• Journal of KIISE
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• v.44 no.10
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• pp.1112-1123
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• 2017

As content providers further offload content-centric services to the cloud, data retrieval over the cloud typically results in many redundant items because there is a prevalent near-duplication of content on the Internet. Simply fetching all data from the cloud severely degrades efficiency in terms of resource utilization and bandwidth, and data can be encrypted by multiple content providers under different keys to preserve privacy. Thus, locating near-duplicate data in a privacy-preserving way is highly dependent on the ability to deduplicate redundant search results and returns best matches without decrypting data. To this end, we propose an efficient near-duplicate detection scheme for encrypted data in the cloud. Our scheme has the following benefits. First, a single query is enough to locate near-duplicate data even if they are encrypted under different keys of multiple content providers. Second, storage, computation and communication costs are alleviated compared to existing schemes, while achieving the same level of search accuracy. Third, scalability is significantly improved as a result of a novel and efficient two-round detection to locate near-duplicate candidates over large quantities of data in the cloud. An experimental analysis with real-world data demonstrates the applicability of the proposed scheme to a practical cloud system. Last, the proposed scheme is an average of 70.6% faster than an existing scheme.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.374-385
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• 2010

Wireless mesh networks can be deployed for various networks from home networking to last-mile broadband Internet access. Wireless mesh networks are composed of mesh routers and mesh clients. In these networks, static nodes form a multi-hop backbone of a large wireless access network that provides connectivity to end-users' mobile terminals. The network nodes cooperate with each other to relay data traffic to its destinations. In order to increase connectivity and better performance, researchers are getting interested in multi-channel and multi-interface wireless mesh networks. In these networks, non-overlapping multiple frequency channels are used simultaneously to increase the aggregate bandwidth available to end-users. Recently, researches have focused on finding suitable channel assignments for wireless network interfaces, equiped in a mesh node, together with efficient routing to improve overall system throughput in wireless mesh networks. This goal can be achieved by minimize channel interference. Less interference among using channels in a network guarantees more aggregated channel capacity and better connectivity of the networks. In this thesis, we propose interference aware channel assignment and routing algorithms for multi-channel multi-hop wireless mesh networks. We propose Channel Assignment and Routing algorithms using Traffic Profiles(CARTP) and Routing algorithms allowing detour routing(CARTP+2). Finally, we evaluate the performance of proposed algorithms in comparison to results from previous methods using ns-2 simulations. The simulation results show that our proposed algorithms can enhance the overall network performance in wireless mesh networks.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.263-272
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• 2017

The concept of Internet of Things (IoT) has been increasingly popular these days, and its areas of application have been broadened. However, if the data stored in an IoT system is damaged and cannot be recovered, our society would suffer considerable damages and chaos. Thus far, most of the studies on fault-tolerance have been focused on computer systems, and there has not been much research on fault-tolerance for IoT systems. In this study, therefore, a fault-tolerance method in IoT environments is proposed. In other words, based on the EVENODD method, one of the traditional fault-tolerance methods, a fault-tolerance storage and recovery method for the data stored in the IoT server is proposed, and the method is implemented on an oneM2M IoT system. The fault-tolerance method proposed in this paper consists of two phases - fault-tolerant data storage and recovery. In the fault-tolerant data storage phase, some F-T gateways are designated and fault-tolerant data are distributed in the F-T gateways' storage using the EVENODD method. In the fault-tolerant recovery phase, the IoT server initiates the recovery procedure after it receives fault-tolerant data from non-faulty F-T gateways. In other words, an EVENODD array is reconstructed and received data are merged to obtain the original data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.551-559
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• 2017

In this paper, a real-time training method to improve the performance of bearing fault diagnosis. The traditional bearing fault diagnosis cannot classify a condition which is not trained by the classifier. The proposed 4-step method trains and recognizes new condition in real-time, thereby it can classify the condition accurately. In the first step, we calculate the maximum distance value for each class by calculating a Euclidean distance between a feature vector of each class and a centroid of the corresponding class in the training information. In the second step, we calculate a Euclidean distance between a feature vector of new acquired data and a centroid of each class, and then compare with the allowed maximum distance of each class. In the third step, if the distance between a feature vector of new acquired data and a centroid of each class is larger than the allowed maximum distance of each class, we define that it is data of new condition and increase count of new condition. In the last step, if the count of new condition is over 10, newly acquired 10 data are assigned as a new class and then conduct re-training the classifier. To verify the performance of the proposed method, bearing fault data from a rotating machine was utilized.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.611-613
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• 2021

Recently, research and development of quantum computers, which exceed the limits of classical computers, have been actively carried out in various fields. Quantum computers, which use quantum mechanics principles in a way different from the electrical signal processing of classical computers, have various quantum mechanical phenomena such as quantum superposition and quantum entanglement. It goes through a very complicated calculation process compared to the calculation of a classical computer for performing an operation using its characteristics. In order to utilize each element efficiently and accurately, it is necessary to visualize the data before driving the actual quantum computer and perform error verification, optimization, reliability, and verification. However, when visualizing all the data of various elements configured inside the quantum computer, it is difficult to intuitively grasp the necessary data, so it is necessary to visualize the data selectively. In this paper, we visualize the data of various elements that make up a quantum computer, and hierarchically visualize the internal circuit components of a quantum computer that are complicatedly configured so that the data can be observed and utilized intuitively.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.87-96
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• 2022

