• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.367-376
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• 2021

Strawberry cultivation in Korea is grown in greenhouse, but most farms manage their water supply using a timer control method based on the experience of growers. The timer control has problems in that it is difficult to consider the weather condition, the growth stage of crops, and the moisture content of the substrate, so that the crops cannot be managed at an optimal level, and the accuracy of cultivation management are lacking. The watering methods using integrated solar irradiance and substrate moisture contents are control systems that provide eco-friendly and precise water supply considering the growth conditions of crops. The purpose of this study was to compare the combined water supply control with integrated solar irradiance and substrate moisture contents and timer control method in hydroponic cultivation of strawberries using coir, and to set the optimal integrated solar irradiance level for complex water supply control. The irrigation system was automatically watered when it reached 100, 150, 250 J·cm^-2 based on the external solar irradiance, and forced irrigation was performed at a substrate moisture content of less than 60% in all treatments. The amount of irrigation at once was 50 mL. The timer treatment was applied as a control. The smaller the level of integrated radiation to start watering, the greater the daily amount of irrigation. Both the fresh weight and dry weight per plant were higher in the complex irrigation control method than the timer control, and the 100 and 150 J·cm^-2 treatment had the highest fresh weight, and the 100 J·cm^-2 treatment showed a significantly higher dry weight. The yield was also significantly higher in the complex control method than in the timer, and the early yield increased as the level of integrated solar irradiance was smaller. The fresh weight of fruit was the lowest in the timer-controlled irrigation. As a result of this study, the possibility of combined control irrigation method using integrated solar irradiance and substrate moisture content was confirmed for precise water supply management of strawberries in hydroponics.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.816-825
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• 2018

Recently, it was emerged to need the necessity of LPG residues management due to the finding some substances such as rust. This study is performed to investigate the characteristics of LPG residues in the production and distribution stage of LPG. For the qualitative analysis of LPG residues, it was operated to be set up the analysis conditions(the flow rate, etc) of GC-MS and was performed to analyze the component of LPG residues. From the analysis result using GC-MS, it was shown that the component of LPG residues was turned out the plasticizer to be used in the rubber manufacturing process. The inorganic components of LPG residues were analyzed using ICP-OES. At the results of inorganic analysis, it was shown that the Si element was detected, which was presumably derived from defoamers used mainly in the LPG production. Also, the P and Zn element, which are estimated to be components of grease additives used for filling facilities, were also partially detected. No trace of rusting was detected in the LPG residues in the production and distribution stages analyzed in this study. But, as plasticizers and grease additives can affect to the LPG fuel system in vehicles, it will be necessary to use the proper quality of rubber and to expand the use of low boiling grease additives.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.325-334
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• 2018

Requirement traceability should be continuously maintained in software development and evolution. However, it is usually updated in practice in the quality assurance phase. The gap between "is" and "should" exists due to the fact that developers must invest considerable effort to update requirement traceability while being able to obtain only marginal benefit from the updated traceability. To close this gap, we propose a spreadsheet application that enables developers to flexibly change mappings in a requirement traceability matrix. In this way, developers can reduce their effort in updating the requirement traceability matrix, but still obtain the common form of a requirement traceability matrix on a spreadsheet. The proposed application maintains the mappings between two artifacts on each sheet so that, whenever an artifact item changes, developers can instantly insert the relevant mapping changes. Then, when developers desire the common form of a requirement traceability matrix, the proposed application calculates the mappings among several artifacts and creates the matrix. The application also checks traceability errors and calculates the metrics so that developers can understand the completeness of the matrix. To understand the applicability of the proposed approach, we conducted a case study, which shows that the proposed application can be applied to the real project and easily incorporate the mapping changes.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Horticultural Science & Technology
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• v.33 no.3
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• pp.303-308
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• 2015

