• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.1-12
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• 2013

This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for the many-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of $256{\times}256$ (60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384) and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30 frames-per-second (30 fps) for the algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.7 s.349
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• pp.1-13
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• 2006

In this paper, we introduce a novel key management scheme that is based on the key pre-distribution but provides the key re-distribution method, in order to manage keys for message encryption and authentication of lower-layer sensor nodes on hierarchical mobile sensor networks. The characteristics of our key management are as follows: First, the role of key management is distributed to aggregator nodes as well as a sink node, to overcome the weakness of centralized management. Second, a sink node generates keys using regression model, thus it stores only the information for calculating the keys using the key information received from nodes, but does not store the relationship between a node and a key, and the keys themselves. As the disadvantage of existing key pre-distributions, they do not support the key re-distribution after the deployment of nodes, and it is hard to extend the key information in the case that sensor nodes in the network enlarge. Thirdly, our mechanism provides the resilience to node capture(${\lambda}$-security), also provided by the existing key pre-distributions, and fourth offers the key freshness through key re-distribution, key distribution to mobile nodes, and scalability to make up for the weak points in the existing key pre-distributions. Fifth, our mechanism does not fix the relationship between a node and a key, thus supports the anonymity and untraceability of mobile nodes. Lastly, we compare ours with existing mechanisms, and verify our performance through the overhead analysis of communication, computation, and memory.

• Korean Journal of Community Nutrition
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• v.24 no.6
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• pp.525-534
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• 2019

Objectives: Reduced glucose utilization in the main parts of the brain involved in memory is a major cause of Alzheimer's disease, in which ketone bodies are used as the only and effective alternative energy source of glucose. This study examined the effects of a low-carbohydrate and high-fat (LCHF) diet supplemented with a ketogenic nutrition drink on cognitive function and physical activity in the elderly at high risk for dementia. Methods: The participants of this study were 28 healthy elderly aged 60-91 years showing a high risk factor of dementia or whose Korean Mini-Mental State Examination (K-MMSE) score was less than 24 points. Over 3 weeks, the case group was given an LCHF diet with nutrition drinks consisting of a ketone/non-ketone ratio of 1.73:1, whereas the control group consumed well-balanced nutrition drinks while maintaining a normal diet. After 3 weeks, K-MMSE, body composition, urine ketone bodies, and physical ability were all evaluated. Results: Urine ketone bodies of all case group subjects were positive, and K-MMSE score was significantly elevated in the case group only (p=0.021). Weight and BMI were elevated in the control group only (p<0.05). Grip strength was elevated in all subjects (p<0.01), and measurements of gait speed and one leg balance were improved only in the case group (p<0.05). Conclusions: We suggest that adherence to the LCHF diet supplemented with a ketogenic drink could possibly influence cognitive and physical function in the elderly with a high risk factor for dementia. Further, we confirmed the applicability of this dietary intervention in the elderly based on its lack of any side effects or changes in nutritional status.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.73-80
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• 2004

In this paper, we present the cell resequencing buffer to solve the cell sequence integrity problem of the Cyclic banyan network that is a high-performance fault-tolerant cell switch. By offering multiple paths between input ports and output ports, using the deflection self-routing, the Cyclic banyan switch offer high reliability, and it also solves congestion problem for the internal links of the switch. By the way, these multiple paths can be different lengths for each other. Therefore, the cells departing from an identical source port and arriving at an identical destination port can reach to the output port as the order that is different from the order arriving at input port. The proposed cell resequencing buffer is a hardware sliding window mechanism. to solve such cell sequence integrity problem. To calculate the size of sliding window that cause the prime cost of the presented device, we analyzed the distribution of the cell delay through the simulation analyses under traffic load that have a nonuniform address distribution that express tile Property of traffic of the Internet. Through these analyses, we found out that we can make a cell resequencing buffer by which the cell sequence integrity is to be secured, by using a, few of ordinary memory and control logic. The cell resequencing buffer presented in this paper can be used for other multiple paths switching networks.

• Geophysics and Geophysical Exploration
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• v.4 no.3
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• pp.70-79
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• 2001

The integral equation method is a powerful tool for numerical electromagnetic modeling. But the difficulty of this technique is the size of the linear equations, which demands excessive memory and calculation time to invert. This limitation of the integral equation method becomes critical in inverse problem. The conventional Born approximation, where the electric field in the anomalous body is approximated by the background field, is very rapid and easy to compute. However, the technique is inaccurate when the conductivity contrast between the body and the background medium is large. Quasi-linear, quasi-analytical and extended Born approximations are novel approaches to 3-D EM modeling based on the linearization of the integral equations for scattered EM field. These approximation methods are much less time consuming than full integral equation method and more accurate than conventional Born approximation. They we, however, still approximate methods for 3-D EM modeling. Iterative series methods such as modified Born, quasi-linear and quasi-analytical can be used to increase the accuracy of various approximation methods. Comparisons of numerical performance against a full integral equation and various approximation codes show that the iterative series methods are very accurate and almost always converge. Furthermore, they are very fast and easy to implement on a computer. In this study, extended Born series method is developed and it shows more accurate result than that of other series methods. Therefore, Iterative series methods, including extended Born series, open principally new possibilities for fast and accurate 3-D EM modeling and inversion.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1051-1060
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• 2014

