• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.189-192
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• 2021

three degree of freedom (3DoF), three degree of freedom plus (3DoF+), six degree of freedom(6DoF) 등 몰입형 비디오의 높은 몰입감을 제공하기 위해 다중 비디오 영상을 효율적으로 처리하는 기법이 활발히 연구되고 있다. 이를 위해 원본의 몰입형 비디오가 입력되면 기본 시점 영상과 추가 시점 영상에서의 중복을 제거하고 기본 시점(basic view)에서는 보이지 않지만 추가 시점(additional view)에서는 보이는 영역을 추출하는 프루닝 과정이 이뤄지는 부호기에서의 부호화 모드 결정은 매우 중요하다. 본 논문은 test model for immersive video (TMIV)의 모드 중 하나인 MPEG immersive video (MIV) view mode 를 통해 만들어진 프루닝 (pruning) 그래프에서 선택된 시점들을 활용하여 뷰 간 중복성을 제거할 수 있는 효율적인 부호화 구조로 클러스터를 기반으로 병렬적으로 부호화하는 클러스터 기반 정렬 기법을 제안한다. 선택된 시점들을 인덱스 순서에 따라 부호화하는 기존 방법에 비해 제안하는 방법은 peak signal-to-noise ratio (Y-PSNR)에서 평균 3.9%의 BD-rate 절감을 보여주었다. 본 연구는 또한 더 객관적인 품질 측정을 위해 immersive video peak signal-to-noise ratio (IV-PSNR)에 의한 비교 결과도 함께 제공하며, 참조 순서에 맞게 정렬한 프루닝 기반 정렬 기법과의 비교도 함께 제공한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.305-307
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• 2020

이머시브 비디오 서비스를 위해 MPEG-I Visual 그룹에서는 3DoF+ 기술과 관련하여 MIV(Metadata for Immersive video)의 표준화를 진행하고 있으며, 이를 위해 다시점 영상 및 전방위 장면을 촬영한 ERP 규격의 영상들이 주어진 경우에 운동시차를 제공할 수 있는 영상 합성 레퍼런스 소프트웨어인 TMIV SW를 제공한다. TMIV는 기본적으로 송신부인 인코더와 수신부인 디코더로 구성이 되어있으며, 인코더에서 가장 중요한 기능은 다수의 입력 시점영상 간의 중복된 데이터를 찾아내서 제거하는 프루닝 과정이다. 프루닝 방법에 따라 데이터 전송량과 디코더에서의 합성 품질이 달라지기 때문에 인코더에서 핵심이라고 할 수 있다. 본 논문은 인코더의 프루닝의 효율을 높이기 위해 전체 흐름도에서 프루닝 순서 변경 과정을 추가하고 그 과정에서 시점 영상간 중첩 영역을 계산하여 이를 토대로 프루닝 순서를 결정하는 방법을 제안하였고 이를 통해 데이터 압축률이 향상됨을 확인할 수 있었고, 또한 수신부에서 영상 합성의 품질이 달라짐을 확인할 수 있었다.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.259-266
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• 2005

Recently data centric routing or application dependent routing protocols are emerged in mobile ad hoc networks. In this paper, we propose a routing method for query processing in MANET(Mobile Ad hoc NETwork) environment, called road-based query routing, with consideration on real time traffic information of large number of vehicles. In particular, we focus on the method that process arrival time dependent shortest path query in MANET without a central server on the road networks. The main idea of our approach lies in a routing message that includes query predicates based on the road connectivity and on data gathering method in real time from vehicles on the road by ad-hoc network. We unify route discovery phase and data delivery(query processing) phase in our mechanism and reduce unnecessary flooding messages by pruning mobile nodes which are not on the same or neighboring road segments. In order to evaluate the performances of the proposed method, we established a model of road networks and mobile nodes which travel along the roads. The measurement factor is the number of nodes to whom route request is propagated according to each pruning strategy. Simulation result shows that road information is a dominant factor to reduce the number of messages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2373-2377
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• 2009

In this paper, the threshold voltage characteristics have been analyzed using three dimensional Poisson's equation for FinFET. The FinFET is extensively been studing since it can reduce the short channel effects as the nano device. We have presented the short channel effects such as subthreshold swing and threshold voltage for PinFET, using the analytical three dimensional Poisson's equation. We have analyzed for channel length, thickness and width to consider the structural characteristics for FinFET. Using this model, the subthreshold swing and threshold voltage have been analyzed for FinFET since the potential and transport model of this analytical three dimensional Poisson's equation is verified as comparing with those of the numerical three dimensional Poisson's equation.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.3-10
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• 1999

This paper describes the development and evaluation of a Korean address input system employing automatic speech recognition technique as user interface for input Korean address. Address consists of cities, provinces and counties. The system works on a window 95 environment of personal computer with built-in soundcard. In the speech recognition part, the Continuous density Hidden Markov Model(CHMM) for making phoneme like units(PLUs) and One Pass Dynamic Programming(OPDP) algorithm is used for recognition. For address recognition, Finite State Automata(FSA) suitable for Korean address structure is constructed. To achieve an acceptable performance against the variation of speakers, microphones, and environmental noises, Maximum a posteriori(MAP) estimation is implemented in adaptation. And to improve the recognition speed, fast search method using variable pruning threshold is newly proposed. In the evaluation tests conducted for the 100 connected words uttered by 3 males the system showed above average 96.0% of recognition accuracy for connected words after adaption and recognition speed within 2 seconds, showing the effectiveness of the system.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.9-16
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• 2013

Data analysis is the universal classification techniques, which requires a lot of effort. It can be easily analyzed to understand the results. Decision tree which is developed by Breiman can be the most representative methods. There are two core contents in decision tree. One of the core content is to divide dimensional space of the independent variables repeatedly, Another is pruning using the data for evaluation. In classification problem, the response variables are categorical variables. It should be repeatedly splitting the dimension of the variable space into a multidimensional rectangular non overlapping share. Where the continuous variables, binary, or a scale of sequences, etc. varies. In this paper, we obtain the coefficients of precision, reproducibility and accuracy of the classification tree to classify and evaluate the performance of the new cases, and through experiments to evaluate.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.