• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.164-172
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• 2014

Recently, as the number of IoT (Internet of Things) devices for personal or home intelligence increases, the need for unified control and cooperative utilization is required. This paper presents a novel idea, proposes methods for virtualizing Internet of Things (IoT) devices and hosting them on the home AP instead of relying on a cloud service or purchasing a new device to do so. For this, the process and profile of the IoT gadgets need to be virtualized into JavaScript-based objects. Then, to execute and control the instances of the virtualized IoT objects on the wireless AP, a novel instance management method and their interfaces are designed. The implementation and performance section demonstrates the proposed system's stability and operability by showing the stress test results while the wireless AP is running for its wireless routing.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.1-23
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• 2005

Personalized reminder systems have to identify the user's current needs dynamically and proactively based on the user's current context. However, need identification methodologies and their feasible architectures for personalized reminder systems have been so far rare. Hence, this paper aims to propose a proactive need awaring mechanism by applying agent, semantic web technologies and RFID-based context subsystem for a personalized reminder system which is one of the supporting systems for a robust ubiquitous service support environment. RescorlaWagner model is adopted as an underlying need awaring theory. We have created a prototype system called NAMA(Need Aware Multi-Agent)-RFID, to demonstrate the feasibility of the methodology and of the mobile settings framework that we propose in this paper. NAMA considers the context, user profile with preferences, and information about currently available services, to discover the user's current needs and then link the user to a set of services, which are implemented as web services. Moreover, to test if the proposed system works in terms of scalability, a simulation was performed and the results are described.

• Journal of IKEEE
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• v.23 no.4
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• pp.1328-1336
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• 2019

To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.293-304
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• 2011

The world of ubiquitous computing is one in which we will be surrounded by an ever-richer set of networked devices and services. Especially, mobile phone now becomes one of the key issues in ubiquitous computing environments. Mobile phones have been infecting our normal lives more thoroughly, and are the fastest technology in human history that has been adapted to people. In Korea, the number of mobile phones registered to the telecom company, is more than the population of the country. Last year, the numbers of mobile phone sold are many times more than the number of personal computer sold. The new advanced technology of mobile phone is now becoming the most concern on every field of technologies. The mix of wireless communication technology (wifi) and mobile phone (smart phone) has made a new world of ubiquitous computing and people can always access to the network anywhere, in high speed, and easily. In such a world, people cannot expect to have available to us specific applications that allow them to accomplish every conceivable combination of information that they might wish. They are willing to have information they want at easy way, and fast way, compared to the world we had before, where we had to have a desktop, cable connection, limited application, and limited speed to achieve what they want. Instead, now people can believe that many of their interactions will be through highly generic tools that allow end-user discovery, configuration, interconnection, and control of the devices around them. Serendipity is an application of the architecture that will help people to solve a concern of achieving their information. The word 'serendipity', introduced to scientific fields in eighteenth century, is the meaning of making new discoveries by accidents and sagacity. By combining to the field of ubiquitous computing and smart phone, it will change the way of achieving the information. Serendipity may enable professional practitioners to function more effectively in the unpredictable, dynamic environment that informs the reality of information seeking. This paper designs the Serendipity Service based on NFC (Near Field Communication) technology. When users of NFC smart phone get information and services by touching the NFC tags, serendipity service will be core services which will give an unexpected but valuable finding. This paper proposes the architecture, scenario and the interface of serendipity service using tag touch data, serendipity cases, serendipity rule base and user profile.

• Journal of Navigation and Port Research
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• v.26 no.4
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• pp.473-479
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• 2002

The estimation of the volume of a pit excavation is often required in many surveying, soil mechanics, highway applications and transportation engineering situations. The calculation of earthwork plays a major role in plan or design of many civil engineering projects such as seashore reclamation, and thus it has become very important to improve the accuracy of earthwork calculation. In this paper the spot height method, proposed formulas(A, B, C), and chen and Line method are compared with the volumes of the pits in these examples. And we proposed an algorithm of finding a terrain surface with the free boundary conditions and both direction spline method drawback, i.e., the modeling curves form peak points at the joints. To avoid this drawback, the cubic spline polynomial was chosen as the methematical model of the new method. From the characteristics of the cubic spline polynomial, the modeling curve of the new method was smooth and matched the ground profile well. As a result of this study, algorithm of proposed three methods to estimate pit excavation volume provided a better accuracy than spot height, chamber, chen and Lin method. And the mathematical model mentioned makes is thought to give a maximum acccuracy in estimating the volume of a pit excavation.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.61-75
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• 2011

