• Korean Journal of Oriental Medicine
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• v.16 no.2
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• pp.91-99
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• 2010

In this paper, we designed and implemented a semantic social network system in Korea Institute of Oriental Medicine (abbreviated as KIOM). Our social network system provides the capabilities such as tracking search, ontology reasoning, ontology graph view, and personal information input, update and management. Tracking search provides the search results by the research information of relevant researchers using ontology, in addition to those by keywords. Ontology reasoning provides the reasoning for experts, mentors, and personal contacts. Users can easily browse the personal connections among researchers by traversing the ontology by graph viewer. These allows KIOM researchers to search other researchers who could aid the researches and to easily share their research information.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.150-156
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• 2024

With the advent of personalized search engines, a myriad of approaches came into practice. With social media emergence the personalization was extended to different level. The main reason for this preference of personalized engine over traditional search was need of accurate and precise results. Due to paucity of time and patience users didn't want to surf several pages to find the result that suits them most. Personalized search engines could solve this problem effectively by understanding user through profiles and histories and thus diminishing uncertainty and ambiguity. But since several layers of personalization were added to basic search, the response time and resource requirement (for profile storage) increased manifold. So it's time to focus on optimizing the layered architectures of personalization. The paper presents a layout of the multi agent based personalized search engine that works on histories and profiles. Further to store the huge amount of data, distributed database is used at its core, so high availability, scaling, and geographic distribution are built in and easy to use. Initially results are retrieved using traditional search engine, after applying layer of personalization the results are provided to user. MongoDB is used to store profiles in flexible form thus improving the performance of the engine. Further Weighted Sum model is used to rank the pages in personalization layer.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.61-73
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• 2004

The efficiency of the real-time route guidance system(RGS) depends largely on the quality of route search algorithms. In this paper, we implement the coarse grid method(CGM) in mathematical programming for finding a good quality route of real-time RGS in large-scale networks. The proposed CGM examines coarser and wider networks as the search phase proceeds, in stead of searching the whole network at once. Naturally, we can significantly reduce computational efforts in terms of search time and memory requirement. We demonstrate the practical effectiveness of the proposed CGM with nationwide real road network simulation.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.855-861
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• 2021

Neural Architecture Search (NAS) is cutting-edge technology in the machine learning community. NAS Without Training (NASWOT) recently has been proposed to tackle the high demand of computational resources in NAS by leveraging some indicators to predict the performance of architectures before training. The advantage of these indicators is that they do not require any training. Thus, NASWOT reduces the searching time and computational cost significantly. However, NASWOT only considers high-performing networks which does not guarantee a fast inference speed on hardware devices. In this paper, we propose a multi objectives reward function, which considers the network's latency and the predicted performance, and incorporate it into the Reinforcement Learning approach to search for the best networks with low latency. Unlike other methods, which use FLOPs to measure the latency that does not reflect the actual latency, we obtain the network's latency from the hardware NAS bench. We conduct extensive experiments on NAS-Bench-201 using CIFAR-10, CIFAR-100, and ImageNet-16-120 datasets, and show that the proposed method is capable of generating the best network under latency constrained without training subnetworks.

• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.510-514
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• 2007

The main purpose of this study was to develop Mountain Search and Rescue System for enhancing search and rescue operations in the mountains. This study also focused on presenting an alternative to using a cellular phone for requesting rescue due to their unreliability in remote areas. This system is designed to help in the search and rescue of people in emergency situations in the mountains. It is composed of buzzer sensors, environmental information sensors, and a statistical analysis program. A key feature of this system is that it does not require an infrastructure of internet or CDMA networks for its operation in the mountains. The measure for the study was conducted by using a zigbee protocol analyzer, RF module and 433MHz Helical antenna to analyze the rate of data reception in relation to the distance between nodes. This system is applicable to mountains provided the distance between nodes is over 100 m and under 150 m.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.100-110
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• 1999

The decision method for the selection of search engine and the number of returned documents for meta search engine proposed in this paper could provide a solution to reduce network traffic and to maintain the precision ratio. The experiments are performed to evaluate the proposed scheme using currently popular search engines and most frequently used queries.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.1-6
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• 2011

In this paper, search-efficient Bi-directional-Chord(B-Chord) is proposed in P2P (peer-to-Peer) overlay network. Chord is the most popular P2P Look-up protocol. However, it applied to the mobile environment, the search success rate become lower and the request delay time increases. That is big problem. Thus, by improving the existing Chord, in this paper proposed B-Chord reduces the request delay time to in a mobile environment. Proposed B-Chord have the two Finger table and can search by selecting Finger table depending on the value of Key. By use these bi-directional, it can reduce the number of nodes Hop and search delay time. Thus, As a result, it will be able to increase the search success rate in a mobile environment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.61-71
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• 2014

Social networking services have changed the way people communicate. Rapid growth of information generated by social networking services requires effective search methods to give useful results. Over the last decade, social search methods have rapidly evolved. Traditional techniques become unqualified because they ignore social relation data. Existing social recommendation approaches consider social network structure, but social context has not been fully considered. Especially, the friend recommendation is an important feature of SNSs. People tend to trust the opinions of friends they know rather than the opinions of strangers. In this paper, we propose a levelized data processing method for social search in ubiquitous environment. We study previous researches about social search methods in ubiquitous environment. Our method is a new paradigm of levelelized data processing method which can utilize information in social networks, using location and friendship weight. Several experiments are performed and the results verify that the proposed method's performance is better than other existing method.

• KDI Journal of Economic Policy
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• v.37 no.1
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• pp.1-18
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• 2015

A reduction in search costs is generally believed to make markets more competitive. However, the effect may be mitigated or amplified if consumers must pay costs for switching products. This paper investigates how search costs affect prices in the presence of switching costs using U.S. domestic airfare data for 2000-2010. The airline industry experienced a dramatic decrease in search costs with increasing Internet use in the 2000s. At the same time, the industry is known for its frequent flyer programs (FFPs), which increase switching costs for consumers. We use the average network size of airlines in a market as a proxy for switching costs related to FFPs and Internet usage as a proxy for (the inverse of) search costs. The results show that increasing Internet usage lowers airfares but that the effect is smaller for markets with a larger average network size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.91-95
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• 2010

Using artificial neural network to use a search preference based on the user's information, the ranking of search results that will enable flexible searches can be improved. After trained in several different queries by other users in the past, the actual search results in order to better reflect the use of artificial neural networks to neural network learning. In order to change the weights constantly moving backward in the network to change weights of backpropagation algorithm. In this study, however, the initial training, performance data, look for increasing the number of lessons that can be overfitted. In this paper, we have optimized a lot of objects that have a strong advantage to apply genetic algorithms to the relevant page of the search rankings flexible as an object to the URL list on a random selection method is proposed for the study.