• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.7
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• pp.159-172
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• 2016

This paper presents a novel algorithm for searching evacuation paths in indoor disaster environments. The proposed method significantly improves the time complexity to find the paths to the evacuation exit by introducing a light-weight Disaster Evacuation Graph (DEG) for a building in terms of the size of the graph. With the DEG, the method also considers load balancing and bottleneck capacity of the paths to the evacuation exit simultaneously. The behavior of the algorithm consists of two phases: horizontal tiering (HT) and vertical tiering (VT). The HT phase finds a possible optimal path from anywhere of a specific floor to the evacuation stairs of the floor. Thus, after finishing the HT phases of all floors in parallel the VT phase begins to integrate all results from the previous HT phases to determine a evacuation path from anywhere of a floor to the safety zone of the building that could be the entrance or the roof of the building. It should be noted that the path produced by the algorithm. And, in order to define the range of graph to process, tiering scheme is used. In order to test the performance of the method, computing times and evacuation times are compared to the existing path searching algorithms. The result shows the proposed method is better than the existing algorithms in terms of the computing time and evacuation time. It is useful in a large-scale building to find the evacuation routes for evacuees quickly.

• Journal of Korean Society of Disaster and Security
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• v.13 no.2
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• pp.53-63
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• 2020

In this study, the two-dimensional flow analysis model Hydro_AS-2D model was used to simulate the situation of flooding in Seongsangu and Uichang-gu in Changwon in the event of rising sea levels and extreme flooding, and the results were expressed on three-dimensional topography and the optimal evacuation path was derived using BIM technology. Climate change significantly affects two factors in terms of flood damage: rising sea levels and increasing extreme rainfall ideas. The rise in sea level itself can not only have the effect of flooding coastal areas and causing flooding, but it also raises the base flood level of the stream, causing the rise of the flood level throughout the stream. In this study, the rise of sea level by climate change, the rise of sea level by storm tidal wave by typhoon, and the extreme rainfall by typhoon were set as simulated conditions. The three-dimensional spatial information of the entire basin was constructed using the information of topographical space in Changwon and the information of the river crossing in the basic plan for river refurbishment. Using BIM technology, the target area was constructed as a three-dimensional urban information model that had information such as the building's height and location of the shelter on top of the three-dimensional topographical information, and the results of the numerical model were expressed on this model and used for analysis for evacuation planning. In the event of flooding, the escape route is determined by an algorithm that sets the path to the shelter according to changes in the inundation range over time, and the set path is expressed on intuitive three-dimensional spatial information and provided to the user.

• Korean Journal of Construction Engineering and Management
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• v.5 no.6 s.22
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• pp.72-79
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• 2004

Critical Path-based project management has been applied to the construction projects with a goal of delivering projects within original costs and time estimates. These current project management methods, rarely make early finishes of construction Projects. In addition, current practices on time management seems not to take advantages of early finishes concepts due to student syndrome and Parkinson's Law, This research study applied the Theory of Constraints(n) in the estimation of construction project duration. While the TOC includes variety of management techniques, in this study, it refers to critical chain that has been used to develop the specific management technique in scheduling. The concept of critical chain is applied to this study to solve the problems associated with the current scheduling method. The efforts focus to solve the p개blems associated with current construction project scheduling methods by adopting both stochastic estimation technique and the concept of schedule buffer,

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.59-66
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• 2019

This study was conducted to evaluate the effect of the synthetic forest road network by calculating the optimal road density and layout of the forest road network in order to construct the systematic road network in the forested area. For this, five comparative routes were additionally planed and compared through evaluation indicators. As a result, the optimum road density of the study site was estimated to be 18.4 m/ha, and the synthetic forest road network was the best in the four indicators such as average skidding distance, standard deviation of skidding distance, development index, and circuity factor. In addition, the synthetic forest road network was comparable to the main road network by about 4 %p in the timber volume available and potential area size for logging, but the construction cost of the road was about 20 %p lower. It showed a synthetic forest road network was better in terms of economy.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.556-559
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• 2020

최근 태풍, 지진, 산불, 산사태, 전쟁 등 다양한 재난 상황으로 인한 인명피해와 자금 손실이 꾸준히 발생하고 있고 현재 이를 예방하고 복구하기 위해 많은 인력과 자금이 소요되고 있는 실정이다. 이러한 여러 재난 상황을 미리 감시하고 재난 발생의 빠른 인지 및 대처를 위해 본 논문에서는 인공지능 기반의 재난 드론 시스템을 설계 및 개발하였다. 본 연구에서는 사람이 감시하기 힘든 지역에 여러 대의 재난 드론을 이용하며 딥러닝 기반의 최단 경로 알고리즘을 적용해 각각의 드론이 최적의 경로로 효율적 탐색을 실시한다. 또한 드론의 근본적 문제인 배터리 용량 부족에 대한 문제점을 해결하기 위해 Ant Colony Optimization (ACO) 기술을 이용하여 각 드론의 최적 경로를 결정하게 된다. 제안한 시스템 구현을 위해 여러 재난 상황 중 산불 상황에 적용하였으며 전송된 데이터를 기반으로 산불지도를 만들고, 빔프로젝터를 탑재한 드론이 출동한 소방관에게 산불지도를 시각적으로 보여주었다. 제안한 시스템에서는 여러 대의 드론이 최적 경로 탐색 및 객체인식을 동시에 수행함으로써 빠른 시간 내에 재난 상황을 인지할 수 있다. 본 연구를 바탕으로 재난 드론 인프라를 구축하고 조난자 탐색(바다, 산, 밀림), 드론을 이용한 자체적인 화재진압, 방범 드론 등에 활용할 수 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.50-68
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• 2024

This study developed a bus route sketch (BRS) methodology for utilizing bus route design and operation steps in practice and evaluated the feasibility of the method. The BRS methodology consists of three steps: transportation zoning suitable for the provider and users of bus transit service; determining the bus operation route based on established transportation zones and path combination; optimizing the operation route based on the estimation of route alternatives in terms of the multi-performance measures from the standpoints of bus-transit service provider and user. The results of a case study showed that the estimation scores from the perspectives of provider and user were improved significantly from 8.83 and 7.13 to 9.50 and 9.89, respectively. Because the BRS method was designated and developed to be suitable for field application for route planning and operation, the method can be used instantly and directly to estimate and adjust the on-operation bus transit line and route design.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1749-1757
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• 2010

This paper considers the cost minimization problem to satisfy QoS (Quality of Service) requirements for a given network, in particular when communication resources to each link can be additionally assigned. For the purpose of quantifying QoS requirements such as data transfer delay and packet loss, we introduce the cost function considering both the link utilization factor and the additionally assigned resource. To minimize this cost function, we firstly formulate a Basic Capacity Planning (BCP) problem, a special case of Network Utility Maximization (NUM). We show that the solution of this BCP problem cannot be optimal via a counter example. In this paper, we suggest the cross-layer design of both additionally assigned resource and routing path, whose initial values are set to the result of BCP problem. This cross-layer design is based on a heuristic approach which presents an effective way to plan how much communication resources should be added to support the QoS requirements in future. By simulation study, we investigate the convergence of the cost function in a more general network topology as well as in a given simple topology.