• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.510-519
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• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• 한국지리정보학회지
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• 제26권3호
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• pp.37-51
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• 2023

도시현상의 해석을 위해 공간정보는 필수적이다. 위치정보가 부족한 도시정보를 공간정보로 변환하기 위한 공간정보화 방법론이 꾸준히 개발되어왔다. 정형화된 주소정보나 지명 등을 이용한 Geocoding이나 이미 위치정보가 있는 공간정보와의 공간결합, 참조데이터를 활용한 수작업 형태 등이 대표적이다. 그러나 아직도 행정기관에서 작성되는 수많은 문서정보들은 비정형화된 문서형태로 인해 공간정보화의 수요가 있음에도 그동안 깊이 있게 다루어지지 못하였다. 본 연구는 자연어 처리 모델인 BERT를 활용하여 도시계획과 관련된 공개문서의 공간정보화를 진행한다. 주소가 포함된 문장 요소를 문서로부터 추출하고, 이를 정형화된 데이터로 변환하는 과정을 중점적으로 다룬다. 18년 동안의 도시계획 고시공고문을 학습 데이터로 사용하여 BERT 모델을 학습시켰으며, 모델의 하이퍼파라미터를 직접 조정하여 성능을 향상시켰다. 모델 학습 후의 테스트 결과, 도시계획시설의 유형을 분류하는 모델은 96.6%, 주소 인식 모델은 98.5%, 주소 정제 모델은 93.1%의 정확도를 보였다. 결과 데이터를 GIS 상에 맵핑하였을 때, 특정 지점의 도시계획시설에 관한 변경 이력을 효과적으로 표출할 수 있었다. 본 연구로 도시계획 문서의 공간적 맥락에 대한 깊은 이해를 제공하며, 이를 통해 이해관계자들이 더욱 효과적인 의사결정을 할 수 있게 지원하기를 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권10호
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• pp.2750-2763
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• 2012

The Domain Name System (DNS) is a critical Internet infrastructure that provides name to address mapping services. In the past decade, Denial-of-Service (DoS) attacks have targeted the DNS infrastructure and threaten to disrupt this critical service. While the flooding DoS attacks may be alleviated by the DNS caching mechanism, we show in this paper that flooding DoS attacks utilizing name error queries is capable of bypassing the cache of resolvers and thereby impose overwhelming flooding attacks on the name servers. We analyze the impacts of such DoS attacks on both name servers and resolvers, which are further illustrated by May 19 China's DNS Collapse. We also propose the detection and defense approaches for protecting DNS servers from such DoS attacks. In the proposal, the victim zones and attacking clients are detected through monitoring the number of corresponding responses maintained in the negative cache. And the attacking queries can be mitigated by the resolvers with a sample proportion adaptive to the percent of queries for the existent domain names. We assess risks of the DoS attacks by experimental results. Measurements on the request rate of DNS name server show that this kind of attacks poses a substantial threat to the current DNS service.

• 한국컴퓨터정보학회논문지
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• 제22권2호
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• pp.1-7
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• 2017

Recently, NAND flash memory, which is used as a storage medium, is replacing HDD (Hard Disk Drive) at a high speed due to various advantages such as fast access speed, low power, and easy portability. In order to apply NAND flash memory to a computer system, a Flash Translation Layer (FTL) is indispensably required. FTL provides a number of features such as address mapping, garbage collection, wear leveling, and hot data identification. In particular, hot data identification is an algorithm that identifies specific pages where data updates frequently occur. Hot data identification helps to improve overall performance by identifying and managing hot data separately. MHF (Multi hash framework) technique, known as hot data identification technique, records the number of write operations in memory. The recorded value is evaluated and judged as hot data. However, the method of counting the number of times in a write request is not enough to judge a page as a hot data page. In this paper, we propose hot data identification which considers not only the number of write requests but also the persistence of write requests.

• 대한전자공학회논문지TC
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• 제49권1호
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• pp.59-67
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• 2012

본 논문은 10G G-PON(Gigabit passive optical network) 네트워크와 NGN(Next generation network) 연동 구조를 설계하고, 10G G-PON 연동 시 문제점 해결을 위한 동적 GEM(G-PON encapsulation mode) Port-ID 할당 방법을 제안했다. 동적 GEM Port-ID 할당 방법은 OMCI(ONT management control and interface) 구조 설계를 통해 IP 주소와 포트 번호를 혼합한 형태의 맵핑 구조를 갖는다. 이는 NGN이 요구하는 클래스 기반 QoS(Quality of service) 뿐만 아니라 트래픽 단위의 QoS 지원까지 가능한 구조로서, 기존 G-PON 네트워크를 NGN에 그대로 적용시킨 구조 보다 QoS 지원에 있어 30% 이상 성능을 향상 시킨다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권6호
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• pp.808-816
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• 2016

