• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.3
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• pp.189-196
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• 2003

In this study, the vertical opening of the non-float midwater pair trawl net was maintained by controlling the length of upper warp. This was because the head rope was able to be kept linearly and the working depth was not nearly as changed with the variation of flow speed as former experiments in this series of studies have demonstrated. We confirmed that the opening efficiency of the non-float midwater pair trawl net was able to be developed according to the increase in front weight and wing-end weight. In this study, we described the opening efficiency of the non-float midwater pair trawl net according to the variation of front weight and wing-end weight obtained by model experiment in circulation water channel. We compared the opening efficiency of the proto type with that of the non-float type. The results obtained can be summarized as follows：1. The hydrodynamic resistance was almost increased linearly in proportion to the flow speed and was increased in accordance with the increase in front weight and wing-end weight. The increasing rate of hydrodynamic resistance was displayed as an increasing tendency in accordance with the increase in flow speed. 2. The net height of the non-float type was almost decreased linearly in accordance with the increase in flow speed. As the reduced rate of the net height of the non-float type was smaller than that of the net height of the proto type against increase of flow speed, the net height of the non-float type was bigger than that of the proto type over 4.0 knot. The net width of the non-float type was about 10 m bigger than that of the proto type and the change rate of net width varied by no more than 2 m according to the variation of the front weight and wing-end weight. 3. The mouth area of the non-float type was maximized at 1.75 ton of the front weight and 1.11 ton of the wing-end weight, and was smaller than that of the proto type at 2.0∼3.0 knot, but was bigger than that of the proto type at 4.0∼5.0 knot. 4. The filtering volume was maximized at 3.0 knot in the proto type and at 4.0 knot in the non-float type. The optimal front weight was 1.40 ton.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.341-346
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• 2009

The motion data glove is a representative human-computer interaction tool that inputs human hand gestures to computers by measuring their motions. The motion data glove is essential equipment used for new computer technologiesincluding home automation, virtual reality, biometrics, motion capture. For its popular usage, this paper attempts to develop an inexpensive visual.type motion data glove that can be used without any special equipment. The proposed approach has the special feature; it can be developed as a low-cost one becauseof not using high-cost motion-sensing fibers that were used in the conventional approaches. That makes its easy production and popular use possible. This approach adopts a visual method that is obtained by improving conventional optic motion capture technology, instead of mechanical method using motion-sensing fibers. Compared to conventional visual methods, the proposed method has the following advantages and originalities Firstly, conventional visual methods use many cameras and equipments to reconstruct 3D pose with eliminating occlusions But the proposed method adopts a mono vision approachthat makes simple and low cost equipments possible. Secondly, conventional mono vision methods have difficulty in reconstructing 3D pose of occluded parts in images because they have weak points about occlusions. But the proposed approach can reconstruct occluded parts in images by using originally designed thin-bar-shaped optic indicators. Thirdly, many cases of conventional methods use nonlinear numerical computation image analysis algorithm, so they have inconvenience about their initialization and computation times. But the proposed method improves these inconveniences by using a closed-form image analysis algorithm that is obtained from original formulation. Fourthly, many cases of conventional closed-form algorithms use approximations in their formulations processes, so they have disadvantages of low accuracy and confined applications due to singularities. But the proposed method improves these disadvantages by original formulation techniques where a closed-form algorithm is derived by using exponential-form twist coordinates, instead of using approximations or local parameterizations such as Euler angels.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.175-182
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• 2018

Knowledge of the optimum cultivation period for Chinese cabbage would help growers especially in spring in Korea. Growth and yield of Chinese cabbage in a temperature gradient chamber was evaluated for the growing periods of 64 days from three set of transplanting dates including March 6, March 20, and April 3 in 2017. Air temperature in the chamber was elevated step-by-step, by $2^{\circ}C$ above the ambient temperature. This increment was divided into three phases; i.e. low (ambient+$2^{\circ}C$, A), medium (ambient+$4^{\circ}C$, B), and high temperature (ambient+$6^{\circ}C$, C). The fresh weight of Chinese cabbage was greater under B and C conditions in the first period and A in the second period, which indicated that GDDs affected the fresh weight considerably. However, leaf growth (number, area, length, and width) did not differ by GDDs. Bolting appeared under A condition in the first period, which was caused by low temperature in the early growth stage. Soft rot was developed under C condition in the second period and all temperature conditions in the third period, which resulted from high temperature in the late stage. Fresh weight increased when GDDs ranged from 587 to 729. However, it decreased when GDDs > 729. The maximum expected yield (16.3 MT/10a) was attained for the growing period of 64 days from transplanting date during which GDDs reached 601. The GDDs for optimum cultivation ranged from 478-724 under which the yield was about 95% (15.5 MT/10a) of maximum fresh weight. Such an optimum condition for GDDs was validated at five main cultivation regions including Jindo, Haenam, Naju, Seosan, and Pyeongtaek in Korea. In these regions, GDDs ranged from 619-719. This suggested that the optimum GDDs for Chinese cabbage cultivation would range from 478-724, which would give the useful information to expect the cultivation periods for ensuring maximum yield.

