• Korean Journal of Environment and Ecology
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• v.28 no.1
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• pp.90-98
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• 2014

This study was carried out to provide basic data. his research conducted the survey using face to face survey and board survey during about 2 months from Oct. to Nov. in 2009 for users of Bongje Mt., a small-sized mountain at downtown, and Acha Mt., a big-sized mountain at outskirt so as to compare the differences of using behavior by forms and size of urban forest in living area of Seoul. Characteristics of urban forest users, using behavior, demands and satisfaction of facilities and management and pass pattern were set as research items. The thing in common for using behavior is that both genders of main users were in more than 40s~60s. They showed the highest using rate from 7 a.m. to 12 p.m. and high rate for using nearly everyday or visiting two or three times per a week. In addition, it's judged that the accessibility from dwelling area to entrance of urban forest in living area is good and satisfaction for the standard of facilities and their management in forest way was relatively low. For the complement and essential facilities, 'sanitary facilities' showed the highest rate. For the differences of using behavior, most of Bongje Mt. users were residents living within a 2km radius (under the standard of walking) and they moved by average 1.3km. And, they preferred short-time activities of about 24 minutes. On the other hand, main users of Acha Mt. were residents living within a 4km radius (under the standard of walking) and people of other regions. and 60% of them preferred the passage route taking 3hours half over 6km. Through the survey on using behavior of urban forest in living area of Seoul, with different using form and forest size, introduction of using program for main users or managing method of differentiations for introduced facility's management should be properly applied. Especially, urban forest should be systematically managed like park green as expected that residents's using of urban forest will be increased with the increase of leisure time.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.19-29
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• 2006

As the security of personal information is becoming more important in mobile phones, we are starting to apply iris recognition technology to these devices. In conventional iris recognition, magnified iris images are required. For that, it has been necessary to use large magnified zoom & focus lens camera to capture images, but due to the requirement about low size and cost of mobile phones, the zoom & focus lens are difficult to be used. However, with rapid developments and multimedia convergence trends in mobile phones, more and more companies have built mega-pixel cameras into their mobile phones. These devices make it possible to capture a magnified iris image without zoom & focus lens. Although facial images are captured far away from the user using a mega-pixel camera, the captured iris region possesses sufficient pixel information for iris recognition. However, in this case, the eye region should be detected for accurate iris recognition in facial images. So, we propose a new fast iris detection method, which is appropriate for mobile phones based on corneal specular reflection. To detect specular reflection robustly, we propose the theoretical background of estimating the size and brightness of specular reflection based on eye, camera and illuminator models. In addition, we use the successive On/Off scheme of the illuminator to detect the optical/motion blurring and sunlight effect on input image. Experimental results show that total processing time(detecting iris region) is on average 65ms on a Samsung SCH-S2300 (with 150MHz ARM 9 CPU) mobile phone. The rate of correct iris detection is 99% (about indoor images) and 98.5% (about outdoor images).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.33-43
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• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.363-371
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• 1992

This study was aimed at developing a computer model to determine rational road networks in mountainous forests. The computer model is composed of two major subroutines for digital terrain analyses and route selection. The digital terrain model(DTM) provides various information on topographic and vegetative characteristics of forest stands. The DTM also evaluates the effectiveness of road construction based on slope gradients. Using the results of digital terrain analyses, the route selection subroutine, heuristically, determines the optimal road layout satisfying the predefined road densities. The route selection subroutine uses the area-partitioning method in order to fully of roads. This method leads to unbiased road layouts in forest areas. The size of the unit partitiones area can be calculated as a function of the predefined road density. In addition, the user-defined road density of the area-partitioning method provides flexibility in applying the model to real situations. The rational road network can be easily achived for varying road densities, which would be an essential element for network design of forest roads. The optimality conditions are evaluated in conjuction with longitudinal gradients, investment efficiency earthwork quantity or the mixed criteria of these three. The performance of the model was measured and, then, compared with those of conventional ones in terns of average skidding distance, accessibility of stands, development index and circulated road network index. The results of the performance analysis indicate that selection of roading routes for network design using the digital terrain analysis and the area-partitioning method improves performance of the network design medel.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of radiological science and technology
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• v.37 no.1
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• pp.7-14
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• 2014

