• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.205-213
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• 2011

The Long Term Evolution (LTE) system is designed to provide a high quality data service for fast moving mobile users. It is based on the Orthogonal Frequency Division Multiplexing (OFDM) and relies its channel estimation on the training samples which are systematically built within the transmitting data. Either a preamble or a lattice type is used for the distribution of training samples and the latter suits better for the multipath fading channel environment whose channel frequency response (CFR) fluctuates rapidly with time. In the lattice-type structure, the estimation of the CFR makes use of the least squares estimate (LSE) for each pilot samples, followed by an interpolation both in time-and in frequency-domain to fill up the channel estimates for subcarriers corresponding to data samples. All interpolation schemes should rely on the pilot estimates only, and thus, their performances are bounded by the quality of pilot estimates. However, the additive noise give rise to high fluctuation on the pilot estimates, especially in a communication environment with low signal-to-noise ratio. These high fluctuations could be monitored in the alternating high values of the first forward differences (FFD) between pilot estimates. In this paper, we analyzed statistically those FFD values and propose a postprocessing algorithm to suppress high fluctuations in the noisy pilot estimates. The proposed method is based on a localized adaptive moving-average filtering. The performance of the proposed technique is verified on a multipath environment suggested on a 3GPP LTE specification. It is shown that the mean-squared error (MSE) between the actual CFR and pilot estimates could be reduced up to 68% from the noisy pilot estimates.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.13-20
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• 2009

Recently, as traffic flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems (IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network traffic. In this paper we propose an SNMP-based lightweight and fast detection algorithm for traffic flooding attacks, which minimizes the processing and network overhead of the detection system, minimizes the detection time, and provides high detection rate. The attack detection algorithm consists of three consecutive stages. The first stage determines the detection timing using the update interval of SNMP MIB. The second stage analyzes attack symptoms based on correlations of MIB data. The third stage determines whether an attack occurs or not and figure out the attack type in case of attack.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.8
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• pp.525-529
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• 2017

Molybdenum oxide ($MoO_3$) offers pivotal advantages for high optical transparency and low light reflection. Considering device fabrication, n-type $MoO_3$ semiconductor can spontaneously establish a junction with p-type Si. Since the energy bandgap of Si is 1.12 eV, a maximum photon wavelength of around 1,100 nm is required to initiate effective photoelectric reaction. However, the utilization of infrared photons is very limited for Si photonics. Hence, to enhance the Si photoelectric devices, we applied the wide energy bandgap $MoO_3$ (3.7 eV) top-layer onto Si. Using a large-scale production method, a wafer-scale $MoO_3$ device was fabricated with a highly crystalline structure. The $MoO_3/p-Si$ heterojunction device provides distinct photoresponses for long wavelength photons at 900 nm and 1,100 nm with extremely fast response times: rise time of 65.69 ms and fall time of 71.82 ms. We demonstrate the high-performing $MoO_3/p-Si$ infrared photodetector and provide a design scheme for the extension of Si for the utilization of long-wavelength light.

• Korean Journal of Applied Biomechanics
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• v.22 no.4
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• pp.395-404
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• 2012

This study was to perform the kinetic analysis of tucked backward salto on the balance beam. Eight women's gymnastics players(age: $15.88{\pm}2.45yrs$, career: $6.38{\pm}0.52yrs$, height: $152.38{\pm}7.35cm$, weight: $44.25{\pm}7.54kg$) of the I-region participated in this study. The kinematic variables were analyzed response time of motion, angle, velocity, acceleration and the kinetic variables were analyzed ground reaction force(GRF) of motion. For measure and analysis of kinematic and kinetic variables of this study, used to synchronized to 6 Eagle camera and 1 force plate, used to the Cortex(Ver. 1.0) for analyzed of variables. The results were as follows; To the kinematic variables of tucked backward salto on the balance beam, a time appeared longer landing than air rotation, changes of angle regulated segment of body smaller moment of inertia when air rotation, larger moment of inertia when releasing and landing. A velocity appeared fast motion when releasing and air rotation of body, but appeared more decelerations from landing and acceleration showed to be tended to velocity. A GRF appears jump more than twice the weight at the moment that showed the power of motion to all subject.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.28-35
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• 2000

In order to acquire the precise and fast response for an analytical query, proper selection of the views to materialize in data warehouse is very crucial. In traditional algorithms, the whole relation is considered to be selected as materialized views. However, materializing the whole relation rather than a part of relation results in much worse performance in terms of time and space cost. Therefore, we present a new algorithm for selection of views to materialize using clustering method in order to improve the performance of data warehouse including this problem. In the presented algorithm, ASVMR(Algorithm for Selection of Views to Materialize using Reduced table), we first generate reduced tables in data warehouse using automatic clustering based on attribute-values density, then we consider the combination of reduced tables as materialized views instead of the combination of the original base relations. We also show the experimental results in which both time and space cost are approximately 1.8 times better than the conventional algorithms.

• 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.

• Korean Journal of Materials Research
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• v.29 no.7
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• pp.456-462
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• 2019

ZnO thin-films are grown on a p-Si(111) substrate by RF sputtering. The effects of growth temperature and $O_2$ mixture ratio on the ZnO films are investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and room-temperature photoluminescence (PL) measurements. All the grown ZnO thin films show a strong preferred orientation along the c-axis, with an intense ultraviolet emission centered at 377 nm. However, when $O_2$ is mixed with the sputtering gas, the half width at half maximum (FWHM) of the XRD peak increases and the deep-level defect-related emission PL band becomes pronounced. In addition, an n-ZnO/p-Si heterojunction diode is fabricated by photolithographic processes and characterized using its current-voltage (I-V) characteristic curve and photoresponsivity. The fabricated n-ZnO/p-Si heterojunction diode exhibits typical rectifying I-V characteristics, with turn-on voltage of about 1.1 V and ideality factor of 1.7. The ratio of current density at ${\pm}3V$ of the reverse and forward bias voltage is about $5.8{\times}10^3$, which demonstrates the switching performance of the fabricated diode. The photoresponse of the diode under illumination of chopped with 40 Hz white light source shows fast response time and recovery time of 0.5 msec and 0.4 msec, respectively.

• Tribology and Lubricants
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• v.38 no.3
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• pp.93-100
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• 2022

Micro-light emitting diode (micro-LED) displays offer numerous advantages such as high brightness, fast response, and low power consumption. Hence, they are spotlighted as the next-generation display. However, defective LEDs may be created due to non-uniform contact loads or LED alignment errors. Therefore, a repair process involving the replacement of defective LEDs with favorable ones is necessitated. The general repair process involves the removal of defective micro-LEDs, interconnection material transfer, as well as new micro-LED transfer and bonding. However, micro-LEDs are difficult to repair since their size decreases to a few tens of micron in width and less than 10 ㎛ in thickness. The conventional nozzle-type dispenser for fluxes and the conventional vacuum chuck for LEDs are not applicable to the micro-LED repair process. In this study, transfer conditions are determined using a micro stamp for repairing micro-LEDs. Results show that the aging time should be set to within 60 min, based on measuring the aging time of the flux. Additionally, the micro-LEDs are subjected to a compression test, and the result shows that they should be transferred under 18.4 MPa. Finally, the I-V curves of micro-LEDs processed by the laser and hot plate reflows are measured to compare the electrical properties of the micro-LEDs based on the reflow methods. It was confirmed that the micro-LEDs processed by the laser reflow show similar electrical performance with that processed by the hot plate reflow. The results can provide guidance for the repair of micro-LEDs using micro stamps.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.