• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.109-116
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• 2024

The Rapid Hardening Composite Mat (RHCM) is a product that improves the initial strength development speed of conventional Geosynthetic Cementitious Composite Mats (GCCM). It offers the advantage of quickly securing sufficient strength in railway slopes with insufficient formation level, and provides benefits such as preventing slope erosion and inhibiting vegetation growth. In this study, an analysis of the practical applicability of RHCM in railway settings was conducted through experimentation. The on-site applicability was assessed by categorizing it into fire resistance, durability, and stability, and conducting combustibility test, ground contact pressure test, and daily displacement analyses. In the case of South Korea, where a significant portion of the territory is composed of forested areas, the prevention of slope fires is imperative. To analyze the fire resistance of RHCM, combustibility tests were conducted as an essential measure. Durability was assessed through ground contact pressure tests to analyze the deformation and potential damage of RHCM caused by the inevitable use of small to medium-sized equipment on the construction surface. Furthermore, daily displacement analysis was conducted to evaluate the structural stability by comparing and analyzing the displacement and behavior occurring during the application of RHCM with railway slope maintenance criteria. As a result of the experiments, the RHCM was analyzed to meet the criteria for heat release rate and gas toxicity. Furthermore, the ground contact pressure was observed to be consistently above 50 kPa during the curing period of 4 to 24 hours under all conditions. Additionally, the daily displacement analyzed through field site experiments ranged from -1.7 mm to 1.01 mm, confirming compliance with the criteria.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.59-72
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• 2024

Agricultural reservoirs supply water for various purposes such as irrigation, maintenance, and living. Since agricultural reservoirs respond sensitively to seasonal and climate changes, it is essential to estimate supply and inflow for efficient operation, and water management should be done based on these data. However, in the case of agricultural reservoirs, the measurement of supply and inflow is relatively insufficient compared to multi-purpose dams, and inflow-supply analysis in agricultural reservoirs through water balance analysis is necessary for efficient water management. Therefore, rainfall-runoff analysis models such as ONE model and Tank model have been developed and used for reservoir water balance analysis, but the applicability analysis for ungauged watersheds is insufficient. The ONE model is designed for daily runoff calculation, and the model has one parameter, which is advantageous for calibration and ungauged watershed analysis. In this study, the water balance was analyzed through the ONE model and the Tank model for 15 watersheds upstream of dams, and R² and NSE were used to quantitatively compare the performance of the two models. The simulation results show that the ONE model is suitable for predicting the inflow of agricultural reservoirs with the ungauged watershed

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.314-323
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• 2024

Recently, a score-penalty method has been used for the acoustic passive tracking of marine mammals. The interesting aspect of this technique lies in the loss function, which has a penalty term representing the mismatch between the measured signal and the modeled signal, while the traditional time-domain matched-field processing is positively considering the match between them. In this study, we apply the score-penalty method into the depth estimation of a passive target with a known source waveform. Assuming deep ocean environments with uncertainties in the sound speed profile, we evaluate the score-penalty method, comparing it with the time-domain matched field processing method. We shows that the score-penalty method is more accurate than the time-domain matched field processing method in the ocean environment with weak mismatch of sound speed profile, and has better efficiency. However, in the ocean enviroment with strong mismatch of the sound speed profile, the score-penalty method also fails in the depth estimation of a target, similar to the time-domain matched-field processing method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.236-244
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• 2024

A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.99-107
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• 2024

While the demand for extrapolating video content horizontally or vertically is increasing, even the most advanced techniques cannot successfully extrapolate all videos. Therefore, it is important to determine if a given video can be well extrapolated before attempting the actual extrapolation. This can help avoid wasting computing resources. This paper proposes a video classifier that can identify if a video is suitable for horizontal extrapolation. The classifier utilizes optical flow and an adaptive Gaussian blur network, which can be applied to flow-based video extrapolation methods. The labeling for training was rigorously conducted through user tests and quantitative evaluations. As a result of learning from this labeled dataset, a network was developed to determine the extrapolation capability of a given video. The proposed classifier achieved much more accurate classification performance than methods that simply use the original video or fixed blur alone by effectively capturing the characteristics of the video through optical flow and adaptive Gaussian blur network. This classifier can be utilized in various fields in conjunction with automatic video extrapolation techniques for immersive viewing experiences.

