• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.127-132
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• 2023

Authors report the performance indicator of Roll to Roll MC (hereinafter referred to as RTR) through experiment that it is possible to process 2.0G acceleration, which cannot be coped with in the existing FPCB RTR, by reducing the weight of the Dancer roll system and controlling the torque using the servo motor. Proposed dancer roll system provides uniform tension to FPCB by solving problems such as high rotation speed, heat generation, and low torque, which were impossible to achieve with the conventional magnet clutch type RTR. Through the development of a lightweight processing method for rolls using magnesium material and the development of a torque control algorithm for servo motor, torque stability also increased. Due to the enabling technology developed in this study, the reaction speed of the dancer roll was improved and the target speed was achieved as well.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.412-417
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• 2022

In this paper, multi-GNSS network RTK MAC correction generation software developed based on MATLAB GUI is introduced. The software was developed as a post-processing software based on simulation data to evaluate the feasibility of an algorithm for generating correction for multi-GNSS including GPS, GLONASS, and Galileo. As a result of software operation, network RTK correction for each system of multi-GNSS is output in MATLAB file format. In this paper, to evaluate the performance of the developed software, the residual error was analyzed after applying the correction generated through the software to the user. As a result of the analysis, it was confirmed that effective network RTK correction could be generated by confirming that the residual errors of users were maintained at 10 cm or less.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• The Korean Journal of Malacology
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• v.29 no.4
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• pp.325-334
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• 2013

The purpose of this paper is to compare and analyze family variations for growth-related traits of Pacific abalone, Haliotis discus hannai. Genetic parameters and breeding values were estimated using all measurement data like shell length, shell width, and total weight as 18-month-old growth traits of 5,334 individuals of selected third generation's Pacific abalone produced in 2011. Family variations of 865 individuals of the upper 10 families with the largest number were inspected. Overall mean in phenotypic traits of 18-month-old Pacific abalone which was investigated in this study showed 54.5 mm of shell length, 36.8 mm of shell width and 21.3 g of total weight respectively. And, variation coefficient of total weight was 51.0%, so variability of data was shown to be higher than 21.1% of shell length and 20.7% of shell width. The family effects showed significant difference by each family (p < 0.05), and heritability of shell length, shell width, and total weight was medium with 0.370, 0.382, and 0.367 respectively. So it is considered that family selection is more advantageous than individual selection. On the basis of breeding values of estimated shell length and total weight, to investigate distribution and ranking by each individual about the upper 10 families with the largest number of individuals, the values were used by being changed into standardized breeding values. Based on shell length, it was investigated that the individual number of the upper 5.4% is 152 and the number of the lower 5.4% is 8. In case of total weight, it was inspected that the individual number of the upper 5.4% is 164 and the number of the lower 5.4% is 1. Like these, phenotypic and genetic diverse variations between families could be checked. By estimating genetic parameters and breeding values of a population for production of the next generation, if they are used properly in selection and mating, it is considered that more breeding effects can be expected.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.691-700
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• 2006

Based on the destination IP address of incoming packets, the Internet routers determine next hops and forward packets toward final destinations through If address lookup. The bandwidth of communication links increases exponentially fast as well as the routing table size grows significant as the number of single host networks attached to the Internet increases. Since packets should be processed at wire-speed, the increased link speed reduces the processing time of a packet in routers, and hence more efficient and fast IP address lookup algorithms and architectures are required in the next generation routers. Most of the previous IP lookup schemes compare routing prefixes of shorter length first with a given input IP address. Since IP address lookup needs to find the most specific route of the given input, search continues until the longest matched prefix is found while it keeps remembering the current test matching prefix. In this paper, based on binary search on prefix length, we proposed a new IP address lookup algorithm which compares longer prefixes first. The proposed scheme is consisted of multiple tries with prefixes on leaves only. The trie composed of the longest prefixes is primarily searched whether there is a match with the given input. This processing is repeated for the trio of the next longer prefixes until there finds a match. Hence the proposed algorithm provides the fast search speed. The proposed algorithm also provides the incremental update of prefixes while the previous binary search on length scheme does not provide the incremental update because of pre-processing requirement. In this paper, we performed extensive simulations and showed the performance comparisons with related works.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.