• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1701-1713
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• 1993

This paper proposes a simple and highly effective RACMC(Real-time ATM Cell Monitoring and Control) algorithm and the resulting bandwidth gain effects art considered. RACMC algorithm performs usage parameter controls according to the monitoring informations of input data cells generated from the accepted connections and the controlling informations set by the M/P(Management Plane) for that connection. The results of monitoring and controlling actions for ATM data cells are transmitted to the M/P and the control parameters in lookup table are updated according to the condition of currently used bandwith. Therefore, the proposed algorithm can allocate network resource optimally and solve the several tantalizing problems that the existing cell control algorithm have, that is, the difficulty in controlling as monitoring very bursty traffics, unavoidable processing delay, and limited input buffer size when implemented. By the performance analysis using computer simulation, RACMC algorithm proves to be very effective especially in ATM network as implemented simply.

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.143-163
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• 1997

Incident management is significant far the traffic management systems. The management of incidents determines the smoothness of freeway operations. The dynamic nature of incidents and the uncertainty associated with them require solutions based on the incident operator's judgment. Fuzz systems attempt to adapt such human expertise and are designed to replicate the decision making capability of on operator. Fuzzy systems process complex traffic information, and transmit it in a simplified, understandable form to human traffic operators. In this study, fuzzy rules were developed based on data from real incidents on Santa Monica Freeway in LosAngeles. The fuzzy rules ail linguistic based, and hence, user-friendly. A comparison of the results from the linguistic model with the real incident durations indicate that the outputs from the model reliably correspond to real incident durations conditions. The model reliably predicts the freeway incident duration. The modes can thus be used as an effective management tool for freeway incident response systems. The approach could be applied to other problems regarding dispatch systems in transportation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.199-206
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• 2020

This study was conducted for the purpose of developing a measurement tool for success and failure factors of education and training of Korean bodyguards. conducted a meeting from the fully open questionnaire at first, and then formed the semi-structured questionnaire, finally carried out the survey from the closed questionnaire and analyzed data from SPSS 21.0, AMOS 21.0 and developed the measurements. It was conducted from May, 2019 to December, 2019. This survey was conducted of 150 security guards after the verification of the content validity though the pilot survey and presented the success attribution factors and standards on the basis of the result form this survey. As a result, the success factors of the training of the bodyguards were accidental education (5 item), vocational mental education (2 item), vocational mental education (2 item), work ability enhancement education (2 item), realistic practical education (2 item) ), Including 4 items, 11 items, The failure factors consisted of 12 item of three factors: formal education and training (5 item), lack of leadership qualities (4 item), and lack of education (3 item).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.23-32
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• 2008

The computing power of graphics processing units(GPU) has already surpassed that of CPU and the gap between their powers is getting wider. Thus, research on GPGPU which applies GPU to general purpose becomes popular and shows great success especially in the field of parallel data processing. Since the implementation of cryptographic algorithm using GPU was started by Cook et at. in 2005, improved results using graphic libraries such as OpenGL and DirectX have been published. In this paper, we present skills and results of implementing block ciphers using CUDA library announced by NVIDIA in 2007. Also, we discuss a general method converting source codes of block ciphers on CPU to those on GPU. On NVIDIA 8800GTX GPU, the resulting speeds of block cipher AES, ARIA, and DES are 4.5Gbps, 7.0Gbps, and 2.8Gbps, respectively which are faster than the those on CPU.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.627-632
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• 2021

ITL (information-theoretic learning) has been applied successfully to adaptive signal processing and machine learning applications, but there are difficulties in deciding the kernel size, which has a great impact on the system performance. The correntropy algorithm, one of the ITL methods, has superior properties of impulsive-noise robustness and channel-distortion compensation. On the other hand, it is also sensitive to the kernel sizes that can lead to system instability. In this paper, considering the sensitivity of the kernel size cubed in the denominator of the cost function slope, a new adaptive kernel estimation method using the rate of change in error power in respect to the kernel size variation is proposed for the correntropy algorithm. In a distortion-compensation experiment for impulsive-noise and multipath-distorted channel, the performance of the proposed kernel-adjusted correntropy algorithm was examined. The proposed method shows a two times faster convergence speed than the conventional algorithm with a fixed kernel size. In addition, the proposed algorithm converged appropriately for kernel sizes ranging from 2.0 to 6.0. Hence, the proposed method has a wide acceptable margin of initial kernel sizes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.714-721
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• 2020

DNN is expanding its use in embedded systems such as robots and autonomous vehicles. For high recognition accuracy, computational complexity is greatly increased, and multiple DNNs are running aperiodically. Therefore, the ability processing multiple DNNs in embedded environments is a crucial issue. Accordingly, multicore based platforms are being released. However, most DNN models are operated in a batch process, and when multiple DNNs are operated in multicore together, the execution time deviation between each DNN may be large and the end-to-end execution time of the whole DNNs could be long depending on how they are allocated to the cores. In this paper, we solve these problems by providing a framework that decompose each DNN into individual layers and then distribute to multicores through a global queue. As a result of the experiment, the total DNN execution time was reduced by 31%, and when operating multiple identical DNNs, the deviation in execution time was reduced by up to 95.1%.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.319-328
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• 2021

The purpose of this study is to develop core competencies for gifted education by utilizing Delphi survey methods and to present behavioral element selection and scale questions based on the definition of competencies. First, the core competence for gifted education was selected through literature analysis, and the first Delphi survey was conducted to verify that the definition of each competency is suitable for the competency name. Subsequently, through a second Delphi survey, detailed questions were developed and verified by expressing the capabilities required to develop core competencies as behavior elements. Through two rounds of Delphi surveys, eight key competencies were finally selected: attitude and practice willingness, communication and collaboration, information processing and tool utilization, creative problem solving, convergence and application, higher-order inference, community spirit, and learning achievement orientation. This study is meaningful in that it selects core competencies and behavior elements for gifted education that are necessary to pursue goals that meet social needs and it presents tools to measure the degree of competency improvement for gifted education.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.1-7
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• 2023

Convolutional neural networks have already been applied to various fields beyond human visual processing capabilities in the image processing area. However, they are exposed to a severe risk of deteriorating model performance due to the appearance of adversarial attacks. In addition, defense technology to respond to adversarial attacks is effective against the attack but is vulnerable to other types of attacks. Therefore, to respond to an adversarial attack, it is necessary to analyze how the performance of the adversarial attack deteriorates through the process inside the convolutional neural network. In this study, the adversarial attack of the Alexnet and VGG11 models was analyzed using the population sparseness index, a measure of neuronal activity in neurophysiology. Through the research, it was observed in each layer that the population sparsity index for adversarial examples showed differences from that of benign examples.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.125-134
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• 2023

In this paper, we propose an efficient surveillance RADAR(RAdio Detection And Ranging) interface control unit(ICU) design in the naval combat system. The proposed design applied a standardized architecture for modules that can be shared in ship combat system software. An error detection function for each link was implemented to increase the recognition speed of disconnection. Messages that used to be sent periodically for human-computer interaction(HCI) are now only transmitted when there is a change in the datagram. This can reduce the processing load of the console. The proposed design supplements the radar with the waterfall scope and time-limited splash recognition in relation to the hit check and zeroing of the shot when the radar processing ability is low due to the adoption of a low-cost commercial radar in the ship. Therefore, it is easy for the operator to determine whether the shot is hit or not, the probability of wrong recognition can be reduced, and the radar's resources can be obtained more effectively.