• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2134-2153
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• 2013

A network-coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (Wireless Sensor Networks). We mainly focus on two problems: firstly, consideration is given to effective distributed storage technology; secondly, we address how to effectively repair the data in failed storage nodes. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from this matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network-coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that it can work over a small field and the required computation overhead is very low. The simulation result verifies that the proposed scheme not only reduces the total energy consumption required to implement distributed storage system in WSNs, but also balances energy consumption of the networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4183-4204
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• 2018

The existing en-route filtering schemes only consider some simple false data injection attacks, which results in lower safety performance. In this paper, we propose an efficient geographical information-based en-route filtering scheme (EGEFS), in which each forwarding node verifies not only the message authentication codes (MACs), but also the report identifier and the legitimacy and authenticity of locations carried in a data report. Thus, EGEFS can defend against not only the simple false data injection attacks and the replay attack, but also the collusion attack with forged locations proposed in this paper. In addition, we propose a new method for electing the center-of-stimulus (CoS) node, which can ensure that only one detecting node will be elected as the CoS node to generate one data report for an event. The simulation results show that, compared to the existing en-route filtering schemes, EGEFS has higher safety performance, because it can resist more types of false data injection attacks, and it also has higher filtering efficiency and lower energy expenditure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2258-2276
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• 2019

Resource sharing is one of the main goals achieved by network virtualization technology to enhance network resource utilization and enable resource customization. Though resource sharing can improve network efficiency by accommodating various users in a network, limited infrastructure capacity is still a challenge to ultra-low latency service operators. In this paper, we propose an inter-slice resource borrowing schema based on the device-to-device (D2D) potentiality especially for ultra-low latency transmission in cellular networks. An extended and modified Kuhn-Munkres bipartite matching algorithm is developed to optimally achieve inter-slice resource borrowing. Simulation results show that, proper D2D user matching can be achieved, satisfying ultra-low latency (ULL) users' quality of service (QoS) requirements and resource utilization in various scenarios.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.639-641
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• 2018

고전적으로 이용되던 디렉터리 분류로는 원하는 정보를 빠르게 찾기 어려워지면서, 키워드 기반 검색 시스템이 정보 처리의 중심이 되고 있다. 본 논문에서는 개인용 컴퓨터에서의 빠른 자료 검색을 위한 키워드 기반 정보검색 시스템을 제안한다. 시스템에서는 동적 색인을 통하여 기존 시스템들보다 빠른 시간 내에 검색 결과를 제공한다. 내용 기반 검색과 다양한 포맷에 대한 문서 검색 기능을 포함하여 사용자에게 편리한 환경을 제공할 뿐만 아니라, 한글 문장이 포함된 문서에 대해서 원활한 검색을 제공하고자 한다. 성능 비교 검증을 수행한 결과 기존 시스템에 비해 보다 빠른 시간 내에 많은 문서를 탐지할 수 있음을 확인하였다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.492-495
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• 2019

휴대전화 메시지로는 결제, 인증번호, 택배, 광고 등의 다양한 문자들이 수신된다. 이 문자들은 서로 섞여 있어 이용자가 찾고자 하는 문자를 찾는 데 어려움이 있다. 본 논문에서는 기계학습과 단어 임베딩을 통해 메시지들을 카테고리로 분류하는 방법을 제안하고, 이를 구현한 안드로이드 앱을 소개한다. 앱에서는 택배, 카드, 인증, 공공기관, 통신사, 대화, 기타의 7개의 분류로 메시지를 분류하며, 자동 분류에서는 수동 태깅한 5802건의 문자메시지를 사용한다. 앱에서는 저장된 문자메시지간 유사도에 기반한 오프라인에 서의 자동 분류를 지원하여 개인정보 노출에 대한 거부감이 있는 사용자의 요구를 반영한다.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.9-19
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• 2015

The most important challenge in the region-based abstraction method as an approach to compute the state space of time Petri Nets (TPNs) for model checking is that the method results in a huge number of regions, causing a state explosion problem. Thus, region-based abstraction methods are not appropriate for use in developing practical tools. To address this limitation, this paper applies a modification to the basic region abstraction method to be used specially for computing the state space of TPN models, so that the number of regions becomes smaller than that of the situations in which the current methods are applied. The proposed approach is based on the special features of TPN that helps us to construct suitable and small region graphs that preserve the time properties of TPN. To achieve this, we use TPN-TCTL as a timed extension of CTL for specifying a subset of properties in TPN models. Then, for model checking TPN-TCTL properties on TPN models, CTL model checking is used on TPN models by translating TPN-TCTL to the equivalent CTL. Finally, we compare our proposed method with the current region-based abstraction methods proposed for TPN models in terms of the size of the resulting region graph.

• Journal of Information Processing Systems
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• v.5 no.2
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• pp.105-116
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• 2009

To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency, and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.13-21
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• 2005

In this study, we study and analysis on e-Learning based Information Security curriculum. e-Learning based university education courses will be much more established in Korea based on advanced IT technology. Computer related majors such as 'Computer Science' and 'Software' can be easily combined with e-Learning system. And Advanced Information Security Expert (AISE) educational course must be broadly opened for satisfying national requirements. In this study, we analyze e-Learning course on Information Security major based on off-line curriculum and suggest new model for further research.