• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.113-118
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• 2015

Mobile wireless networks have received a lot of attention as a future wireless network due to its rapid deployment without communication infrastructure. In these networks communication path between two arbitrary nodes break down because some links in the path are beyond transmission range($r_0$) due to the mobility of the nodes. The set of total path break down time(${\bigcup}T_i$), which is the union of path break down time of every node pair, can be a good measure of the connectivity of the dynamic mobile wireless network. In this paper we show that the distribution of the total path break down time can be approximated as a exponential probability density function and confirms it through experimental data. Statistical knowledge of break down time enables quantitative prediction of delay, packet loss between two nodes, thus provides confidence in the simulation results of mobile wireless networks.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.166-166
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• 2017

모돈의 건강 상태를 정량 지수화 하기 위한 연구를 수행 중이다. 지제이상, 섭식 불량, 수면 패턴 등의 운동 특성 분석을 위하여 복수의 초음파 센서를 이용하였다. 시계열 계측 신호를 분석하여 정량 지수화를 수행하는 과정에서 주파수 도메인 분석을 시도하였다. 이 과정에서 주파수 도메인의 분해능에 따른 편차 극복을 위한 비선형 모델링을 수행하였다. 또한 인접한 시계열 데이터 구간 간의 상관성 분석이 가능하면 대용량 데이터의 실시간 처리로 인한 지연 시간 극복 및 기대되는 예후에 대한 조기 진단이 가능할 것이다. 본 연구에서는 구글에서 제공하는 Tensorflow와 NVIDIA에서 제공하는 CUDA 엔진을 동시 적용한 심층 학습 시스템을 이용하였다. 전 처리를 위하여 주파수 분해능 (2분, 3분, 5분, 7분, 11분, 13분, 17분, 19분)에 따른 데이터 집합을 1단계로 두고, 상위 10 순위 안에 드는 파워 스펙트럼 밀도의 크기를 2단계로 하여, 총 2~10개의 입력 노드를 순차적으로 선정하였고, 동일한 방식으로 인접한 시계열의 파워 스펙터럼 밀도를 순위를 변화시켜 지정하였다. 대표적인 심층학습 모델인 Softmax regression with a multilayer convolutional network를 이용하여 Recursive feature selection 경우의 수를 $8{\times}9{\times}9$로 총 648 가지 선정하고, Epoch는 10,000회로 지정하였다. Calibration 모델링의 경우 Cost function이 10% 이하인 경우 해당 경우의 학습을 중단하였으며, 모델 간 상호 교차 검증을 수행하기 위하여 $_8C_2{\times}_8C_2{\times}_8C_2$ 경우의 수에 대한 Verification test를 수행하였다. Calibration 과정 상 모든 경우에 대하여 10% 이하의 Cost function 값을 보였으나, 검증 테스트 과정에서 모든 경우에 대하여 $r^2$ < 0.5 인 결정 계수 값이 나타났다. 단적으로 심층학습 모델의 과도한 적합(Over fitting) 방식의 한계를 보인 것이라 판단할 수 있다. 적합한 Feature selection 및 심층 학습 모델에 대한 지속적이고 추가적인 고려를 통해 과도적합을 해소함과 동시에 실효적이고 활용 가능한 Classification을 위한 입, 출력 노드 단의 전후 Indexing, Quantization에 대한 고려가 필요할 것이다. 이를 통해 모돈 생체 정보 정량화를 위한 지능형 현장 진단 기술 연구를 지속할 것이다.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.167-177
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• 2010

This study proposes a methodology for assessing the improvement of network connectivity of transport hubs. Extending a previously developed model that measures the connectivity of a node in transportation networks, we define two quantities called the supplied connectivity and the experienced connectivity. Using these quantities, we provide a systematic procedure for analyzing the network connectivity of a transport hub and also suggest criteria for determining whether a given project is effective in improving the network connectivity of the transport hub. The application of the methodology to a test site produces reasonable results, and as such it is expected that the methodology can be used for various transport hubs in the national road network. Once enough data from the application of the methodology are accumulated, a further study on the level of service in terms of network connectivity needs to be followed.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.391-401
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• 2006

We propose a method for geographically enhanced representation of social networks and implement a Web-based 3D visualization of geographical contexts in social networks. A renovated social network graph is illustrated by using two key components: (i) GWCMs (geographically weighted centrality measures) that reflect the differences in interaction intensity and spatial proximity among nodes and (ii) MSNG (map-integrated social network graph) that incorporates the GWCMs and the geographically referenced arrangement of nodes on a choroplethic map. For the integrated 3D visualization of the renovated social network graph, we employ X3D (Extensible 3D), a standard 3D authoring tool for the Web. An experimental case study of regional R&D collaboration provides a visual clue to geographical contexts in social networks including how the social centralization relates to spatial centralization.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.39-43
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• 2007

In high speed communication networks, the determination of a transmission rate is critical for the stability of a closed-loop network system with the congestion control scheme. In ATM networks, the available bit rate (ABR) service is based on a feedback mechanism, i.e., the network status is transferred to the ABR source by a resource management (RM) cell. RM cells contain the traffic information of the downstream nodes for the traffic rate control. However, the traffic status of the downstream nodes can not be directly transferred to the source node in the IP based networks. In this paper, a new rate-based congestion control scheme using an exponential weighted moving average algorithm is proposed to build an efficient feedback control law for congestion avoidance in high speed communication networks. The proposed congestion control scheme assures the stability of switch buffers and higher link utilization of the network. Moreover, we note that the proposed congestion scheme can flexibly work along with the increasing number of input sources in the network, which results in an improved scalability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.55-63
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• 2013