The Ministry of Land, Infrastructure and Transport has also been promoting the commercialization of land transport technology to commercialize the technologies owned by small and medium-sized venture companies, and to support the transfer and commercialization of public technologies. At this point, in order to improve the investment effect of subsequent new projects and to select excellent research institutes, it is necessary to establish a valid evaluation index system suitable for the purpose of the project. The evaluation index system for subsequent new projects should be linked to the project objectives and goals of the preceding project, and should be selected in consideration of existing evaluation indicators to prevent interruption of research results. Therefore, this thesis sets the evaluation index system into multiple scenarios through hierarchical cluster analysis using the evaluation result data for each evaluation committee for small and medium venture companies participating in the land transportation technology commercialization support project, and then analyzes the structural equation model. As a result of scenario analysis, considering the measurement effect of each path representing the causal relationship between evaluation indicators and the effect of each evaluation index on evaluation items, the scenario with the highest impact on the evaluation result was selected as an improvement plan.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.999-1004
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• 2006

본 연구에서는 다양한 개념 패싯과 관계 패싯들을 수용한 범용 과학기술 시소러스 구축용 웹 기반 워크벤치 개발에 대해 기술한다. 기존 국내 시소러스 구축용 워크벤치들이 제공하는 기본적인 용어 관계구축 기능을 확장하여 개념 패싯, 범주 관계 패싯, 의미역 관계 패싯, 속성 관계 패싯 및 속성 키워드 처리 기능을 원활히 제공할 수 있는 사용자 중심적 워크벤치를 개발함으로써 시소러스 상의 개념들에 대한 효율적인 구축이 가능하도록 한다. 또한 시멘틱 웹 상의 온톨로지 영역에 보다 근접한 고도화되니 시소러스 구축을 위해 용어들을 개념화시키고, 개념간의 다양한 관계를 설정하는 프로세스 중심적 설계로 분야 적합성이 높은 정보 처리 기반을 갖춘다. 궁극적으로 여러 마이크로 시소러스들을 통합하여 운용할 수 있는 복합 모델을 구축하는 것을 목표로 하고 있다. 이러한 목적에 부합하는 시스템 구현을 위해 CBD(Component Based Development) 개발 방법론으로 MSF/CD를 이용하였으며, 분산 환경에서 이기종간의 데이터 교환을 용이하게 하기 위하여 웹 서비스 (XML Web Services)를 이용하였다. 또한 시멘틱 웹 기반 연구자 간 협업 지원 서비스 구현을 위한 확장 검색용으로서도 활용할 수 있도록 하였다. 시소러스 반출은 CSV, XML 및 RDF를 모두 지원할 수 있도록 함으로써 다양한 사용자 요구 사항에 부합할 수 있도록 하였다. 시소러스 브라우징을 시각화 기반의 3단계 구조를 가진 플래시로 구현하여 사용자가 쉽게 시소러스를 탐색하고 분석할 수 있는 기반을 제공하였다. 또한 다양한 검색 요구를 만족시키고자 기본 검색, 고급 검색, 메타 검색을 선택할 수 있도록 하며, 개념 편집 및 시소러스 브라우징과 연동시켜 효율적인 시소러스 구축이 가능하도록 하였다. 본 연구의 워크벤치를 이용하여 구축된 시소러스는 기존 시소러스들에 비해 사용자가 보다 폭넓은 의미 기반 검색을 수행할 수 있도록 함으로써 다각적인 정보를 쉽게 획득할 수 있는 기반을 마련하고 있다는 데 의의가 있으며, 다국어 시소러스 및 다중 시소러스를 수용할 수 있는 방향으로 발전시킬 계획이다.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.649-657
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• 2018

It is essential that weapons which are mounted on aircraft need to prove its reliability and safety during its developments. A guided missile should have high reliability and safety throughout various tests which are including ground tests, captive flight tests, detailed technical tests and operational tests. In these various tests, it is vital that software of each component in the guided missile should be easily updated in order to correct algorithms or errors. In this paper, we propose a software update technique based on MDTP(mass data transfer protocol) which are defined in MIL-STD-1760. The proposed techniques have the following advantages: First, software of each unit in a weapon can be updated through a test equipment without disassemble a guided missile. Second, development periods for a software update can be reduced by reusing MDTP. Third, we can easily maintenance of the software because it is based on standard. We proved its efficiency and validity through experiments. Therefore, the proposed technique should be effectively utilized for software update of a weapon mounted on an aircraft during development processes.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.940-947
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• 2018

The purpose of this study was to obtain data to support applied motions of bench press and to provide basic data for the efficient execution of the exercise by analyzing muscular activation through measuring electromyogram of pectoralis major and erector spinae when flat bench pressing with feet on the ground versus feet on the bench. For this study, 4 amateur bodybuilders and 2 health trainers were chosen as subjects. Surface electrodes were attached on the measuring muscles; pectoralis major and erector spinae. Motion phases were defined for the flat bench press and each subject, with a 10RM load, performed a set with feet on the ground and another set with feet on the bench. Data was analyzed in SPSS 20.0 and the following results were obtained. The placement of the feet did not affect the muscular activation of the pectoralis major during the flat bench press. however, the muscular activation of the erector spinae was different between the two positions.