The consumption of leaf vegetables has been steadily increasing in Korea. Leaf vegetables are used for "Ssam (vegetable wrap-up), eaf vegetables has been steadily increasing in Korea. Leaf vegetables are used for asoned condiments inside several layers of young vegetable leaves. This study investigated the effect of air temperature on the growth and phytochemical contents of beet (Beta vulgaris L.) and Ssamchoo (Brassica lee L. ssp. namai) grown in a closed-type plant factory system where fluorescent lamps were used as an artificial light source. Seeds of beet and Ssamchoo were sown in a peat-lite germination mix. The roots of 20-day-old seedlings were washed, and the seedlings were planted on a styrofoam board and grown in hydroponic beds for 25 days under fluorescent light. Plants were exposed to one of three different air temperature regimes (20, 25 and $30^{\circ}C$ during the day combined with $18^{\circ}C$ during the night), which were monitored with a sensor at 30 cm above the plant canopy. Increased plant height and leaf area were observed in beet at $25^{\circ}C$ and $30^{\circ}C$ compared to $20^{\circ}C$. For Ssamchoo, the greatest plant height, leaf area, fresh weight and dry weight were obtained at $20^{\circ}C$. Ascorbic acid content of beet and Ssamchoo leaves were highest at $30^{\circ}C$. In beet, total polyphenol and flavonoid contents were higher at $20^{\circ}C$ (42.4, $197.0mg{\cdot}g^{-1}DW$) and $25^{\circ}C$ (46.9, $217.0mg{\cdot}g^{-1}DW$) than $30^{\circ}C$ (22.4, $88.0mg{\cdot}g^{-1}DW$). In Ssamchoo, total polyphenol and flavonoid contents were also higher at $20^{\circ}C$ (79.2, $268.2mg{\cdot}g^{-1}DW$) and $25^{\circ}C$ (66.3, $258.3mg{\cdot}g^{-1}DW$), respectively, than $30^{\circ}C$ (53.7, $134.7mg{\cdot}g^{-1}DW$). Hence, the optimum temperature appears to be $20^{\circ}C$ for growing both beet and Ssamchoo in a closed-type plant factory system with fluorescent light.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.152-157
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• 2008

The customer satisfaction of WAP service greatly relies on the usability of the service due to the limited display size of a mobile phone and limitation in realizing UI (User Interface) for function keys, browser, and OS (operating system) Currently, a number of contents providers develop and deliver varying services, and thus, it is critical to control quality level of UI in consistent standards and manner. This study suggests usability index evaluation system to achieve consistent UI quality control of various WAP services. The system adopts both top-down and bottom-up approaches. The former concerns deriving UI design components and evaluation checklists for the WAP, based on the usability attributes and UI principles. The latter concerns deriving usability-related evaluation checklists from the established UI design features, and then grouping them from the viewpoint of usability principles and attributes. This bidirectional approach has two outstanding advantages: it allows thorough examination of potential elements that can cause usability problems from the standpoint of usability attributes, and also derives specific evaluation elements from the perspective of UI design components that are relevant to the real service environment. The evaluation system constitutes a hierarchical structure by networking usability attributes, UI guideline which indicates usability principles for each attribute, and usability evaluation checklist for each UI component that enables specific evaluation. Especially, each evaluation checklist contains concrete contents and format so that it can be readily marked in O/X. The score is based on the ratio of number of items that received positive answer to the number of total items. This enables a quantitative evaluation of the usability of mobile WAP service. The validity of the proposed evaluation system has been proved through comparative analysis with the real usability problems based on the user test. A software was developed that provides guideline for evaluation objects, criteria and examples for each checklist, and automatically calculates a score. The software was applied to evaluating and improving the real mobile WAP service.

• The Korean Journal of Air & Space Law and Policy
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• v.23 no.2
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• pp.137-172
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• 2008

