• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.695-707
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• 2013

DHT(Distributed Hash Table) networks such as Kademlia are vulnerable to the ID mapping attack caused by the voluntary DHT mapping structure where the location of a node is solely determined by itself on the network topology. This causes security problems such as eclipse, DRDoS and botnet C&C on DHT networks. To prevent ID mapping attacks, we propose a non-voluntary DHT mapping scheme and perform analysis on NAT compatibility, attack resistance, and network dynamicity. Analysis results show that our approach may have an equivalent level of attack resistance comparing with other defense mechanisms and overcome their limitations including NAT compatibility and network dynamicity.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.365-374
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• 2021

This study set off to explore the effect of interactive videos on developing mind mappings skills required for the common first-joint year students at Umm Al-Qura University. Towards this end, the experimental research design of a quasi-experimental of two experimental groups was adopted. The research tools consisted of an achievement test of the cognitive aspects of mind mapping skills and a product evaluation form of mind mapping skills. Results showed statistically significant differences at the significance level (α 0.05) between the mean scores of the two experimental groups who studied the educational video regardless of the type of video in the pre-post cognitive test of the mind mapping skills and the form of product evaluation. Besides, there are statistically significant differences at the significance level (0.05≥α) between the mean scores of the first experimental group who studied the conventional educational video and the mean scores of the second experimental group who studied the interactive educational video. This significant difference was in the posttest of mind mapping skills and in favor of the group who studied the interactive educational video. Nevertheless, there were no statistically significant differences at the significance level (0.05≥α) between the mean scores of the first and second experimental groups in the post-application of the product evaluation form of mind mapping skills. The researcher recommended using the interactive video in teaching courses to common first-year students. It also recommends organizing courses for the faculty members to train them on using interactive videos in their teaching.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.342-347
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• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.37-45
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• 2011

As vehicles become more intelligent, in-vehicle networking (IVN) systems such as controller area network (CAN) or FlexRay are essential for convenience and safety of drivers. To expand the applicability of IVN systems, attention is currently being focused on the communication between heterogeneous networks such as body networking and chassis networking systems. A gateway based on message mapping method was developed to interconnect FlexRay and CAN networks. However, this type of gateways has the following shortcomings. First, when a message ID was changed, the gateway must be reloaded with a new mapping table reflecting the change. Second, if the number of messages to be transferred between two networks increase, software complexity of gateway increases very rapidly. In order to overcome these disadvantages, this paper presents FlexRay-CAN gateway based on node mapping method. More specifically, this paper presents a node mapping based FlexRay-CAN gateway operation algorithm along with the experimental evaluation for ID change.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.819-837
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• 2014

Data centers, which implement cloud service, have been faced up with quick growth of energy consumption and low efficiency of energy. 60GHz wireless communication technology, as a new option to data centers, can provide feasible approach to alleviate the problems. Aiming at energy optimization in 60GHz wireless data centers (WDCs), we investigate virtualization technology to assign virtual resources to minimum number of servers, and turn off other servers or adjust them to the state of low power. By comprehensive analysis of wireless data centers, we model virtual network and physical network in WDCs firstly, and propose Virtual Resource Mapping Packing Algorithm (VRMPA) to solve energy management problems. According to VRMPA, we adopt packing algorithm and sort physical resource only once, which improves efficiency of virtual resource allocation. Simulation results show that, under the condition of guaranteeing network load, VPMPA algorithm can achieve better virtual request acceptance rate and higher utilization rate of energy consumption.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Korea Logistics Conference
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• v.14 no.3
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• pp.161-180
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• 2006