• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• The KIPS Transactions:PartC
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• v.18C no.5
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• pp.343-360
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• 2011

The objective of this paper is to analyze and characterize to simulate routing observations on end-to-end routing circuits and a ping experiment of a virtual network after modeling, such as IPv6 migration, an OSPFv3 routing experiment based on an IPv6 environment, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing using IPv6 planning and operations in an OPNET Modeler. IPv6 deployment based largely on the integrated wired and wireless network was one of the research tasks at hand. The previous studies' researchers recommended that future research work be done on the explicit features of both OSPFv3 and EIGRP protocols in the IPv4/IPv6 environment, and more research should be done to explore how to improve the end-to-end IPv6 performance. Also, most related work was performed with an IPv4 environment but lacked studies related to the OSPFv3 virtual network based on an end-to-end IPv6 environment. Hence, this research continues work in previous studies in analyzing IPv6 migration, an OSPFv3 routing experiment based on IPv6, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing. In the not too distant future, before enabling the default IPv6, it would help to understand network design and deployment based on an IPv6 environment through IPv6 planning and operations for the end-user perspective such as success or failure of connection on IPv6 migration, exploration of an OSPFv3 routing circuit based on an end-to-end IPv6 environment, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing. We were able to observe an optimal route for modeling of an end-to-end virtual network through simulation results as well as find what appeared to be a fast ping response time VC server to ensure Internet quality of service better than an HTTP server.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.21-41
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• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Journal of KIISE:Information Networking
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• v.34 no.5
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• pp.348-359
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• 2007

The conventional mobile VPN services assumed the mobile communications occur between the MN in foreign networks and the CN in the home network. However, if a MN wants to communicate with another MN in a foreign network, it could degrade the performance of the mobile VPN service because of the triangular routing problem. In this paper, we propose a route optimization mechanism based on the mobile VPN using an x-HA allocated by diameter MIP in order to support the efficient communication between the mobile VPN users in foreign networks. The i-HA maintains the VPN-TIA as well as the x-HoA as the CoAs to solve the security problem and to provide an efficient route optimization simultaneously. Moreover, we proposed revised IPSec tunnel configuration to reduce the IPSec tunnel overheads at a MN when the MN communicates with several MNs in the foreign networks at the same time. The VPN server, a security management entity in the home network, notifies an additional IPSec tunnel establishment between the x-HAs where the communication peers are registered. The simulation result showed that the proposed scheme decreases the end-to-end packet delay time and improves the throughput after the handoff compared to the existing mechanism.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.487-498
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• 2013

A variety of location-based services applications, such as missing children search, emergency rescue requests and so on that requiring high-precision location information are increasing. Precision of GPS that can be used in most systems, however, is still low. In this paper, we design and propose a low cost differential global positioning system(DGPS) based on Web services using object-oriented modeling technique which can offer useable location service, variety device and safe service in wireless environment. The proposed system is designed with UML based on object-oriented modeling to maximize system recyclability and system scalability. In addition, we would like to improve the precision of the GPS in accordance with mobile station location when build low cost mobile station, location differential framework and server. We implement a communication interface based on web-service which is available in the form of a variety of services and can offer stable according to mobile environments. Finally, as performance evaluation results, we can obtain precision location within 1 ~ 2m through proposed system and 88.5% probability of less than 2m.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.81-94
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• 2016

The Korean real-time traffic responsive control systems, Cycle Offset Split Model of Seoul (COSMOS), employs a single theoretical model to estimate the degree-of-saturation (DS) on approaches. However, the deployment of the system has been accomplished without practical consideration of its field performance. This paper delivers a diagnosis study performed to find the relationships yet known on the DS values against the operational conditions unproved in theory but ordinarily observed in field practice. Based on the analysis of the historical log data (476,505 cycles) obtained from the COSMOS server, it was found; (1) full coverage of lane detections should perform better than the sample coverage of detection in ordinary conditions, (2) the sample coverage of detection perform better than the other case with an exclusive bus lane, (3) detection in which a shared lane is involved provide poor estimation of DS, (4) poor DS estimation when a detection lane is adjacent to a shared lane, and (5) the DS values obtained during a day can hardly be stable all time. The findings suggest traffic engineers a progressive direction to move forward for the next real-time traffic control systems.

• Journal of KIISE
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• v.44 no.2
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• pp.115-123
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• 2017

A message processor is server software that receives non-realtime messages as well as realtime messages from clients that need to be processed within a deadline. With the recent advances of micro-processor technologies and Linux, the message processor is often implemented in Linux-based multi-core servers and it is important to use cores efficiently to maximize the performance of system in multi-core environments. Numerous research efforts on a real-time scheduler for the efficient utilization of the multi-core environments have been conducted. Typically, though, they have been conducted theoretically or via simulation, making a subsequent real-system application difficult. Moreover, many Linux-based real-time schedulers can only be used in a specific Linux version, or the Linux source code needs to be modified. This paper presents the design of a Linux-based message processor for multi-core environments that maps the threads to the cores at user level. The message processor is implemented through a modification of the traditional RM algorithm that consolidates the real-time messages into certain cores using a first-fit-based bin-packing algorithm; this minimizes the response-time delay of the non-real-time messages, while guaranteeing the violation rate of the real-time messages. To compare the performances, the message processor was implemented using the two multi-core-scheduling algorithms GSN-EDF and P-FP, which are provided by the LITMUS framework. The benchmarking results show that the response-time delay of non-real-time messages in the proposed system was improved up to a maximum of 17% to 18%.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.