• Journal of dental hygiene science
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• v.16 no.2
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• pp.150-156
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• 2016

In this study, the surface treatment of a self-cured temporary crown was polished using a denture bur, silicone bur, or pumice. The color stability of the temporary crown resin specimen was evaluated by immersing it in coffee, and cola, wine, beer, red pepper paste, or soybean paste. Two-hundred eighty-five identical resin specimens with six types of staining solution and three types of surface treatment were placed in a shaking incubator at $37^{\circ}C$. The degree of discoloration was observed using a time-lapse recording of days 1, 5, and 7. $L^*$, $a^*$, and $b^*$ were measured using a spectrophotometer, which shows the quantitative value of discoloration, and statistically processed after calculating ${\Delta}E^*$. The results show that as time passed, all the specimens showed a color change (p<0.001). The amount of color change was the greatest in in crowns with denture bur polishing on the day 1, 5, and 7. As the precipitation time increased, the ${\Delta}E^*$ value was also increased. Of the specimens immersed on day 1, the greatest color change in crowns polished with denture bur was observed in those immersed in red pepper paste, while the smallest color change was observed in those immersed in cola. On days 5 and 7, the greatest color change in crowns polished with denture bur was observed in those immersed in red wine. Crowns polished with silicone bur and immersed in soybean paste exhibited the smallest color change. Based on the results, compared to pumice polishing, silicone bur polishing results in better color stability, saves time and money, and is recommended for patients with temporary crowns.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.131-144
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• 2021

The organizational ambidexterity is an organizational strategy designed to pursue exploration activities to seize new opportunities and exploitation activities to efficiently use resources. Most of these ambidextrous structures have been studied for large corporations with slack resources, and there are still not many studies on the necessity of an ambidextrous structure for startups with relatively low-level resources. However, recently, the startup ecosystem is being advanced globally, and the amount of VC investment is rapidly increasing. This is a time when a lot of venture fund is invested in startups and a startup-friendly environment for rapid growth is created. This is the time to discuss the necessity and applicability of an ambidextrous organizational structure for startups. Therefore, this study conducted a hypothesis test whether the importance and necessity of balance that startups solving market problems with new ideas and utilizing accumulated resources have. To conduct this study, we analyzed 140 startups data gathered from the survey and the moderation effect was also analyzed. As a result of the study, it was verified that the balance of startup exploration and exploitation had a significant effect on startup performance, and the moderating effect of environmental dynamics was found to have a significant effect on the relationship with non-financial performance. Therefore, for startups with insufficient resources, it was concluded that the surplus resources generated in the process of a firm's growth should be effectively utilized and the balance between exploration and exploitation should be balanced from the initial stage of searching for a new business. In other words, it was confirmed that it is important for continuous growth and survival to seek the structure of an ambidextrous organization in order to internalize a mechanism that enables startups to pursue both effectiveness and efficiency in the long term. This study suggests a strategic direction for the growth of startups from the perspective of organizational structure. We expect that this meaningful results on the relationship between the ambidextrous capabilities of startups and performance contribute to the growth of startups in the rapidly growing startup venture environment.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.108-126
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• 2023

This study explored the sustainability of a blockchain-based cultural art performance video platform through the construction of Gyeonggi Art On, a new media art broadcasting station in Gyeonggi-do. In addition, the technical limitations of video content transaction using block chain, legal and institutional issues, and the protection of personal information and intellectual property rights were reviewed. As for the research method, participatory observation methods such as in-depth interviews with developers and operators and participation in meetings were conducted. The researcher participated in and observed the entire development process, including designing and developing blockchain nodes, smart contracts, APIs, UI/UX, and testing interworking between blockchain and content distribution services. Research Question 1: The results of the study on 'Which technology model is suitable for a blockchain-based performance video content distribution public platform?' are as follows. 1) The blockchain type suitable for the public platform for distribution of art performance video contents based on the blockchain is the private type that can be intervened only when the blockchain manager directly invites it. 2) In public platforms such as Gyeonggi ArtOn, among the copyright management model, which is an art based on NFT issuance, and the BC token and cloud-based content distribution model, the model that provides content to external demand organizations through API and uses K-token for fee settlement is suitable. 3) For public platform initial services such as Gyeonggi ArtOn, a closed blockchain that provides services only to users who have been granted the right to use content is suitable. Research question 2: What legal and institutional problems should be reviewed when operating a blockchain-based performance video distribution public platform? The results of the study are as follows. 1) Blockchain-based smart contracts have a party eligibility problem due to the nature of blockchain technology in which the identities of transaction parties may not be revealed. 2) When a security incident occurs in the block chain, it is difficult to recover the loss because it is unclear how to compensate or remedy the user's loss. 3) The concept of default cannot be applied to smart contracts, and even if the obligations under the smart contract have already been fulfilled, the possibility of incomplete performance must be reviewed.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.