• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.1-8
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• 2016

Current indoor intrusion detection and location tracking methods have the weakness in seamless operations in tracking the objective because the object must possess a communicating device and the limitation of the single cell size (approximate $100cm{\times}100cm$) exits. Also, the utilization of CCTV technologies show the shortcomings in tracking when the object disappear the area where the CCTV is not installed or illumination is not enough for capturing the scene (e.g. where the context-awarded system is not installed or low illumination presents). Therefore, in this paper we present an improved in-door tracking system based on sensor networks. Such system is built on a simulated scenario and enables us to detect and extend the area of surveillance as well as actively responding the emergency situation. Through simulated studies, we have demonstrated that the proposed system is capable of supplementing the shortcomings of signal cutting, and of estimating the location of the moving object. We expect the study will improve the better analysis of the intruder behavior, the more effective prevention and flexible response to various emergency situations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.131-142
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• 2009

While users of information security products require high-quality products that are secure and have high performance, there are neither examples for evaluating the quality of information security products nor studies on the quality model and metrics for the quality evaluation. In this paper, information security products are categorized into three different types and the security and performance of various information security products are analyzed. Through this process and after consideration of information security products' security and performance, a new quality model that possesses 7 characteristics and 24 sub-characteristics has been defined. In addition, metrics consisting of 62 common and 45 extended metrics that can be used to evaluate the quality of information security products are introduced, and a proposition for a method of generating the quality evaluation metrics for specific information security products is included. The method of generating metrics proposed in this paper can be extended in order to be applied to a variety of information security products, and by generating and verifying the quality evaluation metrics for firewall, intrusion detection systems and fingerprint systems it is shown that it applicable on a variety of information security products.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.221-229
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• 2023

This research aimed to conduct an empirical assessment of the penetration of chloride and sulfate ions into mortar sections using an anion exchange membrane(AEM) and laser-induced breakdown spectroscopy(LIBS). The study involved a simultaneous ion chromatography(IC) analysis and LIBS analysis performed on mortars immersed in varying concentrations of chloride and sulfate. The findings revealed that at the wavelengths specific to Chloride(837.59nm) and Sulfur(921.30nm), the LIBS intensity achieved using AEM surpassed that obtained with a paper substrate at equivalent penetration concentrations. A robust correlation was confirmed between LIBS intensity and chloride ion concentration. Furthermore, when juxtaposed with IC analysis concentration outcomes at identical depths, the AEM displayed a higher intensity. The research noted an enhancement in LIBS intensity and a diminution in errors within the low-concentration section when deploying AEM. However, for the Sulfur wavelength of 921.3nm, there remains a need to augment the sensitivity of the LIBS signal within the low-concentration section in future studies. The findings underscore the potential of employing AEM and LIBS for precise analysis of chloride and sulfate ion penetration into mortar sections. This strategy can aid in bolstering assessment precision and mitigating errors, particularly in regions with low concentrations. It is recommended to further research and develop methods to amplify the sensitivity of the LIBS signal for sulfur detection in low-concentration sections. In sum, the study accentuates the significance of employing advanced techniques like AEM and LIBS for efficacious and precise analysis in the domain of mortar section assessment.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Molecules and Cells
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• v.45 no.12
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• pp.896-910
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• 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible and potentially fatal virus. So far, most comprehensive analyses encompassing clinical and transcriptional manifestation have concentrated on the lungs. Here, we confirmed evident signs of viral infection in the lungs and spleen of SARS-CoV-2-infected K18-hACE2 mice, which replicate the phenotype and infection symptoms in hospitalized humans. Seven days post viral detection in organs, infected mice showed decreased vital signs, leading to death. Bronchopneumonia due to infiltration of leukocytes in the lungs and reduction in the spleen lymphocyte region were observed. Transcriptome profiling implicated the meticulous regulation of distress and recovery from cytokine-mediated immunity by distinct immune cell types in a time-dependent manner. In lungs, the chemokine-driven response to viral invasion was highly elevated at 2 days post infection (dpi). In late infection, diseased lungs, post the innate immune process, showed recovery signs. The spleen established an even more immediate line of defense than the lungs, and the cytokine expression profile dropped at 7 dpi. At 5 dpi, spleen samples diverged into two distinct groups with different transcriptome profile and pathophysiology. Inhibition of consecutive host cell viral entry and massive immunoglobulin production and proteolysis inhibition seemed that one group endeavored to survive, while the other group struggled with developmental regeneration against consistent viral intrusion through the replication cycle. Our results may contribute to improved understanding of the longitudinal response to viral infection and development of potential therapeutics for hospitalized patients affected by SARS-CoV-2.