• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.4
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• pp.3-10
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• 2007

In this paper, we proposed the methodology for security testing using block-based file fault injection. When fault is inserted into software, we consider the format of file in order to efficiently reduce the error that is caused by mismatch of format of file. The Vulnerability the methodology focuses on is related to memory processing, such as buffer overflow, null pointer reference and so on. We implemented the automatic tool to apply the methodology to image file format and named the tool ImageDigger. We executed fault-injection focused on WMF and EMF file format using ImageDigger, and found 10 DOS(Denial of Service) in Windows Platform. This methodology can apply to block-based file format such as MS Office file.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.312-318
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• 2022

Currently, the Republic of Korea is facing the problem of a decrease in military service resources due to the demographic cliff, and is pursuing military restructuring and changes in the military force structure in order to respond to this. In this situation, the Army is pushing forward the deployment of a drone-bot combat system that will lead the future battlefield. The battlefield of the future will be changed into an integrated battlefield concept that combines command and control, surveillance and reconnaissance, and precision strike. According to these changes, unmanned combat system, including dronebots, will be widely applied to combat situations that are high risk and difficult for humans to perform in actual combat. In this paper, as one of the countermeasures to these changes, autonomous behavior software with a BDI architecture-based decision-making system was developed. The autonomous behavior software applied a framework structure to improve applicability to multiple models. Its function was verified in a PC-based environment by assuming that the target UAV is a battalion-level surveillance and reconnaissance UAV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.51-58
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• 2021

Microservices are not only developed independently, but can also be run and deployed independently, ensuring more flexible scaling and efficient collaboration in a cloud computing environment. This impact has led to a surge in migrating to microservices-oriented application environments in recent years. In order to introduce microservices, the problem of identifying microservice units in a single application built with a single architecture must first be solved. In this paper, we propose an algorithm-based approach to identify microservices from legacy systems. A graph is generated using the meta-information of the legacy code, and a microservice candidate is extracted by applying a clustering algorithm. Modularization quality is evaluated using metrics for the extracted microservice candidates. In addition, in order to validate the proposed method, candidate services are derived using codes of open software that are widely used for benchmarking, and the level of modularity is evaluated using metrics. It can be identified as a smaller unit of microservice, and as a result, the module quality has improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.267-270
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• 2021

In order to diagnose a vehicle, it is achieved by collecting diagnostic data within the ECU or between ECUs and managing the diagnostic data by utilizing various communication methods through an electronic device composed of an ECU(Electronic Control Unit), which is an automotive electronic device. As communication methods, LIN, CAN, FlexRay are mainly used. Recently, wired/wireless communication is being used based on Ethernet. In order to perform vehicle diagnosis, it is necessary to know the diagnosis code generated by the ECU and to collect diagnosis data using diagnosis communication. In addition, diagnostic data can be managed from the ECU only when the application software required for vehicle diagnosis is configured. If many automobile manufacturers are manufacturing ECUs based on the AUTOSAR standard, which is an automotive electronic standard, the software structure is also configured to be applied according to the standard. In this paper, we understand the vehicle diagnosis communication method of the AUTUSAR standard, study the configuration and processing method of diagnosis data, and study the contents of software components, diagnosis communication, and diagnosis event processing.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.22-28
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• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• Journal of Distribution Science
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• v.16 no.12
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• pp.69-80
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• 2018

