• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.1-8
• /
• 2021

The subject of navigation has drawn a large interest in the last few years. Navigation problem (or path planning) finds the path between two points, source location and destination location. In smart cities, solving navigation problem is essential to all residents and visitors of such cities to guide them to move easily between locations. Also, the navigation problem is very important in case of moving robots that move around the city or part of it to get some certain tasks done such as delivering packages, delivering food, etc. In either case, solution to the navigation is essential. The core to navigation systems is the navigation algorithms they employ. Navigation algorithms can be classified into navigation algorithms that depend on maps and navigation without the use of maps. The map contains all available routes and its directions. In this proposal, we consider the first class. In this paper, we are interested in getting path planning solutions very fast. In doing so, we employ a parallel platform, Reconfigurable mesh (R-Mesh), to compute the path from source location to destination location. R-Mesh is a parallel platform that has very fast solutions to many problems and can be deployed in moving vehicles and moving robots. This paper presents two algorithms for path planning. The first assumes maps with linear streets. The second considers maps with branching streets. In both algorithms, the quality of the path is evaluated in terms of the length of the path and the number of turns in the path.

• Journal of applied mathematics & informatics
• /
• v.14 no.1_2
• /
• pp.97-113
• /
• 2004

In this paper we shall study moving boundary problems, and we introduce an approach for solving a wide range of them by using calculus of variations and optimization. First, we transform the problem equivalently into an optimal control problem by defining an objective function and artificial control functions. By using measure theory, the new problem is modified into one consisting of the minimization of a linear functional over a set of Radon measures; then we obtain an optimal measure which is then approximated by a finite combination of atomic measures and the problem converted to an infinite-dimensional linear programming. We approximate the infinite linear programming to a finite-dimensional linear programming. Then by using the solution of the latter problem we obtain an approximate solution for moving boundary function on specific time. Furthermore, we show the path of moving boundary from initial state to final state.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.21 no.1
• /
• pp.1-17
• /
• 1996

There have been considerable researches for solving large-scale separable convex optimization ptoblems. In this paper we present a method for large-scale nonseparable smooth convex optimization problems with block-angular linear constraints. One of them is occurred in reconfiguration of the virtual path network which finds the routing path and assigns the bandwidth of the path for each traffic class in ATM (Asynchronous Transfer Mode) network [1]. The solution is approximated by solving a sequence of the block-angular structured separable quadratic programming problems. Bundle-based decomposition method [10, 11, 12]is applied to each large-scale separable quadratic programming problem. We implement the method and present some computational experiences.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.147-151
• /
• 1991

In this paper a time-optimal path planning scheme for the multiple robot manipulators will be proposed by using hopfield neural network. The time-optimal path planning, which can allow multiple robot system to perform the demanded tasks with a minimum execution time and collision avoidance, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to rearrange the problem as MTSP(Multiple Travelling Salesmen Problem) and then apply the Hopfield network technique, which can allow the parallel computation, to the minimum time problem. This paper proposes an approach for solving the time-optimal path planning of the multiple robots by using Hopfield neural network. The effectiveness of the proposed method is demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.310-316
• /
• 1986

The problem of finding an allowable object trajectory for a cooperating two-robot system is investigated. The method proposed in this paper is based on reformulating the problem as a nonlinear optimization problem with equality constants in terms of the joint variables. The optimization problem is then solved numerically on a computer. The solution automatically gives the corresponding joint variable trajectories as well, thus eliminating the need for solving the inverse kinematic problem. The method has been succesfully applied to an experimental system.

• Journal of Technology Innovation
• /
• v.27 no.4
• /
• pp.85-110
• /
• 2019

This study examines the social problem-solving R&D policies from the perspective of 'Mission-oriented innovation policy'. To this end, we analyzed the 'second science and technology-based social problem solving plan' in terms of civil society's participation, securing the government's dynamic capabilities, and government's risk investments. The plan introduces an institutional framework for civic participation for social problem-solving innovation, strengthening R&D program coordination and integration, and new innovation ecosystem formation. However, there is a need for a concrete program to overcome a path dependency of existing activities. Otherwise new institutions are likely to be formalized. In addition, in order to derive risk investment, it is necessary to integrate innovation policy with social policy fields such as community care and climate change. It is necessary to establish an policy process that combines the agenda of social policy beyond with R & D policy, and to forms a platform for problem solving, integrates various technologies, industries and resources.

• Journal of The Korean Association For Science Education
• /
• v.26 no.1
• /
• pp.1-8
• /
• 2006

This study investigated the causal relationships between high school student multiple-choice algorithmic chemistry problem solving and 1) the motivational variables of achievement goal (task goal/performance goal/performance-avoidance) and perceived ability, and 2) the cognitive variables of learning strategy (deep learning/surface learning) and self-regulation. Path analysis supported a causal model in which perceived ability and task goal were found to positively influence algorithmic chemistry problem-solving ability via self-regulation. In particular it was found that perceived ability directly influenced algorithmic chemistry problem-solving ability. Moreover, deep learning was found to have been influenced by perceived ability and task goal, while surface learning was influenced by performance-avoidance goal. Lastly, there did not appear to be any causal relationship between learning strategy and algorithmic chemistry problem-solving ability.

• Industrial Engineering and Management Systems
• /
• v.12 no.3
• /
• pp.198-206
• /
• 2013

The team orienteering problem (TOP) or the multiple tour maximum collection problem can be considered as a generic model that can be applied to a number of challenging applications in logistics, tourism, and other fields. This problem is generally defined as the problem of determining P paths, in which the traveling time of each path is limited by $T_{max}$ that maximizes the total collected score. In the TOP, a set of N vertices i is given, each with a score $S_i$. The starting point (vertex 1) and the end point (vertex N) of all paths are fixed. The time $t_{ij}$ needed to travel from vertex i to j is known for all vertices. Some exact and heuristics approaches had been proposed in the past for solving the TOP. This paper proposes a new solution methodology for solving the TOP using the particle swarm optimization, especially by proposing a solution representation and its decoding method. The performance of the proposed algorithm is then evaluated using several benchmark datasets for the TOP. The computational results show that the proposed algorithm using specific settings is capable of finding good solution for the corresponding TOP instance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.143-146
• /
• 1996

In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

• International Journal of Quality Innovation
• /
• v.4 no.1
• /
• pp.131-147
• /
• 2003

This paper considers that a public department under specialized TQM manpower constraints have to implement multiple total quality management (TQM) policies to promote its service performance (fundamental goal) by adopting a centralized sequential advancement strategy (CSAS). Under CSAS, the decision-makers (DMs) start off by focusing specialized TQM manpower on a single policy, then transfer the specialized TQM manpower to the next policy when the first policy reaches the predetermined implementation time limit (in terms of education and training). Suppose that each TQM policy has a different desirous education and training goal. When the desirous goals for all TQM policies are achieved, we say that the fundamental goal will be satisfied. Within the limitation of total implementation period of time for all policies, assume the desirous goals for all TQM policies cannot be achieved completely. Under this premise, the optimal implementation sequence for all TQM policies must be calculated to maximize the weighted achievement of the desirous goal. We call this optimization problem a TQM case of "centralized sequential decision-making problem (CSDMP)". The achievement of the desirous goal for each TQM policy is usually affected by the experience in prior implemented policies, which makes solving CSDMP quite difficult. As a result, this paper introduces the concepts of sequential effectiveness and path effectiveness. The structural properties are then studied to propose theoretical methods for solving CSDMP. Finally, a numerical example is proposed to demonstrate CSDMP′s usability.