• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2340-2349
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• 2009

In this paper we proposed a hopping routing scheme to resolve the hot spot problem for periodic monitoring services in wireless sensor networks. Our hopping routing scheme constructs load balanced routing path, where an amount of energy consumption of all nodes in the sensor networks is predictable. Load balanced routing paths can be obtained from horizontal hopping transmission scheme which balances the load of the sensor nodes in the same area, and also from vertical hopping transmission scheme which balances the load of the sensor nodes in the other area. The direct transmission count numbers as load balancing parameter for vertical hopping transmission are derived using the energy consumption model of the sensor nodes. The experimental results show that the proposed hopping scheme resolves the hot spot problem effectively. The efficiency of hopping routing scheme is also shown by comparison with other routing scheme such as multi-hop, direct transmission and clustering.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.567-576
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• 2011

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.317-324
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• 2010

In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.83-92
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• 2009

As the innovative internet technologies and multimedia are being rapidly developed, malicious codes are a remarkable new growth part and supplied by various channel. This project presents a classification methodology for malicious codes in Windows OS (Operating System) environment, develops a test classification system. Thousands of malicious codes are brought in every day. In a result, classification system is needed to analyzers for supporting information which newly brought malicious codes are a new species or a variety. This system provides the similarity for analyzers to judge how much a new species or a variety is different to the known malicious code. It provides to save time and effort, to less a faulty analysis. This research includes the design of classification system and test system. We classify the malicious codes to 9 groups and then 9 groups divide the clusters according to the each property.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.1
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• pp.60-70
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• 2021

Recently, with the development of technologies for the fourth industrial revolution, convergence and complex technology are being applied to aircraft, electronic home appliances and mobile devices, and the number of parts used is increasing. Increasing the number of parts and the application of convergence technologies such as HW (hardware) and SW (software) are increasing the No Defect Found (NDF) phenomenon in which the defect is not reproduced or the cause of the defect cannot be identified in the subsequent investigation systems after the discovery of the defect in the product. The NDF phenomenon is a major problem when dealing with complex technical systems, and its consequences may be manifested in decreased safety and dependability and increased life cycle costs. Until now, NDF-related prior studies have been mainly focused on the NDF cost estimation, the cause and impact analysis of NDF in qualitative terms. And there have been no specific methodologies or examples of a working-level perspective to reduce NDF. The purpose of this study is to present a practical methodology for reducing NDF phenomena through data mining methods using quantitative data accumulated in the enterprise. In this study, we performed a cluster analysis using market defects and design-related variables of mobile devices. And then, by analyzing the characteristics of groups with high NDF ratios, we presented improvement directions in terms of design and after service policies. This is significant in solving NDF problems from a practical perspective in the company.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.451-457
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• 2020

Diverse sensors are equipped on P-3C Maritime Patrol Aircraft for RoKN to detect and monitor tactical targets. Tactical targets are maintained/shared by tactical computer stations which consist of a clustering network in the aircraft and displayed in various ways on TSDs(Tactical Situation Displays) for mission operators to perform their specified missions. Korea peninsula is widely covered with the sea areas and neighboured with several countries; which makes huge number of ships and aircraft deployment around the place. Due to an increase in number of sensors and enhancement of their sensitivities; we were aware of the necessity of TSD improvements to provide huge number of tactical targets and to display them efficiently. In this paper, we propose a solution for the improvements by using previous backup data and re-usage of the data, then we verify the proposal through implementation and evaluation results.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.55-59
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• 2022

Recently, IoT technology has great attention and plays a key role in 4^th industrial revolution in order to design customized products and services. Additive Manufacturing (AM) is applied to fabricate IoT sensor directly or IoT sensor embedded structure. Also, design methods for AM are developing to consolidate various parts of IoT devices. Part consolidation leads to assembly time and cost reduction, reliability improvement, and lightweight. Therefore, a design method was proposed to guide designers to consolidate parts. The design method helps designers to define product architecture that consists of functions and function-part relations. The product architecture is converted to a network graph and then Girvan Newman algorithm is applied to cluster the graph network. Parts in clusters are candidates for part consolidation. To demonstrate the usefulness of the proposed design method, a case study was performed with e-bike fabricated by additive manufacturing.