• The Journal of Fisheries Business Administration
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• v.32 no.1
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• pp.99-123
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• 2001

The relatively recent emphasis on a total allowable catch(TAC) system is placing new demands on fisheries management. Korean fisheries law has provided recently for implementation of the TAC based on fishery management system, in order to conserve and manage fisheries resources rationally in its exclusive economic zone(EEZ). In 1998, the TAC system was first applied to Korean fisheries. This TAC system is currently undergoing a second trial period, having been put under the system for 20012002 and continuous trial basis until the complete settlement of EEZ system agreement among three countries, Korea, China and Japan. The TAC system implementation needs are sophisticated information collection, analysis and modeling that will continue to increase and require the high management resources. In addition, data on social and economic impacts on TAC system is sometimes inadequate. The implementation of the TAC system provides a unique opportunity to examine the limits of management information and resources, and to solve the problems in Korean fisheries management system, These limits and problems are complicated by an inadequate biologically and economically information and insufficient management resources. Government and fisheries cooperatives must be cooperated in the management process in order to minimize its conflicts and maximize commitment to sustain fishery development. Recognition of the ineffectiveness and its potential consequences leads to the adoption of the cooperative-based co-management approach in implementation of TAC system. In 1998, the TAC system was first applied to Korean fisheries, where traditional fishery management has consisted mainly of technical measures and input controls. The QMS of TAC system has been implemented in the form of cooperative-based co-management framework. This QMS framework was chosen to overcome many difficulties and limits that a competitive TAC system would impose on Korean traditional fisheries management. The implementation of the QMS of TAC system provides a unique opportunity to examine the limits of management information and resources, and to solve the problems in Korean fisheries management system.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.51-71
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• 2017

This study conducted a survey to measure recognition distance between the materials which are located separately in a children's library targeting 200 elementary school lower grade students, higher grade students, and school parents(adults). And compared recognition distance between the elements of materials of individual visitor group with multidimensional scaling and K-mean group analysis. Multidimensional Scaling (MDS) is a technique for projecting the cognitive state in space by evaluating the similarity or attribute of the analysis target. Even though it is mainly used for market diagnosis in marketing, It can also be applied to present an ideal physical layout plan by analyzing the distance. As a result of analysis, the main discoveries are as follows. First, elementary school students cognize child, baby and computer materials should be adjacent as a same group. But recognition of adults(school parents) is reflected by differing from elementary school students vastly. They cognize that computer materials should be formed as a special group separated from child and baby's materials. Second, elementary school higher graders and adults(school parents) groups also want to separate their main reading materials from baby's book, therefore They both want to secure silent reading space separating from baby. Third, as a result to confirming how this recognition distance system of materials is reflected in a real children's library through three children's libraries in Y-gu, Incheon, there is no library with structure according perfectly with a recognition system of a particular class, but a recognition system of adults and elementary school students is partially reflected because baby, child and computer materials, and baby and child materials are commonly separated and placed. It is difficult to insist that a recognition system of a visitor group, especially a recognition system of children is absolute consideration conditions in material placement of a children's library. However, understanding cognition of the user groups can be an important evidentiary factors to offer differentiated service space according to visitors and effective placement of the elements of library resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1744-1751
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• 2017

We are striving to strengthen environmental regulations and reduce household waste in all countries around the world. Korea is also striving for the circulation of energy resources by enacting laws to promote resource saving and recycling. The government has implemented an empty bottle deposit system for the recycling of empty bottles, but there is a limit to the collection through manpower and the reverse vending machine is not localized. In this paper, we propose a recyclable bottle recognition and classification algorithm which is essential in the reverser vending machine to promote energy resource circulation. The proposed algorithm is a complex identification algorithm using OpenCV and CNN(Convolution Neural Network). In order to evaluate the effectiveness of the proposed algorithm, we implement a classification system that operates in an reverse vending machine, so that it can easily acquire information about bottles and reverse vending machine in various devices.

