• Journal of KIISE:Software and Applications
• /
• v.32 no.3
• /
• pp.192-201
• /
• 2005

For the past ten years, the goal of ubiquitous computing research has been the establishment of a new technology system with the aim 'Anytime, Anywhere, and Any form'. The needs for agent technology innovations such as ontology-based structure, ontology-based agent communication language, and multi-agents frameworks have been identified. This paper proposes a noble multi-agents architecture for ubiquitous systems. We suggest four major steps in the interaction between human and agents which enable ubiquitous agents to process by themselves to provide adaptive service to meet human's needs. First, we propose a semantic web technology to represent the association between information resources more explicitly Second, we construct a semantic ontology so that agents can recognize web contents.'Third, we propose a method to communicate between agents using OWL ontologies. Finally, we suggest a multi-agents structure based on the JADE of FIPA to analyze messages and get information. The semantic multi-agents framework proposed in this paper infers semantic situations using semantic web technology based on ontologies. A service provided is inferred differently according to user state because the multi-agents communicate by using OWL ontology language. Therefore, our system better infers context information than other without ontologies.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.4
• /
• pp.504-510
• /
• 2009

Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the K-means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.545-550
• /
• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of Broadcast Engineering
• /
• v.12 no.3
• /
• pp.230-241
• /
• 2007

This paper presents the requirements analysis for the Terrestrial DMB Automatic Emergency Alert Service (AEAS) Standard. First, the basic concepts in disaster management and the AEAS system structure are presented as a background. Next, other emergency alert systems and the related standards are analyzed. We propose taxonomy to categorize the emergency alert systems and analyze the characteristics of each system. Next, we analyze advantages of T-DMB for the delivery medium of emergency alert message and problems to resolve for the enhanced performance. Finally, we propose service requirements which will achieve general/special-purpose, non-interrupting, location-adaptive, automatic, message delivery service. The paper will contribute as a guideline to the development for emergency alert service standards for other broadcasting media.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.1
• /
• pp.1-11
• /
• 2005

Several Solid Freeform Fabrication Systems(SFFS) are commercialized in a few companies for rapid prototyping. However, they have many technical problems including the limitation of applicable materials. A new method of agile prototyping is required for the recent manufacturing environments of multi-item and small quantity production. The objectives of this paper include the development of a novel method of SFFS, the CAFL/sup VM/(Computer Aided Fabrication of Lamination for Various Material), and the manufacture of the various material samples for the certification of the proposed system and the creation of new application areas. For these objectives, the technologies for a highly accurate robot path control, the optimization of support structure, CAD modeling, adaptive slicing was implemented. However, there is an important problem with the conventional 2D lamination method. That is the inaccuracy of 3D model surface, which is caused by the stair-type surface generated in virtue of vertical 2D cutting. In this paper, We design the new control algorithm that guarantees the constant speed, precise positioning and tangential cutting on the 5DOF SFFS. We develop the tangential cutting algorithm to be controlled with constant speed and successfully implemented in the 5DOF CAFL/sup VM/ system developed in this paper. Finally, this paper confirms its high-performance through the experimental results from the application into CAFL/sup VM/ system.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.5
• /
• pp.91-99
• /
• 2019

Existing node-to-node based optimal path searching is built on the assumption that all destination nodes can be arrived at from an origin node. However, the recent appearance of the adaptive path search algorithm has meant that the optimal path solution cannot be derived in node-to-node path search. In order to reflect transportation data at the links in real-time, the necessity of the node-to-link (or link-to-node; NL) problem is being recognized. This research assumes existence of a network with link-label and non-additive path costs as a solution to the node-to-link optimal path problem. At the intersections in which the link-label has a turn penalty, the network retains its shape. Non-additive path cost requires that M-similar paths be enumerated so that the ideal path can be ascertained. In this, the research proposes direction deletion and turn restriction so that regulation of the loop in the link-label entry-link-based network transformation method will ensure that an optimal solution is derived up until the final link. Using this method on a case study shows that the proposed method derives the optimal solution through learning. The research concludes by bringing to light the necessity of verification in large-scale networks.

• Journal of Internet Computing and Services
• /
• v.14 no.5
• /
• pp.1-10
• /
• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.95-108
• /
• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.291-300
• /
• 1999

인터넷을 기반으로 한 정보통신의 급속한 발전이라는 기업환경의 변화에 적응하기 위해서 기업은 점차 모든 경영시스템을 인터넷을 기반으로 하도록 변화시키고 있을 뿐만 아니라, 기업 조직 또한 전세계를 기반으로한 글로벌 기업 형태로 변화하고 있다. 이러한 급속한 경영환경의 변화로 인해서 기업 내에서는 종전과는 다른 형태의 부서간 상호의사결정조정 과정이 필요하게 되었다. 일반 기업들을 대상으로 한 상호의사결정의 지원과정에 대해서는 기존에 많은 연구들이 있었으나 글로벌기업과 같은 네트워크 형태의 새로운 형태의 기업에 있어서의 상호의사결정과정을 지원할 수 있는 의사결정지원시스템에 대해서는 단순한 그룹의사결정지원시스템 또는 분산의사결정지원시스템과 같은 연구들이 주를 이루고 있다. 따라서 본 연구에서는 인터넷 특히, 웹을 기반으로 한 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 메커니즘을 제시하고 이에 기반한 프로토타입 형태의 시스템을 구현하여 성능을 검증하고자 한다. 특히, 기업 내에서 가장 대표적으로 상호의사결정지원이 필요한 생산과 마케팅 부서를 대상으로 상호의사결정지원 메커니즘을 개발하고 실험을 진행하였다. 그 결과 글로벌 기업내의 생산과 마케팅 부서간 상호의사결정을 효율적으로 지원 할 수 있는 상호조정 메카니즘인 개선된 PROMISE(PROduction and Marketing Interface Support Environment)를 기반으로 한 웹 분산의사결정지원시스템 (Web-DSS : Web-Decision Support Systems)을 제안하는 바이다.자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer

• Journal of KIISE:Software and Applications
• /
• v.27 no.5
• /
• pp.529-542
• /
• 2000

Recent years, the use of multimedia information is rapidly increasing, and the video media is the most rising one than any others, and this field Integrates all the media into a single data stream. Though the availability of digital video is raised largely, it is very difficult for users to make the effective video access, due to its length and unstructured video format. Thus, the minimal interaction of users and the explicit definition of video structure is a key requirement in the lately developing image and video management systems. This paper defines the terms and hierarchical video structure, and presents the system, which construct the clustering-based video hierarchy, which facilitate users by browsing the summary and do a random access to the video content. Instead of using a single feature and domain-specific thresholds, we use multiple features that have complementary relationship for each other and clustering-based methods that use normalization so as to interact with users minimally. The stage of shot boundary detection extracts multiple features, performs the adaptive filtering process for each features to enhance the performance by eliminating the false factors, and does k-means clustering with two classes. The shot list of a result after the proposed procedure is represented as the video hierarchy by the intelligent unsupervised clustering technique. We experimented the static and the dynamic movie videos that represent characteristics of various video types. In the result of shot boundary detection, we had almost more than 95% good performance, and had also rood result in the video hierarchy.