为了降低系统的碳排放,对热电联产机组加装碳捕集(carbon capture and stora...针对风光出力的不确定性容易对虚拟电厂调度产生影响的问题,提出了基于信息间隙决策理论(information gap decision theory,IGDT)的新型虚拟电厂优化调度模型。为了降低系统的碳排放,对热电联产机组加装碳捕集(carbon capture and storage,CCS)系统;为了提高可再生能源的利用率,在系统中引入电转气(power to gas,P2G)装置,提出CCS-P2G耦合的运行模式;在CCSP2G耦合运行的基础上基于信息间隙决策理论考虑了风光出力的不确定性。通过CPLEX求解器对所建模型进行求解,结果表明,在CCS-P2G耦合运行下,风光出力的利用率达到100%,系统的运行成本降低了12.3%,有效提升了系统的经济性和低碳性;在IGDT策略下通过成本预留,在风光出力的不确定度不超过上限时,能实现调度周期内对虚拟电厂运行的有效管控。展开更多
为充分利用源荷双侧资源实现低碳调度目标,文中提出一种基于信息间隙决策理论(Information Gap Decision Theory,IGDT)的含碳捕集发电厂和热负荷集群电热联合系统优化调度方法。在源侧建立具有储液式碳捕集装置的热电联产电厂模型,分析...为充分利用源荷双侧资源实现低碳调度目标,文中提出一种基于信息间隙决策理论(Information Gap Decision Theory,IGDT)的含碳捕集发电厂和热负荷集群电热联合系统优化调度方法。在源侧建立具有储液式碳捕集装置的热电联产电厂模型,分析其净出力特性。在负荷侧提出考虑用户舒适度的住宅热负荷聚合模型。通过不确定集对风电预测误差的不确定性进行建模,提出一种基于风险规避型IGDT的电热联合系统优化调度模型,可同时满足系统鲁棒性和经济性要求。结果表明,所提模型可有效提高风电消纳水平,提升系统运行的经济性。展开更多
Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emerg...Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.展开更多
文摘Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.
文摘在“双碳”目标背景下,为有效降低虚拟电厂(virtual power plant,VPP)系统中的碳排量及源、荷不确定性对系统经济优化调度的影响,并进一步挖掘电转气(power-to-gas,P2G)过程中氢能的高品位利用,将富氧燃烧碳捕集技术引入虚拟电厂中,建立了考虑信息间隙决策理论(information gap decision theory,IGDT)和富氧燃烧碳捕集技术的虚拟电厂优化调度。首先,分析富氧燃烧碳捕集机组运行原理与能流特性,并建立数学模型;其次,构建富氧燃烧碳捕集机组与P2G协同运行框架,搭建细化两阶段的P2G和热电比可调的氢燃料电池模型,实现P2G过程中氢能高品位利用,减少能量的梯级损耗;然后,引入奖惩阶梯碳交易机制,以系统总运行成本最小为目标建立VPP优化调度模型;接着,利用IGDT构建风险规避和机会寻求策略下的优化调度模型,并调用GUROBI商业求解器进行求解。最后,通过设置不同的方案进行算例验证,结果表明文中所提的方案在考虑富氧燃烧碳捕集技术和源、荷不确定性下能满足VPP低碳经济运行。
文摘Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network(CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.
基金the project supported by the National Natural Science Foundation of China(Grant No.61702524)the Natural Science Foundation of Shaanxi Province(Grant No.2016JQ6052).
文摘As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc.)of multi-actual trainees may be obscured when they perform the collaborative interactive operation.To address this issue,motion data compensation method based on the additional feature information provided by the electromagnetic spatial position tracking equipment is proposed in this paper.The main working principle and detailed realization process of the proposed method are introduced step by step,and the practical implementation is presented to illustrate its validity and efficiency.The results show that the missing capture data and motion information of relevant obscured markers on arms can be retrieved with the proposed method,which can avoid the simulation motions of corresponding virtual operators being interrupted and deformed during the collaborative interactive operation process performed by multiactual trainees with optical human motion capture system in a limited capture range.
文摘Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.
文摘针对风光出力的不确定性容易对虚拟电厂调度产生影响的问题,提出了基于信息间隙决策理论(information gap decision theory,IGDT)的新型虚拟电厂优化调度模型。为了降低系统的碳排放,对热电联产机组加装碳捕集(carbon capture and storage,CCS)系统;为了提高可再生能源的利用率,在系统中引入电转气(power to gas,P2G)装置,提出CCS-P2G耦合的运行模式;在CCSP2G耦合运行的基础上基于信息间隙决策理论考虑了风光出力的不确定性。通过CPLEX求解器对所建模型进行求解,结果表明,在CCS-P2G耦合运行下,风光出力的利用率达到100%,系统的运行成本降低了12.3%,有效提升了系统的经济性和低碳性;在IGDT策略下通过成本预留,在风光出力的不确定度不超过上限时,能实现调度周期内对虚拟电厂运行的有效管控。
文摘为充分利用源荷双侧资源实现低碳调度目标,文中提出一种基于信息间隙决策理论(Information Gap Decision Theory,IGDT)的含碳捕集发电厂和热负荷集群电热联合系统优化调度方法。在源侧建立具有储液式碳捕集装置的热电联产电厂模型,分析其净出力特性。在负荷侧提出考虑用户舒适度的住宅热负荷聚合模型。通过不确定集对风电预测误差的不确定性进行建模,提出一种基于风险规避型IGDT的电热联合系统优化调度模型,可同时满足系统鲁棒性和经济性要求。结果表明,所提模型可有效提高风电消纳水平,提升系统运行的经济性。
基金supported by the National Natural Science Foundation of China(No.62376197)the Tianjin Science and Technology Program(No.23JCYBJC00360)the Tianjin Health Research Project(No.TJWJ2025MS045).
文摘Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.
