• ETRI Journal
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• v.37 no.6
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• pp.1065-1076
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• 2015

Network densification is regarded as the dominant driver for wireless evolution into the era of 5G. However, in this context, interference-limited dense small cell deployments are facing technical challenges in mobility management. The recently announced results from an LTE field test conducted in a dense urban area show a handover failure (HOF) rate of over 21%. A major cause of HOFs is the transmission failure of handover command (HO CMD) messages. In this paper, we propose two enhancements to HO performance in LTE networks - radio link failure-proactive HO, which helps with the reliable transmission of HO CMD messages while the user equipment is under a poor radio link condition, and Early Handover Preparation with Ping-Pong Avoidance (EHOPPPA) HO, which assures reliable transmission of HO CMD under a good radio link condition. We analyze the HO performance of EHOPPPA HO theoretically, and perform simulations to compare the performance of the proposed schemes with that of standard LTE HO. We show that they can decrease the HOF rate to nearly zero through an analysis, and based on the simulation results, by over 70%, without increasing the ping-pong probability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.117-126
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• 2013

Recently, as Social Network Services(SNS), such as Twitter, Facebook, are becoming more popular, much research has been doing on opinion mining. However, current related researches are mostly focused on sentiment classification or feature selection, but there were few studies about opinion document retrieval. In this paper, we propose a new retrieval method of short opinion documents. Proposed method utilizes previous sentiment classification methodology, and applies several features of documents for evaluating the quality of the opinion documents. For generating the retrieval model, we adopt Learning-to-rank technique and integrate sentiment classification model to Learning-to-rank. Experimental results show that proposed method can be applied successfully in opinion search.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1141-1144
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• 2009

인공신경망(artificial neural network) 기법은 인간의 두뇌 신경세포의 활동을 모형화한 것으로 오랜 시간동안 발전해 왔으며 여러 분야에서 활용되고 있고 수문분야에서도 인공신경망을 이용한 연구가 활발히 진행되어 왔다. RDAPS와 같은 단기수치예보 자료는 강우의 유무 판단과 같은 정성적인 분석에서 비교적 정확도가 높지만 정확한 강우량의 추정과 같은 정량적인 부분에서는 정확도가 매우 낮으므로 인공신경망 기법과 같은 후처리 기법을 통해서 정확도를 높이게 된다. 인공신경망 기법을 수행할 때, 가장 중요한 것은 입력변수선택(input variable selection)으로 입력 변수의 적절한 선택이 결과값에 큰 영향을 주게 된다. 본 연구에서는 mutual information을 입력 변수 선택 기법으로 채택하여, 인공신경망의 입력변수 선정의 정확도를 알아보고자 한다. Mutual information은 주어진 자료의 엔트로피값을 이용하여 변수들 간의 독립과 종속의 관계를 나타내는 기법으로서, MI값은 '0'에서 '1'의 값을 가지며 '0'에 가까울수록 변수들 간의 관계가 독립적이고 '1'에 가까울수록 종속적인 관계를 나타낸다. 인공신경망의 입력변수선정에 대한 mutual information의 정확도를 알아보기 위해, 기존 입력변수선택 기법과 mutual information을 이용했을 경우의 인공신경망의 처리능력, 정확도를 비교 검토하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1015-1016
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• 2015

VANET is a technology for building a robust ad-hoc network between mobile vehicles as well as, between vehicles and RSU. Vehicles are moving and they only stay in the RSU area for a short time. When the number of requests is increased, an important challenge is to implement a suitable scheduling algorithm which serves as more requests as possible. $D^*S$ algorithm uses a priority weight, DS_Value, for selection of a request to get service. Priority weight is influenced only by deadline and data size parameters. We propose a packet scheduling using multilevel queue and show that using this idea leads to higher service ratio compare to previous algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.105-106
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• 2015

본 논문에서는 DTN(Delay Tolerant Network) 환경에서 효율적인 메시지 전달을 위해 확률적으로 중계 노드를 선택하는 기법을 제안한다. DTN은 종단 간 연결이 불확실한 네트워크에서의 통신을 Store-Carry-Forward 방식을 사용하여 메시지를 목적 노드에 전달한다. 또한 종단 간 연결이 불확실한 상황에서도 중계 노드를 통해 메시지를 목적 노드에 전달하여 높은 전송률을 보장한다. 하지만 에피데믹(Epidemic) 라우팅이나 Spray and Wait 라우팅과 같은 기존 다중 복사 라우팅 알고리즘은 접촉한 모든 노드에게 메시지를 복사하여 메시지 복사로 인한 오버헤드가 높아진다. 반면에 PROPHET 라우팅과 같은 단일 복사 알고리즘은 적은 오버헤드를 발생시키지만 중계 노드 수 감소로 인한 메시지 전송률 감소 현상이 나타난다. 본 논문에서 제안하는 알고리즘은 기존 DTN 라우팅의 문제점을 보완하기 위해 확률적으로 노드 분포를 분석하여 현재 네트워크에 효율적인 메시지 복사 방식을 선택하여 작동한다. 본 논문에서는 제안하는 알고리즘이 기존 DTN 라우팅 알고리즘과 오버헤드와 전송률을 비교하여 더 효율적임을 증명한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1281-1286
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• 2010

Ant-based routing methods belong to a class of ant colony optimization algorithms which apply the behavior of ants in nature to routing mechanism. Since the topology of mobile ad-hoc network(MANET) changes dynamically, it is needed to establish paths based on the local information. Subsequently, it is known that routing in MANET is one of applications of ant colony optimization. In this paper, we propose a routing method, namely EPMAR, which enhances SIR in terms of route selection method and the process upon link failure. The performance of the proposed method is compared with those of AntHocNet and SIR. Based on he analysis, it is proved that the proposed method provided higher packet delivery ratio and less critical link failure than AntHocNet and SIR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.143-150
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• 2009

In this paper, we propose a novel shadow clustering mechanism including a mobility estimation algorithm based on Gauss-Markov mobility model which analyses patterns of moving direction and speed of a mobile terminal respectively and a selection algorithm of the most suitable network for the requirements of mobile terminals. The proposed mechanism makes much less shadow cluster area than that of the legacy methods, and reduces unnecessary resource reservation. It is compared the proposed algorithm with traditional methods under various scenarios.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2330-2332
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• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.