• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.75-83
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• 2009

This paper presents a new mobile robot localization method for indoor robot navigation. The method uses color-coded landmarks on the ceiling and a camera is installed on the robot facing the ceiling. The proposed "cell-coded map", with the use of only nine different kinds of color-coded landmarks distributed in a particular way, helps reduce the complexity of the landmark structure. This technique is applicable for navigation in an unlimited size of indoor space. The structure of the landmarks and the recognition method are introduced. And 2 rigid rules are also used to ensure the correctness of the recognition. Experimental results prove that the method is useful.

• Bulletin of the Korean Chemical Society
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• v.32 no.1
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• pp.251-262
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• 2011

Human ether-a-go-go related gene (hERG) potassium channel blockade, an undesirable side effect which might cause sudden cardiac death, is one of the major concerns facing the pharmaceutical industry. The purpose of this study is to develop an in silico QSAR model which uncovers the structural parameters of T-type calcium channel blockers to reduce hERG blockade. Comparative molecular similarity indices analysis (CoMSIA) was conducted on a series of piperazine and benzimidazole derivatives bearing methyl 5-(ethyl(methyl)amino)-2-isopropyl-2-phenylpentanoate moieties, which was synthesized by our group. Three different alignment methods were applied to obtain a reliable model: ligand based alignment, pharmacophore based alignment, and receptor guided alignment. The CoMSIA model with receptor guided alignment yielded the best results : $r^2$ = 0.955, $q^2$ = 0.781, $r^2_{pred}$ = 0.758. The generated CoMSIA contour maps using electrostatic, hydrophobic, H-bond donor, and acceptor fields explain well the structural requirements for hERG nonblockers and also correlate with the lipophilic potential map of the hERG channel pore.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2885-2890
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. Raspberrypi is a linux system which can be used as a sensor node. Pi can be used to construct IP based Hadoop clusters. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time. The experimentation showed that with more execution power and memory volume, Pi could be appropriate for a member node of the cluster, accomplishing precise classification for sensor localization using machine learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.668-676
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• 2009

Hierarchical Mobile 1Pv6(HM1Pv6) have been proposed by IETF(Internet Engineering Task Force) beginning from Mobile 1Pv6 for mobility support in IP networks, however these researches relying on client based mobility support have not been drawn attention due to excessive consumption of wireless resources and long handover delay. In this paper, we propose the Router-based Binding Update(RBU) scheme to solve problems in existing client-based mobility support schemes. The router registers location information of mobile nodes through Neighbor Discovery protocol without additional codes for the RBU scheme to a terminal operated by existing HMlPv6. By using this the RBU scheme is designed so that it can support partial nupport based mobility and reduce handover latency rather than using HMIPv6. It is analysed and compared with existing HMIPv6 to verify efficiency of the RBU scheme. As a result, the RBU scheme has outperformed existing HMIPv6 by 15% in terms of macro handover delay especially when long delay on wireless links exists.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.271-280
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• 2012

Currently, contents generated by SNS services are increasing exponentially, as the number of SNS users increase. The SNS is commonly used to post personal status and individual interests. Also, the SNS is applied in socialization, entertainment, product marketing, news sharing, and single person journalism. As SNS services became available on smart phones, the users of SNS services can generate and spread the social issues and controversies faster than the traditional media. The existing indexing methods for web contents have limitation in terms of real-time indexing for SNS contents, as they usually focus on diversity and accuracy of indexing. To overcome this problem, there are real-time indexing techniques based on RDBMSs. However, these techniques suffer from complex indexing procedures and reduced indexing targets. In this regard, we introduce the TK-Indexing method to improve the previous indexing techniques. Our method indexes the generation time of SNS contents and keywords by way of NoSQL to indexing SNS contents in real-time.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.22-32
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• 1995

The purpose of this study is to estimate not only the watershed soil loss but also its temporal changes of Kyungan River basin, the study area, due to the land development. To analyze the soil loss of the river basin, USLE was employed. GIS and remote sensing were also utilized to estimate the soil loss. The data for this analysis consist of a series of thematic map and remotely sensed data. The remotely sensed images for this study are Landsat TM(Oct, 28, 1997 & Sep. 22, 1992), In Kyungan River basin, not only the detection of temporal changes of land use and GVI, but also the estimation of soil loss provided very significant factors that affect to the watershed environment quality. The management of the factors of vegetative cover, slope steepness and length were the keys to reduce soil loss and solve conservation and protection issues of Kyungan River basin. GIS application with USLE to the watershed analysis allows the planner to recognize sensitive sites and to plan strategies to minimize soil loss.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.367-373
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• 2017

The study aims to suggests mobile application service that helps to reduce physical and mental suffering after surgery for breast cancer patients constantly increasing. At first, I analyze literature research to understand the breast cancer and similar services for achieving study purposes. After I finally design a mobile application that offers to customed one-to-one specialist coaching service focused on post-treatment management by using a double diamond design process model. In detail, first, I analyze the current program and comprehend user needs through interviews with the person such as breast patient, protector, nurse, salesperson in cancer center. Second, I specify the entire scenario, concept and keyword based on the persona and the customer journey map representing user. Third, I propose the mobile application for the ultimate goal by producing prototype to effectively communicate with specific functional descriptions. I hope this service will improve the quality of their lives.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.313-320
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• 2019

Nowadays, as the availability of tiny, low-cost microcomputer increases at a high level, mobile robots are experiencing remarkable enhancements in hardware design, software performance, and connectivity advancements. In order to control Turtlebot 2, several algorithms have been developed using the Robot Operating System(ROS). However, ROS requires to be run on a high-cost computer which increases the hardware cost and the power consumption to the robot. Therefore, design an algorithm based on low-cost hardware is the most innovative way to reduce the unnecessary costs of the hardware, to increase the performance, and to decrease the power consumed by the computer on the robot. In this paper, we present a path-finding algorithm for TurtleBot 2 based on low-cost hardware. We implemented the algorithm using Raspberry pi, Windows 10 IoT core, and RPLIDAR A2. Firstly, we used Raspberry pi as the alternative to the computer employed to handle ROS and to control the robot. Raspberry pi has the advantages of reducing the hardware cost and the energy consumed by the computer on the robot. Secondly, using RPLIDAR A2 and Windows 10 IoT core which is running on Raspberry pi, we implemented the path-finding algorithm which allows TurtleBot 2 to navigate from the starting point to the destination using the map of the area. In addition, we used C# and Universal Windows Platform to implement the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.586-594
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• 2012

The drastic growth of mobile communication and spreading of smart phone make the significant attention on Location Based Service. The one of most important things for vitalization of LBS is the accurate estimating position for mobile object. Focusing on AP's probabilistic position estimation, we develop an AP distribution map and new pattern matching algorithm for position estimation. The developed approaches can strengthen the advantages of Radio fingerprint based Wi-Fi Positioning System, especiall on the algorithms and data handling. Compared on the existing approaches of fingerprint pattern matching algorithm, we achieve the comparable higher performance on both of average error of estimation and deviation of errors. Furthermore all fingerprint data have been harvested from the actual measurement of radio fingerprint of Seoul, Kangnam area. This can approve the practical usefulness of proposed methodology.