• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.110-112
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• 2017

본 논문에서는 최신의 비디오 코덱 표준인 HEVC(High Efficiency Video Coding)의 화면 내 예측 부호화의 성능 향상을 위하여 SRCNN(Super Resolution Convolutional Neural Networks)을 이용하는 방법을 제안한다. SRCNN 은 비교적 최신 기술인 CNN(Convolutional Neural Network)을 사용하여 이미지를 추가적인 데이터 없이 보간 하여 해상도를 증가시키는 기술이다. HEVC 에서는 화면 내 예측의 잔차신호를 부호화 하기 위해 많은 비트를 소모하는데, 본 논문에서는 이 잔차신호들의 해상도를 낮추어 부호화 되는 비트를 줄이며, 복호화기에서 SRCNN 을 이용하여 원래의 해상도로 복원을 수행하여 압축성능을 향상 시키는 방법에 대하여 제안한다. 제안하는 기술은 HM 16.6 에 구현하였으며, CNN 트레이닝에 Caffe 라이브러리를 사용하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.62-63
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• 2016

딥러닝의 대표적 개발 환경 중 하나인 Caffe를 임베디드 시스템의 메모리 구조를 고려하여 최적화하고 실제 측정 실험으로 기존의 방식보다 처리시간과 소비 전력량의 이득이 있다는 것을 확인하였다. 구체적으로 통합 메모리를 사용하는 임베디드 시스템 환경의 특성에 적합한 zero-copy기법을 적용하여 CPU와 GPU 모두 접근이 가능하도록 메모리 영역을 맵핑하는 방식으로 메모리 복제에 따른 오버헤드를 줄였으며, GoogLeNet 네트워크 모델에 대하여 10%의 처리 속도 향상과, 36% 소비 전력 감소를 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6018-6033
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• 2018

Fires are a common cause of catastrophic personal injuries and devastating property damage. Every year, many fires occur and threaten human lives and property around the world. Providing early important sign for early fire detection, and therefore the detection of smoke is always the first step in fire-alarm systems. In this paper we propose an automatic smoke detection system built on camera surveillance and image processing technologies. The key features used in our algorithm are to detect and track smoke as moving objects and distinguish smoke from non-smoke objects using a convolutional neural network (CNN) model for cascade classification. The results of our experiment, in comparison with those of some earlier studies, show that the proposed algorithm is very effective not only in detecting smoke, but also in reducing false positives.

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.

• The Korean Journal of Franchise Management
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• v.3 no.2
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• pp.39-54
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• 2012

Franchise system in Korea has been developed in different way compared with American way of franchising based on mutual contract and intellectual property context. Korean franchising is mostly based on product distribution franchise concept rather than business format franchise in which franchisor makes revenue sources from providing their products as much as possible thru group purchasing and logistics rather than receiving royalty. Many franchise enterprises from Korea drive to enter into global franchise market based on the successful performance of Korean way of franchising. Korean enterprises are required to prepare completely for research and survey regarding local culture, custom, way of life and legal matters etc. when entering into global franchise market to gain a substantial performance. CaffeBene recently entered into American franchise business with success, and many other Korean franchise enterprises have a deep interest in proceeding with global franchise business modeling CaffeBene case. There is no Korean franchise enterprise in United Kingdom in which service franchise area in particular with personal service is considered to become a promising and potential franchise business and many people show a great interest in Oriental foods and beverages with well-being trend. Korean franchise enterprises have now access to United Kingdom easier because IT industry including internet of the country have been developed by leaps and bounds since London Olympic in 2012. The purpose of this study is to suggest key success factors and basic strategy such as situation analysis, selecting business format, and marketing strategy for successful launching of franchise business in United Kingdom.

• ETRI Journal
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• v.41 no.6
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• pp.760-770
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• 2019

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.299-303
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• 2017

By piling up hidden layers in artificial neural networks, deep learning is delivering outstanding performances for high-level abstraction problems such as object/speech recognition and natural language processing. Alternatively, deep-learning users often struggle with the tremendous amounts of time and resources that are required to train deep neural networks. To alleviate this computational challenge, many approaches have been proposed in a diversity of areas. In this work, two of the existing Apache Spark-based acceleration frameworks for deep learning (SparkNet and DeepSpark) are compared and analyzed in terms of the training accuracy and the time demands. In the authors' experiments with the CIFAR-10 and CIFAR-100 benchmark datasets, SparkNet showed a more stable convergence behavior than DeepSpark; but in terms of the training accuracy, DeepSpark delivered a higher classification accuracy of approximately 15%. For some of the cases, DeepSpark also outperformed the sequential implementation running on a single machine in terms of both the accuracy and the running time.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.346-357
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• 2012

This study classified the various quality features of coffee shop by Kano model with customers' perspective. Also both satisfaction coefficient and dissatisfaction coefficient are calculated to analyse the relative influence of quality features on customer satisfaction. This study also dragged the potential customer satisfaction improvement index to scrutinize the quality improvement possibility for coffee shops. The analysis results showed that low price, luxurious interior, restfulness of table and chair, usability of wireless internet are belonged to the Attractive quality. On the other hand, cleanliness and hygiene, quality to price are identified as the One-dimensional quality. The current satisfaction level for both 'Caffe Bene' and 'Starbucks' are measured to draw the potential customer satisfaction improvement index. The result showed that low price and quality to price appeared to be the highest in its quality improvement possibility. The findings of this study help understanding the quality features to focus on and strengthening the competitiveness for coffee shops.

• Culinary science and hospitality research
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• v.22 no.5
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• pp.37-51
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• 2016

The purposes of this study were (1) to investigate the important selection attributes of coffee brands and the relative positions of major coffee brands on the positioning map through similarities and preferences of coffee brands that customers perceive and (2) to provide Korean coffee brands with strategic information about how to differentiate themselves from existing competitors in the Chinese market. A total of 268 samples were used for data analysis that comprised frequency analysis, descriptive statistics, ALSCAL, and regression analysis. The results showed that among 8 coffee brand selection attributes the most important was atmosphere, followed by cleanness and sanitary condition, and convenient location and transportation. Friendly service, taste of coffee, price and variety of menu were next and brand was the least important among 8 selection attributes. The results of positioning map showed that Maan Coffee was evaluated the highest iin atmosphere, cleanness and sanitary condition, and convenient location and transportation compared to other coffee brands. Marketing strategies for Korean coffee franchisers (Angel-in-us Coffee and Caffe Bene) were provided for successful repositioning in the Beijing coffee market.