• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.65-72
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• 2015

According to recent statistics published by the National Industrial Security Center, former and current employees are responsible for 80.4% of companies' technology leakages, and employees of cooperative firms are responsible for another 9.6%. This means that 90% of technology leakages are intentionally or mistakenly caused by insiders. In a recent incident, a credit card company leaked private information, and the person responsible was an employee of a cooperative firm. These types of incidents have an adverse effect not only on a company's assets but also on its reputation. Therefore, most institutions implement various security solutions to prevent information from being leaked. However, security solutions are difficult to analyze and distinguish from one another because their logs are independently operated and managed. A large number of logs are created from various security solutions. This thesis investigates how to prevent internal data leakage by setting up individual scenarios for each security solution, analyzing each scenario's logs, and applying a monitoring system to each scenario.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.123-126
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• 2011

본 논문에서는 한국형 스마트 앱 저작도구로 미국, 일본, 한국에서 세계특허를 획득한 원더풀소프트의 M-Bizmaker를 이용하여 식품영양학과와 비서행정과 등 비전공자 회사원과 학생이 협력하는 관계에서도 중급수준의 비즈니스 앱 개발이 단기간에 가능하며 최고의 효율성을 검증할 수 있음을 확인하였다. 즉 저작도구인 M-Bizmaker를 이용하면 초중고, 대학, 일반인까지 모든 계층에서 초단기 1일 교육을 통하여 개인의 아이디어와 개성을 살린 앱을 제작할 수 있다는 결론을 도출하였다. 비전공자들이 제작한 스마트 앱의 수준은 본문에서 설명한 것 같이 단체의 일반홍보, 식단관리, 그래프를 이용한 취업현황, 구글맵 연계 주소 관리, 자동전화걸기, 사진 등의 이미지 관리, 친구 찾기와 같이 구성원을 등록하여 용이하게 관리할 수 있고, 설문조사도 쉽게 할 수 있다. 현재 세계 모바일 시장은 애플, 구글 등 미국시장이 세계시장을 선도하고 있는 상황으로 구글의 앱인벤터, 애플의 앱쿠커 등의 저작도구가 베타버젼으로 존재하지만 세계특허 수준의 한국형 저작도구인 비즈니스용 전문개발인 M-Bizmaker와는 기술수준에서 많은 격차가 존재하므로 국가적 차원에서 앱 저작도구 기술개발 인력 양성에 투자한다면 다가오는 미래에는 우리나라가 세계시장을 선도할 수 있을 것이라 사려 된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.987-999
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• 2021

Recently, Federated learning has become an issue due to privacy invasion caused by data. Federated learning is safe from privacy violations because it does not need to be collected into a server and does not require learning data. As a result, studies on application methods for utilizing distributed devices and data are underway. However, Federated learning is no longer safe as research on the reconstruction attack to restore learning data from gradients transmitted in the Federated learning process progresses. This paper is to verify numerically and visually how well data reconstruction attacks work in various data situations. Considering that the attacker does not know how the data is constructed, divide the data with the class from when only one data exists to when multiple data are distributed within the class, and use MNIST data as an evaluation index that is MSE, LOSS, PSNR, and SSIM. The fact is that the more classes and data, the higher MSE, LOSS, and PSNR and SSIM are, the lower the reconstruction performance, but sufficient privacy invasion is possible with several reconstructed images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.254-260
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• 2019

Recently, a self-camera that generates and distributes a large amount of moving images has been rapidly increasing due to the appearance of SNS such as Facebook, Instagram, and Tweet using mobile. In particular, the amount of SNS connections using mobile phones is significantly increasing in terms of usage, number of connections, and usage time. However, the use of a self-recording system using a smartphone by itself is extremely limited not only in terms of usage but also in frequency of use. In addition, the conventional unattended recording system is a very expensive system that automatically records and tracks an object to be photographed using an infrared signal. Therefore, this paper developed a low cost unmanned recording system using mobile phone. The system consists of a commercial mobile camera, a servomotor for moving the camera from side to side, a microcontroller for controlling the motor, and a commercial wireless Bluetooth earset for video audio input. And it is an unmanned automation system using mobile, and anyone can record image by self image tracking.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.179-189
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• 2019

