• Journal of the Korea Computer Industry Society
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• v.3 no.3
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• pp.321-330
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• 2002

A 2-D median filter has many applications in various image and video signal processing areas. The rapid development in VLSI technology makes it possible to implement a real-time or near real-time 2-D median filter with reasonable cost. For the efficient VLSI implementation, the algorithm should have characteristics such as small memory requirements, regular computations, and local data transfers. This paper presents an architecture of the real-time two-dimensional separable median filter which has appropriate characteristics for the VLSI implementation. For the efficient two-dimensional median filter, a separable two-dimensional median filtering structure and a bit-sliced pipelined median searching algorithm are used. A behavioral simulator is implemented with C language and used for the analysis of the presented architecture.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.395-404
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• 2010

Solid-State Disks (SSDs) have been currently considered as a promising candidate to replace hard disks, due to their significantly short access time, low power consumption, and shock resistance. SSDs, however, have drawbacks such that their write throughput and life span are decreased by random-writes, nearly regardless of SSDs controller designs. Previous studies have mostly focused on better designs of SSDs controller and reducing the number of write operations to SSDs. We suggest another method that reallocates data pages that tend to be simultaneously written to contiguous blocks. Our method gathers write operations during a period of time and generates write traces. After transforming each trace to a set of transactions, our method mines frequent itemsets from the transactions and reallocates the pages of the frequent itemsets. In addition, we introduce an algorithm that reallocates the pages of the frequent itemsets with moderate time complexity. Experiments using TPC-C workload demonstrated that our method successfully reduce 6% of total logical block access.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.11
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• pp.409-418
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• 2014

The amount of data located in storage servers has dramatically increased with the growth in cloud and social networking services. Storage systems with very large capacities may suffer from poor reliability and long latency, problems which can be addressed by the use of a hybrid disk, in which mechanical and flash memory storage are combined. The Linux-based SSD(solid-state disk) uses a caching technique based on the DM-cache utility. We assess the limitations of DM-cache by evaluating its performance in diverse environments, and identify problems with the caching policy that it operates in response to various commands. This policy is effective in reducing latency when Linux is running in native mode; but when Linux is installed as a guest operating systems on a virtual machine, the overhead incurred by caching actually reduces performance.

• 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.

• Korean Journal of Family Social Work
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• no.56
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• pp.69-104
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• 2017

This research aims to understand in depth and contextually of the grandchildren 's growth with their grandparents. The data were collected through in-depth interviews with observation and documents and analyzed using life history approach. The life history method was based on Mandelbaum(1973)'s framework including 'life dimensions', 'turning points', and 'adaptations'. After the analysis, central themes in each domain emerge as follows; 'social prejudice', 'growing poverty', 'a painstaking smile', 'more polite and honest', and 'being alone' in life dimensions, 'parentless children', 'a painful memory, outcast', and 'going to college' in turning points, and 'a willing person, parents', 'a blessed person', 'self-reliance and scale of economic life', and 'diligence and inborn cheerfulness' in adaptations, respectively. Based on these results, several intervention strategies and implications for healthy growth of grandchildren.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.101-107
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• 2019

This paper proposes a method for detecting faults in conveyor systems used for transportation of raw materials needed in the thermal power plant and cement industries. A small drone was designed in consideration of the difficulty in accessing the industrial site and the need to use it in wide industrial site. In order to apply the system to the embedded microprocessor, hardware and algorithms considering limited memory and execution time have been proposed. At this time, the failure determination method measures the peak frequency through the measurement, detects the continuity of the high frequency, and performs the failure diagnosis with the high frequency components of noise. The proposed system consists of experimental environment based on the data obtained from the actual thermal power plant, and it is confirmed that the proposed system is useful by conducting virtual environment experiments with the drone designed system. In the future, further research is needed to improve the drone's flight stability and to improve discrimination performance by using more intelligent methods of fault frequency.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.675-687
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• 2010

