• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.413-413
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• 2023

Wetlands are a critical component of the global carbon cycle and are essential in mitigating climate change. Accurately quantifying wetland carbon emissions is crucial for understanding and predicting the impact of wetlands on the global carbon budget. The uncertainty quantifying carbon in wetlands may comes from the ecosystem's hydrological, biochemical, and microbiological variability. The Community Land Model is a sophisticated and flexible land surface model that offers several configuration options such as energy and water fluxes, vegetation dynamics, and biogeochemical cycling, necessitating careful consideration for the alternative configurations before model implementation to develop a practical model framework. We conducted a systematic literature review, analyzing the alternatives, focusing on the carbon stock pools configurations and the parameters with significant sensitivity for carbon quantification in wetlands. In addition, we evaluated the feasibility and availability of in situ observation data necessary for validating the different alternatives. This analysis identified the most suitable option for our study site, the Binbong Wetland, in Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.232-235
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• 2010

최근 국방 소프트웨어 분야에서는 모델링 및 시뮬레이션 기술이 각광받으면서 무기체계 개발을 위한 시뮬레이션 소프트웨어 개발 사업을 늘리고 있다. 특히, 시뮬레이션 요소의 재사용성 및 신뢰성 확보를 위한 개발 프레임워크 제공이 핵심기술로 떠오르면서, 시뮬레이션을 위한 공통 서비스를 제공하는 M&S 프레임워크가 개발되었다. 하지만 고객의 요구사항이 프레임워크의 기능 변경을 요구하는 경우 프레임워크가 적용된 모든 시뮬레이션 요소의 수정이 불가피하며, 추가 구성요소의 상호작용을 위한 인터페이스 재설계가 요구된다. 본 논문은 이러한 문제점을 해결하기 위해서 프레임워크의 요소를 DLL로 구현하여 기능 구성을 용이하게 하고, 구성요소 간 상호작용을 위해 데이터 기반 Publish-Subscribe 방식을 사용함으로써 프레임워크와 독립적으로 인터페이스를 설계할 수 있도록 한다. 특히, 프레임워크와 DLL 간 교환 메시지 객체에 대한 구조 설계를 제시한다.

• Smart Structures and Systems
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• v.32 no.2
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• pp.111-121
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• 2023

This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.741-754
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• 2022

Openstack is widely used as a representative open-source infrastructure of the service (IaaS) platform. The Openstack Identity Service is a centralized approach component based on the token including the Memcached for cache, which is the in-memory key-value store. Token validation requests are concentrated on the centralized server as the number of differently encrypted tokens increases. This paper proposes the practical Byzantine fault tolerance (PBFT) blockchain-based Openstack Identity Service, which can improve the performance efficiency and reduce security vulnerabilities through a PBFT blockchain framework-based decentralized approach. The experiment conducted by using the Apache JMeter demonstrated that latency was improved by more than 33.99% and 72.57% in the PBFT blockchain-based Openstack Identity Service, compared to the Openstack Identity Service, for 500 and 1,000 differently encrypted tokens, respectively.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.22-32
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• 2008

To overcome the weakness of the image retrieval system using the existing Ontology and the distributed image based on the database having a simple structure, HERMES was suggested to ensure the self-control of various image suppliers and support the image retrieval based on semantic, the mentioned framework could not solve the problems which are not considered the deterioration in the capacity and scalability when many users connect to broker server simultaneously. In this paper the tables are written which in the case numerous users connect at the same time to the supply analogous level of services without the deterioration in the capacity installs Broker servers and then measures the performance time of each inner Broker Component through Monitoring System and saved and decides the ranking in saved data. As many Query performances are dispersed into several Servers User inputted from the users Interface with reference to Broker Ranking Table, Load Balancing system improving reliability in capacity is proposed. Through the experiment, the scheduling technique has proved that this schedule is faster than existing techniques.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.96-106
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• 2002

this paper, we propose a flexible, reusable, and extensible HL7 encoding and decoding framework using a Message Object Model (MOM) and Message Definition Repository (MDR). The MOM provides an abstract HL7 message form represented by a group of objects and their relationships. It reflects logical relationships among the standard HL7 message elements such as segments, fields, and components, while enforcing the key structural constraints imposed by the standard. Since the MOM completely eliminates the dependency of the HL7 encoder and decoder on platform-specific data formats, it makes it possible to build the encoder and decoder as reusable standalone software components, enabling the interconnection of arbitrary heterogeneous hospital information systems(HISs) with little effort. Moreover, the MDR, an external database of key definitions for HL7 messages, helps make the encoder and decoder as resilient as possible to future modifications of the standard HL7 message formats. It is also used by the encoder and decoder to perform a well formedness check for their respective inputs (i. e., HL7 message objects expressed in the MOM and encoded HL7 message strings). Although we implemented a prototype version of the encoder and decoder using JAVA, they can be easily packaged and delivered as standalone components using the standard component frameworks like ActiveX, JAVABEAN, or CORBA component.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.421-435
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• 2022

As natural disasters have been increasing due to climate change, sustainable solutions are in need to alleviate the degree of drought hazard, assess and project the drought influence based on future climate change scenarios. In assessing drought risk, socio-economic factors of the region must be considered along with meteorological factors. This study categorized drought hazard, exposure, and vulnerability as three major components of drought risk according to the Intergovernmental panel on Climate Change (IPCC) risk assessment framework, and selected indices for each component to quantify the drought risk in South Korea according to the mid-size basins. Combinations of climate scenarios (Representative Concentration Pathway; RCP 2.6 and RCP 8.5) and socio-economic scenarios (Shared Socio-economic Pathways; SSP 1, SSP2 and SSP3) for the near future (2030-2050) ant the far future (2080-2099) were utilized in drought risk analysis, and results were compared with the historical data (1986-2005). In general, the drought risks for all scenarios shows large increases as time proceeds to the far furture. In addition, we analyzed the rank of drought hazard, exposure, vulnerability for drought risk, and each of their contribution. The results showed that the drought hazard is the most contributing component to the increase of drought risk in future and each basin shows varying contributing components. Finally, we suggested countermeasures for each basin according to future climate change scenarios, and thus this study provides made the basis for establishing drought management measures.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.163-174
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• 2022

Despite the great interest in the metaverse from academia and industry, research so far has been focused on a specific area, and the background of the study is in the recognition that research is necessary from the perspective of the entire metaverse ecosystem. The purpose of this study was to derive the metaverse research framework and each component to study from the perspective of the metaverse ecosystem, and to study the development stage of the metaverse ecosystem. From an academic point of view, the ecosystem components were derived through the Metaverse IDC-Platform, a framework for applying Michael Porter's diamond model to the metaverse. From a practical point of view, the four components of the metaverse ecosystem interact with each other in terms of metaverse application and development. As the basis of this study, it can be used strategically because it is possible to identify areas for reinforcement in academia and industry and provide basic data for insight by closely examining the strengths and weaknesses of each component. The contribution of research is that it has created a foundation for research that has been limited to specific areas from an ecosystem perspective, unlike before.