Purpose - Our primary goal of this study is to investigate the positive relationship between female managers' self-determined motivations (i.e., RAI: relative autonomy index) and job performances with the mediation of their job creativity in service industries. This study also examines the moderating role of creative efficacy on the relationship between female managers' self-determined motivations and creativities. Finally, based on mediation and moderation hypotheses, we also tested moderating effect of creative efficacy on the mediation effect of job creativity. Research design, data, and methodology - Drawing on SDT(Self-determination theory) and COR(conservation of resources) theories, we developed three research hypotheses. Service female managers from a several service organizations(i.e. banking, retailing, and restaurant/hospitality service) in South Korea were surveyed using self-administered instrument for data collection. A total of 331 usable questionnaires were obtained after list-wise deletion. To test reliability and validity of measurement model, we employed the CFA(confirmatory factor analysis) using M-plus 8.1 Software. Also, internal consistency was tested by Cronbach's α. We, furthermore, used the SPSS PROCESS MACRO 2.16, which was suggested by Hayes (2013; 2015), to test mediation, moderation, and moderated mediation. Results - Our results revealed that self-determined motivation and job performance were positively and fully mediated by job creativity. Furthermore, the positive relationship between female managers' self-determined motivations and job creativities was stronger when their creative self-efficacies were high than when it was low. In addition, female managers' creative self-efficacies also amplified the positive relationship between their self-determined motivations and job performances with the mediation of job creativity. Conclusions - Our research empirically elaborated the previous model of self-determined motivation and manager/female creativity literature by presenting the findings that female managers' self-determined motivations significantly influence their job performances via job creativity and that creative self-efficacy effectively strengthen these positive impacts. Also, our research offered new insight for practitioners (i.e. top service managers) by suggesting that they may enhance female service managers' job performance if they pay more attention to employee creativity in service marketing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.469-478
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• 2019

Car-sharing services have been settled on as a new type of public transportation owing to their enhanced convenience, expanded awareness of practical consumption patterns, the inspiration for environmental conscientiousness, and the diffusion of smart phones following the economic crisis. With development of the market, many people have started using such services. However, security is still an issue. Damage is expected since IDs and passwords are required for log-in when renting and controlling the vehicles. The protocol suggested in this study uses bio-information, providing an optimized service, and convenient (but strong) authentication with various service-provider clouds registering car big data about users through brokers. If using the techniques suggested here, it is feasible to reduce the exposure of the bio-information, and to receive service from multiple service-provider clouds through one particular broker. In addition, the proposed protocol reduces public key operations and session key storage by 20% on mobile devices, compared to existing car-sharing platforms, and because it provides convenient, but strong, authentication (and therefore constitutes a secure channel), it is possible to proceed with secure communications. It is anticipated that the techniques suggested in this study will enhance secure communications and user convenience in the future car-sharing-service cloud environment.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.693-704
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• 2017

The present descriptive study investigated the influence of violence experienced by nursing staff in long-term care hospitals and their emotional intelligence on the quality of nursing service. The study participants included 167 nursing staff from 9 different long-term care hospitals in G and C Provinces. Data collected from questionnaires were analyzed using SPSS 23.0 software. While slight differences were found among the subtypes of violence experience, it was found that verbal violence was the most common form in violence, experienced by the nursing staff, followed by physical threat and physical violence. A hierarchical regression analysis performed to investigate the degree of influence of violence experience and emotional intelligence on the quality of nursing service found that violence experience did not significantly affect the quality of nursing service when the general characteristics were controlled whereas emotional intelligence had a significant influence on the quality of nursing service. The results of this study show that, although it is commonly believed that violence experience is a major factor compromising the quality of nursing service, emotional intelligence, which reflects one's ability to utilize and control one's emotions, may actually have a more significant impact on the quality of nursing service. Emotional intelligence can be improved through education and training; therefore, it is necessary to explore ways to improve emotional intelligence of nursing staff such as development of various programs.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.800-815
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• 2009

Recently, Web services based service-oriented architecture is widely used to integrate effectively various applications distributed on the networks. In the service-oriented architecture BPEL as a standard modeling language for the business processes provides the way to integrate various services provided by applications. Over the past few years, some types of studies have been made on testing compatibility of services and on discriminating and tracing of the business processes in the services composition. Now a lot of studies about the services composition with BPEL are going on. However there were few efforts to solve the problems caused by the services composition. Especially, there is no effort to evaluate whether a composite service is reliable and whether it is robust against to exceptional situations. In this paper, we suggest a test framework and a testing method for robustness of the composite service written in WS-BPEL. For this, firstly we extract some information from the BPEL process and the participant services. Next, with the extracted information we construct the virtual testing environment that generates various faults and exceptional cases which may be raised within the real services. Finally the testing work for robustness of a composite service is performed on the test framework.