A Geographic Information System(GIS) is a system that captures, stores, analyzes, manages and presents data with reference to geographic location data. In the later 1990s and earlier 2000s it was limitedly used in government sectors such as public utility management, urban planning, landscape architecture, and environmental contamination control. However, a growing number of open-source packages running on a range of operating systems enabled many private enterprises to explore the concept of viewing GIS-based sales and customer data over their own computer monitors. K telecommunication company has dominated the Korean telecommunication market by providing diverse services, such as high-speed internet, PSTN(Public Switched Telephone Network), VOLP (Voice Over Internet Protocol), and IPTV(Internet Protocol Television). Even though the telecommunication market in Korea is huge, the competition between major services providers is growing more fierce than ever before. Service providers struggled to acquire as many new customers as possible, attempted to cross sell more products to their regular customers, and made more efforts on retaining the best customers by offering unprecedented benefits. Most service providers including K telecommunication company tried to adopt the concept of customer relationship management(CRM), and analyze customer's demographic and transactional data statistically in order to understand their customer's behavior. However, managing customer information has still remained at the basic level, and the quality and the quantity of customer data were not enough not only to understand the customers but also to design a strategy for marketing and sales. For example, the currently used 3,074 legal regional divisions, which are originally defined by the government, were too broad to calculate sub-regional customer's service subscription and cancellation ratio. Additional external data such as house size, house price, and household demographics are also needed to measure sales potential. Furthermore, making tables and reports were time consuming and they were insufficient to make a clear judgment about the market situation. In 2009, this company needed a dramatic shift in the way marketing and sales activities, and finally developed a dedicated GIS_based market analysis and sales management system. This system made huge improvement in the efficiency with which the company was able to manage and organize all customer and sales related information, and access to those information easily and visually. After the GIS information system was developed, and applied to marketing and sales activities at the corporate level, the company was reported to increase sales and market share substantially. This was due to the fact that by analyzing past market and sales initiatives, creating sales potential, and targeting key markets, the system could make suggestions and enable the company to focus its resources on the demographics most likely to respond to the promotion. This paper reviews subjective and unclear marketing and sales activities that K telecommunication company operated, and introduces the whole process of developing the GIS information system. The process consists of the following 5 modules : (1) Customer profile cleansing and standardization, (2) Internal/External DB enrichment, (3) Segmentation of 3,074 legal regions into 46,590 sub_regions called blocks, (4) GIS data mart design, and (5) GIS system construction. The objective of this case study is to emphasize the need of GIS system and how it works in the private enterprises by reviewing the development process of the K company's market analysis and sales management system. We hope that this paper suggest valuable guideline to companies that consider introducing or constructing a GIS information system.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.177-192
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• 2012

This is a study of match-making systems that considers the mutual satisfaction of matching partners. Recently, recommendation systems have been applied to people recommendation, such as recommending new friends, employees, or dating partners. One of the prominent domain areas is match-making systems that recommend suitable dating partners to customers. A match-making system, however, is different from a product recommender system. First, a match-making system needs to satisfy the recommended partners as well as the customer, whereas a product recommender system only needs to satisfy the customer. Second, match-making systems need to include as many participants in a matching pool as possible for their recommendation results, even with unpopular customers. In other words, recommendations should not be focused only on a limited number of popular people; unpopular people should also be listed on someone else's matching results. In product recommender systems, it is acceptable to recommend the same popular items to many customers, since these items can easily be additionally supplied. However, in match-making systems, there are only a few popular people, and they may become overburdened with too many recommendations. Also, a successful match could cause a customer to drop out of the matching pool. Thus, match-making systems should provide recommendation services equally to all customers without favoring popular customers. The suggested match-making system, called Mutually Beneficial Matching (MBM), considers the reciprocal satisfaction of both the customer and the matched partner and also considers the number of customers who are excluded in the matching. A brief outline of the MBM method is as follows: First, it collects a customer's profile information, his/her preferable dating partner's profile information and the weights that he/she considers important when selecting dating partners. Then, it calculates the preference score of a customer to certain potential dating partners on the basis of the difference between them. The preference score of a certain partner to a customer is also calculated in this way. After that, the mutual preference score is produced by the two preference values calculated in the previous step using the proposed formula in this study. The proposed formula reflects the symmetry of preferences as well as their quantities. Finally, the MBM method recommends the top N partners having high mutual preference scores to a customer. The prototype of the suggested MBM system is implemented by JAVA and applied to an artificial dataset that is based on real survey results from major match-making companies in Korea. The results of the MBM method are compared with those of the other two conventional methods: Preference-Based Matching (PBM), which only considers a customer's preferences, and Arithmetic Mean-Based Matching (AMM), which considers the preferences of both the customer and the partner (although it does not reflect their symmetry in the matching results). We perform the comparisons in terms of criteria such as average preference of the matching partners, average symmetry, and the number of people who are excluded from the matching results by changing the number of recommendations to 5, 10, 15, 20, and 25. The results show that in many cases, the suggested MBM method produces average preferences and symmetries that are significantly higher than those of the PBM and AMM methods. Moreover, in every case, MBM produces a smaller pool of excluded people than those of the PBM method.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.