By using a Chest Phantom(DUKE Phantom) focusing on dose reduction of diagnostic radiation field with the most use of artificial radiation, and attempt to reduce radiation dose studies technical radiation. Publisher of the main user of the X-ray Radiological technologists, Examine the effect of reducing the radiation dose to apply additional filtering of the X-ray generator. In order to understand the organ dose and effective dose by using the PC-Based Monte Carlo Program(PCXMC) Program, the patient receives, was carried out this research. In this experiment, by applying a complex filter using a copper and Al(aluminum,13) and filtered single of using only aluminum with the condition set, and measures the number of the disk of copper indicated by DUKE Phantom. The combination of the composite filtration and filtration of a single number of the disk of the copper is the same, with the PCXMC 2.0. Program looking combination of additional filtration fewest absorbed dose was calculated effective dose and organ dose. Although depends on the use mAs, The 80 kVp AP projection conditions, it is possible to reduce the effective amount of about 84 % from about 30 % to a maximum at least. The 120 kVp PA projection conditions, it is possible to reduce the effective amount of about 71 % from about 41 % to a maximum of at least. The organ dose, dose reduction rate was different in each organ, but it showed a decrease of dose rate of 30 % to up 100 % at least. Additional filtration was used on the imaging conditions throughout the study. There was no change in terms of video quality at low doses. It was found that using the DUKE Phantom and PCXMC 2.0 Program were suitable to calculate the effect of reducing the effective dose and organ dose.

• Journal of Korea Water Resources Association
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• v.44 no.4
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• pp.315-325
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• 2011

There are not many practical measures to solve a water conflict, when a hydromorphologically asymmetric situation in international rivers exists whereby downstream users may not affect upstream users but upstream users do cause downstream impacts. In taking advantage of this merit, North Korea has built Imnam Dam in upstream of North Han River and uses water for trans-basin hydropower generation. As an impact of this dam South Korean' area as a downstream user has been suffered from water deficit and dry river. It is very critical for South Korea to solve a key problem such as water allocation for water supply and river maintenance. Therefore, this study is aim to suggest alternatives for equitable water allocation in consideration of special circumstances between the South and the North. For this, reviewing the allocation methods of water rights is carried using lessons obtained from international river cases. The results show that the minimum desired streamflow is calculated at 7.3 $m^3/sec$; water budget analysis by the equitable distribution of streamflows at the border line of the North Han River, the difference in water supply deficiency is at 3.7 $m^3/sec$ before and after Imnam Dam; in the determined distribution method, the difference in water deficiency is at 11.38 $m^3/sec$. These results show that South Korea should be secured 11.38 $m^3/sec$ from North Korean's Imnam Dam in respective of water use right and sound river maintenance.

• Archives of design research
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• v.16 no.4
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• pp.385-394
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• 2003

An investigation by KRNIC (Korea Network Information Center) on the real state of usage of internet has shown that 96.9％ of children investigated had experiences of using internet. Especially the firstly ranked item that had been answered by children as a necessity of internet was 'Studying to solve tasks' rated by 83.9％. As seen from the research result, the need as a homework sonics is actually so dominant that it cannot be ignored when considering the profitability at the area of education contents, but any profound research has not been accomplished yet. Internet has been positioned as a more effective and fruitful learning tool, and also all activities done by users for exploring informations and choosing learning items under the on-line circumstances are based on the successive mutual reactions between users and computers. Up to now much of the web based learning circumstances has been introducing the User Interface using metaphor, and the same is found dominantly from the sites for children. But in spite of the availability of metaphor mentioned above the current status is much lack of profound researches about metaphor interface; and what is more, in the case of the site for elementary school students the gap of the ability recognizing metaphor is very large between lower classes and higher classes according to the degree of mental growth but that is used to be simply ignored, then a common concept is adapted to interface for all grades of classes and moreover for infant and kindergarten without any objections. Based on foregoing problems this research has put the main focus on the groping and presenting desirable directions on the prospect design of interface for children-oriented sites by analyzing the status of practical usage of metaphor interface in the field of the sites for children-oriented learning sites with concentration upon homework supporting domains.