Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured...Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.展开更多
The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challen...The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.展开更多
Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees ...Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.展开更多
Equipment selection is an essential work in the research and development planning of equipment.The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimizatio...Equipment selection is an essential work in the research and development planning of equipment.The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimization of equipment architecture design,the adequate resources allocation,and the joint combat performance.From the system view,this paper proposes a method of weapons equipment portfolios selection(WEPS)based on the contribution rate of weapon systems,providing a new idea for weapon equipment portfolio selection.Firstly,we analyze the WEPS problem and the concept of the contribution rate under the systems background.Secondly,we propose a combat network modeling method for weapon equipment systems based on the function chain.Thirdly,we propose a WEPS method based on the contribution rate,fully considering the correlation relationships between potential weapons and the old weapon systems by the combat network model,under the limitation of capability demands and budget resources,with the objective to maximally increasing the combat ability of weapon systems.Finally,we make a case study with a specific WEPS problem where the whole calculation processes and results are analyzed and exhibited to verify the feasibility and effectiveness of the proposed method model.展开更多
Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-par...Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.展开更多
Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision make...Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.展开更多
Equipment development planning(EDP)is usually a long-term process often performed in an environment with high uncertainty.The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with ...Equipment development planning(EDP)is usually a long-term process often performed in an environment with high uncertainty.The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations.To deal with this problem,a multi-stage EDP model based on a deep reinforcement learning(DRL)algorithm is proposed to respond quickly to any environmental changes within a reasonable range.Firstly,the basic problem of multi-stage EDP is described,and a mathematical planning model is constructed.Then,for two kinds of uncertainties(future capabi lity requirements and the amount of investment in each stage),a corresponding DRL framework is designed to define the environment,state,action,and reward function for multi-stage EDP.After that,the dueling deep Q-network(Dueling DQN)algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme.Finally,a case of ten kinds of equipment in 100 possible environments,which are randomly generated,is used to test the feasibility and effectiveness of the proposed models.The results show that the algorithm can respond instantaneously in any state of the multistage EDP environment and unlike traditional algorithms,the algorithm does not need to re-optimize the problem for any change in the environment.In addition,the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.展开更多
In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD）...In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.展开更多
基金supported by the National Natural Science Foundation of China(71690233,71901214)。
文摘Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.
文摘The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.
基金supported by the National Natural Science Foundation of China(71690233)
文摘Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.
基金supported by the National Natural Science Foundation of China(71690233)the Scientific Research Foundation of National University of Defense Technology(ZK19-16)the PLA military graduate student funding project.
文摘Equipment selection is an essential work in the research and development planning of equipment.The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimization of equipment architecture design,the adequate resources allocation,and the joint combat performance.From the system view,this paper proposes a method of weapons equipment portfolios selection(WEPS)based on the contribution rate of weapon systems,providing a new idea for weapon equipment portfolio selection.Firstly,we analyze the WEPS problem and the concept of the contribution rate under the systems background.Secondly,we propose a combat network modeling method for weapon equipment systems based on the function chain.Thirdly,we propose a WEPS method based on the contribution rate,fully considering the correlation relationships between potential weapons and the old weapon systems by the combat network model,under the limitation of capability demands and budget resources,with the objective to maximally increasing the combat ability of weapon systems.Finally,we make a case study with a specific WEPS problem where the whole calculation processes and results are analyzed and exhibited to verify the feasibility and effectiveness of the proposed method model.
基金supported by the National Natural Science Foundation of China(7169023371901214)the National Key R&D Program of China(2017YFC1405005)。
文摘Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.
基金the National Key R&D Program of China(2017YFC1405005)the National Natural Science Foundation of China(71901214,71690233).
文摘Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.
基金supported by the National Natural Science Foundation of China(71690233,72001209)the Scientific Research Foundation of the National University of Defense Technology(ZK19-16)。
文摘Equipment development planning(EDP)is usually a long-term process often performed in an environment with high uncertainty.The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations.To deal with this problem,a multi-stage EDP model based on a deep reinforcement learning(DRL)algorithm is proposed to respond quickly to any environmental changes within a reasonable range.Firstly,the basic problem of multi-stage EDP is described,and a mathematical planning model is constructed.Then,for two kinds of uncertainties(future capabi lity requirements and the amount of investment in each stage),a corresponding DRL framework is designed to define the environment,state,action,and reward function for multi-stage EDP.After that,the dueling deep Q-network(Dueling DQN)algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme.Finally,a case of ten kinds of equipment in 100 possible environments,which are randomly generated,is used to test the feasibility and effectiveness of the proposed models.The results show that the algorithm can respond instantaneously in any state of the multistage EDP environment and unlike traditional algorithms,the algorithm does not need to re-optimize the problem for any change in the environment.In addition,the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.
文摘In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.