This study is attempted to realize an improved computation performance by combining the MPI (Message Passing Interface) Technique, a standard model of the parallel programming in the distributed memory environment, with the DHM(Diffusion Hydrodynamic Model), a inundation analysis model. With parallelizing inundation model, it compared with the existing calculation method about the results of applications to complicate and required long computing time problems. In addition, it attempted to prove the capability to estimate inundation extent, depth and speed-up computing time due to the flooding in protected lowlands and to validate the applicability of the parallel model to the actual flooding analysis by simulating based on various inundation scenarios. To verify the model developed in this study, it was applied to a hypothetical two-dimensional protected land and a real flooding case, and then actually verified the applicability of this model. As a result of this application, this model shows that the improvement effectiveness of calculation time is better up to the maximum of about 41% to 48% in using multi cores than a single core based on the same accuracy. The flood analysis model using the parallel technique in this study can be used for calculating flooding water depth, flooding areas, propagation speed of flooding waves, etc. with a shorter runtime with applying multi cores, and is expected to be actually used for promptly predicting real time flood forecasting and for drawing flood risk maps etc.

• Journal of Life Science
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• v.19 no.7
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• pp.917-922
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• 2009

The effect of adding docosahexaenoic acid into an n-3 fatty acid adequate diet on the improvement of learning related brain function was investigated. On the second day after conception, Sprague Dawley strain dams were subjected to a diet containing either n-3 fatty acid adequate (Adq, 3.4% linolenic acid) or n-3 fatty acid adequate+docosahexaenoic acid (Adq+DHA, 3.31%linolenic acid plus 9.65% DHA). After weaning, male pups were fed on the same diet of their respective dams until adulthood. Motor activity and Morris water maze tests were measured at 10 weeks. In the motor activity test, there were no statistically significant differences in moving time and moving distance between the Adq and Adq+DHA diet groups. The n-3 fatty acid adequate with DHA (Adq+DHA) group tended to show a shorter escape latency, swimming time and swimming distance compared to the n-3 fatty acid adequate group (Adq), but the differences were not statistically significant. There was no difference in resting time, but the Adq+DHA group showed a higher swimming speed compared to the Adq group. In memory retention trials, the numbers of crossing of the platform position (region A), in which the hidden platform was placed, were significantly greater than those of other regions for both Adq and Adq+DHA groups. Based on these results, adding DHA into the n-3 fatty acid adequate diet from gestation to adulthood tended to induce better spatial learning performance in Sprague Dawley rats as assessed by the Morris water maze test, although the difference was not significant.

• Journal of the Korea Computer Graphics Society
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• v.17 no.3
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• pp.1-7
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• 2011

Recently, various researches have been proposed to accelerate GPU-based volume ray-casting. However, those researches may cause several problems such as bottleneck of data transmission between CPU and GPU, requirement of additional video memory for hierarchical structure and increase of processing time whenever opacity transfer function changes. In this paper, we propose an efficient GPU-based empty space skipping technique to solve these problems. We store maximum density in a brick of volume dataset on a vertex element. Then we delete vertices regarded as transparent one by opacity transfer function in geometry shader. Remaining vertices are used to generate bounding boxes of non-transparent area that helps the ray to traverse efficiently. Although these vertices are independent on viewing condition they need to be reproduced when opacity transfer function changes. Our technique provides fast generation of opaque vertices for interactive processing since the generation stage of the opaque vertices is running in GPU pipeline. The rendering results of our algorithm are identical to the that of general GPU ray-casting, but the performance can be up to more than 10 times faster.

• The KIPS Transactions:PartA
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• v.18A no.5
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• pp.193-204
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• 2011

Since web applications are accessed by anonymous users via web, more security risks are imposed on those applications. In particular, because security vulnerabilities caused by insecure source codes cannot be properly handled by the system-level security system such as the intrusion detection system, it is necessary to eliminate such problems in advance. In this paper, to enhance the security of web applications, we develop a static analyzer for detecting the well-known security vulnerability of PHP file inclusion vulnerability. Using a semantic based static analysis, our vulnerability analyzer guarantees the soundness of the vulnerability detection and imposes no runtime overhead, differently from the other approaches such as the penetration test method and the application firewall method. For this end, our analyzer adopts abstract interpretation framework and uses an abstract analysis domain designed for the detection of the target vulnerability in PHP programs. Thus, our analyzer can efficiently analyze complicated data-flow relations in PHP programs caused by extensive usage of string data. The analysis results can be browsed using a JAVA GUI tool and the memory states and variable values at vulnerable program points can also be checked. To show the correctness and practicability of our analyzer, we analyzed the source codes of open PHP applications using the analyzer. Our experimental results show that our analyzer has practical performance in analysis capability and execution time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.36-43
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• 2005

This paper suggests a method to effectively apply an application model of fuzzy-neural network to the optimal load distribution algorithm, considering the complication and non-linearity of the web server environment. We use the clustering web server in the linux system and it consists of a load balancer that distributes the network loads and some of real servers that processes the load and responses to the client. The previous works considered only with the scrappy decision information such as the connections. That is, since the distribution algorithm depends on the input of the whole network throughput, it was proved inefficient in terms of performance improvement of the web server. With the proposed algorithm, it monitors the whole states of both network input and output. Then, it infers CPU and memory states of each real server and effectively distributes the requests of the clients. In this paper, the proposed model is compared with the previous method through simulations and we analysis the results to develop the optimal and intelligent load balancing model.