• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1603-1618
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• 2024

Emergency operation plans are indispensable elements for effective process safety management especially when unanticipated events occur under extreme situations. In the paper, a synthesis framework is proposed for the integration success path planning and performance analysis. Within the synthesis framework, success path planning is implemented through flow-directed signal tracing, renaming and reconstruction from a complete collection of Minimal Path Sets (MPSs) that are obtained using graph traversal search on GO-FLOW model diagram. The performance of success paths is then evaluated and prioritized according to the task complexity and probability calculation of MPSs for optimum action plans identification. Finally, an Auxiliary Feed Water System of Pressurized Water Reactor (PWR-AFWS) is taken as an example system to demonstrate the flow-directed MPSs search method for success path planning and performance analysis. It is concluded that the synthesis framework is capable of providing procedural guidance for emergency response and safety management with optimal success path planning under extreme situations.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.250-259
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• 2021

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree^* (RRT^*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT^*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.171-179
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• 2010

A fundamental technology of UGV(Unmanned Ground Vehicle) to perform a given mission with success in various environment is a path planning method which generates a safe and optimal path to the goal. In this paper, we suggest a local path-planning method of UGV based on the binary map using world model data which is gathered from terrain perception sensors. In specially, we present three core algorithms such as shortest path computation algorithm, path optimization algorithm and path smoothing algorithm those are used in the each composition module of LPP component. A simulation is conducted with M&S(Modeling & Simulation) system in order to verify the performance of each core algorithm and the performance of LPP component with scenarios.

• Journal of Distribution Science
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• v.16 no.8
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• pp.15-27
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• 2018

Purpose - The purpose of the study is to examine the relationships among the following retail operations variables: retail store operations (i.e., store management, sales personnel, promotion of merchandise), success of assortment planning, firm performance (i.e., market share, overall competitive position, profitability, product quality, consumer satisfaction), and retail buyer's demographics and firm's characteristics. Research design, data, and methodology - After conducting a pilot test, the survey was conducted in Seoul, South Korea. With using the listwise deletion method, 378 usable data sets were analyzed. For data analysis, descriptive statistics, factor analysis, and Structural Equation Modeling (SEM) methods were employed. Results - As evidenced from the path diagram, the relationship between retail store operations and the success of assortment planning is strong and significant. Retail store operations affect firm performance, though at a weaker significance than it affects the success of assortment planning. The relationship between the success of assortment planning and firm performance, is the strongest relationship observed by this research. Conclusions - The findings of this empirical study contribute to the retail/fashion buying/management field by confirming (a) the importance of assortment planning for retail firm performance and (b) the role of store operations for successful assortment planning and firm performance for fashion retailers.

• Journal of Korean Society of Rural Planning
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• v.15 no.4
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• pp.59-74
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• 2009

This study reviews and modifies general IS success models to find success factors of WIS(Web-based Information Systems) and to confirm the relationship between WIS success and user's satisfaction of web use. A WISSM(Web-based Information Success Model extended to include EQ(E-Quality) is developed to anticipate user's intention to use Web-based Agricultural Information System and fit into the survey data from 252 WIS users of RDA(Rural Development Administration). PLS is applied to estimate a structural model based on EQ-WISSM to test hypotheses including 1) users reach a high level of intention to use Web-based Information Systems when they feel a high level of interactivity among an 'E-Quality', 'Decision Making Support Satisfaction' and 'Task Support Satisfaction', and E-Quality boosts intention to use Web-based Information Systems. The results show high path coefficients and $R^2$ values and find followings; First, the EQ-WISSM explains the user's intention to use WAIS quite well. Second, E-Quality can be used well in web-based IS environment to predict IS Success. Finally, this research finds the importance of 'Task Support Satisfaction' as a mediator between 'Decision Making Support Satisfaction', 'E-Quality' and 'Intention to Use'.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• 지역과문화
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• v.7 no.1
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• pp.59-98
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• 2020

An empirical study utilizing data was performed with a variable called 'strategic planning' for festivals in order to look into the impact of a cultural tourism festival on urban regeneration. As a success factor of a festival, strategic festival planning was drawn up, and the following hypotheses were set: Seven strategic factors verified through an exploratory factor analysis will have a positive impact on festival satisfaction (festival success) and on urban regeneration, and festival satisfaction will have a positive impact on urban regeneration by having a mediating effect on it. For the analysis, the Gimje Horizon Festival was selected as it was considered as a typical case of urban regeneration through a festival, and the relationship between the festival and urban regeneration was understood by conducting a combined analysis of a quantitative analysis through a survey, a literature search, field investigations and in-depth interviews. The quantitative analysis indicates that strategic planning has a positive impact on festival satisfaction (festival success) and on urban regeneration and that festival satisfaction has a positive impact on urban regeneration. The same study result as the quantitative analysis result was obtained even through a qualitative analysis. This shows that the higher the path coefficient of strategic planning, the higher the path coefficient of festival satisfaction and urban generation and that with better strategic planning, the effects of festival satisfaction and urban regeneration are maximized. In other words, when planning and implementing a festival by actively incorporating the seven strategic planning factors which were suggested as festival success factors earlier in this study beginning from the stage of festival planning, it is likely to have a positive impact not only on the success of the festival but also on urban regeneration. This is an implication that gives a new alternative to software-based urban regeneration through festivals. It is meaningful to present the importance of festival planning and the direction of planning to maximize the effect of urban regeneration to festival planners and urban regeneration experts. This study is believed to serve as a momentum for people to take a new approach to studies on festivals and urban regeneration utilizing software in the future.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.143-154
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• 2023

In this study, a fast motion planning method for the swing motion of a 6x6 wheel-legged robot to traverse large obstacles and gaps is proposed. The motion planning method presented in the previous paper, which was based on trajectory optimization, took up to tens of seconds and was limited to two-dimensional, structured vertical obstacles and trenches. A deep neural network based on one-dimensional Convolutional Neural Network (CNN) is introduced to generate keyframes, which are then used to represent smooth reference commands for the six leg angles along the robot's path. The network is initially trained using the behavioral cloning method with a dataset gathered from previous simulation results of the trajectory optimization. Its performance is then improved through reinforcement learning, using a one-step REINFORCE algorithm. The trained model has increased the speed of motion planning by up to 820 times and improved the success rates of obstacle crossing under harsh conditions, such as low friction and high roughness.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.488-496
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• 2015

This paper proposes a framework to determine the routes of multiple unmanned aerial vehicles (UAVs) to conduct multiple tasks in different locations considering the survivability of the vehicles. The routing problem can be formulated as the vehicle routing problem (VRP) with different cost matrices representing the trade-off between the safety of the UAVs and the mission completion time. The threat level for a UAV at a certain location was modeled considering the detection probability and the shoot-down probability. The minimal-cost path connecting two locations considering the threat level and the flight distance was obtained using the Dijkstra algorithm in hexagonal cells. A case study for determining the optimal routes for a persistent multi-UAVs surveillance and reconnaissance missions given multiple enemy bases was conducted and its results were discussed.