A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function lev...A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function level, including dynamic binding and unbinding of base object and behavior. We implement this model with RoleJava Language,which is our self linguistic extension of the Java Language.Meta Objects are generated automatically at compile\|time,this makes the reflecton mechanism transparent to programmers.Finally an example applying this model to a banking system is presented.展开更多
This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are...This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.展开更多
A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta...A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.展开更多
This paper deals with the goals and visions of a consortium in Japan, named Consortium for Business Object Promotion (CBOP), discussing its background, activities, and basic technical approaches to share and exchangin...This paper deals with the goals and visions of a consortium in Japan, named Consortium for Business Object Promotion (CBOP), discussing its background, activities, and basic technical approaches to share and exchanging various types of Business Objects. Especially, Object Pattern Technologies used in CBOP should be discussed.展开更多
The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and ...The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and literature Instances, was divided into three stages: Stage 1, data treatment;Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II);Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF<sub>1</sub>;minimization of CO<sub>2</sub> emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF<sub>2</sub>;minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances. In this way, the procedure presented here can bring benefits to society as it considers environmental issues and also balancing work between the routes, ensuring savings and satisfaction for the users.展开更多
战斗部破片群运动参数对弹药毁伤威力评估具有重要的意义。针对破片尺寸较小、背景信息复杂以及破片数据样本少导致的破片检测精度较低的问题,本文提出一种YOLOv5-FD的战斗部破片群目标检测方法。首先,在网络输出端增加微小目标检测层,...战斗部破片群运动参数对弹药毁伤威力评估具有重要的意义。针对破片尺寸较小、背景信息复杂以及破片数据样本少导致的破片检测精度较低的问题,本文提出一种YOLOv5-FD的战斗部破片群目标检测方法。首先,在网络输出端增加微小目标检测层,将原始的三尺度改为四尺度,并在特征融合网络中引入内容感知特征重组(Content Aware ReAssembly of Features,CARAFE)上采样模块替换原有的最近邻插值上采样,减少小目标特征信息损失,提高弱小破片的提取能力。其次,在特征提取网络引入坐标注意力模块(Coordinate Attention,CA),加强对破片特征的提取,弱化背景信息,抑制复杂背景的干扰。最后,在模型训练过程中引入模型不可知元学习方法(Model Agnostic Meta Learning,MAML),达到仅用小样本破片数据集实现较高的检测性能。实验结果表明,YOLOv5-FD破片检测算法在自制破片数据集中,精确率达到了90.5%,召回率达到了85.4%,平均精度mAP_0.5达到了88.2%,与原始YOLOv5s算法相比分别提高了7.1%,7.9%和7.5%,有效提高了破片目标检测准确性。展开更多
基金Supported by the National Natural Science Foundation of China(60373086)
文摘A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function level, including dynamic binding and unbinding of base object and behavior. We implement this model with RoleJava Language,which is our self linguistic extension of the Java Language.Meta Objects are generated automatically at compile\|time,this makes the reflecton mechanism transparent to programmers.Finally an example applying this model to a banking system is presented.
文摘This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.
基金supported by the National Natural Science Foundation of China(Grant 11572134)
文摘A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.
文摘This paper deals with the goals and visions of a consortium in Japan, named Consortium for Business Object Promotion (CBOP), discussing its background, activities, and basic technical approaches to share and exchanging various types of Business Objects. Especially, Object Pattern Technologies used in CBOP should be discussed.
文摘The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and literature Instances, was divided into three stages: Stage 1, data treatment;Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II);Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF<sub>1</sub>;minimization of CO<sub>2</sub> emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF<sub>2</sub>;minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances. In this way, the procedure presented here can bring benefits to society as it considers environmental issues and also balancing work between the routes, ensuring savings and satisfaction for the users.
文摘战斗部破片群运动参数对弹药毁伤威力评估具有重要的意义。针对破片尺寸较小、背景信息复杂以及破片数据样本少导致的破片检测精度较低的问题,本文提出一种YOLOv5-FD的战斗部破片群目标检测方法。首先,在网络输出端增加微小目标检测层,将原始的三尺度改为四尺度,并在特征融合网络中引入内容感知特征重组(Content Aware ReAssembly of Features,CARAFE)上采样模块替换原有的最近邻插值上采样,减少小目标特征信息损失,提高弱小破片的提取能力。其次,在特征提取网络引入坐标注意力模块(Coordinate Attention,CA),加强对破片特征的提取,弱化背景信息,抑制复杂背景的干扰。最后,在模型训练过程中引入模型不可知元学习方法(Model Agnostic Meta Learning,MAML),达到仅用小样本破片数据集实现较高的检测性能。实验结果表明,YOLOv5-FD破片检测算法在自制破片数据集中,精确率达到了90.5%,召回率达到了85.4%,平均精度mAP_0.5达到了88.2%,与原始YOLOv5s算法相比分别提高了7.1%,7.9%和7.5%,有效提高了破片目标检测准确性。
