• 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.

• 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 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.623-625
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• 2021

Rubber o-rings are produced by conventional injection molding methods. In this case, products that are not normally molded are determined to be defective. However, if images acquired during image-based reading are read as original, there is a problem of poor accuracy. We have thus learned from convolutional neural networks using adaptive binarization and Convex Hull algorithms by extracting only rubber oring parts from the original images through pre-processing. During the test process, it was confirmed that the defect detection performance of the learning method applied pre-processing was better than the standard suggested.

• 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.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1113-1116
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• 2006

Security in wireless sensor networks is very pressing especially when sensor nodes are deployed in hostile environments. To obtain security purposes, it is essential to be able to encrypt and authenticate messages sent amongst sensor nodes. Keys for encryption and authentication must be agreed upon by communicating nodes. Due to resource limitations and other unique features, obtaining such key agreement in wireless sensor network is extremely complex. Many key agreement schemes used in general networks, such as trusted server, Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks [1], [2], [5], [7], [8]. In that situation, key pre-distribution scheme has been emerged and considered as the most appropriate scheme [2], [5], [7]. Based on that sense, we propose a new resource-optimal key pre-distribution scheme utilizing merits of the two existing key pre-distribution schemes [3], [4]. Our scheme exhibits the fascinating properties: substantial improvement in sensors' resource usage, rigorous guarantee of successfully deriving pairwise keys between any pair of nodes, greatly improved network resiliency against node capture attack. We also present a detailed analysis in terms of security and resource usage of the scheme.

• BMB Reports
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• v.39 no.6
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• pp.717-721
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• 2006

In vitro analyses of type I signal peptidase activities require protein precursors as substrates. Usually, these pre-proteins are expressed in vitro and cleavage of the signal sequence is followed by SDS polyacrylamide gel electrophoresis coupled with autoradiography. Radioactive amino acids have to be incorporated in the expressed protein, since the amount of the in vitro expressed protein is usually very low and processing of the signal peptide cannot be followed by SDS polyacrylamide gel electrophoresis alone. Here we describe a rapid and simple method to express large amounts of a protein precursor in E. coli. We have analyzed the effect of ionophors as well as of azide on the accumulation of expressed protein precursors. Azide blocks the function of SecA and the ionophors dissipate the electrochemical gradient across the cytoplasmic membrane of E. coli. Addition of azide ions resulted in the formation of inclusion bodies, highly enriched with pre-apo-plastocyanine. Plastocyanine is a soluble copper protein, which can be found in the periplasmic space of cyanobacteria as well as in the thylakoid lumen of cyanobacteria and chloroplasts, and the pre-protein contains a cleavable signal sequence at its N-terminus. After purification of cyanobacterial pre-apo-plastocyanine, its signal sequence can be cleaved off by the E. coli signal peptidase, and protein processing was followed on Coomassie stained SDS polyacrylamide gels. We are optimistic that the presented method can be further developed and applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.