• The Journal of Information Technology
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• v.4 no.3
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• pp.87-100
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• 2001

IETF has proposed integrated services model(Int-Serv) and differentiated service(Diff-Serv) to supply IP QoS in Internet[1][2]. Int-Serv model uses state information of each IP flow, so satisfies QoS according to traffic characteristics, but increases the amount of flow state information with increasing flow number. Diff-Serv uses PHP(Per Hop Behaviour) and there are well-defined classes to provide differentiated traffics with different services according to delay and loss sensitivity. Diff-Serv model can provide diverse services in Internet because of having no the state and signal information of each flow. As MPLS uses the packet forwarding technology based on label, it implements the forwarding engine of high performance easily. The MPLS can set up the path having different and variable bandwidth and assign each path to particular CoS (Class of Service). Therefore it is possible to support the Diff-Serv model of well- defined classes that can provide the differentiated traffic with different services according to delay and loss sensitivity in IP QoS models of IETF. In this paper we propose a scheme that can accommodate Diff-Serv model to provide QoS. The system performance has been estimated by scheduling plan according to traffic classes.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.1
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• pp.52-59
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• 2015

Recently the dairy cow industry have faced environmental issues such as eutrophication, global warming, etc. An LCA was used to quantify the environmental impact of a dairy cow system and to identify key issues contributing to the impact. The system boundary crop cultivation for feeding dairy cow, feed production, rearing and manure management (cradle-to-gate). The functional unit was 1 kg of milk (fat protein corrected milk, FPCM) produced. Rearing and cultivation of feed crops stages in system boundary to the environmental impact of the domestic dairy cow system were dominant issues. Techniques such as suppression of enteric fermentation, improvement of the energy efficiency of farm equipment and apparatuses, management of leachate generated during the crop cultivation, and development of controling the loss of fertilizer during crop production would be necessary for the improvement of the environmental key issues of the dairy cow system.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.59-69
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• 2019

The present study explored consumer's perception and preference on providing information of fashion products by using QR code and suggested the possibility for consumer-to-consumer and consumer-to-company connection. A survey was conducted on males and females in their 20s-a population among whom the rate of smart phone penetration is higher than in any other age group and who tend to exchange information online. The results showed that consumers are dissatisfied with the amount of information, terms of instructions, and ambiguous washing symbols currently provided. Therefore, the study identified the need for better methods of providing information and found that QR code, which is able to deliver high-quality information on fashion products, can be an efficient alternative. Moreover, respondents felt the need for detailed washing instructions, information on handling, and functionality of material on high-involvement fashion products such as outdoor, padding, suit, and underwear worn next to the skin. They also desire styling tips or purchasing information such as SNS OOTD (Outfit Of The Day) utilizing the product, other products that may go well with the one purchased, and similar products on casual wear and coat used on a daily basis. Therefore, QR code used as a link to information web pages or a social network can help consumers to satisfy information needs and to use the products effectively.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.4
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• pp.251-255
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• 2021

a-Si is commonly considered as a primary candidate for the formation of passivation layer in heterojunction (HIT) solar cells. However, there are some problems when using this material such as significant losses due to recombination and parasitic absorption. To reduce these problems, a wide bandgap material is needed. A wide bandgap has a positive influence on effective transmittance, reduction of the parasitic absorption, and prevention of unnecessary epitaxial growth. In this paper, the adoption of a-SiO_x:H as the intrinsic layer was discussed. To increase lifetime and conductivity, oxygen concentration control is crucial because it is correlated with the thickness, bonding defect, interface density (D_it), and band offset. A thick oxygen-rich layer causes the lifetime and the implied open-circuit voltage to drop. Furthermore the thicker the layer gets, the more free hydrogen atoms are etched in thin films, which worsens the passivation quality and the efficiency of solar cells. Previous studies revealed that the lifetime and the implied voltage decreased when the a-SiOx thickness went beyond around 9 nm. In addition to this, oxygen acted as a defect in the intrinsic layer. The D_it increased up to an oxygen rate on the order of 8%. Beyond 8%, the D_it was constant. By controlling the oxygen concentration properly and achieving a thin layer, high-efficiency HIT solar cells can be fabricated.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.2
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• pp.117-125
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• 2000

The waveform and spectrum analysis of Tursiops truncatus(bottlenose dolphin) sonar signals were carried out on the basis of data collected during the dolphin show at the aquarium of Cheju Pacificland from October 1998 to February 1999. When greeting to audience, the pulse width, peak frequency and spectrum level from the five dolphins'sonar signals were 3.0ms, 4.54kHz and 125.6dB, respectively. At the time of warm-up just before the show, their figures were 5.0㎳, 5.24kHz and 127.0dB, respectively. During the performance of dolphins, with singing, peak frequency ranged 3.28∼5.78kHz and spectrum level ranged 137.0∼142.0dB. With playing ring, pulse width, peak frequency and spectrum level were 7.0㎳, 2.54kHz and 135.9dB, and when playing the ball, the values were 9.0㎳, 2.78kHz and 135.2dB, respectively. The values determined from the five dolphins during jump-up out of water were : pulse width 2.0㎳, peak frequency 4.50kHz and spectrum level 126.8dB. When they responded to trainer's instructions, the values were 2.25㎳, 248kHz and 148.7dB, respectively, and greeting to audience, the peak frequency and spectrum level were 5.84kHz and 122.5dB. During swimming under water, peak frequency and spectrum level were determined to be 10.10kHz and 126.8dB. It was found that there exited close consistencies in pulse width, frequency distribution and spectrum level between whistle sounds and dolphin's sonar signals. Accordingly, the dolphins can be easily trained by using whistle sound based on the results obtained from the waveform and spectrum of the dolphin's sonar signals.