In cognitive radio networks, acquirement of position information of primary user is very important to secondary network since localization information of primary users can be utilized for improving the spectrum efficiency of secondary network and for avoiding harmful interference to primary users by using proper power control. Among various location methods, Received Signal Strength (RSS)-based localization has been widely used for distance measurements in the location detection process despite its inherent inaccuracy because it can be easily implemented without any additional hardware cost. In the RSS-based localization, the distance is measured by the received signal strength, and distance error can be caused by many factors such as fading, shadowing and obstacle between two nodes. In the paper, therefore we propose a localization scheme based on estimation of path-loss exponent to localize the location of primary users more accurately. Through simulations, it is shown that the proposed scheme can provide less localization error and interference rate to primary users than other schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1367-1372
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• 2007

This paper suggests One-time Password key exchange authentication technique for a strong authentication based on MANET and through identify wireless environment security vulnerabilities, analyzes current authentication techniques. The suggested authentication technique consists of 3 steps: Routing, Registration, and Running. The Routing step sets a safe route using AODV protocol. The Registration and Running step apply the One-time password S/key and the DH-EKE based on the password, for source node authentication. In setting the Session key for safe packet transmission and data encryption, the suggested authentication technique encrypts message as H(pwd) verifiers, performs key exchange and utilizes One time password for the password possession verification and the efficiency enhancement. EKE sets end to end session key using the DH-EKE in which it expounds the identifier to hash function with the modula exponent. A safe session key exchange is possible through encryption of the H(pwd) verifier. The suggested authentication technique requires exponentiation and is applicable in the wireless network environment because it transmits data at a time for key sharing, which proves it is a strong and reliable authentication technique based on the complete MANET.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.889-900
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• 2012

In wireless sensor networks, the positioning scheme using received signal strength (RSS) has been widely considered. Appropriate estimation of path-loss exponent (PLE) between a sensor node and an anchor node plays a key role in reducing position error in this RSS-based positioning scheme. In the conventional researches, a sensor node directly uses the PLEs measured by its nearest anchor node to calculate its position. However, the actual PLE between a sensor node and the anchor node can be different from the PLE measured by its nearest anchor node. Thus, if a sensor node directly uses the PLEs measured by its nearest anchor node, the estimated position is different from the actual position of the sensor node with a high probability. In this paper, we describe the method how a sensor node estimates PLEs from the anchor nodes of interest by itself and calculates its position based on these self-estimated PLEs. Especially, our proposal suggests the mechanism to iteratively calculate the PLEs depending on the estimated distances between a sensor node and anchor nodes. Based on the recalculated PLEs, the sensor node reproduces its position. Through simulations, we show that our proposed positioning scheme outperforms the traditional scheme in terms of position error.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.831-838
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• 1995

Geometric transformations require many operations in displaying moving 3D objects on the screen and a fast computation is a important problem in CAD or animation applications. The general method to compute the transformation coordinates of an object represented by an octree must perform the operations on every node. This paper proposes an efficient method that computes the rectangular coordinates of the vertices of the octree nodes into the coordinates of the universe space using the basicvectors in order to compute quickly geometric transformations of 3D images represented by an octree. The coordinates of the vertices of each octant are computed by using the formula presented here, which requies additions and multiplications by powers of 2. This method has a very fast execution time and is compared with the general computation method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.191-194
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• 2005

본 논문은 최적 탐색 알고리즘인 유전자 알고리즘을 이용하여 다항식 뉴럴네트워크(Polynomial Neural Networks : PNN)의 최적 설계가 그 목적이다. 기존의 다항식 뉴럴네트워크는 확장된 GMDH(Group Method of Data Handling) 방법에 기반을 두며, 네트워크의 성장과정을 통하여 각 층의 다항식뉴런(혹은 노드)에서 고정된 (설계자에 의해 미리 선택된) 노드 입력들의 수뿐만 아니라 다항식 차수(1차, 2차, 그리고 수정된 2차식)를 이용하였다. 더구나, 그 방법은 학습을 통해 생성된 PNN이 최적 네트워크 구조를 가진다는 것을 보증하지 못한다. 그러나, 제안된 GA-based PW 모델은 다음의 파라미터들- 즉 입력변수의 수, 입력변수, 및 다항식 차수-을 유전자 알고리즘을 이용하여 선택 동조함으로써 그 구조를 구조적으로 더 최적화된 네트워크가 되도록 하고, 기존의 PNN보다 훨씬 더 유연하고, 선호된 뉴럴 네트워크가 되도록 한다. 하중계수를 가진 합성성능지수가 그 모델의 근사화 및 일반화(예측) 능력 사이의 상호 균형을 얻기 위해 제안된다. GA-based PNN의 성능을 평가하기 위해 그 모델은 가스 터빈발전소의 NOx 배출 공정 데이터로 실험된다. 비교해석은 제안된 GA-based PNN이 앞서 나타난 다른 지능모델보다 더 우수한 예측능력뿐만 아니라 높은 정확성을 가진 모델임을 보인다.