装配序列规划问题的CSP模型及其符号OBDD求解技术

完全、正确的可行装配序列的表示和生成是装配序列评价、优化和选择的前提,为此建立了单调非线性装配意义下的可行装配序列规划问题的约束满足问题(CSP)模型,并给出了基于有序二叉决策图(OBDD)的符号求解算法.首先以装配联接图和移动向量函数为装配体模型,给出了装配联接图模型的共享二叉决策图(SBDD)表示、移动向量函数的OBDD表示,以及装配序列规划问题的CSP描述;然后将生成所有可行装配序列的问题转化为对CSP求解所有可能解的问题,利用回溯算法对CSP问题进行符号OBDD求解,得到了满足几何可行性约束的所有可行装配序列.最后通过装配体实验验证了基于CSP模型和OBDD推理的装配序列生成技术的正确性和可行性.     

产生式知识的有序二叉决策图表示及其推理

为了更完整地表示产生式规则构成的知识库及更有效地进行知识推理,给出了基于有序二叉决策图OBDD(Ordered Binary Decision Diagram)的产生式知识表示模型.在此基础上实现了基于OBDD的知识推理规则及相关算法,并结合实例对OBDD模型及其推理规则的可行性进行了分析.     

一种基于熵的OBDD变量排序算法

有序二叉决策图（OBDD）是一种有效表示布尔函数的数据结构，其大小依赖于所采用的变量序。熵是定量描述布尔函数中变量重要性的一种方法。基于变量的熵值分析了高质量变量序的特征，给出了一种基于熵的OBDD变量排序算法。实验结果表明：该算法与模拟退火算法和遗传算法结果相当。时间仅为相应算法的80．84％和29．79％。     

装配几何可行性判别的符号OBDD技术研究

采用了有序二叉决策图(OBDD)描述装配几何可行性,给出了装配操作及其几何可行性的符号表示,设计了装配操作几何可行性的符号OBDD判别算法,给出了装配几何可行性的研究实例,开发了装配的原型系统以支持装配规划,原型系统运行结果表明符号OBDD技术用于装配几何可行性判别的有效性。     

基于OBDD的描述逻辑εL循环术语集推理

循环术语集推理是描述逻辑研究中面临的难点问题,尚未得到很好的解决.有序二叉决策图（ordered binary decision diagram,简称OBDD）是一种对布尔函数进行紧凑表示和高效操作的数据结构,适用于表示和处理大规模问题.将OBDD应用于描述逻辑循环术语集的推理.首先,针对描述逻辑εL中的循环术语集,给出了描述图上关于最大模拟关系的重要性质,并借助集合表示和集合运算对该性质进行了表述和证明.在此基础上,应用布尔函数对描述图进行编码,给出了基于OBDD求解最大模拟关系的方法,进而给出了最大不动点语义下基于OBDD对概念包含关系进行判定的算法;接下来,基于OBDD给出了求解描述图中可以到达循环路径的所有结点的方法,进而给出了最小不动点语义下基于OBDD对概念包含关系进行判定的算法;最后,对算法的正确性、复杂度等进行了分析和证明,并对算法进行了编程实现,给出了关于计算性能的实验结果.该工作为循环术语集的推理提供了一条有效途径,也为OBDD在逻辑推理中的应用提供了新的案例.     

基于OBDD的有向图的存储与操作研究

本文讨论了一种基于OBDD的有向图的存储结构,给出了基于OBDD的有向图的操作方法及搜索算法.实验结果表明,该存储结构与传统的邻接表的存储结构相比,在处理大规模的有向图时,具有较高的存储效率.     

约束满足问题求解的符号OBDD桶消元算法

桶消元算法是求解约束满足问题的一种典型推理方法.针对桶消元算法面临的状态空间爆炸问题,将有序二叉决策图( OBDD)技术与该算法结合起来,给出了约束满足问题的一种求解算法.通过对约束满足问题中变量和域值的编码,将CSP问题转化为命题可满足性问题,给出了约束满足问题的OBDD表示方法;基于桶消元的算法思想,在约束满足问题...     

同一变量排序下的多OBDD合并算法

有序决策图（OBDD）是一种用于表示布尔表达式的数据结构,并在许多领域得到了广泛应用。在分布式或者动态环境下,利用已知布尔表达式的OBDD构造目标布尔表达式的OBDD是一个决定实际问题解决效率的关键问题。基于Shannon分解原理提出了一个同一变量排序下的OBDD合并算法。该算法首先建立目标布尔表达式的表存储模型,然后按照变量排序的逆序,依次处理各个变量,并且合并取值相同的行,直到所有变量处理完毕。     

基于SBDD图的布尔匹配

在逻辑验证和综合中,布尔匹配利用有序二叉判定图OBDD来检验两个给定的逻辑函数是否相等。为了提高匹配算法的效率,文中用最小项数作为标签标定变量（变量组）。对比两函数中变量（变量组）的“标签”,可以删除不可能的排序,从而加快匹配过程。在提取变量标签时,提出简约二分决策图－SBDD,并利用其节点少的特性进一步提高“标签”提取算法的效率。实验结果表明本算法执行速度快,变量区分能力强。     

基于OBDD的信号处理任务智能加卸载算法

为实现信号处理平台中应用任务的快速动态加卸载和智能切换,提出一种基于有序二叉决策图(OBDD)的智能加卸载算法。在分析智能加卸载整体系统构成的基础上,构建信号处理平台软硬件资源模型、态势参数定义字典和智能加卸载规则库。通过基于OBDD的智能规则决策算法,实现最小人工干预下的应用任务智能加卸载。实验结果表明,与顺序规则存储算法相比,该算法的效率较高,圈复杂度较低。     

Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm

To reduce waste during disassembly production and improve disassembly efficiency, this study investigates a type of partial parallel disassembly line applicable for the simultaneous disassembly of different products. A multi-objective mathematical model for a partial parallel disassembly line balancing problem is built considering four optimisation goals, namely, the minimisation of the cycle time, number of workstations, idle index, and quantity of disassembly resources. In addition, a novel multi-objective hybrid group neighbourhood search algorithm is proposed. First, a certain set of neighbourhood individuals (from the current population of individuals) is generated via neighbourhood search mechanisms based on optimal embedding and exchange operations. Then, a Pareto filtering process is performed on a mixed population composed of the individuals of the current population and all neighbourhoods. Subsequently, the current population individuals are renewed based on the mixed population. To prevent the algorithm from falling into a local optimum and to enhance the algorithm’s global search performance, we conduct a local search strategy based on a simulated annealing operation on the newly generated population individuals. The effectiveness and superiority of the proposed algorithm are proven by solving two complete disassembly line balancing problems at different scales and a partial disassembly line balancing problem, and also by comparison with several algorithms investigated in existing literature. Finally, the proposed model and algorithm are applied to a partial parallel disassembly line designed for the simultaneous disassembly of two types of waste products in a household appliance disassembly enterprise. The results of the partial parallel disassembly line are compared with those of an initial single-product straight disassembly line, and the comparison results show that the solution results of the optimisation goals for the partial parallel disassembly line are more superior than those of the initial single-product straight disassembly line.     

异步时序电路的功能测试自动生成方法

异步时序电路的测试一直是一个比较困难的问题。该文通过在前人研究的基础上，提出了一种实用、高效的自动测试生成方法。该方法通过使用基于OBDD(有序二元判决图)的布尔特征函数的运算求解来确定电路的状态转换图，然后通过对转换图的强连通图的搜索运算简化状态转换图，最后使用图论的方法求出测试序列。     

异步时序电路分析一种OBDD方法

对异步时序电路的分析和使用是一个比较困难的问题,所以,异步时序电路的实际应用范围远不如同步时序电路,通过改进JRBurch等提出的分析方法,使之适用于异步时序电路,该方法使用基于OBDD的布尔特征函数来表示电路的转移关系,并通过基于OBDD的布尔函数的运算涞确定异步时序电路的稳定状态,及当输入改变时电路的下一个稳定状态,由此可实现对电路特性的精确描述。     

基于OBDD的编码算法

论文提出一个新的无损图像压缩算法,主要是通过有序二叉决策图(OBDD)的方法,寻找图像中重复的模式来减少其存储空间的一种变换编码,因而成为表示图像的另一种数据结构。我们通过该算法来寻找OBDD以精确地表示图像,并给出了其OBDD的有效编码,所获得的结果表明,所提出的算法及其编码是实现无损图像压缩的一种有效的方法。     

Lower bounds on the OBDD size of two fundamental functions' graphs

Ordered Binary Decision Diagrams (OBDDs) are a data structure for Boolean functions which supports many useful operations. Among others it finds applications in CAD, model checking, and symbolic graph algorithms. Nevertheless, many simple functions are known to have exponential OBDD size with respect to their number of variables. In order to investigate the limits of symbolic graph algorithms which work on OBDD-represented graph instances, it is useful to have simply-structured graphs whose OBDD representation has exponential size. Therefore, we consider two fundamental functions with exponential lower bounds on their OBDD size and transfer these results to their corresponding graphs. Concretely, we consider the Indirect Storage Access function and the Hidden Weighted Bit function.     

基于OBDD的灰度图像无损压缩算法

提出一种基于有序决策二叉图(OBDD)的灰度图像无损压缩算法,该算法将灰度图像划分成独立的位平面,利用二值图像的开关性,用OBDD模型来表征位平面,然后对OBDD进行有效的编码,最后用算术编码压缩进一步减少冗余.实验结果表明,本算法的压缩比优于WinZIP.     

一种动静态结合的代码反汇编技术

为在不修改二进制代码的情况下提高反汇编的准确性和覆盖率,提出一种静态分析与动态仿真相结合的反汇编技术。在传统静态反汇编算法的基础上,利用代码仿真环境构造动态基本块标记算法,通过监控代码的执行路径达到反汇编求精的目的。测试结果证明了该方法的有效性。     

Human-robot collaboration disassembly planning for end-of-life product disassembly process

The disassembly process is the main step of dealing with End-Of-Life (EOL) products. This process is carried out mostly manually so far. Manual disassembly is not efficient economically and the robotic systems are not reliable in dealing with complex disassembly operations as they have high-level uncertainty. In this research, a disassembly planning method based on human-robot collaboration is proposed. This method employs the flexibility and ability of humans to deal with complex tasks, alongside the repeatability and accuracy of the robot. Besides, to increase the efficiency of the process the components are targeted based on the remanufacturability parameters. First, human-robot collaboration tasks are classified, and using evaluation of components remanufacturability parameters, human-robot collaboration definition and characteristics are defined. To target the right components based on their remanufacturability factors, the PROMETHEE II method is employed to select the components based on Cleanability, Reparability, and Economy. Then, the disassembly process is represented using AND/OR representation and the mathematical model of the process is defined. New optimization parameters for human-robot collaboration are defined and the genetic algorithm was modified to find a near-optimal solution based on the defined parameters. To validate the task classification and allocation, a 6-DOF TECHMAN robot arm is used to test the peg-out-hole disassembly operation as a common disassembly task. The experiments confirm the task classification and allocation method. Finally, an automotive component was selected as a case study to validate the efficiency of the proposed method. The results in comparison with the Particle Swarm algorithm prove the efficiency and reliability of the method. This method produces a higher quality solution for the human-robot collaborative disassembly process.