• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.493-506
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• 2018

This paper proposes the OOPP(Optimized Online Portfolio Platform) design for the job seekers to search for the job competency necessary for employment and to write and manage portfolio online efficiently. The OOPP consists of three modules. First, JDCM(Job Data Collection Module) stores the help-wanted advertisements of job information sites in a spreadsheet. Second, CSM(Competency Statistical Model) classifies core competencies for each job by text-mining the collected help-wanted ads. Third, OBBM(Optimize Browser Behavior Module) makes users to look up data rapidly by improving the processing speed of a browser. In addition, The OBBM consists of the PSES(Parallel Search Engine Sub-Module) optimizing the computation of a Search Engine and the OILS(Optimized Image Loading Sub-Module) optimizing the loading of image text, etc. The performance analysis of the CSM shows that there is little difference in accuracy between the CSM and the actual advertisement because its data accuracy is 99.4~100%. If Browser optimization is done by using the OBBM, working time is reduced by about 68.37%. Therefore, the OOPP makes users look up the analyzed result in the web page rapidly by analyzing the help-wanted ads. of job information sites accurately.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.425-431
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• 2020

Recent advancements in industries and technologies have resulted in an increase in the volume of transportation, management, and distribution of logistics. Radio-frequency identification (RFID) technologies have been developed to efficiently manage such a large amount of logistics information. The use of RFID for management is being applied not only to the logistics industry, but also to the power transmission and energy management field. However, due to the limitation of program development capacity, the RFID device is limited in development, and this limitation is vulnerable to security because the existing strong encryption method cannot be used. For this reason, we designed a chaotic system for security with simple operations that are easy to apply to such a restricted environment of RFID. The designed system is a two-dimensional tent map chaotic system. In order to solve the problem of a biased distribution of signals according to the parameters of the chaotic dynamical system, the system has a cryptographic parameter(𝜇₁), a distribution parameter(𝜇₂), and a parameter(𝜃), which is the constant point, ID value, that can be used as a key value. The designed RFID authentication system is similar to random numbers, and it has the characteristics of chaotic signals that can be reproduced with initial values. It can also solve the problem of a biased distribution of parameters, so it is deemed to be more effective than the existing encryption method using the chaotic system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.437-441
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• 2006

Wireless sensor network consists of sensor nodes which are disseminated closely to each other to collect informations for the various requests of a sensor application applied for sensing phenomenons in real world. Each sensor node delivers sensing informations to an end user by conducting cooperative works such as processing and communicating between sensor nodes. In general, the power supply of a sensor node is depends on a battery so that the power consumption of a sensor node decides the entire life time of a sensor network. To resolve the problem, optimal routing algorithm can be used for prolong the entire life time of a sensor network based on the information on the energy level of each sensor node. In this paper, different from the existing Directed Diffusion and SPTN method, we presents a data dissemination protocol based on lower-power consumption that effectively maximizes the whole life time of a sensor network using the informations on the energy level of a sensor node and shortest-path hops. With the proposed method, a data transfer path is established using the informations on the energy levels and hops, and the collected sensing information from neighboring nodes in the event-occurring area is merged with others and delivered to users through the shortest path.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.307-312
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• 2016

Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.399-404
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• 2002

Along the evolution of information and communication technologies, manufacturing embedded systems such as PDA (personal digital assistant), HPC (hand -held PC), settop box. and information appliance became realistic. And RTOS (real-time operating system) and filesystem have been played essential re]os within the embedded systems as well. For the filesystem of embedded systems, flash memory has been used extensively instead of traditional hard disk drives because of embedded system's requirements like portability, fast access time, and low power consumption. Other than these requirements, nonvolatile storage characteristic of flash memory is another reason for wide adoption in industry. However, there are some technical challenges to cope with to use the flash memory as an indispensable component of the embedded systems. These would be relatively slow cleaning time and the limited number of times to write-and-clean. In this paper, a new cleaning policy is proposed to overcome the problems mentioned above and relevant performance comparison results will be provided. Ranking cleaning policy(RCP) decides when and where to clean within the flash memory considering the cost of cleaning and the number of times of cleaning. This method will maximize not only the lifetime of flash memory but also the performance of access time and manageability. As a result of performance comparison, RCP has showed about 10 ~ 50% of performance evolution compared to traditional policies, Greedy and Cost-benefit methods, by write throughputs.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.2
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• pp.103-112
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• 2010

In sensor networks, because sensors are deployed in an unprotected environment, they are prone to be targets of compromise attack, If the number of compromised nodes increases considerably, the key management in the network is paralyzed. In particular, compromise of Cluster Heads (CHs) in clustered sensor networks is much more threatening than that of normalsensors. Recently, rekeying schemes which update the exposed keys using the keys unknown to the compromised nodes are emerging. However, they cause some security and efficiency problems such as single group key employment in a cluster, passive eviction of compromised nodes, and excessive communication and computation overhead. In this paper, we present a proactive rekeying scheme using renewals of duster organization for clustered sensor networks. In the proposed scheme, each sensor establishes individual keys with neighbors at network boot-up time, and these keys are employed for later transmissions between sensors and their CH. By the periodic cluster reorganization, the compromised nodes are expelled from network and the individual keys employed in a cluster are changed continuously. Besides, newly elected CHs securely agree a key with sink by informing their members to sink, without exchangingany keying materials. The simulation results shows that the proposed scheme remarkably improves the confidentiality and integrity of data in spite of the increase of compromised nodes. Also, they show that the proposed scheme exploits the precious energy resource more efficiently than SHELL.