• Journal of Korean Society of Environmental Engineers
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• v.35 no.6
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• pp.422-429
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• 2013

It is generally known that the increase of the Earth surface temperature due to the global warming together with the land desertification by rapid urban development has caused severe climate and weather change. In desert or desertification land, it is observed that there are always severe flooding phenomena, even if desert sand has the high porosity, which could be believed as the favorable condition of rain water infiltration into ground water. The high runoff feature causes possibly another heavy rain by quick evaporation with the depletion of underground water due to the lack of infiltration. The basic physics of desert flooding is reasonably assumed due to the thermal buoyancy of the higher temperature of the soil temperature than that of the rain drop. Considering the importance of this topic associated with water resource management and climate disaster prevention, no systematic investigation has, however, been reported in literature. In this study, therefore, a laboratory scale experiment together with the effort of numerical calculation have been performed to evaluate quantitatively the basic hypothesis of run-off mechanism caused by the increase of soil temperature. To this end, first, of all, a series of experiment has been made repeatedly with the change of soil temperature with well-sorted coarse sand having porosity of 35% and particle diameter, 2.0 mm. In specific, in case 1, the ground surface temperature was kept at $15^{\circ}C$, while in case 2 that was high enough at $70^{\circ}C$. The temperature of $70^{\circ}C$ was tested as this try since the informal measured surface temperature of black sand in California's Coachella Valley up to at 191 deg. $^{\circ}F$ ($88^{\circ}C$). Based on the experimental study, it is observed that the amount of runoff at $70^{\circ}C$ was higher more than 5% compared to that at $15^{\circ}C$. Further, the relative amount of infiltration by the decrease of the surface temperature from 70 to $15^{\circ}C$ is about more than 30%. The result of numerical calculation performed was well agreed with the experimental data, that is, the increase of runoff in calculation as 4.6%. Doing this successfully, a basic but important research could be made in the near future for the more complex and advanced topic for this topic.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.149-157
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• 2013

The weirs built so far are mainly overflow type weirs overflowing to the upstream. Main advantages of overflow type weirs are, effective water resources management and easy design, construction and maintenance due to many accumulated studies. However, due to the special feature of the overflow type weir where water overflows through the upstream of the weir, the silt coming from the upstream is not discharged to the downstream of the weir. This increases the river bed and reduces the reservoir capacity, and as a result, the weir loses its function. A underflow type weir with a water gate has been implemented in order to solve such sediment deposit and weir maintenance problems. However due to the design problem of recently constructed underflow type weirs, the river bed of the downstream of a weir has been scoured. And this leds to a structural problem. In this study, the flow characteristics of overflow type weirs and underflow type weir, hydraulic jump length analysis depending on change of water depth and the amount of specific energy loss generated per unit length depending on a weir type have been compared and analyzed, for the effective design and management of the weirs. The experiment results show that, when identical upstream conditions of underflow type weir and an overflow type weir were maintained, the hydraulic jump length was up to twice longer with Fr(Froude number) 3.5 of the hydraulic jump length at the underflow type weir, and the hydraulic jump length gradually decreased as the downstream water depth increased. The comparative analysis result of the amount of specific energy loss generated per unit length showed that the amount of energy loss per unit length was twice higher for an overlfow type weir than a underflow type weir. Therefore, in case of a underflow type facility, an additional energy reduction facility is determined to be necessary for safety of water construction structures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Geophysics and Geophysical Exploration
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• v.14 no.1
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• pp.80-87
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• 2011

Autonomous underwater vehicles (AUVs) present the important advantage of being able to approach the seafloor more closely than surface vessel surveys can. To collect bathymetric data, bottom material information, and sub-surface images, multibeam echosounder, sidescan sonar (SSS) and subbottom profiler (SBP) equipment mounted on an AUV are powerful tools. The 3000m class AUV URASHIMA was developed by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). After finishing the engineering development and examination phase of a fuel-cell system used for the vehicle's power supply system, a renovated lithium-ion battery power system was installed in URASHIMA. The AUV was redeployed from its prior engineering tasks to scientific use. Various scientific instruments were loaded on the vehicle, and experimental dives for science-oriented missions conducted from 2006. During the experimental cruise of 2007, high-resolution acoustic images were obtained by SSS and SBP on the URASHIMA around the northern Kumano Basin off Japan's Kii Peninsula. The map of backscatter intensity data revealed many debris objects, and SBP images revealed the subsurface structure around the north-eastern end of our study area. These features suggest a structure related to the formation of the latest submarine fan. However, a strong reflection layer exists below ~20 ms below the seafloor in the south-western area, which we interpret as a denudation feature, now covered with younger surface sediments. We continue to improve the vehicle's performance, and expect that many fruitful results will be obtained using URASHIMA.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.6
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• pp.57-65
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• 2010