• Journal of IKEEE
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• v.28 no.3
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• pp.469-473
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• 2024

In this paper proposes a study on the development of a multi-camera inline inspection system using machine vision for quality inspection of pharmaceutical containers. The proposed technique captures the pharmaceutical containers from multiple angles using several cameras, allowing for more accurate quality assessment. Based on the captured data, the system inspects the dimensions and defects of the containers and, upon detecting defects, notifies the user and automatically removes the defective containers, thereby enhancing inspection efficiency. The development of the multi-camera inline inspection system using machine vision is divided into four stages. First, the design and production of a control unit that fixes or rotates the containers via suction. Second, the design and production of the main system body that moves, captures, and ejects defective products. Third, the design and development of control logic for the embedded board that controls the entire system. Finally, the design and development of a user interface (GUI) that detects defects in the pharmaceutical containers using image processing of the captured images. The system's performance was evaluated through experiments conducted by a certified testing agency. The results showed that the dimensional measurement error range of the pharmaceutical containers was between -0.30 to 0.28 mm (outer diameter) and -0.11 to 0.57 mm (overall length), which is superior to the global standard of 1 mm. The system's operational stability was measured at 100%, demonstrating its reliability. Therefore, the efficacy of the proposed multi-camera inline inspection system using machine vision for the quality inspection of pharmaceutical containers has been validated.

• Sleep Medicine and Psychophysiology
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• v.12 no.1
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• pp.27-31
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• 2005

Objectives: We attempted to compare the performance of 2 commercially available actigraphies with focus on sleep parameters, using polysomnography as standard comparison tool. Methods: Fourteen normal volunteers (5 males and 9 females, mean age of $28{\pm}4.6\;years$) participated in this study. All the participants went through one night of polysomnography, simultaneously wearing 2 different kinds of actigraphies on each wrist. Polysomnographic and actigraphic data were stored, downloaded, and processed according to standard protocols and then statistically compared. Results: Both $ActiWatch^{(R)}$ and $SleepWatch^{(R)}$ tended to overestimate the total sleep time, compared to the polysomnography. $SleepWatch^{(R)}$ tended to underestimate the sleep latency. The two actigraphs and the polysomnograph did not show significant difference of sleep efficiency, when compared with one another. In addition, all of the sleep parameters from the instruments showed linear correlations except in $SleepWatch^{(R)}'s$ sleep latency. The sleep parameters from the two actigraphs did not show much noteworthy difference, and linear relationships were found between the sleep parameters from the two actigraphs. There was no significant distinction in the results of the two different actigraphs. Conclusion: The results of two actigraphies can be used interchangeably since the sleep parameters of the two different actigraphies do not show significant differences statistically. Overall, it is not legitimate to use actigraphy as a substitute for polysomnography. However, since sleep parameters except sleep latency show linear correlations, actigraphy might possibly be used to follow up patients after polysomnography.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.83-98
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• 2015

