• Journal of the Korea Society of Computer and Information
• /
• v.26 no.1
• /
• pp.45-55
• /
• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.1-18
• /
• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.199-219
• /
• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Korean Society of Transportation
• /
• v.17 no.4
• /
• pp.59-69
• /
• 1999

A Freeway corridor is a network consisting of a few Primary longitudinal roadways (freeway or major arterial) carrying a major traffic movement with interconnecting roads which offer the motorist alternative paths to his/her destination. Control measures introduced to ameliorate traffic performance in freeway corridors typically include ramp metering at the freeway entrances, and signal control at each intersections. During a severe freeway incident, on-ramp metering usually is not adequate to relieve congestion effectively. Diverting some traffic to the Parallel surface street to make full use of available corridor capacity will be necessary. This is the purpose of the traffic management system. So, an integrated traffic control scheme should include three elements. (a)on-ramp metering, (b)off-ramp diversion and (c)signal timing at surface street intersections. The purpose of this study is to develop an integrated optimal control model in a freeway corridor. By approximating the flow-density relation with a two-segment linear function. the nonlinear optimal control problem can be simplified into a set of Piecewise linear programming models. The formulated optimal-control Problem can be solved in real time using common linear program. In this study, program MPL(ver 4.0) is used to solve the formulated optimal-control problem. Simulation results with TSIS(ver 4.01) for a sample network have demonstrated the merits of the Proposed model and a1gorithm.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_1
• /
• pp.455-467
• /
• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Journal of the HCI Society of Korea
• /
• v.11 no.3
• /
• pp.13-22
• /
• 2016

Vocal and instrument apprenticeship in applied music has been accepted without significant changes since the introduction of applied music education to South Korea. Few discussions or suggestions about other types of teaching than 'one-to-one lessons' or 'education of apprentices by assigned specialists' have been made. Since the introduction of applied music education to South Korea late in the 1980s, the CAI(Computer Aided Instruction) courseware development for applied music education has not actively been under way. The area of applied music has also made rapid progress in terms of music producing or music videos using computers. Actually, the improved computer program is not positively applied to applied music education. This study aimed to present learning methods using the improved functions of music production softwares to improve the traditional apprenticeship system in the area of vocal training in applied music. In particular, it used the technique of auto tune-pitch shift developed for interval correction in sound sources. By giving real-time feedbacks concerning intervals or monitors visually after recording, it intended to present a learning method to induce improvement in accuracy of intervals in vocal training. This study is expected to present a method that allows vocal trainers to overcome temporal and spatial limitations in applied music and make their vocal training more efficient.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• The Journal of Korean Philosophical History
• /
• no.27
• /
• pp.197-222
• /
• 2009

The Reform Movement(戊戌變法) of 1898 was a boundary tablet of modern history of science and technology which inherited the past and ushered in the future. Kang Youwei(康有爲), as a leader, his scientific thoughts opened up the way of Chinese enlightenity campaign and pushed the development of Chinese modem science and had an important position in modem history of scientific thoughts. The dissertation analyses the source, establishment and content of Kang Youwei. Kang Youwei developed the useful and discarded the useless of the view of implement science held by the Westernized Party, undertook a deep and throughout thinking on the nature of science, had cognition of scientific methods and spirit, by which he criticized negative proneness of ancient Chinese views of science. He put forwards a series of practical suggestions on political reform that provided a solid guarantee and support in system for scientific development. Kang Youwei rooted in the soil of Chinese traditional academic culture, but also western learning in modern western civilization. Kang go through Westernization Movement since the in-depth study of Western natural and inevitable outcome of the social sciences, are giving to science and technology. Although he was originally of Western "science" has a lot of misunderstandings and prejudices, but these shallow hazy perceptual knowledge, his view of science which constitutes the basis of the formation. In the course of scientific inquiry, Kang has begun to explore the essence of scientific development. He has a gut feeling that behind the scientific discovery of the existence of a force, which is the scientific truth and is used to grasp the scientific method. After contact with the Western world, with the traditional "Heaven(天)", and modern Chinese intellectuals began to "axiom(公理)" to recover his traditional "Heaven" of the new understanding is reflected mainly in "Zhutianjiang(諸天講)". "Zhutianjiang" is the Kang Yuwei in the absorption of traditional astronomy knowledge base, will the traditional arithmetic, as well as Buddhism and the West since the twentieth century, new knowledge of astronomy combines written. Kang while recognizing that scientific instruments, is nothing more than an extension of the role of the human senses and make the "Dao(道)" is more clear, but the "artifacts(器物)" caused by the inherent limitations of the limited nature of human knowledge, which is "Heaven" boundless nature of the broad terms, refused to concede defeat to. In reality, the activities of political reform, he gradually recognize this real-world helpless, and he recognized that the real world to achieve common ground of social ideal is impossible, so he chose comfort in life that people really get a stage of "Tianyou(天遊)". This is the cause that his writing "Datongshu(大同書)", at the same time, followed by writing "Zhutianjiang" talk "Tianyou".

• Journal of Korean Medical Science
• /
• v.33 no.49
• /
• pp.323.1-323.12
• /
• 2018

Background: Vitamin K antagonist (VKA) to prevent thromboembolism in non-valvular atrial fibrillation (NVAF) patients has limitations such as drug interaction. This study investigated the clinical characteristics of Korean patients treated with VKA for stroke prevention and assessed quality of VKA therapy and treatment satisfaction. Methods: We conducted a multicenter, prospective, non-interventional study. Patients with $CHADS_2{\geq}1$ and treated with VKA (started within the last 3 months) were enrolled from April 2013 to March 2014. Demographic and clinical features including risk factors of stroke and VKA treatment information was collected at baseline. Treatment patterns and international normalized ratio (INR) level were evaluated during follow-up. Time in therapeutic range (TTR) > 60% indicated well-controlled INR. Treatment satisfaction on the VKA use was measured by Treatment Satisfaction Questionnaire for Medication (TSQM) after 3 months of follow-up. Results: A total of 877 patients (age, 67; male, 60%) were enrolled and followed up for one year. More than half of patients (56%) had $CHADS_2{\geq}2$ and 83.6% had $CHA_2DS_2-VASc{\geq}2$. A total of 852 patients had one or more INR measurement during their follow-up period. Among those patients, 25.5% discontinued VKA treatment during follow-up. Of all patients, 626 patients (73%) had poor-controlled INR (TTR < 60%) measure. Patients' treatment satisfaction measured with TSQM was 55.6 in global satisfaction domain. Conclusion: INR was poorly controlled in Korean NVAF patients treated with VKA. VKA users also showed low treatment satisfaction.

• The KIPS Transactions:PartC
• /
• v.10C no.5
• /
• pp.625-634
• /
• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.