• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.47-58
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• 2024

The serving robot is defined as a robot that carries cooked food to a customer table or collects and carries bowls after eating. This study presented a research model to find out the causal relationship between customer satisfaction and continuous use intention through the three factors of serving robot service quality. In addition, in order to empirically verify the above research model, a survey was conducted on customers using serving robots within the last 5 months. 300 copies were analyzed using SPSS 20 as the final analysis, excluding unfaithful responses. The main findings of this study are as follows. First, serving robot service quality (typical) found to have significant effect on customer satisfaction and continuous use intention. Second, it was found that serving robot service quality (reliability) had a significant effect on customer satisfaction and continuous use intention. Third, it was found that serving robot service quality (ease) did not significantly affect customer satisfaction and continuous use intention.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.97-110
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• 2022

Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.432-438
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• 2023

Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.135-142
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• 2021

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers' locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasksin shipyards.

• Journal of the Korean Society of Food Culture
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• v.33 no.5
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• pp.426-436
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• 2018

This study assessed effective strategies to reduce the sodium intake among consumers using pork cutlet sauce as a model food system. Original pork cutlet sauce and sodium-reduced sauce (29% reduced by a salt substitute) were analyzed to characterize the sensory properties using descriptive analysis. The effects of sodium-reduction of the sauce, consumer type (nutrition teachers vs. general consumers), information related to the sodium content, serving method, and consumer's health, taste and sodium-related attitudes on the consumer's preference, perception, and intake of the sauce were analyzed using a consumer test. In descriptive analysis, the original and sodium-reduced sauce showed similar sensory characteristics but did not differ in saltiness. In the consumer test, there were no significant differences in the overall preference levels between the two sauces. On the other hand, there were significant differences in preference and perception between nutrition teachers and general consumer groups, which were due largely to their age as well as the health and sodium-related attitudes and nutritional knowledge differences. Sodium-reduced information decreased the perceived saltiness intensity. In addition, reducing sodium intake by serving pork cutlet sauce in a bottle can be an effective strategy because this serving method increased the acceptance and induced the smaller intake of sauce.

• Journal of the Korean Society for Library and Information Science
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• v.22
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• pp.397-441
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• 1992

This study aims to present an improved model of the organizational structure for national university libraries in Korea. The results of this study are as follows : 1. A small scale library serving to a population of less than 15,000 should be organized with a staff of 50-60 persons, 3 departments and 10-13 sections (3 departments and 9-12 sections of central library + 1 section of branch library) 2. A medium scale library serving to 15,000-25,000 population should be organized with a staff of 80-120 persons, an associate librarian, 5-6 departments and 18-21 sections (4-5 departments and 15-18 sections of central library + 1 department and 3 sections of branch library) 3. A large scale library serving to a population of over 25,000 should be organized with a staff of 140-170 persons, an associate librarian, 7-8 departments and 25-28 sections (5-6 department and 20-21 sections of central library + 2 departments and 6 sections of branch libraries)

• The Korean Journal of Food & Health Convergence
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• v.8 no.1
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• pp.9-16
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• 2022

Recently, the use of serving robots has been increasing due to the increase in preference for non-face-to-face services and the rise in the minimum wage due to the coronavirus. When analyzing previous studies related to serving robots, it was confirmed that most of the studies on the functions and technologies of serving robots were conducted. Therefore, this study analyzed the factors affecting the attitude and customer satisfaction of restaurant consumers toward serving robots by adding performance expectations, effort expectations, and speed factors among the UTAUT2 models. The survey period was conducted from July 28, 2021 to September 9, 2021, and 306 out of a total of 310 surveys were used for analysis, excluding 4 unfaithful surveys. For the analysis, exploratory factor analysis, reliability analysis, confirmatory factor analysis, and hypothesis test were performed using SPSS 20.0 and AMOS 20.0, and the research results are as follows. First, it was found that performance expectation, effort expectation, and speed had a significant positive (+) effect on attitudes. Second, it was found that attitude had a significant positive (+) effect on customer satisfaction. This study researched customer selection attributes of robot service restaurants using the UTAUT2 model, and also provided academic and practical implications.

• Journal of Korean Library and Information Science Society
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• v.5
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• pp.33-65
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• 1978

This study is an attempt to present a model of the organization structure for public libraries based on Standards for Public Libraries in Korea. The result of this study is as follows : (1) A library serving $50,000{\sim}80,000$ population should be organized with a staff of 4 persons and a part-time typist without actual departments. (2) A library serving $100,000{\sim}150,000$ population should be organized with a staff of 10 persons without actual departments. (3) A library serving $400,000{\sim}500,000$ population should be organized with a staff of 33 persons. $4{\sim}5$ sections and 2 branch libraries leveled with a section. (4) A library serving $900,000{\sim}1,000,000$ population should be organized with a staff of $62{\sim}66$ persons, an associate librarian, $4{\sim}5$ departments, $9{\sim}10$ sections and $4{\sim}5$ branch libraries leveled with a section. (5) A library serving $1,400,000{\sim}1,500,000$ population should be organized with a staff of 95 persons, an associate librarian, $5{\sim}6$ departments, $13{\sim}16$ sections, $6{\sim}8$ branch libraries leveled with a section and a full-time person taking charge of publicity. (6) A library serving $1,900,000{\sim}2,000,000$ population should be organized with a staff of 124 persons, 2 associate librarians, $7{\sim}8$ departments, 14 sections, 4 separate rooms without actual departments and $9{\sim}11$ branch libraries leveled with a section.

• Korean Journal of Social Welfare
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• v.29
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• pp.68-104
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• 1996

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.