• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2251-2259
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• 2009

We propose an algorithm and system which generates 3D stereo image by composition of 2D image from 4 multiple clusters which 1 cluster was composed of 4 multiple cameras based on network. Proposed Schemes have a network-based client-server architecture for load balancing of system caused to process a large amounts of data with real-time as well as multiple cluster environments. In addition, we make use of JPEG compression and RAM disk method for better performance. Our scheme first converts input images from 4 channel, 16 cameras to binary image. And then we generate 3D stereo images after applying edge detection algorithm such as Sobel algorithm and Prewiit algorithm used to get disparities from images of 16 multiple cameras. With respect of performance results, the proposed scheme takes about 0.05 sec. to transfer image from client to server as well as 0.84 to generate 3D stereo images after composing 2D images from 16 multiple cameras. We finally confirm that our scheme is efficient to generate 3D stereo images in multiple view and multiple clusters environments with real-time.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.795-799
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• 2007

최근 정보통신산업이 급속도로 발전함에 따라 Internet을 이용하여 각종 정보를 쉽게 얻을 수 있게 되어 수자원 분야에도 정보의 공유와 자료의 통합이 활발히 이루어지고 있다. 또한 재해정보를 효율적이고 직관적으로 표현하기 위해서 방재업무에 GIS를 도입하고 있다. Internet을 통한 손쉬운 정보의 취득은 효율적인 재해관리에 매우 중요하며, 자료의 효율적인 관리와 표현을 위한 GIS기법 또한 필수적이다. 따라서 본 연구에서는 Internet과 GIS를 결합하여 보다 많은 사람들이 신속하고 정확한 재해정보를 얻을 수 있게 하며, 방재 업무에 활용토록 하여 재해에 의한 주민들의 생명의 위협과 재산피해를 줄이도록 하는데 그 목적이 있다. 효과적인 재해관리를 위해서는 계획과 관련된 정보, 재해발생 이력, 대상지역의 각종 정보가 통합되어 실시간으로 전달될 필요가 있다. 또한 이러한 정보들을 빠른시간내에 이해하고 분석하여 대책을 수립하고 시행해야한다. Internet GIS는 비상상황에 대비한 계획과 대처 그리고 복구사업에 있어 가장 정확하고 신속한 정보를 제공해 줄 수 있다. 즉, Internet GIS는 방대한 양의 정보를 효과적이고 지능적으로 분석이 가능하며, 이해하기 쉬운 그래픽 기반의 자료를 보여주므로 방재업무에 큰 도움을 줄 수 있다. Internet GIS를 방재정보 시스템에 이용할 경우 에 있어 장점, 약점, 기회, 위기에 대한 SWOT분석을 실시하였다. Internet이라는 환경의 장점을 그대로 이어받아 비용면에서 효과적이며 적용범위와 사용자층도 넓어지게 된다. 또한 누구나 간편하게 이용할 수 있어 협력체계 또한 쉽게 구축할 수 있으며 빠른 정보의 교환이 가능하다. 하지만, 인터넷에 의존한다는 점에 있어 서버가 자연재해에 노출될 경우 시스템 자체가 제 기능을 할 수 없으며 여러명이 동시에 서버에 접속을 하기 때문에 컴퓨터에 부하가 많이 걸리는 모델링이나 복잡한 분석은 실시하기 어려우며, 대용량 데이터를 전송할 수 있는 대역폭이 확보 되어야 한다. 또한, Internet 환경으로 개발을 해야되기 때문에 데스크탑용 GIS에 비해 개발속도가 느리며 개발 초기비용이 많이 들게 된다. 하지만, 네트워크 기술의 발달과 모바일과의 연계 등으로 이러한 약점을 극복할 수 있을 것으로 판단된다. 따라서 본 논문에서는 인터넷 GIS를 이용하여 홍수재해 정보를 검색, 처리, 분석, 예경보할 수 있는 홍수방재정보 시스템을 구축토록 하였다.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.59-70
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• 2009

By the time the interest on the u-City is continuously growing as a test bed for verifying the potentials of ubiquitous convergence industries, research on the u-Safety gradually increases as well, as a typical service and application area of u-City. Like the other service areas of u-City, in order to provide u-Safety services smoothly, it is crucial to integrally connect u-City services and infrastructures that have operated under distributed environment. In this study, we suggest an approach for design of u-Safety service and monitoring architecture by combing CIM/WBEM standard with GMA. CIM/WBEM and GMA are broadly applied in the distributed resource monitoring environment and are widely recognized as data acquisition architecture under massive monitoring data volumes respectively. Considering the growing research needs for standardization and extension of u-City service infrastructure, it is expected that our integrated infrastructure model will be used as a reference model for effective integration of distributed resources with newly added services.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.37-44
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• 2021

