• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.33-38
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• 2019

Recently, to support location-based services, there have been many researches which consider the spatial network. For this, there are many experimental data for data processing on the road network. However, the data to process the trajectory of moving objects are not suitable. Therefore, we propose index structure to process the trajectory data on the road network and the trajectory data generation algorithm. In addition, to prove efficiency of our index structure and algorithm, we show that edge-based trajectory data are generated through the proposed algorithm using the map data of San Francisco Bay.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.198-201
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• 2022

Recently, new cyber threats targeting new technologies are increasing, and hackers' attack targets are becoming broader and more intelligent. To counter these attacks, major security companies are using traditional EDR (Endpoint Detection and Response) solutions. However, the conventional method does not consider the context, so there is a limit to the accuracy and efficiency of responding to an advanced attack. In order to improve this problem, the need for a security solution centered on XDR (Extended Detection and Response) has recently emerged. In this study, we present effective threat detection and countermeasures in a changing environment through XDR trends and development roadmaps using machine learning-based context analysis.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.31-37
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• 2021

In this paper proposed a feature fusion and spatial attention-based modified YOLOv4S for small and occluded detection. Conventional YOLOv4S is a lightweight network and lacks feature extraction capability compared to the method of the deep network. The proposed method first combines feature maps of different scales with feature fusion to enhance semantic and low-level information. In addition expanding the receptive field with dilated convolution, the detection accuracy for small and occluded objects was improved. Second by improving the conventional spatial information with spatial attention, the detection accuracy of objects classified and occluded between objects was improved. PASCAL VOC and COCO datasets were used for quantitative evaluation of the proposed method. The proposed method improved mAP by 2.7% in the PASCAL VOC dataset and 1.8% in the COCO dataset compared to the Conventional YOLOv4S.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• Spatial Information Research
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• v.18 no.4
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• pp.129-140
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• 2010

Large portal sites such as Google, NAVER provide Various services based on the map. Thus, interest and demand of users who want to obtain the region-related information has been increased. And services that combine the regional information with the map are provided currently at the large portal sites. However, the existing services of large portal sites do not provide enough detailed information and are inconvenient because acquisition process of related information is repeated. Therefore, the system that enables users to obtain detailed information related on the specific region synthetically and easily is needed. In this paper, we propose a system model using map interface and knowledge-processing in order to build the system that is useful for acquiring regional information. The model consists of 3-Layers: 'Regional Information Web-Documents Layer', 'Unique Regional Information Layer', and "Map-Interface Layer'. The Integrated Region~Related Information Searching System based on the model is implemented through the following 4-steps: (1) extracting the keywords that represent specific region (2) collecting the related web pages (3) extracting a set of related keywords and computing an association between the keywords (4) implementing a user interface. We verified validity on the model we proposed. knowledge-processing algorithm using affinity matrix, and UI that help users conveniently search by applying the system to region of the Goyang City. This system integrates regional information existing merely individual 'information' and provides users the 'knowledge' that is newly produced and organized. Users can obtain various detailed regional information and easily get related information through this system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.74-84
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• 2010

In this paper, we propose a preference-based shortest path algorithm which is combined with Ant Colony Optimization (ACO) and A* heuristic algorithm. In recent years, with the development of ITS (Intelligent Transportation Systems), there has been a resurgence of interest in a shortest path search algorithm for use in car navigation systems. Most of the shortest path search algorithms such as Dijkstra and A* aim at finding the distance or time shortest paths. However, the shortest path is not always an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose a preference-based shortest path search algorithm which uses the properties of the links of the map. The preferences of the links are specified by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links. The experimental results show that the proposed algorithm is suitable to find preference-based shortest paths as well as distance shortest paths.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.197-205
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• 2021

Recently, the retail industry has been increasingly demanding information technology convergence and utilization to respond to various external environmental threats such as COVID-19 and to be competitive using AI technologies, but there is a very lack of research and application services. This study is a CCTV video data-driven AI application case study, using CCTV image data collection in retail space, object detection and tracking AI model, time series database to store real-time tracked objects and tracking data, heatmap to analyze congestion and interest in retail space, social access zone.We present the orientation and verify its usability in the direction designed through practical implementation.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• Convergence Security Journal
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• v.14 no.7
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• pp.65-73
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• 2014

Recently, the Internet of things has become issue as the new techniques the cloud computing and the grid computing etc. The Internet of Things is can grow even more that utilization of the range with the development of smart devices. and it has a lot of interest in several industries. In these circumstances, By analyzing the technologies and trends in the Internet of Things, I think you are ready to adapt to future IT field when needed. therefore, this paper are analyzed a various technologies and a case studies of the Internet of things, and it is expected to be used as the road map and material to build environment of the Internet of things in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.373-376
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• 2014

최근 모바일 기기 및 무선기기의 발달로 인하여 센서 네트워크가 다양한 분야에서 응용되고 있다. 따라서 센서에서 실시간으로 발생하는 스트리밍 데이터에서 이벤트를 감지하고 분석하는 것은 중요한 연구 분야로 부각되고 있다. 단순 이벤트의 발생 조건을 빠르게 판별하기 위해 비트맵 인덱스 기반 복합 이벤트 검출 기법 등 여러 가지 방법들이 사용되고 있지만, 아직까지 이기종 센서에서 발생하는 각기 다른 형태의 데이터를 융합하여 이벤트를 검출하는 복합 이벤트 처리에 대한 연구는 미비한 실정이다. 본 논문에서는 각기 다른 형태를 가지는 스트리밍 데이터에 멤버쉽 함수를 적용하여 퍼지화 함으로서 이기종 센서에서 발생하는 데이터를 융합 처리가능하며, Quine-Mccluskey 감축기법을 통하여 규칙의 신뢰도 및 속도가 향상된 의사결정을 하는 고속 이벤트 탐지기법을 제안한다.