• Asia-Pacific Journal of Business
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• v.9 no.1
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• pp.39-55
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• 2018

Exports play an important role in Korea's economy and industry. Korea's share in world trade is also increasing. Governments and trade organizations are making great efforts to promote exports. However, since SMEs lack resources of enterprises, SMEs have a lot of difficulties in increasing exports compared to large enterprises. For this reason, in this study, we analyzed the difference of perception of export difficulties according to characteristics of small and medium export companies. As a result, four factors such as overseas market information, price and cost, competitiveness and regulation of importing country were derived. In addition, we investigated the differences in recognition of export difficulties by the stage of growth of company, period of export, products exported, export department, size of company. There was a significant difference in recognition. Especially, The companies which are the early stages of growth, short export period, finished product, the smaller the size of exports companies are more difficult in export difficulties.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.25-33
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• 2012

In this paper, we propose a rule-based model to correct errors in a speech recognition result in the mobile device environment. The proposed model considers the mobile device environment with limited resources such as processing time and memory, as follows. In order to minimize the error correction processing time, the proposed model removes some processing steps such as morphological analysis and the composition and decomposition of syllable. Also, the proposed model utilizes the longest match rule selection method to generate one error correction candidate per point, assumed that an error occurs. For the purpose of deploying memory resource, the proposed model uses neither the Eojeol dictionary nor the morphological analyzer, and stores a combined rule list without any classification. Considering the modification and maintenance of the proposed model, the error correction rules are automatically extracted from a training corpus. Experimental results show that the proposed model improves 5.27% on the precision and 5.60% on the recall based on Eojoel unit for the speech recognition result.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.205-217
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• 2018

Deep learning using convolutional neural networks (CNNs) is being studied in various fields of image recognition and these studies show excellent performance. In this paper, we compare the performance of CNN architectures, KCR-AlexNet and KCR-GoogLeNet. The experimental data used in this paper is obtained from PHD08, a large-scale Korean character database. It has 2,187 samples of each Korean character with 2,350 Korean character classes for a total of 5,139,450 data samples. In the training results, KCR-AlexNet showed an accuracy of over 98% for the top-1 test and KCR-GoogLeNet showed an accuracy of over 99% for the top-1 test after the final training iteration. We made an additional Korean character dataset with fonts that were not in PHD08 to compare the classification success rate with commercial optical character recognition (OCR) programs and ensure the objectivity of the experiment. While the commercial OCR programs showed 66.95% to 83.16% classification success rates, KCR-AlexNet and KCR-GoogLeNet showed average classification success rates of 90.12% and 89.14%, respectively, which are higher than the commercial OCR programs' rates. Considering the time factor, KCR-AlexNet was faster than KCR-GoogLeNet when they were trained using PHD08; otherwise, KCR-GoogLeNet had a faster classification speed.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1692-1705
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• 2024

In this paper, we present a method that integrates a Grammar Transducer as an external language model to enhance the accuracy of the pre-trained Korean End-to-end (E2E) Automatic Speech Recognition (ASR) model. The E2E ASR model utilizes the Connectionist Temporal Classification (CTC) loss function to derive hypothesis sentences from input audio. However, this method reveals a limitation inherent in the CTC approach, as it fails to capture language information from transcript data directly. To overcome this limitation, we propose a fusion approach that combines a clause-level n-gram language model, transformed into a Weighted Finite-State Transducer (WFST), with the E2E ASR model. This approach enhances the model's accuracy and allows for domain adaptation using just additional text data, avoiding the need for further intensive training of the extensive pre-trained ASR model. This is particularly advantageous for Korean, characterized as a low-resource language, which confronts a significant challenge due to limited resources of speech data and available ASR models. Initially, we validate the efficacy of training the n-gram model at the clause-level by contrasting its inference accuracy with that of the E2E ASR model when merged with language models trained on smaller lexical units. We then demonstrate that our approach achieves enhanced domain adaptation accuracy compared to Shallow Fusion, a previously devised method for merging an external language model with an E2E ASR model without necessitating additional training.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.191-195
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• 2013

This paper proposes an automatic method for discovering cross-lingual links from English Wikipedia documents to Korean ones in order to increase connectivity among vast web resources. Compared to the existing methods roughly estimating link probability of phrases, candidate anchors are selected from English documents by using various information such as title lists and linking probability extracted from Wikipedia dumps and the results of named-entity recognition, and the anchors are translated into Korean words, and then the most suitable Korean documents with the words are selected as cross-lingual links. The experimental results showed 0.375 of MAP.