The market participation and development of major manufacturers of next-generation green vehicles is accelerating in recent years. The results of this study are as follows: Consumer type (personal value pursuit type, price value pursuit type) was significant difference between consumer. The results of this study were as follows: First, there was no significant difference in the characteristics of consumers (gender, age, monthly average income) and purchase intention according to age, gender and monthly average income, Image has a positive (+) impact on eco-friendly vehicles. This suggests that the development and market participation of next - generation green vehicles is accelerating and consumers 'interest is increasing, and the characteristics of environment - friendly vehicles and the government' s policy support are important factors.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.297-302
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• 2022

Contemporary viewers familiar with the digital space show their desire for self-expression and use voice, text and gestures as tools for expression. The purpose of this study is to create interactive art that expresses the narrative uttered by the viewer in the form of a collage using the viewer's figure, and reproduces and expands the story by the viewer's movement. The proposed interactive art visualizes audio and video information acquired from the viewer in a text collage, and uses gesture information and a natural user interface to easily and conveniently interact in real time and express personalized emotions. The three pieces of information obtained from the viewer are connected to each other to express the viewer's current temporary emotions. The rigid narrative of the text has some degree of freedom through the viewer's portrait images and gestures, and at the same time produces and expands the structure of the story close to reality. The artwork space created in this way is an experience space where the viewer's narrative is reflected, updated, and created in real time, and it is a reflection of oneself. It also induces active appreciation through the active intervention and action of the viewer.

• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.89-102
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• 2020

This study aimed to compare the effects of two emotion regulation strategies, namely, cognitive reappraisal and expressive suppression in female college students. Specifically, the effects of these two emotion regulation strategies were tested and the intrapersonal factors related to the effects of these strategies were explored. The participants included 60 female college students. In Study 1, 40 participants were randomly assigned to each of the two different strategy groups, i.e., a between-subject design was employed. In Study 2, 20 participants were asked to use both strategies to regulate their emotion, i.e., a within-subject design was employed. The results revealed that both emotion regulation strategies effectively reduced negative emotion of emotional stimuli. However, the use of emotion regulation assessed with a questionnaire was not matched to the actual usage of regulation strategies examined with a task. Finally, the use of a suppression strategy was related to the extroversion psychological adaptive variable. Our findings suggest that the subjective assessment of the use of an emotion regulation strategy may not be the same as the actual use of an emotion regulation strategy. Furthermore, we demonstrated that when participants have an option to use both strategies, the cognitive reappraisal is more functional than expression suppression. This concurs with the previous findings on the effects of emotion regulation strategies.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.548-553
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• 2014

In order to evaluate the performance of arrow that manufactures through production process, it is used that personal experiences such as hunters who have been using bow and arrow for a long time, technicians who produces leisure and sports equipment, and experts related with this industries. Also, the intensity of arrow's impact point which obtains from repeated shooting experiments is an important indicator for evaluating the performance of arrow. There are some ongoing researches for evaluating performance of arrow using intensity of the arrow's impact point and the arrow's flying image that obtained from high-speed camera. However, the research that deals with mutual relation between distribution of the arrow's impact point and characteristics of the arrow (length, weight, spine, overlap, straightness) is not enough. Therefore, this paper suggests both the system that could describes the distribution of the arrow's impact point into numerical representation and the correlation model between characteristics of arrow and impact points. The inputs of the model are characteristics of arrow (spine, straightness). And the output is MAD (mean absolute distance) of triangular shaped coordinates that could be obtained from 3 times repeated shooting by changing knock degree 120. The input-output data is collected for learning the correlation model, and ANN (artificial neural network) is used for implementing the model.

• Management & Information Systems Review
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• v.39 no.3
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• pp.1-16
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• 2020

The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.