The earliest safety data programs, the FDR and CVR, were electronic reporting systems that generate data "automatically." The FDR program, originally instituted in 1958, had no publicly available restrictions for protections against sanctions by the FAA or an airline, although there are agreements and union contracts forbidding the use of FDR data for FAA enforcement actions. This FDR program still has the least formalized protections. With the advent of the CVR program in 1966, the precursor to the current FAR 91.25 was already in place, having been promulgated in 1964. It stated that the FAA would not use CVR data for enforcement actions. In 1982, Congress began restricting the disclosure of the CVR tape and transcripts. Congress added further clarification of the availability of discovery in civil litigation in 1994. Thus, the CVR data have more definitive protections in place than do FDR data. The ASRS was the first non-automatic reporting system; and built into its original design in 1975 was a promise of limited protection from enforcement sanctions. That promise was further codified in an FAR in 1979. As with the CVR, from its inception, the ASRS had some protections built in for the person who might have had a safety problem. However, the program did not (and to this day does not) explicitly deal with issues of use by airlines, litigants, or the public media, although it appears that airlines will either take a non-punitive stance if an ASRS report is filed, or the airline may ignore the fact that it has been filed at all. The FAA worked with several U.S. airlines in the early 1990s on developing ASAP programs, and the FAA issued an Advisory Circular about the program in 1997. From its inception, the ASAP program contained some FAA enforcement protections and company discipline protections, although some protection against litigation disclosure and public disclosure was not added until 2003, when FAA Order 8000.82 was promulgated, placing the program under the protections of FAR 193, which had been added in 2001. The FOQA program, when it was first instituted through a demonstration program in 1995, did not contain protections against sanctions. Now, however, the FAA cannot take enforcement action based on FOQA safety data, and an airline is limited to "corrective action" under the program. Union contracts can exclude FOQA from the realm of disciplinary action, although airline practice may be for airlines to require retraining if there is no contract in place forbidding it. The data is protected against disclosure for litigation and public media purposes by FAA Order 8000.81, issued in 2003, which placed FOQA under the protections of FAR 193. The figure on the next page shows when each program began, and when each statute, regulation, or order became effective for that program.

• Science of Emotion and Sensibility
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• v.21 no.2
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• pp.101-112
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• 2018

This study was carried out to enhance the competitiveness of blackbox design for domestic and international companies, based on the explosive growth of the blackbox market due to development of blackbox design for vehicle accident prevention and post-treatment. In the past, the blackbox market has produced products indiscriminately to meet the ever-increasing demand of consumers. Therefore, we thought a new design method was necessary to effectively investigate the needs of rapidly changing consumers. In this study, we aimed to identify the best-selling blackbox to understand the design flow, and the optimum area for a blackbox, considering the uniqueness of associated vehicle. Based on discussion with blackbox design experts, we studied the direction of design and the problems with blackbox use, which were reflected in blackbox development. Through this research, two types of design - leading blackbox (A type) and mass production blackbox (B type) - were proposed for compatibility of the blackbox with the car. The leading type of blackbox was positioned so that it was wrapped with the room mirror hinge before the screw was fastened, in order to achieve an integrated design. Therefore, we designed an integrated form and resolved the placement problem of an adhesive blackbox. To blend, the mass production blackbox implemented material and surface processing in the same way with the car, and adopted the slide structure to automatically turn off the main body power when removing the SDcard, reflecting consumer needs. This study considers evolving consumer needs through a case study and collective intelligence and deals with implementation of the whole design process during mass production. In this study, we aimed to strengthen the competitiveness of the blackbox design based on design method and its realization.

• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.171-182
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• 2018

We performed the seismic field data processing using an open-source software (Madagascar) to verify if it is applicable to processing of field data, which has low signal-to-noise ratio and high uncertainties in velocities. The Madagascar, based on Python, is usually supposed to be better in the development of processing technologies due to its capabilities of multidimensional data analysis and reproducibility. However, this open-source software has not been widely used so far for field data processing because of complicated interfaces and data structure system. To verify the effectiveness of the Madagascar software on field data, we applied it to a typical seismic data processing flow including data loading, geometry build-up, F-K filter, predictive deconvolution, velocity analysis, normal moveout correction, stack, and migration. The field data for the test were acquired in Gunsan Basin, Yellow Sea using a streamer consisting of 480 channels and 4 arrays of air-guns. The results at all processing step are compared with those processed with Landmark's ProMAX (SeisSpace R5000) which is a commercial processing software. Madagascar shows relatively high efficiencies in data IO and management as well as reproducibility. Additionally, it shows quick and exact calculations in some automated procedures such as stacking velocity analysis. There were no remarkable differences in the results after applying the signal enhancement flows of both software. For the deeper part of the substructure image, however, the commercial software shows better results than the open-source software. This is simply because the commercial software has various flows for de-multiple and provides interactive processing environments for delicate processing works compared to Madagascar. Considering that many researchers around the world are developing various data processing algorithms for Madagascar, we can expect that the open-source software such as Madagascar can be widely used for commercial-level processing with the strength of expandability, cost effectiveness and reproducibility.

• 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.