• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.355-358
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• 2010

As CMOS technology continues to scale down, signal processing is favorably done in the digital domain, which requires Analog-to-Digital (A/D) Converter to be integrated on-chip. This paper presents a design methodology of 12-bit 1-MS/s Rail-to-Rail fully differential SAR ADC using Deep N-well Switch based on binary search algorithm. Proposed A/D Converter has the following architecture and techniques. Firstly, chip size and power consumption is reduced due to split capacitor array architecture and charge recycling method. Secondly, fully differential architecture is used to reduce noise between the digital part and converters. Finally, to reduce the mismatch effect and noise error, the circuit is designed to be available for Rail-to-Rail input range using simple Deep N-well switch. The A/D Converter fabricated in a TSMC 0.18um 1P6M CMOS technology and has a Signal-to-Noise-and-Distortion-Ratio(SNDR) of 69 dB and Free-Dynamic-Range (SFDR) of 73 dB. The occupied active area is $0.6mm^2$.

• Journal of Adhesion and Interface
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• v.7 no.4
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• pp.60-64
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• 2006

In modern vehicle construction the search for means of weight reduction, improving durability, increasing comfort and raising body stiffness are issues of priority to the design engineer. The intelligent usage of many materials such as high strength steel, light-alloys and plastics enables a significant vehicle weight reduction to be achieved. The classical joining techniques used in the automobile industry need to be newly-evaluated since they often do not present workable solutions for such mixed-material connections, for example aluminium/steel. Calculation/simulation methods have made progress as a key factor for broader and more cost-effective implementation of structural bonding. This will lead to reduction of spotwelds and accelerate the car development. A special focus of the paper is the use of high strength steel grades. It will be shown that adhesive bonding is a key tool for yielding the potential of advanced high strength steel for low gauging without compromising the stiffness. The latest status of adhesive development has been described. Improvements with physical strength and glass temperature as well as of process relevant properties are shown. Also the situation regarding occupational hygiene is treated, showing that by further spotweld point reduction the emission around the working area can be even lowered against the current praxis. High performing lightweight design cannot longer do without high performing crash durable adhesives.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• International Journal of Highway Engineering
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• v.11 no.3
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• pp.129-139
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• 2009

The transportation network is a critical infrastructure in the event of natural and human caused disasters such as rainfall, snowfall, and terror and so on. Particularly, the transportation network in an urban area where a large number of population live is subject to be negatively affected from such events. Therefore, efficient traffic operation plans are required to assist rapid evacuation and effective detour of vehicles on the network as soon as possible. Recently, ubiquitous communication and sensor network technology is very useful to improve data collection and connection related emergency information. In this study, we develop a specific algorithm to provide evacuation route and detour information only for vehicles under emergency situations. Our algorithm is based on shortest path search technique and dynamic traffic assignment. We perform the case study to evaluate model performance applying hypothetical scenarios involved terror. Results show that the model successfully describe effective path for each vehicle under emergency situation.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.293-306
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• 2003

In recent years, distributed computing environments have been radically changing to a structure of global, heterogeneous, federative and wide-area systems. This structure's environments consist of a let of objects which are implemented on telecommunication network to provide a wide range of services. Furthermore, all of objects existing on the earth have the duplicated characteristics according to how to categorize their own names or properties. But, the existing naming or trading mechanism has not supported the binding services of duplicated objects, because of deficiency of independent location service. Also, if the duplicated objects which is existing on different nodes provide the same service, it is possible to distribute the client requests considering each system's load. For this reason, we designed and implemented a new model that can not only support the location management of replication objects, but also provide the dynamic binding service of objects located in a system with minimum overload for maintaining load balancing among nodes in wide-area object computing environments. Our model is functionally divided into two parts; one part is to obtain an unique object handle of replicated objects with same property as a naming and trading service, and the other is to search one or more contact addresses by a location service using a given object handle. From a given model mentioned above, we present the procedures for the integrated binding mechanism in design phase, that is, Naming/Trading Service and Location Service. And then, we described in details the architecture of components for Integrated Binding Service implemented. Finally, we showed our implement environment and executing result of our model.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1421-1434
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• 2006

Spatial data warehouses provide analytical information for decision supports using SOLAP (Spatial On-Line Analytical Processing) operations. Many researches have been studied to reduce analysis cost of SOLAP operations using pre-aggregation methods. These methods use the index composed of fixed size nodes for supporting the concept hierarchy. Therefore, these methods have many unused entries in sparse data area. Also, it is impossible to support the concept hierarchy in dense data area. In this paper, we propose a dynamic pre-aggregation index method based on the spatial hierarchy. The proposed method uses the level of the index for supporting the concept hierarchy. In sparse data area, if sibling nodes have a few used entries, those entries are integrated in a node and the parent entries share the node. In dense data area, if a node has many objects, the node is connected with linked list of several nodes and data is stored in linked nodes. Therefore, the proposed method saves the space of unused entries by integrating nodes. Moreover it can support the concept hierarchy because a node is not divided by linked nodes. Experimental result shows that the proposed method saves both space and aggregation search cost with the similar building cost of other methods.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.631-636
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• 2001

The technologies relevant to e-business have rapidly developed during very short period of time and recently it is expanding to the area of B2B. Keeping pace with this development in e-business, the information of comparison or analysis on commodities of a lot of sites is also required. Though the information on price comparison among internal shopping malls are now being offered, its not efficient for its renewing intervals are long and, due to some indiscreet collection of information for the purpose of fast renewal, much loads are being generated on the pertinent shopping malls. In this article, the retrieval engine on demand is designed and realized using a dynamci robot agent changing kinetically on the status of the pertinent shopping malls that can offer the customized service and presents the shopping malls with the lowest price for each commodity under e-business after the shortest time of collection and analysis while not giving loads to the pertinent shopping malls.