• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1145-1152
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• 2014

The RFID (Radio-Frequency Identification) system is a technology to discern things by radio and an epoch-making new method to improve product management such as distribution, transport, mobilization, inventory control. However, RFID, which uses radio, is at risk for information leakage and falsification due to the vulnerability of security of the communication section. We designed the new authentication protocol by applying the tent map, which is the representative complex systems, to the RFID communication system. A more solid and simple authentication system was designed by applying the initial value sensitivity and irregularity, which are the representative characteristics of the complex system, to the reader and tag of RFID. The purpose of this paper is to verify the usability of the RFID authentication protocol design that uses the nonlinear system shown in this thesis by the new system differentiated from the authentication system that depends on the existing hash function or random numbers.

• Journal of Sensor Science and Technology
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• v.5 no.2
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• pp.37-46
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• 1996

This paper describes a system that can be used to recognize unknown materials regardless of the change in ambient temperature by using temperature response curve fitting and fuzzy neural network(FNN). There are problems with a recognition system which utilize temperature responses. It requires too many memories to store the vast temperature response data and it has to be filtered to remove the noise which occurs in experiments. Thus, this paper proposes a practical method using curve fitting to remove the above problems of memories and noise. Also, the FNN is proposed to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient temperatures and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be recognized via its thermal conductivity.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.287-295
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• 2017

This study is aimed at observing effects of fingerprint image quality on various photographing conditions in the aspect of resolution. Discrimination between two friction ridges plays an important role in the value of fingerprint image, and it can be confirmed with quantification of pixels of boundary region which is existing between two friction ridges. In this study, several factors were estimated with same fingerprint image using Adobe photoshop CS 6 for analysis: changes of image quality by ISO, movement when photographing, and photographers' experience and skill. Consequently, there was no significant change of image quality by ISO. Furthermore, there was no significant difference in the hand-held images between crime scene investigators and laymen, yet there was significant difference between hand-held images and images using tripod in the aspect of resolution. This study shows that using tripod is very important in forensic fingerprint photography through empirical methods.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.638-641
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• 2003

This paper suggests that construction management services should be performed considering the owner's and the project's characteristics through the examples of the successful projects. For this study, the critical CM services in each phase regarding schedule management, cost management and information management are presented and the problems of the CM delivery system in Korea are analyzed with respect to policies, users, suppliers/CMrs and construction environments. Besides, the situation of the delivery systems in Korea and the differences of each delivery system are presented. It is concluded that, for the settlement of CM delivery system in public construction market and, ultimately, the successful execution as an agent of the owner, CMr should diversify the services and give the optimum CM services appropriate for each project's characteristics.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of KIISE
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• v.41 no.12
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• pp.1110-1116
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• 2014

Nowadays, social networking sites (SNSs), such as Twitter, LinkedIn, and Tumblr, are coming into the forefront, due to the growth in the number of users. While users voluntarily provide their information in SNSs, privacy leakages resulting from the use of SNSs is becoming a problem owing to the evolution of large data processing techniques and the raising awareness of privacy. In order to solve this problem, the studies on protecting privacy on SNSs, based on graph and machine learning, have been conducted. However, examples of privacy leakages resulting from the advent of a new SNS are consistently being uncovered. In this paper, we propose a technique enabling a user to detect privacy leakages beforehand in the case where the service provider or third-party application developer threatens the SNS user's privacy maliciously.

• Journal of KIISE
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• v.44 no.1
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• pp.21-26
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• 2017

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.417-423
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• 2017

Recently, many embedded devices that have the computing capability required for deep learning have become available; hence, many new applications using these devices are emerging. However, these embedded devices have an architecture different from that of PCs and high-performance servers. In this paper, we propose a method that improves the performance of deep-learning framework by considering the architecture of an embedded device that shares memory between the CPU and the GPU. The proposed method is implemented in Caffe, an open-source deep-learning framework, and is evaluated on an NVIDIA Jetson TK1 embedded device. In the experiment, we investigate the image recognition performance of several state-of-the-art deep-learning networks, including AlexNet, VGGNet, and GoogLeNet. Our results show that the proposed method can achieve significant performance gain. For instance, in AlexNet, we could reduce image recognition latency by about 33% and energy consumption by about 50%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.951-960
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• 2015

The smart screening in the medical field as diffusion of smart devices and development of communication technologies is emerging some medical security concerns. Among of them its necessary to taking risk management measures to identify, evaluate and control of the security risks that can occur in Telemedicine because of the Medical information interchanges as Doctor to Doctor (D2D), Doctor to Patient (D2P). This research paper studies and suggests the risk analysis and evaluation methods of risk security that can occur in Telemedicine based on the verified results of Telemedicine system and equipment from the direct site which operating in primary clinics, public health centers and it's branches, etc.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1161-1167
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• 2018

In order to cope with dramatically increasing malware variant, malware classification research is getting diversified. Recent research tend to grasp individual limits of existing malware analysis technology (static/dynamic), and to change each method into "hybrid analysis", which is to mix different methods into one. Futhermore, it is applying machine learning to identify malware variant more accurately, which are difficult to classify. However, accuracy and scalability of trade-off problems that occur when using all kinds of methods are not yet to be solved, and it is still an important issue in the field of malware research. Therefore, to supplement and to solve the problems of the original malware classification research, we are focusing on developing a new malware classification system in this research.