Structural Health Monitoring (SHM) is the science and technology of monitoring and assessing the condition of aerospace, civil and mechanical infrastructures using a sensing system integrated into the structure. Impedance-based SHM measures impedance of a structure using a PZT (Lead Zirconate Titanate) patch. This paper presents a low-power wireless autonomous and active SHM node called Autonomous SHM Sensor 2 (ASN-2), which is based on the impedance method. In this study, we incorporated three methods to save power. First, entire data processing is performed on-board, which minimizes radio transmission time. Considering that the radio of a wireless sensor node consumes the highest power among all modules, reduction of the transmission time saves substantial power. Second, a rectangular pulse train is used to excite a PZT patch instead of a sinusoidal wave. This eliminates a digital-to-analog converter and reduces the memory space. Third, ASN-2 senses the phase of the response signal instead of the magnitude. Sensing the phase of the signal eliminates an analog-to-digital converter and Fast Fourier Transform operation, which not only saves power, but also enables us to use a low-end low-power processor. Our SHM sensor node ASN-2 is implemented using a TI MSP430 microcontroller evaluation board. A cluster of ASN-2 nodes forms a wireless network. Each node wakes up at a predetermined interval, such as once in four hours, performs an SHM operation, reports the result to the central node wirelessly, and returns to sleep. The power consumption of our ASN-2 is 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it is estimated to run for about 2.5 years with two AAA-size batteries ignoring the internal battery leakage.

• Proceedings of the Ginseng society Conference
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• 2002.10a
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• pp.531-544
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• 2002

Ginseng is the best known and most popular herbal medicine used worldwide. Ameliorating effects of ginseng were observed on the models of scopolamine-induced, aged or hippocampal lesioned learning and memory deficits. Further beneficial effects of ginseng were observed on neuronal cell death associated with ischemia or glutamate toxicity. In spite of these beneficial effects of ginseng on the CNS, little scientific evidence shows at the cellular level. In the present study, we have employed cultures of rat hippocampal neurons and examined the direct modulation of ginseng on NMDA receptor-induced changes in $[Ca^{2+}]_i$ and -gated currents using fura-2-based digital imaging and perforated whole-cell patch-clamp techniques, respectively. We found that ginseng total saponins inhibited NMDA-induced but less effectively glutamate-induced increase in $[Ca^{2+}]_i$ Ginseng total saponins also modulated $Ca^{2+}$ transients evoked by depolarization with 50 mM KCI along with its own effects on $[Ca^{2+}]_i$. Among ginsenosides tested, ginsenoside $Rg_3$ was found to be the most potent component for ginseng actions on NMDA receptors. Furthermore, we examined the inhibitory effects ofbiotransformants of ginsenosides on NMDA receptor using purified stereoisomers of ginsenosides. 20(S)-ginsenoside $Rg_3$ and its metabolite, 20(S)-ginsenoside $Rh_3$, produced the strongest inhibition while 20(S)-ginsenoside $Rh_1$ and Compound K produced the moderate inhibition on NMDA-induced increase in $[Ca^{2+}]_i$. The data obtained suggest that the inhibition of NMDA receptors by ginseng, in particular by 20(S)-ginsenoside $Rg_3$ and its metabolite, 20(S)-ginsenoside $Rh_2$, could be one of mechanisms for ginsengmediated neuroprotective actions.

• Korean Journal of Optics and Photonics
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• v.14 no.6
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• pp.663-668
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• 2003

We studied a localized hologram recording method that can be used in a disk-shaped medium. In this method, the reference beam is focused by use of a cylindrical lens to get a thin spot in the medium, and then a hologram is recorded in that spot by illuminating the signal beam. Many localized holograms are multiplexed by shifting the medium by a distance more than the thin spot size of the reference beam. This method does not need recording-time scheduling for uniform diffraction efficiencies. We show that a minimal required thickness of the recording medium exists for a given spot size of the signal beam. We performed experiments for data storage and retrieval, and obtained a storage density of 20 bits/${\mu}{\textrm}{m}$$^2$ and a raw-bit error rate (RBER) of 2.5${\times}$10$^{-3}$ , when a 2 mm-thick Fe-doped LiNbO$_3$ crystal was used.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.463-468
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• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.