• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.86-88
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• 2004

Personal Robot System in developing, have a module architecture, each module are connected through variety network system like ethernet, WLAN (802.11), IEEE 1394 (firewire), bluetooth, CAN, or RS-232C. In developing personal robot system. We think that the key of robot performance is interoperablity among modules. Each network protocol are well connected in the view of network system for the interoperability. So we make a bridging architecture that can routing converting, and transporting packets with matching each network's properties. Furthermore, we suggest a advanced design scheme for realtime / non-realtime and control signal (short, requiring hard-realtime) / multimedia data (large, requiring soft-realtime).

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1995.11a
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• pp.331-337
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• 1995

In this paper, We present that protocols have already proposed an applicable key distribution and authentication protocol based discrete logarithm and prime-factorization problem in PCN(Personal Communication Network) is anaysised. We newly propose identiity-based protocol using smart card. This proposed potocol is that Fiat-Shamir identification scheme and a new key distribution scheme based on Fiat-Shamir identification scheme are joined. Proposed protocol is compared with suiting protocols with respect to security and efficiency to evalate performance, so its calculation is reduced in key distribution and authentication to evaluate performance.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1808-1815
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• 2016

TPSN(Timing-sync Protocol for Sensor Networks), the representative of time synchronization protocol, has been already developed to provide time synchronization among nodes in wireless sensor networks. Even though the TPSN's method has been referenced by so many other time synchronization schemes for resource-constrained networks like wireless sensor networks or low power personal area networks, it has some inefficiency in terms of power consumption and network-wide synchronization time (or called convergence time). The main reason is that each node in TPSN needs waiting delay to solve the collision problem due to simultaneous transmission among competing nodes, which causes more power consumption and longer network convergence time for a network-wide synchronization. In this paper an improved scheme is proposed by changing message exchange method among nodes. The proposed scheme not only shortens network-wide synchronization time, but also reduce collision traffic which lead to needless power consumption. The proposed scheme's performance has been evaluated and compared with an original scheme by simulation. The results are shown to be better than the original algorithm used in TPSN.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.67-76
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• 2024

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.136-138
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• 2006

The high-rate Wireless Personal Area Networks (WPANs) which IEEE 802.15.3 standard support, foster the Digital Home Network (DHN) scenarios with high rate multimedia data transmission. Actually, there are a few routing protocols for ad-hoc networks which considered the terminal location information and routing metric to reduce the energy consumption and optimize the routing path in mobile system. Based on other routing protocols, this paper presents the reliable location-based routing algorithm which is an adaptation to these networks.

• The Journal of Information Technology and Database
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• v.2 no.2
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2452-2454
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• 2001

KEPRI, the research institute for KEPCO, has started developing a DAS using wireless communication networks since 1999. The wireless networks adopted Radio Link Protocol (RLP) of Personal Communication Service (PCS) as communication protocol. It is the first time that PCS is applied to data networks for DAS. The communication protocol, RLP, makes the DAS networks simple and economically affordable when they are installed at widely dispersed small cities. But, RLP has problem when it send unsolicited message. This paper describes the implementing method of a wireless network using RLP and TCP/IP Network to cope with unsolicited message problem.

• Smart Media Journal
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• v.8 no.3
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• pp.62-69
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• 2019

Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.1-8
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• 2017

Recently, with the development of ICT technology such as cloud and mobile, image utilization through social networks is increasing rapidly. These images contain personal information, and personal information leakage accidents may occur. As a result, studies are underway to recognize and mask personal information in images. However, optical character recognition, which recognizes personal information in images, varies greatly depending on brightness, contrast, and distortion, and Korean recognition is insufficient. Therefore, in this paper, we design and implement a personal information identification and masking system based on image recognition through deep learning application using CNN algorithm based on optical character recognition method. Also, the proposed system and optical character recognition compares and evaluates the recognition rate of personal information on the same image and measures the face recognition rate of the proposed system. Test results show that the recognition rate of personal information in the proposed system is 32.7% higher than that of optical character recognition and the face recognition rate is 86.6%.