• The KIPS Transactions:PartA
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• v.15A no.4
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• pp.227-238
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• 2008

Recently, as network environment has broadened, the demands on VOD have been increased. The VOD services can be categorized into two types, RVOD and NVOD. Practical VOD services adopt one of them exclusively. Since a method using only one of RVOD and NVOD is not able to deal with frequently variable demand of clients, it leads to a result of overload on a server and a waste of server bandwidth. The efficiency of the network resource usage becomes lower. Hence this paper presents a study on the protocol for efficient VOD services. We propose a new protocol appliable for the existing VOD service algorithm, analyze its performance through simulation, and developed server/client systems applying the new protocol. We propose a mode switching protocol combined with protocols used in RVOD and NVOD. The proposed protocol is not able only to control both RVOD and NVOD but also to change the mode between RVOD and NVOD. As a result of using the proposed protocol to meet frequently variable demand, server bandwidth can be used efficiently. Especially, it can be applied to the existing VOD service algorithms. Therefore, we expect that the proposed protocol in this paper will be widely used in emerging VOD markets.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.7
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• pp.778-787
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• 1999

이제까지 대부분의 다중 에이전트 시스템에서는 에이전트 사회에 속한 모든 응용 에이전트를 작업 요청에 관계없이 처음부터 구동시킨다고 가정하였다. 이러한 에이전트 정적 구동 방법은 에이전트 관리를 단순하게 해주는 이점을 제공하지만 워크플로우 관리나 전자상거래와 같이 매우 많은 수의 에이전트로 구성되는 응용 분야에서는 시스템 과부하와 자원의 낭비 등 많은 문제점을 초래한다. 동적 에이전트 관리는 이에 대한 해결책으로 아주 많은 수의 에이전트를 포함하는 다중 에이전트 시스템에서 현재 수행중인 작업에 관련된 에이전트만을 선별하여 구동시키고, 작업이 끝난 에이전트는 종료시킴으로써 자원의 낭비를 막고 에이전트간의 상호작용 시에 요구되는 에이전트 통신의 복잡도 부담을 감소시키는 효과를 낸다. 본 논문에서는 자바로 에이전트 관리 시스템을 구현하고, 이 관리 시스템을 통해 각기 다른 언어로 개발된 응용 에이전트가 분산된 환경에서 상호 협력을 통해 작업을 수행할 수 있는 기법을 제안한다. 사용자나 다른 에이전트의 요청으로 에이전트를 동적으로 수행시키기 위해 다른 언어로의 확장을 가능하게 하는 Java Native Interface(JNI)를 사용한 기술 및 이러한 이질적인 에이전트간의 원활한 통신을 위해서 KQML 언어 인터페이스를 통한 통신 기능을 제안한다. 이질적 에이전트의 동적 관리를 가능하게 함으로써 다중 에이전트 시스템의 자원 이용 효율성과 확장성을 높이고 다양한 환경 변화에 대한 적응성과 개선된 협동능력을 제공한다.Abstract It has been assumed that all application agents in a multi-agent system are pre-invoked and remain active regardless of whether they are actually used. Although this kind of static agent invocation simplifies the management of agents, it causes several problems such as the system overload and a waste of resources, especially in the areas of the workflow management and the electronic commerce that consist of tens and even hundreds of application agents. A solution for these problems is the scheme of dynamic agent management that selectively invokes only agents that are actually requested and terminates them when they are no longer needed. This method prevents a waste of system resources and alleviates the complexity of agent communications.This paper proposes an agent management system implemented in Java that supports interactions between application agents that are developed using different languages. Dynamic agent invocation is accomplished by Java Native Interface(JNI) that links two heterogeneous methods, and by KQML language interface that facilitates the communications between heterogeneous agents. This scheme of dynamic agent management provides efficient resource usage, easy extensibility, dynamic adaptibility to changes in the environment, and improved cooperation.

