• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.62-70
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• 2016

Ensuring walkability in a city where pedestrians and vehicles coexist is an issue of critical importance. The relative relationship between vehicle transit and walkability improvements complicates the evaluation of walkability, which thus necessitates the formation of a quantitative standard by which a methodological measurement of walkability can be achieved inside the pedestrian network. Therefore, a model is determined whereby quantitative indices such as, but not limited to, experiences of accessibility, mobility, and convenience within the network are estimated. This research proposes the integration of space syntax theory and the logit path choice model in the evaluation of walkability. Space syntax theory assesses adequacy of the constructed pedestrian network through calculation of the link integration value, while the logit model estimates its safety, mobility, and accessibility using probability. The advantage of the integrated model hence lies in its ability to sufficiently reflect such evaluation measures as the integration value, mobility convenience, accessibility potential, and safety experienced by the demand in a quantitative manner through probability computation. In this research, the Dial Algorithm is used to arrive at a solution to the logit model. This process requires that the physical distance of the pedestrian network and the perceptive distance of space syntax theory be made equivalent. In this, the research makes use of network expansion to reflect wait times. The evaluation index calculated through the integrated model is reviewed and using the results of this sample network, the applicability of the model is assessed.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.75-81
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• 2003

User authentication, including confidentiality, integrity over untrusted networks, is an important part of security for systems that allow remote access. Using human-memorable Password for remote user authentication is not easy due to the low entropy of the password, which constrained by the memory of the user. This paper presents a new password authentication and key agreement protocol suitable for authenticating users and exchanging keys over an insecure channel. The new protocol resists the dictionary attack and offers perfect forward secrecy, which means that revealing the password to an attacher does not help him obtain the session keys of past sessions against future compromises. Additionally user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the server. It does not have to resort to a PKI or trusted third party such as a key server or arbitrator So no keys and certificates stored on the users computer. Further desirable properties are to minimize setup time by keeping the number of flows and the computation time. This is very useful in application which secure password authentication is required such as home banking through web, SSL, SET, IPSEC, telnet, ftp, and user mobile situation.

• Research in Community and Public Health Nursing
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• v.12 no.3
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• pp.795-814
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• 2001

To evaluate the effect of sex education on middle school students' access to the obscene online computer and video film contents. 154 students were selected as experimental group. and 154 students were selected as control group, sampled randomly from Andong. Kyungbook, Korea. An analysis was performed. A non-equivalent control group pre test-post test research design was used. The data were collected from April 2nd to April 19th. 2001. A pre-survey was done on general characteristics and the condition of accessing obscene online computer and video film contents on both experimental and control group. From the survey results information. sex education contents were put together. The researcher organized 3 ready-made sex education program and explained to the four school health nurses about the ready-made sex education program step by step and they educated their selected students with three classes of 45 minutes lecture. Two weeks after the last lecture, a post-test was conducted. Four weeks from the last lecture, another post-test was conducted. The existing studies by Choi Yongseon(1998) and Kim Hyeok(1998) were reviewed and two professors in the department of community health nursing advised on the study questionnaire writing. An SPSS Win 10.0 was used. The data of respondents' general characteristics were analyzed using frequency and percentage. $X^2$ test was used to verify the homogeneity of the experimental group and the control group. Repeated Measures ANOVA was used to find out whether sex education had an effect on the awareness of obscene online computer and video film contents and under-age prostitution through the online computer networks. and time and frequency of access to the obscene online computer and video film contents. The results of the study are as follow. 1. The results of the verification of homogeneity between the experimental group and the control group showed that there was no significant difference between the experimental group and the control group. 2. The first hypothesis, 'the experimental group which received sex education would have a higher level of awareness of accessing obscene contents than the control group which did not receive the education' was supported at p<0.0001. 3. The second hypothesis. 'the experimental group which received sex education would have a higher level of awareness of underage prostitution on computer networks than the control group which did not receive the education' was supported at p<0.05. 4. The third hypothesis, 'the experimental group which received sex education would spend time less accessing obscene video and computer contents than the control group which did not receive the education' was rejected at p>.05. 5. The 4-1 hypothesis. 'the experimental group which received sex education would access obscene computer contents less frequently than the control group which did not receive the education' was supported at p<0.0001. 6. The 4-2 hypothesis, 'the experimental group which received sex education would access obscene video contents less frequently than the control group which did not receive the education' was supported at p<0.0001. In conclusion, a systematic step-by-step sex education program should be developed to protect middle school students from the harmful online computer and video film access. An effective teaching material for sex education should be prepared to decrease middle school students' access to obscene online computer and video film contents.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.8
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• pp.52-59
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• 2007

In this paper, we analyzed and measured implant isolation characteristics for a 1.25 Gbps monolithic integrated hi-directional (M-BiDi) optoelectronic system-on-a-chip, which is a key component to constitute gigabit passive optical networks (PONs) for a fiber-to-the-home (FTTH). Also, we derived an equivalent circuit of the implant structure under various DC bias conditions. The 1.25 Gbps M-BiDi transmit-receive SoC consists of a laser diode with a monitor photodiode as a transmitter and a digital photodiode as a digital data receiver on the same InP wafer According to IEEE 802.3ah and ITU-T G.983.3 standards, a receiver sensitivity of the digital receiver has to satisfy under -24 dBm @ BER=10-12. Therefore, the electrical crosstalk levels have to maintain less than -86 dB from DC to 3 GHz. From analysed and measured results of the implant structure, the M-BiDi SoC with the implant area of 20 mm width and more than 200 mm distance between the laser diode and monitor photodiode, and between the monitor photodiode and digital photodiode, satisfies the electrical crosstalk level. These implant characteristics can be used for the design and fabrication of an optoelectronic SoC design, and expended to a mixed-mode SoC field.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.