• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.93-101
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• 2020

Regional CRL(certificate revocation list) in vehicular communications is to partition Full CRL into several small CRLs according to geographic location to keep the size of individual CRLs with smaller. However, since a Regional CRL includes vehicle's revoked certificates within its administrative region, it has to know vehicle' location. For this, how to know vehicle' location effectively corresponding to every region represents a major challenge. This paper proposes a Regional CRL scheme which is envisioned to achieve vehicle's location and to make regional CRLs according to vehicles current location efficiently. The scheme is based on the short-lived pseudonyms defined by WAVE standard. It also acquires issued pseudonyms, vehicle's id and region information whenever a vehicle initiates pseudonyms refill after that, utilizes them to create and distribute the Regional CRL. To keep location privacy-preserving for vehicles, the scheme uses the blockchain technology in the network. The analysis results show that it reduces CRL size and database query time for finding revoked certificates sharply in the vehicle's on-board unit.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.527-531
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• 2003

Previous researches on scalability problem of distributed virtual environment (DVE) have been mainly focused on spatial partitioning of area of interest (AOI). Congestion phenomena by avatar groups in AOI have been neglected relatively. However, AOI congestion is highly related to scalability of DVT because it exhausts system resources such as network bandwidth and rendering time, and could be a bar to perform collaboration among participants. In this paper, this will be defined as the problem that must be solved for the realization of the scalable DVE, and a model will be proposed to measure and control congestion situation in AOI. The purposes of the proposed model are to prevent high density of participants in AOI, and to protect stable collaboration in DVE. For evaluation of the performance it is compared with a previous method by defining the resource cost model which is dynamically activated to AOI congestion.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.249-261
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• 2014

Ever increasing "Big data" can only be effectively processed by parallel computing. Parallel computing refers to a high performance computational method that achieves effectiveness by dividing a big query into smaller subtasks and aggregating results from subtasks to provide an output. However, it is well-known that parallel computing does not achieve scalability which means that performance is improved linearly by adding more computers because it requires a very careful assignment of tasks to each node and collecting results in a timely manner. Hadoop is one of the most successful platforms to attain scalability. In this paper, we propose a measurement for Hadoop optimization by utilizing a Lorenz curve which is a proxy for the inequality of hardware resources. Our proposed index takes into account the intrinsic overhead of Hadoop systems such as CPU, disk I/O and network. Therefore, it also indicates that a given Hadoop can be improved explicitly and in what capacity. Our proposed method is illustrated with experimental data and substantiated by Monte Carlo simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2839-2846
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• 2014

New VHF band for use in VDE is determined by WRC-12 due to the overload of AIS VDL, and the system characteristics of the VDE is recommended as ITU-R M. 1842-1. CS-TDMA, a multiple access method of AIS class B, is recommended as that of the VDE. It is inefficient for CS-TDMA just applying the report interval used in AIS class B to transmit high speed data with higher payload in the aspect of efficiency. In this paper, a simulation is executed in order to determine adequate report interval according to the number of active ships that affects directly network traffic. To this end, the performance of CS-TDMA, which includes the number of received packets, reception success rate, channel utilization, and collision rate, is investigated via a simulation.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.549-560
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• 2001

Cluster system provides attractive scalability in terms of compution power and memory size. With the advances in high speed computer network technology, cluster systems are becoming increasingly competitive compared to expensive MPPs (massively parallel processors). Load balancing is very important issue since an inappropriate scheduling of tasks cannot exploit the true potential of the system and can offset the gain from parallelization. In parallel processing program, it is difficult to predict the load of each task before running the program. Furthermore, tasks are interdependent each other in many ways. The dynamic load balancing algorithm, which evaluates each processor's load in runtime, partitions each task into the appropriate granularity and assigns them to processors in proportion to their performance in cluster systems. However, if the communication cost between processing nodes is expensive, it is not efficient for all nodes to attend load balancing process. In this paper, we restrict a processor that attend load balancing by the communication cost and the deviation of its load from the average. We simulate various models of the cluster system with parameters such as communication cost, node number, and range of workload value to compare existing load balancing methods with the proposed dynamic algorithms.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.49-63
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• 2019

