• Annual Conference of KIPS
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• 2008.11a
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• pp.1383-1386
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• 2008

분산된 자원의 실시간 정보교환과 그리드를 통한 효율적인 자원 재구성을 위해서는 기존의 단일 도메인에서 구성되는 네트워크와는 다르게 대규모 가상도메인(Large-Scale Multi-domain)을 위한 네트워크의 성능과 기능 향상이 필요하다. 그리드네트워크를 기반으로 활발히 진행 중인 글로벌한 연구자원을 대상으로 공유된 자원의 성능 개선과 자원 간 데이터전달의 효율 개선을 위해 TDM(Time Division Multiplexing)기반의 Multi-Point Lambda-Path Ring 구현 기술을 제안하고 이를 Multi-Domain 간 Control Plane하에서 최적의 가상도메인으로 구성 할 수 있는 기법을 제안한다.

• Annual Conference of KIPS
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• 2019.10a
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• pp.1004-1006
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• 2019

Different scale of information is an important component in computer vision systems. Recently, there are considerable researches on utilizing multi-scale information to solve the scale-invariant problems, such as GoogLeNet and FPN. In this paper, we introduce the notion of different scale information fusion (DSIF) and show that it has a significant effect on the performance of object recognition systems. We analyze the DSIF in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to clear suggestions for ways of the DSIF to choose.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1351-1367
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• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.239-248
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• 2005

The reliability and efficiency of network must be considered in the large scale wireless sensor networks. Broadcast method must be used rather than unicast method to enhance the reliability of networks. In recently proposed GRAB (GRAdient Broadcast) can certainly enhance reliability of networks fy using broadcast but its efficiency regarding using energy of network is low due to using only one sink. Hence, the lifetime of networks is reduced. In the paper we propose the scheme of SMSGB (Selective Multi Sink Gradient Broadcast) which uses single sink of multi-sink networks. The broadcast based SMSGB can secure reliability of large scale wireless sensor networks. The SMSGB can also use the network's energy evenly via multi sink distribution. Our experiments show that using SMSGB was reliable as GRAB and it increased the network's lifetime by 18% than using GRAB.

• Annual Conference of KIPS
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• 2023.11a
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• pp.648-651
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• 2023

Video anomaly detection aims to detect abnormal events. Motivated by the power of transformers recently shown in vision tasks, we propose a novel transformer-based network for video anomaly detection. To capture long-range information in video, we employ a multi-scale transformer as an encoder. A convolutional decoder is utilized to predict the future frame from the extracted multi-scale feature maps. The proposed method is evaluated on three benchmark datasets: USCD Ped2, CUHK Avenue, and ShanghaiTech. The results show that the proposed method achieves better performance compared to recent methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.229-236
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• 2005

This paper is contribute to Inverse continuous wavelets transform(ICWT) which permits to determine real 'time-scale' plan. The application of ICWT is not yet represented because of the numerical difficulty. If the signal can be reconstructed stably by ICWT, the multi scale filter bank system which composed by analysis and synthesis process can be designed. In this work, we represent the ICWT which leads to nearly perfect reconstruction of signal and the multi-scale filter bank system.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.149-158
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• 2019

Due to the rapid development of IT (information technology) and internet, products become smart and able to collect, process and produce information and can think of themselves to provide better service to consumers. However, research on the characteristics of smart product is still sparse. In this paper, we report the systemic development of a scale to measure the perceived product smartness associated with smart product. To develop product smartness scale, this study follows systemic scale development processes of item generation, item reduction, scale validation, reliability and validity test consequently. And, after acquiring a large amount of qualitative interview data asking the definition of smart product, we add a unique process to reduce the initial items using both a text mining method using 'r' s/w and traditional reliability and validity tests including factor analysis. Based on an initial qualitative inquiry and subsequent quantitative survey, an eight-factor scale of product smartness is developed. The eight factors are multi-functionality, human-like touch, ability to cooperate, autonomy, situatedness, network connectivity, integrity, and learning capability consequently. Results from Korean samples support the proposed measures of product smartness in terms of reliability, validity, and dimensionality. Implications and directions for further study are discussed. The developed scale offers important theoretical and pragmatic implications for researchers and practitioners.