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.40-45
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• 2019

This paper is a purpose to study the failure example for heavy-duty vehicle fire. The first example, the researcher found the engine over-heating phenomenon causing a coolant leakage by the sealing poor of head-gasket because of D-ring part deformation contacting with cylinder liner top-part and cylinder head. He certified a fire breakout by short transferred to surrounding wiring of air-cleaner. The second example, a brake lining by return fault of break operating S cam causing with much wear of a rear 4 wheel brake lining repeatably was worn by friction. In the long run, it became the cause of fire. The third example, the researcher knew the fire cause was came about the short of wire by overload of tilting motor when the driver tilted up the cap to inspect a engine. Therefore, a heavy-duty fire must minimize the fire occurrence by thorough controlling.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.69-79
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• 2024

Companies should resolve consumer dissatisfaction and increase brand loyalty by actively identifying the factors of consumer dissatisfaction and proactively responding to expected complaint behavior to induce repurchase. This is a management goal that should be pursued in common regardless of the size of the company. The specific purpose of this study is to find out whether the degree of dissatisfaction differs depending on whether or not consumers' expected performance before purchase and the actual perceived performance after purchase is compared, whether the degree of dissatisfaction affects the type of complaint behavior, which is a subsequent behavior, and whether the attributable behavior has a moderating effect in this process and whether the persistence of the result and the controllability of the cause act as a factor that determines the attribution position. In particular, compared to general companies, venture companies are more likely to overload the information processing ability of managers and are likely to make various irrational errors in decision making, so this study has important academic and practical implications. As a result of the analysis, the negative inconsistency group had the highest degree of dissatisfaction, and the higher the degree of inconsistency, the higher the dissatisfaction. The attributable behavior of unsatisfied consumers had a moderating effect on the degree of dissatisfaction, and the dissatisfaction was significantly higher in the external attributable group than the internal attributable group, which was statistically significant. On the other hand, the persistence of the result had a statistically significant effect on the attribution position, but the controllability of the cause was not. The degree of attributable behavior and dissatisfaction did not affect the type of complaining behavior, showing limited influence. Along with the interpretation of these results, this study presents various implications, especially for small and medium-sized/venture companies that provide new durable products.

• Management & Information Systems Review
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• v.38 no.4
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• pp.1-24
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• 2019

This study confirms the responses of consumers when the composition of emoticon bundles can be selected by individuals in MIM service. This aims to verify that customized bundling is a valid marketing strategy in the MIM emoticon market. Currently, the emoticon bundling used in Korean MIM services is in the form of pure bundling. As a result, Consumers must purchase an entire bundle even though he/she doesn't need to use all the emoticons contained in it. Some researches(e.g. Hitt & Chen, 2005; Wu & Anandalingam, 2002) show that when consumers value only part of the products or services included in pure bundling, customized bundling is much more profitable. In their works, customized bundling is appropriate when marginal costs are near zero. Information goods, such as emoticons, meet the condition. On the other hand, customized bundling increase the choosable options, so it can pose a problem of complexity (Blecker et al., 2004). And consumers may experience information overload(Huffman & Kahn, 1998). Thus, judgement on the necessity to introduce customized bundling needs to be made through empirical analyses in the light of characteristics of the product and the reaction of consumers. Results show that when customized bundling was introduced, consumers' purchase intention and willingness to pay significantly increased. Purchase intention for customized bundles has increased by 0.44 based on the five point Likert scale than the purchase intention for existing pure bundles. The increase in purchase intention for customized bundles was statistically independent of the existing purchasing experience. In addition, the willingness to pay was increased by about 2.8% compared to the price of the existing emoticon bundles in the whole group. The group with experience in purchasing pure bundles were willing to pay 5.9% more than pure bundles. The other group without experience in purchasing pure bundles were willing to buy if they were about 5% cheaper than the existing price. Overall, introducing customized bundling into emoticon bundles can lead to positive consumers responses and be a viable marketing strategy.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.