• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.103-126
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• 2021

This study aims to propose a plan to restructure libraries as a complex space for education and culture for 19 public libraries belonging to the Chungcheongnam-do Office of Education. For the purpose of this, case surveys and user surveys of complex facilities of domestic and foreign public institutions were conducted. Based on the findings, a space restructuring strategy was suggested by dividing the library space into the following ways: a space to learn (a comprehensive data room, a theme data inquiry room, a future classroom, a blended learning center, a STEAM training room, and an online lecture learning room), a space to express (a foyer, a maker room, a digital media creation room and an online lecture production room), a space to share (a club room, a group study room, a well-being complex culture space, a convenient living space, rest area, and a browsing area), and a space to enjoy (a performance-thought playground, infant and child archives, a digital virtual experience room, a specialized alcove room, and an outdoor reading room). In addition, a restructuring model of public libraries belonging to the Office of Education was proposed, such as a leading model, a basic model, a joint model, and a minimum model, in consideration of the size of the building, the size of the library, and the level of service and space.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Korean journal of food and cookery science
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• v.17 no.1
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• pp.13-22
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• 2001

This study was to investigate the reason of family restaurant cook part employee's turnover. One hundred and forty seven family restaurant employees in Seoul area were surveyed to obtain the information from Oct. 1 to 10 in 2000. There were sixty males and eighty seven females. The group of twenty years old to twenty nine years old(95,2%) was the largest one by age, and the group of junior college graduated(71.4%) was the largest one by learning. On order, manager was 4.1%, captain was 13.6%, and employee was 82.3%. Except 15.6% employee, almost family restaurant cook part employees' service of duty was under 2 years. The highest scored turnover factor was work system(3.59), and then human relation(3.18), another way(3.11), unbelievable management(3.04). The rest factors effected on turnover not too much. The mean of female's turnover factor score(3.06) is higher then male(3.00), the group of over fifty years 0Id(3.32) had the highest mean score in aged group, on learning, the group of Master degree's mean score(4.24) is highest. The manager's mean score(3.23) was highest in order, and the employees who's service duty was over five years(3.35) had the highest mean score in service duty group.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.180-190
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• 2014

Social network services are increasingly being used in organizational settings to improve relationships among employees and enhance prospects for information exchange and cooperative work. Social Networking Service(SNS) has deeply penetrated organizational job settings, influencing multiple aspects of employee's life. This study is designed to explore the impact of SNS engagement on the job performance mediated as team cooperation and decision making quality effects. Data were collected from 146 employees who use organizational SNS in there company. Factor analysis and structural equation method are employed. Results from a survey accompanied by the substantial impacts of organizational employee's social networking engagement on social learning processes and outcomes. SNS engagement not only directly influences organizational employee's job performance, but also helps their team cooperation and decision making quality from others and adapt to organizational culture, both of which play prominent roles in improving their job performance.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.115-123
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• 2010

Because of development of information technology, moving picture can run various platforms. We should consider and apply users' attitude as well as production technique because convergence between mobile and media technology may be increased full-browsing service using mobile device. Previous research related to production technique in various platforms only focus on video quality and adjustment of screen size. However, besides of technical side, production techniques should be changed such as image production as well as image editing by point of view aesthetic. Mise-en-scene such as camera angle, composition, and lighting is changed due to HD image. Also image production should be changed to a suitable full-browsing service using mobile device. Therefore, we would explore a new suitable production techniques and image editing for smart phone. To propose production techniques for smart phone, we used E-learning production system, which are transition, editing technique for suitable converting system. Such as new attempts are leading to new paradigm and establishing their position by applying characteries such as openness, timeliness to mobile. Also it can be extended individual area and established as expression and play tool.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.839-846
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• 2006

Context-aware adaptive service for overcoming the limitations of wireless devices and maintaining adequate service levels in changing environments is becoming an important issue. However, most existing studies concentrate on an adaptation module on the client, proxy, or server. These existing studies thus suffer from the problem of having the workload concentrated on a single system when the number of users increases md, and as a result, increases the response time to a user's request. Therefore, in this paper the adaptation module is dispersed and arranged over the client, proxy, and server. The module monitors the contort of the system and creates a proposition as to the dispersed adaptation system in which the most adequate system for conducting operations. Through this method faster adaptation work will be made possible even when the numbers of users increase, and more stable system operation is made possible as the workload is divided. In order to evaluate the proposed system, a prototype is constructed and dispersed operations are tested using multimedia based learning content, simulating server overload and compared the response times and system stability with the existing server based adaptation method. The effectiveness of the system is confirmed through this results.

• Journal of Service Research and Studies
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• v.6 no.1
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• pp.29-50
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• 2016

The purpose of this study is to identify the motivation of using and choosing factors in credit cards. These results indicate that considerate of other people factor may emphasize the convenience and safety in using credit cards, while the leisure factor may prefer the real-worth factor as they know that the more they use, the more benefits they would get. As the personality factor and the fashion factor, we can pay much attention to the advertisements, promotions, card designs, the colors, the characters and the social positions of the card holders and that considerate other people factor may like the low annual fees and the accumulating points, buying items on the installments without paying additional interest, which had offered them cash service and card loan. The authors of this paper has suggested an information by which credit card companies can establish the strategies according to the lifestyle of the elderly in silver business market.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.