• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.46 no.8
• /
• pp.631-638
• /
• 2018

In this paper, we propose a method to improve the performance by guidance technique and applying it to the precision guided ammunition. The proposed method is a technique designed to reduce the target error of ammunition by reducing the projectile error without analyzing the motion characteristics of the shot. This technique is applied to the moving average filter technique which is widely used as signal processing technique to reduce the fluctuation of the output of the inboard mounting inertial sensor caused by the rotation and the coning motion of the ammunition. In order to compare the performance of the applied technique including the simple 3D guided control technique and the proposed improvement technique. It is confirmed that the application of this technique improves the accuracy of impact of the cannon - launched ammunition with severe environmental conditions and irregular motion characteristics unlike the missile.

• Journal of Broadcast Engineering
• /
• v.23 no.3
• /
• pp.409-420
• /
• 2018

In this paper, we propose a Motion Vector-based Particle Filter(MVPF) for object tracking on bitstreams and a object tracking system using the MVPF. The MVPF uses motion vectors to both the transition and the observation models of a general particle filter to improve the accuracy while maintaining the number of particles. In the proposed object tracking system, the state of the target object can be predicted using the histogram of motion vectors extracted from the bitstream. In terms of precision, F-measure and IOU(Intersection Of Union), the proposed method is about 30%, 17%, and 17% better on average, respectively, in MPEG test sequences and VOT2013 sequences. Furthermore, When the tracking results are displayed in box form for subjective performance evaluation, the proposed method can track moving objects more robust than the conventional methods in all test sequences.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.6
• /
• pp.591-598
• /
• 2016

For the mobile healthcare environment, it is important to measure the exact biomedical signals in real time, and another key point is to design mobile healthcare devices with low power consumption. In this paper, we propose a method in which the piezo film sensor(PFS), having a low power characteristic, is used to measure the pulse signal synchronized with the heart rate from the radial artery. The critical issue in the bio-signal processing is the existence of the motion artifacts. To dissolve this problem, we have applied the periodic moving average filter using the quasi-periodicity of the pulse signal in addition to the conventional method of the adaptive filtering using the reference signal. Results of simulation and experiments are presented to confirm that the quasi-periodicity of the PFS signal can be used to eliminate completely the motion artifacts which still appears after the adaptive filtering.

• Journal of Korea Water Resources Association
• /
• v.34 no.6
• /
• pp.701-711
• /
• 2001

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles ; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.1
• /
• pp.196-204
• /
• 2013

In this paper, a scheduling algorithm is proposed for military satellite networks to improve QoS(Quality of Service) based on WDRR(Weighted Deficit Round Robin) method. When the packet size that has been queued to be larger, the proposed scheme DWDRR(Dynamic WDRR) method give appropriate additional quantum using EWMA(Exponentially Weighted Moving Average). To demonstrate an usefulness of proposed algorithm using OPNET modeler that built the simulation environment, reliability and real-time availability of the proposed algorithm is analyzed. The simulation results show an availability of proposed scheme in terms of reduce queuing delay and packet drop rate compared and analyzed the existing algorithms WRR(Weighted Round Robin), DRR(Deficit Round Robin) and WDRR with DWDRR.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.7 no.3
• /
• pp.95-99
• /
• 2002

3.5KHz subbottom profiling systems are useful for delineating of shallow (up to 10~100m below the sea bottom) geological structure. These systems are generally used to image geological structures with less than 1m of vertical resolution. However swell in the sea is quite often higher than 1m, causing degradation in the quality of the 3.5KHz subbottom profiles. In this paper, we show the quality of digitally recorded data can be enhanced by the suppression of swell effect. Prior to suppression of swell effect, sea bottom detection procedure was applied using the characteristics that the amplitude of sea bottom reflection is high. To suppress the swell effect, we applied moving average method and high-cut filtering method using the extracted water depth of adjacent traces. Acceptable results were obtained from both methods. In the case of bad quality data or shallow data interfered with direct wave, the suppression of swell effect is difficult due to incorrect sea bottom detection.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.6 no.1
• /
• pp.35-39
• /
• 2001

Multibeam echosounder data, collected to investigate seabed features and topography, are usually subject to outliers resulting from the ship's irregular movements and insufficient correction for pressure calibration to the positions of beams. We introduce a statistical method which adjusts the outliers using the ARMA (Autoregressive Moving Average) technique. Our method was applied to a set of real data acquired in the East Sea. In our approach, autocorrelation of the data is modeled by an AR (1) model. If an observation is substantially different from that obtained from the estimated AR (1) model, it is declared as an outlier and adjusted using the estimated AR (1) model. This procedure is repeated until no outlier is found. The result of processing shows that outliers that are far greater than signals in amplitude were successfully removed.

• Journal of the Korean Society of Safety
• /
• v.25 no.6
• /
• pp.47-52
• /
• 2010

Many industrial accidents have occurred over the years in the manufacturing and construction industries in Korea. However, as the service industry has increased continuously, the share of the accident rate in the service industry was 39.07% in 2009, while the manufacturing industry share was 33.73%. The service industry share overtook the manufacturing industry share for the first time. Therefore, this research considers prevention of industrial accidents in the service industry as well as manufacturing and construction industries. This paper describes a procedure and a method to estimate efficient accident rate forecasting and estimated zero accident time in the service industry in order to prevent industrial accidents in the transportation, storage, and telecommunication divisions. This paper proposes a model using an analytical function for the sake of very efficient accident rate forecasting. Accordingly, this paper has develops a program for accident rate forecasting, zero accident time estimating, and calculation of achievement probability through MFC (Microsoft Foundation Class) software Visual Studio 2008 in the transportation, storage, and telecommunication divisions. In results of this paper, ARIMA (Auto Regressive Integrating Moving Average) is regarded as a very efficient forecasting model for the transportation, storage, and telecommunication division. In testing this model, value minimizing the Sum of Square Errors (SSE) was calculated as 0.2532. Finally the results of this paper are sure to help establish easy accident rate forecasting and strategy or method of zero accident time in the service industry for prevention of industrial accidents.

• Journal of the Korea Society of Computer and Information
• /
• v.8 no.3
• /
• pp.113-123
• /
• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.12
• /
• pp.1245-1255
• /
• 2012

There are various anti-jamming algorithms for the GNSS system which is vulnerable to jamming, and the methods using array antenna show the best performance. Among them, the DOA estimation algorithms to identify the location of the jammer is very important. However, in case of the rotorcraft, the wireless channel which amplitude and phase changes with time is generated by the rotation of the rotor and it affects the performance of existing anti-jamming algorithms. In this paper, we modeled the effect of the rotor in four scenarios according to the correlation of antennas and assured that the performance of DOA estimation algorithms are degraded and saturated regardless of JNR due to the rotor effect. When we use the averaging method to solve this problem, the performance is improved as increasing samples for estimating. And in case of using moving average method with averaging, it shows similar performance. In addition, it reduces the required memory and moderates the variation of DOA estimation.