• Smart Media Journal
• /
• v.12 no.11
• /
• pp.113-124
• /
• 2023

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.263-270
• /
• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1085-1092
• /
• 2022

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.

• The Journal of Korea Robotics Society
• /
• v.16 no.2
• /
• pp.122-129
• /
• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.6
• /
• pp.593-599
• /
• 2011

With increasing user applicability of geospatial data, user demand for manifold and accurate information has increased. The usefulness of these services derives from their combination of the advantages of as-built geospatial data in making new content. There is a spatial inconsistency and shape disagreement in fusing heterogeneous data. Conflation, defined as the combining of information from diverse sources so as to reconcile spatial inconsistencies and shape disagreement, is possible solution to the problem. In this research, we developed the technique for removing shape disagreement between aerial image and road map removed spatial inconsistency in advanced research. The process includes four processes: producing of a road candidate image, extraction of vertices, and generation of a graph by connecting the vertices. We could remove the shape disagreement using the extracted road that was derived from finding the road possible path.

• Journal of Oriental Neuropsychiatry
• /
• v.12 no.2
• /
• pp.103-122
• /
• 2001

The clinical study was carried out the 58 patients with Headache who were treated in Department of Neuropsychiatry, College of Oriental Medicine, Dae Jeon University from 14 October 1999 to 15 October 2001. The results were summarized as follows. 1. The ratio of male and female was 15:43, 40s(36.2%) was frequent, the ratio of Tension headache and Migraine was 43:12, hypernoia and overwork oneself were the most inducing factor. 2. In distribution of the period of the clinical history, Tension headache was comparatively short term within 1 month(62.8%) and Migraine was comparatively long term over 1 year(91.7%), Tension headache was frequent at whole portion(41.3%) and occipital portion(26.1%), Migraine was frequent at temporal portion(76.9%). 3. In pain type, Tension headache has many vandlike discomport type, Migraine has many pulsatile type, neck-stiffness-pain and dizziness were mainly coexited. 4. Toung aspect has many SULDAMHONGTAEBAEKHOO(舌淡紅苔白厚), GINMAEK(緊脈) and HEUNMAEK(弦脈) were frequent in Pulse type, the GAEDAMSUNKIJEETONG(祛淡順氣止痛) prescription drugs were frequent such as GEYNTONGA(肩痛A), GEYNTONGDODAMTANG(?通導淡湯), Tension headache patients were well treated(90.7%). 5. In Tension headache and Migraine, the Curve has many SL except Tension headache‘s 2th SANGHAN(상한), in Regulation RR was frequent at 1th, 2th, 3th, 4th, 7th SANGHAN and RL was frequent at 5th, 6th SANGHAN, the result of Graph, Activity and Reactivity have many low response at the whole. 6. The Curve was within normal limit at whole portion and frequent SL at temporal portion, the whole and temporal portion s Regulation also have many RR at 1th, 2th, 3th, 4th, 7th SANGHAN and RL at 5th, 6th SANGHAN, Activity and Reactivity have many low response at the whole. 7. The occipital and frontal portion‘s Curve have many SL at 1th SANGHAN, the occipital portion’s Regulation has many RR at 1th, 2th, 4th, 7th SANGHAN and RL at 5th, 6th SANGHAN, Activity has many low response at the whole, Reactivity has many low response at 1th, 4th, 5th, 6th SANGHAN and high response 2th, 3th SANGHAN, the frontal portion s Regulation has many RL at 1th, 3th, 5th, 6th, 7th SANGHAN and RR at 4th SANGHAN, Activity and Reactivity also have many low response at the whole except 6th, 7th SANGHAN respectively.