• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.257-262
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• 2021

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.679-684
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• 2005

There are a few representative wireless network access technologies used widely. WWAN is celluar based telecommunication networks supporting high mobility, WLAN ensures high data rate within hotspot coverage, and WDMB support both data and broadcasting services correspondingly. However, these technologies include some limitations especially on the mobility, data rate, transmission direction, and so on. In order to overvome these limitations, there are various studies have been proposed in terms of 'Vortical Handoff' that offers seamless connectivity by switching active connection to the appropriate interface which installed in the mobile devices. In this paper, we propose the interface selection algorithm and network architecture to maximize the life time of entire system by minimizing the unnecessary energy consumption of another interfaces such as WLAN, WDMB that are taken in the user equipment. In addition, by using the results of analyzing multiple types of traffic and managing user buffer as a metric for vertical handoff, we show that the energy efficiency of our scheme is $75\%$ and $34\%$ than typical WLAN for WDMB and WLAN preferred schemes, correspondingly.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.603-612
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• 2006

Recently the web service area is rapidly growing as the next generation IT paradigm because of increase of concern about SOA(Services-Oriented Architecture) and growth of B2B market. Since a service discovery through UDDI(Universal Description, Discovery and Integration) is limited to a functional requirement, it is not considered an effect on frequency of service using and reliability of mutual relation. That is, a quality as nonfunctional aspect of web service is regarded as important factor for a success between consumer and provider. Therefore, the web service selection method with considering the quality is necessary. This paper suggests the agent-based quality broker architecture and selection process which helps to find a service providing the optimum quality that the consumer needs in a position of service consumer. A theory of agent is accepted widely and suitable for proposed system architecture in the circumstance of distributed and heterogeneous environment like web service. In this paper, we considered the QoS and CoS in the evaluation process to solve the problem of existing researches related to the web service selection and used PROMETHEE(Preference Ranking Organization MeTHod for Enrichment Evaluations) as an evaluation method which is most suitable for the web service selection among MCDM approaches. PROMETHEE has advantages that solve the problem that a pair-wise comparison should be performed again when comparative services are added or deleted. This paper suggested a case study with the service composition scenario in order to verify the selection process. In this case study, the decision making problem was described on the basis of evaluated values for qualities from a consumer's point of view and the defined service level.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.140-146
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• 2021

As the environment using intelligent IoT devices increases, various studies are underway to ensure the integrity of information sent and received from intelligent IoT devices. However, all IoT information generated in heterogeneous environments is not fully provided with reliable protocols and services. In this paper, we propose an intelligent-based multi-blockchain model that can extract only critical information among various information processed by intelligent IoT devices. In the proposed model, blockchain is used to ensure the integrity of IoT information sent and received from IoT devices. The proposed model uses the correlation index of the collected information to trust a large number of IoT information to extract only the information with a high correlation index and bind it with blockchain. This is because the collected information can be extended to the n-tier structure as well as guaranteed reliability. Furthermore, since the proposed model can give weight information to the collection information based on blockchain, similar information can be selected (or bound) according to priority. The proposed model is able to extend the collection information to the n-layer structure while maintaining the data processing cost processed in real time regardless of the number of IoT devices.