Many TV viewers use mainly portal sites in order to retrieve information related to broadcast while watching TV. However retrieving information that people wanted needs a lot of time to retrieve the information because current internet presents too much information which is not required. Consequentially, this process can't satisfy users who want to consume information immediately. Interactive video is being actively investigated to solve this problem. An interactive video provides clickable objects, areas or hotspots to interact with users. When users click object on the interactive video, they can see additional information, related to video, instantly. The following shows the three basic procedures to make an interactive video using interactive video authoring tool: (1) Create an augmented object; (2) Set an object's area and time to be displayed on the video; (3) Set an interactive action which is related to pages or hyperlink; However users who use existing authoring tools such as Popcorn Maker and Zentrick spend a lot of time in step (2). If users use wireWAX then they can save sufficient time to set object's location and time to be displayed because wireWAX uses vision based annotation method. But they need to wait for time to detect and track object. Therefore, it is required to reduce the process time in step (2) using benefits of manual annotation method and vision-based annotation method effectively. This paper proposes a novel annotation method allows annotator to easily annotate based on face area. For proposing new annotation method, this paper presents two steps: pre-processing step and annotation step. The pre-processing is necessary because system detects shots for users who want to find contents of video easily. Pre-processing step is as follow: 1) Extract shots using color histogram based shot boundary detection method from frames of video; 2) Make shot clusters using similarities of shots and aligns as shot sequences; and 3) Detect and track faces from all shots of shot sequence metadata and save into the shot sequence metadata with each shot. After pre-processing, user can annotates object as follow: 1) Annotator selects a shot sequence, and then selects keyframe of shot in the shot sequence; 2) Annotator annotates objects on the relative position of the actor's face on the selected keyframe. Then same objects will be annotated automatically until the end of shot sequence which has detected face area; and 3) User assigns additional information to the annotated object. In addition, this paper designs the feedback model in order to compensate the defects which are wrong aligned shots, wrong detected faces problem and inaccurate location problem might occur after object annotation. Furthermore, users can use interpolation method to interpolate position of objects which is deleted by feedback. After feedback user can save annotated object data to the interactive object metadata. Finally, this paper shows interactive video authoring system implemented for verifying performance of proposed annotation method which uses presented models. In the experiment presents analysis of object annotation time, and user evaluation. First, result of object annotation average time shows our proposed tool is 2 times faster than existing authoring tools for object annotation. Sometimes, annotation time of proposed tool took longer than existing authoring tools, because wrong shots are detected in the pre-processing. The usefulness and convenience of the system were measured through the user evaluation which was aimed at users who have experienced in interactive video authoring system. Recruited 19 experts evaluates of 11 questions which is out of CSUQ(Computer System Usability Questionnaire). CSUQ is designed by IBM for evaluating system. Through the user evaluation, showed that proposed tool is useful for authoring interactive video than about 10% of the other interactive video authoring systems.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.306-320
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• 2018

Emission inventory is the essential component for improving the performance of air quality forecasting system. This study evaluated the simulated daily mean $PM_{2.5}$ concentrations in South Korea and China for 1-year period (Sept. 2016~Aug. 2017) using air quality forecasting system which was applied by the emission inventory of E2015 (predicted CAPSS 2015 for South Korea and KORUS 2015 v1 for the other regions). To identify the impacts of emissions on the simulated $PM_{2.5}$, the emission inventory replaced by E2010 (CAPSS 2010 and MIX 2010) were also applied under the same forecasting conditions. These results showed that simulated daily mean $PM_{2.5}$ concentrations had generally suitable performance with both emission data-sets for China (IOA>0.87, R>0.87) and South Korea (IOA>0.84, R>0.76). The impacts of the changes in emission inventories on simulated daily mean $PM_{2.5}$ concentrations were quantitatively estimated. In China, normalized mean bias (NMB) showed 5.5% and 26.8% under E2010 and E2015, respectively. The tendency of overestimated concentrations was larger in North Central and Southeast China than other regions under both E2010 and E2015. Seasonal differences of NMB were higher in non-winter season (28.3% (E2010)~39.3% (E2015)) than winter season (-0.5% (E2010)~8.0% (E2015)). In South Korea, NMB showed -5.4% and 2.8% for all days, but -15.2% and -11.2% for days below $40{\mu}g/m^3$ to minimize the impacts of long-range transport under E2010 and E2015, respectively. For all days, simulated $PM_{2.5}$ concentrations were overestimated in Seoul, Incheon, Southern part of Gyeonggi and Daejeon, and underestimated in other regions such as Jeonbuk, Ulsan, Busan and Gyeongnam, regardless of what emission inventories were applied. Our results suggest that the updated emission inventory, which reflects current status of emission amounts and spatio-temporal allocations, is needed for improving the performance of air quality forecasting.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.