Crowdedness management at subway platforms is essential to improve services, including the prevention of train delays and ensuring passenger safety. Establishing effective crowdedness mitigation measures for platforms requires accurate estimation of the congestion level. There are temporal and spatial constraints since crowdedness on subway platforms is assessed at certain locations every 1-2 years by hand counting. However, smart cards generate real-time big data 24 hours a day and could be used in estimating congestion. This study proposes a model based on data from transit cards to estimate crowdedness dynamically. Crowdedness was defined as demand, which can be translated into passengers dynamically moving along a subway network. The trajectory of an individual passenger can be identified through this model. Passenger flow that concentrates or disperses at a platform is also calculated every minute. Lastly, the platform congestion level is estimated based on effective waiting areas for each platform structure.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.89-107
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• 2023

Public libraries should plan and provide services that satisfy various needs of the local community users. In order to understand library users, it is essential first to grasp the service areas of libraries. The current service areas of public libraries are primarily set based on administrative boundaries of the areas where the libraries are located, which limits the consideration of actual user access patterns to the libraries. In this study, we aim to create service areas that incorporate the transportation and geographical characteristics of the library's surroundings and reflect the access patterns of library users. Specifically, we utilized street network data from 502 libraries in 7 metropolitan cities to determine the travel distance and time from user locations, considering gradients, to the libraries. Subsequently, we applied the shortest path algorithm to generate service areas within a 30-minute walking or driving range. As a result, we confirmed that there are differences in the service area patterns of libraries depending on topographical factors, and this better reflects the realistic conditions of library access compared to service areas based on straight-line distances. This method of generating service areas contributes to a more accurate understanding of library users' numbers, characteristics, and needs.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.357-360
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• 2011

최근 인터넷과 통신기술, 특히 모바일과 관련된 기술의 급속한 발전으로 소셜 커뮤니케이션 수단으로 대표되는 SNS(Social Networking Service)가 중요한 이슈로 부각되어지고 있다. SNS 서비스 제공시 중요하게 고려되어져야 할 사항은 정확하고 의미 있는 데이터를 통해서 사용자가 원하고 관심 있는 분야의 정보를 어떻게 제공할 것인가에 초점이 맞춰져 있어야 한다. 그러나 최근 폭발적으로 증가되어지고 있는 소셜 데이터 때문에 사용자는 의미 분석이 정확하게 이루어지지 않은 신뢰성이 결여된 소셜 커뮤니케이션 서비스를 제공받고 있다. 이러한 소셜데이터 분석의 문제점을 해결하기 위해서 본 논문에서는 소셜 네트워크 서비스에 필요한 데이터를 수집하고, 클라우드 컴퓨팅 환경에서 수집된 대용량 SNS 데이터의 의미를 분석 할 수 있는 MapReduce 기반의 분석 모듈의 구조를 제안하였다. 제안한 모듈은 의미 분석에 필요한 소셜 데이터를 수집하는 수집 기능과 수집된 소셜데이터의 의미 분석을 수행하는 분석 기능을 포함하고 있다. 수집 기능은 SNS에서 생성되는 텍스트 형태의 데이터를 수집하고 MapReduce를 통해서 데이터를 분석하기 쉽게 적절한 크기로 생성된 파일을 분할한다. 수집된 소셜 데이터의 의미 분석은 기존 TF-IDF 방식에 개선된 Weighted-MINMAX 적용한 알고리즘을 통해서 구현하였다. 개선된 알고리즘은 단어의 중요도를 평가하고, 중요도가 높은 단어로 구성된 의미정보 제공 서비스를 지원한다. 시스템의 성능 평가를 위해서 노드별 데이터 처리시간과 추출 키워드의 정확도를 측정하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6A
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• pp.893-899
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• 2000

WDM technology has been an emerging issue for the efficient use of optical links. WDM uses a number of different wavelengths that are assigned to each channel. The minimal number of optical transcievers and receivers should be used in a node to build an economic WDM transmission system without degrading system performance. Hence, the analysis of performance parameters such as throughput and delay is important to guarantee the WDM system performance. In this paper, the performance of a MAC protocol on a slotted WDM system that has a tunable transmitter(TTX), a tunable receiver(TRX), and a fixed receiver(FRX), respectively, on each node, was statistically analyzed. The computer simulation validates the performance analysis.