• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.424-433
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• 2008

An ownship with towed array sonar (TAS) has limited maneuvers due to its dynamic feature, bearing and frequency measurements of a target which are not detected continuously but are often lost in ocean environment. We propose a pre-processing algorithm for the faded bearing and frequency measurements to solve the BFTMA problem of TAS under limited detection conditions. The proposed pre-processing algorithm to restore the faded bearing and frequency measurements is implemented to perform a BFTMA filter even if the measurements of a target are not continuously detected. The Modified Gain Extended Kalman Filter (MGEKF) method based on the Interacting Multiple Model (IMM) structure is applied for a BFTMA filter algorithm to estimate the target. Simulations for the various conditions were carried out to verify the applicability of the proposed algorithms, and confirmed superior estimation performance compared with the existing Bearings-Only TMA (BOTMA).

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.299-307
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• 2022

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

• Analytical Science and Technology
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• v.25 no.4
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• pp.214-222
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• 2012

ASTM (American Society for Testing and Materials) D6751-10 suggests analytical methods as well as specifications for biodiesel quality. However, it is expensive and time-consuming to follow the ASTM testing methods to analyze biodiesel and various impurities. This paper develops a quantitative analysis system for biodiesel and impurities based on Infrared spectroscopy and a multivariate statistical method, PLS (partial least squares). In addition, four different pre-processing techniques were compared for spectrum correction and noise reduction. Savitzky-Golay pre-processing showed the best performance.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3166-3179
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• 2013

A pre-processing system for adaptive noise removal is proposed based on the principle of identifying and filtering object regions and background regions. Human perception of images depends on bright, well-focused object regions; these regions can be treated with the best filters, while simpler filters can be applied to other regions to reduce overall computational complexity. In the proposed method, bright region segmentation is performed, followed by segmentation of object and background regions. Noise in dark, background, and object regions is then removed by the median, fast bilateral, and bilateral filters, respectively. Simulations show that the proposed algorithm is much faster than and performs nearly as well as the bilateral filter (which is considered a powerful noise removal algorithm); it reduces computation time by 19.4 % while reducing PSNR by only 1.57 % relative to bilateral filtering. Thus, the proposed algorithm remarkably reduces computation while maintaining accuracy.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.11-19
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• 2008

Recently, advances in mobile devices and wireless communication technologies require research on various location-based services. As a result, many studies on processing k-nearest neighbor query, which is most im portant one in location-based services, have been done. Most of existing studies use pre-computation technique to improve retrieval performance by computing network distance between POIs and nodes beforehand in spatial networks. However, they have a drawback that they can not deal with effectively the update of POIs to be searched. In this paper, we propose a distributed grid scheme using S-GRID to overcome the disadvantage of the existing work as well as to manage the location information of a large number of moving objects in efficient way. In addition, we describe a k-nearest neighbor(k-NN) query processing algorithm for the proposed distributed grid scheme. Finally, we show the efficiency of our distributed grid scheme by making a performance comparison between the k-NN query processing algorithm of our scheme and that of S-GRID.

• Journal of Broadcast Engineering
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• v.6 no.2
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• pp.139-147
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• 2001

Since various noises degrade not only image quality but also compression efficiency in MPEG and H.263, pre-processing is necessary to reduce spatial and temporal noise and to increase ceding efficiency as well. In this paper, we propose a simplified method for noise detection, spatial and temporal noise reduction. Noise detection is based on correlation of the current pixel with its neighboring 4 pixels. Spatial noose reduction utilizes a non-rectangular median filter that is less complex than the conventional rectangular median filter. The proposed temporal filter is an IIR average filter using LUT(Look-up Table) to enhance subjective video quality. The proposed pre-processing method is very simple and efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.41-48
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• 2005

In this paper, we present a low complexity modified Gaussian model based pre-processing filter to improve the performance of H.264 compressed video. Video sequence captured by general imaging system represents the degraded version due to the additive noise which decreases coding efficiency and results in unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.3
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• pp.419-428
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• 2011

Recycling as a stabilizer of industrial by-product can be terms of the proper handling of industrial by-product and positive side in terms of recycling of waste. This study was performed to evaluate has the possibility as stabilizer by primary processing Pre-Red Mud and Bio-Solids which are generated as waste in soils contaminated with heavy metals and compared the efficiency with steel slug being applied in an existing site. In evaluation of the arsenic-fixing ability of stabilizer in batch test, Bio-Solids have the similar arsenic-fixing ability with Pre-Red Mud, which shows 17% h igher arsenic-fixing ability than PS Ball. Since the stabilization periods using Bio-Solids and Pre-Red Mud are faster than the PS Ball, they seems to be better stabilizer than PS Ball to decrease the leaching of arsenic in contaiminated soil.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.12
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• pp.590-596
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• 2001

The pre-emphasis is an essential process for speech signal processing. Widely used two methods are the typical method using a fixed value near unity and te optimal method using the autocorrelation ratio of the signal. This study proposes a new pre-emphasis method using the short-term energy difference of speech signal, which can effectively compensate the glottal source characteristics and lip radiation characteristics. Using the proposed pre-emphasis, speech analysis, such as spectrum estimation, formant detection, is performed and the results are compared with those of the conventional two pre-emphasis methods. The speech analysis with 5 single vowels showed that the proposed method enhanced the spectral shapes and gave nearly constant formant frequencies and could escape the overlapping of adjacent two formants. comparison with FFT spectra had verified the above results and showed the accuracy of the proposed method. The computational complexity of the proposed method reduced to about 50% of the optimal method.