Physical activity of the people has decreased due to a sedentary lifestyle according to developing the economy throughout the world. It is thought to increase the risk of chronic diseases, including obesity, diabetes, etc. People are interested in walking, which is an easy activity to engage in as an antidote to chronic diseases. The aim of this study is to increase the diminishing physical activity of modem society by inducing walking as part of everyday life through building a walking-based activity-friendly city where people can live merrily, safely and pleasantly. For this purpose, this study conducted a satisfaction survey to dwellers of Jinhae on the physical pedestrian environments which affect determining walking participation and intentions of people, and also provided a valid model to evaluate the effects of the physical environmental factors on walking satisfaction using factor analysis and multiple linear regression analysis. The results are summarized as follows. The 18 variables of the physical pedestrian environments were selected based on pre-literature reviews. The results of the satisfaction surveys showed that the satisfaction of crossing aids in segments was highest, while the building feature was the lowest. Factor analysis was run through a two-step process. The first analysis was conducted to examine the adequacy of this factor analysis on the selected 18 variables. As a result, two variables were removed and the remaining 16 variables were extracted to the four factors by second analysis. Each factor was named function of path, effect of traffic, amenity and safety based on the each factor's commonality. Each factor score of the extracted four factors was set as the independent variable, while the overall walking satisfaction was set as the dependent variable. Then, the multiple linear regression analysis was conducted and showed that all four factors had a positive influence on the overall satisfaction of walking, especially the 'function of path' and 'amenity' factors, followed by 'effect of traffic' and 'safety'. The results of this research will be used as foundational data for creating a walking-based activity-friendly city.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.223-231
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• 2012

The purpose of this study is to investigate basically the mechanism of heat transfer by the resolution of complex fluid flow inside a sophisticated designed screw dryer for the treatment of sewage sludge by using numerical analysis and experimental study. By doing this, the result was quite helpful to obtain the design criteria for enhancing drying efficiency, thereby achieving the optimal design of a multiple screw type dryer for treating inorganic and organic sludge wastes. One notable design feature of the dryer was to bypass a certain of fraction of the hot combustion gases into the bottom of the screw cylinder, by the fluid flow induction, across the delicately designed holes on the screw surface to agitate internally the sticky sludges. This offers many benefits not only in the enhancement of thermal efficiency even for the high viscosity material but also greater flexibility in the application of system design and operation. However, one careful precaution was made in operation in that when distributing the hot flue gas over the lump of sludge for internal agitation not to make any pore blocking and to avoid too much pressure drop caused by inertial resistance across the lump of sludge. The optimal retention time for rotating the screw at 1 rpm in order to treat 200 kg/hr of sewage sludge was determined empirically about 100 minutes. The corresponding optimal heat source was found to be 150,000 kcal/hr. A series of numerical calculation is performed to resolve flow characteristics in order to assist in the system design as function of important system and operational variables. The numerical calculation is successfully evaluated against experimental temperature profile and flow field characteristics. In general, the calculation results are physically reasonable and consistent in parametric study. In further studies, more quantitative data analyses such as pressure drop across the type and loading of drying sludge will be made for the system evaluation in experiment and calculation.

• Resources Recycling
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• v.23 no.2
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• pp.17-25
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• 2014

The thermal effect on the compositional change of the $SO_2$ absorption process product was investigated compared with the composition of raw material when dolomite is employed in place of lime in the scrubbing process based on thermodynamic estimation. It was considered that the equilibrium reactions which directly related with the formation of $CaSO_4$ and $MgSO_4$, the absorption process products, are those between $Ca^{2+}$ and $Ca(OH)_2$, $Mg^{2+}$ and $Mg(OH)_2$, and the secondary dissociation reaction of $H_2SO_4$. It was thought to be necessary to examine the enthalpy change for the formation reactions of $CaSO_4$ and $MgSO_4$ along with the thermal feature of the relative reactions to figure out the influence of temperature on the compositional change of absorption process products. The stable regions for $Ca(OH)_2$ and $Mg(OH)_2$ in Pourbaix diagram were found to be increased as temperature rises and the equilibrium reaction between $Ca^{2+}$ and $Ca(OH)_2$ was investigated to be more strongly influence by temperature change compared with the equilibrium reaction between $Mg^{2+}$ and $Mg(OH)_2$. The amounts of $CaSO_4$ and $MgSO_4$ were anticipated to be decreased with temperature considering the thermal characteristics for the equilibrium reactions regarding calcium, magnesium, and $H_2SO_4$. It was understood that the formation ratio between $CaSO_4$ and $MgSO_4$ is greater than the composition ratio between calcium and magnesium contained in dolomite at specific temperature and the decrease of the formation ratio of $CaSO_4$ and $MgSO_4$ with temperature was estimated to be diminished as the content of calcium in dolomite is increased. In addition, the extent of the change in the compositional ratio between absorption process products was examined to be reduced compared with the composition of raw material as the calcium content in dolomite is raised.