Quality estimation (QE) can evaluate the quality of machine translation output even for those who do not know the target language, and its high utilization highlights the need for QE. QE shared task is held every year at Conference on Machine Translation (WMT), and recently, researches applying Pretrained Language Model (PLM) are mainly being conducted. In this paper, we conduct a survey on the QE task and research trends, and we summarize the features of PLM. In addition, we used a multilingual BART model that has not yet been utilized and performed comparative analysis with the existing studies such as XLM, multilingual BERT, and XLM-RoBERTa. As a result of the experiment, we confirmed which PLM was most effective when applied to QE, and saw the possibility of applying the multilingual BART model to the QE task.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1279-1289
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• 2020

To securely protect users' sensitive information and national secrets, the importance of cryptographic modules has been emphasized. Currently, many companies and national organizations are actively using cryptographic modules. In Korea, To ensure the security of these cryptographic modules, the cryptographic module has been verified through the Korea Certificate Module Validation Program(KCMVP). Most of the domestic cryptographic modules are CPU-based software (S/W). However, CPU-based cryptographic modules are difficult to use in servers that need to process large amounts of data. In this paper, we propose an S/W cryptographic module that provides a high-speed operation using GPU. We describe the configuration and operation of the S/W cryptographic module using GPU and present the changes in the cryptographic module security requirements by using GPU. In addition, we present the performance improvement compared to the existing CPU S/W cryptographic module. The results of this paper can be used for cryptographic modules that provide cryptography in servers that manage IoT (Internet of Things) or provide cloud computing.

• Design Convergence Study
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• v.16 no.5
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• pp.125-140
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• 2017

This study explores potential capability of a machine learning approach for monitoring urban morphology based on an evident case study. The case study conveys year 2006 investigations on interpreting urban morphology of Boston Main Streets by applying a machine learning approach. From the lesson of the precedent study, in 2016, another field research and interview was conducted to compare changes in urban situation, data commons culture, and technology innovation during the decade. This paper describes open possibilities to advance urban monitoring for morphological changes. Most of all, a multi-participatory data platform enables managing urban data system in real time. Second, collaboration with machines with artificial intelligence can intervene the framework of the urban management system as well as transform it through new demands of innovative industries. Recently, urban regeneration became a dominant urban planning strategy in Korean, therefore, urban monitoring is on demand. It is timely important to correspond to in-situ problems based on empirical research.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.671-678
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• 2005

In this paper, we suggest an algorithm that allows us to extract the important obbject from motion pictures and then replace the background with arbitrary images. The suggested technique can be used not only for protecting privacy and reducing the size of data to be transferred by removing the background of each frame, but also for replacing the background with user-selected image in video communication systems including mobile phones. Because of the relatively large size of image data, digital image processing usually takes much of the resources like memory and CPU. This can cause trouble especially for mobile video phones which typically have restricted resources. In our experiments, we could reduce the requirements of time and memory for processing the images by restricting the search area to the vicinity of major object's contour found in the previous frame based on the fact that the movement of major object is not wide or rapid in general. Specifically, we detected edges and used the edge image of the initial frame to locate candidate-object areas. Then, on the located areas, we computed the difference image between adjacent frames and used it to determine and trace the major object that might be moving. And then we computed the contour of the major object and used it to separate major object from the background. We could successfully separate major object from the background and replate the background with arbitrary images.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.63-72
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• 2020

This study aims to develop a video-based smart utterance deep analyser (SUDA) application that analyzes semiautomatically the utterances that child and mother produce during interactions over time. SUDA runs on the platform of Android, iPhones, and tablet PCs, and allows video recording and uploading to server. In this device, user modes are divided into three modes: expert mode, general mode and manager mode. In the expert mode which is useful for speech and language evaluation, the subject's utterances are analyzed semi-automatically by measuring speech and language factors such as disfluency, morpheme, syllable, word, articulation rate and response time, etc. In the general mode, the outcome of utterance analysis is provided in a graph form, and the manger mode is accessed only to the administrator controlling the entire system, such as utterance analysis and video deletion. SUDA helps to reduce clinicians' and researchers' work burden by saving time for utterance analysis. It also helps parents to receive detailed information about speech and language development of their child easily. Further, this device will contribute to building a big longitudinal data enough to explore predictors of stuttering recovery and persistence.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.