As the number of CPU/GPU cores and IPs in SOC increases and applications require explosive memory bandwidth, simultaneously achieving good throughput and fairness in the memory system among interfering applications is very challenging. Recent works proposed priority-based thread scheduling and channel partitioning to improve throughput and fairness. However, combining these different approaches leads to performance and fairness degradation. In this paper, we analyze the problems incurred when combining priority-based scheduling and channel partitioning and propose dynamic priority thread scheduling and adaptive channel partitioning method. In addition, we propose dynamic address mapping to further optimize the proposed scheme. Combining proposed methods could enhance weighted speedup and fairness for memory intensive applications by 4.2% and 10.2% over TCM or by 19.7% and 19.9% over FR-FCFS on average whereas the proposed scheme requires space less than TCM by 8%.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2000년도 춘계 학술대회
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• pp.322-328
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• 2000

This study discusses cognition as a computable mapping in cognitive system and relates G del's Incompleteness Theorems to the computability of cognition from a metamathematical perspective. Understanding cognition as a from of computation requires not only Turing machine models but also neural network models. In previous studies of computation by cognitive systems, it is remarkable to note how little serious attention has been given to the issue of computation by neural networks with respect to G del's Incompleteness Theorems. To address this problem, first, we introduce a definition of cognition and cognitive science. Second, we deal with G del's view of computability, incompleteness and speed-up theorems, and then we interpret G del's disjunction on the mind and the machine. Third, we discuss cognition as a Turing computable function and its relation to G del's incompleteness. Finally, we investigate cognition as a neural computable function and its relation to G del's incompleteness. The results show that a second-order representing system can be implemented by a finite recurrent neural network. Hence one cannot prove the consistency of such neural networks in terms of first-order theories. Neural computability, theoretically, is beyond the computational incompleteness of Turing machines. If cognition is a neural computable function, then G del's incompleteness result does not limit the compytational capability of cognition in humans or in artifacts.

• 한국지능시스템학회논문지
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• 제24권2호
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• pp.209-217
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• 2014

Of several approaches for future Internet, separating two properties of IP address into locator and identifier, is being considered as a highly likely solution. IETF's LISP (Locator ID Separation Protocol) is proposed for this architecture. In particular, the LISP model easily allows for device mobility through simple update of information at MS (Mapping Server) without a separate protocol. In recent years, some of the models supporting device mobility using such LISP attributes have emerged; however, most of them have the limitation for seamless mobility support due to the frequent MS information updates and the time required for the updates. In this paper, PMIPv6 (Proxy Mobile IPv6) model is applied for mobility support in LISP model. PMIPv6 is a method that can support mobility based on network without the help of device; thus, this we define anew the behavior of functional modules (LMA, MAG and MS) to fit this model to the LISP environment and present specifically procedures of device registration, data transfer, route optimization and handover. In addition, our approach improves the communication performance using three tunnels identified with locators between mobile node and corresponding node and using a route optimized tunnel between MN's MAG and CN's MAG. Finally, it allows for seamless mobility by designing a sophisticated handover procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4412-4428
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• 2018

The purpose of this work is to solve the problem of representing an entire video using Convolutional Neural Network (CNN) features for human action recognition. Recently, due to insufficient GPU memory, it has been difficult to take the whole video as the input of the CNN for end-to-end learning. A typical method is to use sampled video frames as inputs and corresponding labels as supervision. One major issue of this popular approach is that the local samples may not contain the information indicated by the global labels and sufficient motion information. To address this issue, we propose a binary hashing method to enhance the local feature extractors. First, we extract the local features and aggregate them into global features using maximum/minimum pooling. Second, we use the binary hashing method to capture the motion features. Finally, we concatenate the hashing features with global features using different normalization methods to train the classifier. Experimental results on the JHMDB and MPII-Cooking datasets show that, for these new local features, binary hashing mapping on the sparsely sampled features led to significant performance improvements.

• 정보관리학회지
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• 제27권2호
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• pp.75-94
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• 2010

현재의 정보환경에서 메타데이터 상호운용은 정보를 관리하고 조직하는 데 있어 유용한 도구가 되고 있다. 하지만 상호운용성 확보를 위한 현재의 방법들은 메타데이터 요소의 의미를 고려하지 않은 피상적인 매핑에만 중점을 두고 있다. 본 연구에서는 이러한 문제들을 해결하기 위해 패싯분석을 적용하여 메타데이터 상호운용성을 확보하고자 한다. 메타데이터 요소들의 의미와 기능적인 유사성에 따라 요소들을 분석하여, 기본패싯, 개념패싯, 관계패싯의 세 가지 유형의 패싯을 확인하고, 이를 통해 의미적, 구문적, 구조적 상호운용성을 제공할 수 있는 패싯 프레임워크를 구축하였다.