• JSTS:Journal of Semiconductor Technology and Science
• /
• v.12 no.4
• /
• pp.418-425
• /
• 2012

In this paper, efficient implementation of error correction code (ECC) processing circuits based on single error correction and double error detection (SEC-DED) code with check bit pre-computation is proposed for memories. During the write operation of memory, check bit pre-computation eliminates the overall bits computation required to detect a double error, thereby reducing the complexity of the ECC processing circuits. In order to implement the ECC processing circuits using the check bit pre-computation more efficiently, the proper SEC-DED codes are proposed. The H-matrix of the proposed SEC-DED code is the same as that of the odd-weight-column code during the write operation and is designed by replacing 0's with 1's at the last row of the H-matrix of the odd-weight-column code during the read operation. When compared with a conventional implementation utilizing the odd-weight- column code, the implementation based on the proposed SEC-DED code with check bit pre-computation achieves reductions in the number of gates, latency, and power consumption of the ECC processing circuits by up to 9.3%, 18.4%, and 14.1% for 64 data bits in a word.

• Nuclear Engineering and Technology
• /
• v.51 no.1
• /
• pp.146-154
• /
• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Korean Journal of Veterinary Service
• /
• v.38 no.1
• /
• pp.25-30
• /
• 2015

Bee venom has been used as to prevent and treat bovine mastitis as natural antimicrobial compounds in some dairy cattle farms in Korea. It is needed to determine the residual of bee venom in milks of dairy cattle treated with bee venom. Since bee venom is not approved as a raw material for animal drugs, the preprocessing method to detect bee venom residual in milk and the tolerance limit for its residue has not been established yet in Korea. Therefore, the purpose of this study was to develop pre-processing method not affecting major component of bee venom for detection of its residue in milks using ultra-high performance liauid chromatography (UHPLC). In addition, bee venom residue was also analyzed in milk samples of dairy cattle treated for mastitis with bee venom using UHPLC with the developed pre-processing method in this study. As a result, melittin, histamin and phospolipase A2, the major components of bee venom, were all detected by UHPLC with the pre-processing method developed in this study. The results of this study suggest that the pre-processing method developed in this study can be useful to detect bee venom residue in dairy cattle milk. We also found that no bee venom residues were detected in milk samples collected from dairy cattle treated with bee venom after 1 and 3 days, respectively.

• Food Science of Animal Resources
• /
• v.41 no.2
• /
• pp.173-184
• /
• 2021

Clean labeling is emerging as an important issue in the food industry, particularly for meat products that contain many food additives. Among synthetic additives, nitrite is the most important additive in the meat processing industry and is related to the development of cured color and flavor, inhibition of oxidation, and control of microbial growth in processed meat products. As an alternative to synthetic nitrite, preconverted nitrite from natural microorganisms has been investigated, and the applications of pre-converted nitrite have been reported. Natural nitrate sources mainly include fruits and vegetables with high nitrate content. Celery juice or powder form have been used widely in various studies. Many types of commercial starter cultures have been developed. S. carnosus is used as a critical nitrate reducing microorganism and lactic acid bacteria or other Staphylococcus species also were used. Pre-converted nitrite has also been compared with synthetic nitrite and studies have been aimed at improving utilization by exploiting the strengths (positive consumer attitude and decreased residual nitrite content) and limiting the weaknesses (remained carcinogenic risk) of pre-converted nitrite. Moreover, as concerns regarding the use of synthetic nitrites increased, research was conducted to meet consumer demands for the use of natural nitrite from raw materials. In this report, we review and discuss various studies in which synthetic nitrite was replaced with natural materials and evaluate pre-converted nitrite technology as a natural curing approach from a clean label perspective in the manufacturing of processed meat products.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.111-118
• /
• 2022

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.41-51
• /
• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• The Research Journal of the Costume Culture
• /
• v.11 no.1
• /
• pp.66-74
• /
• 2003

This study is to improve the worst lightfastness of a natural dye. To modify the fiber surface, low temperature oxygen plasma was carried out on silk fabric. The result is followed below after the examination of surface shape, dyeability, color change, UV absorbent influence and lightfastness. 1. When electric discharge outputs are 60W, 80W and 100w, and processing times are 10minutes, 20minutes and 40minutes, the etching effect of surface increased as electric discharge outputs and processing times increased. 2. When examined UV absorbent for 5hours, 10hours, 20hours, 40hours and 80hours, the value changes of E are 1.47, 2.51, 2.91, 3.71, 4.51 and 5.31 in case of Al pre-mordanting/ prasma 80W, 20min./ UVabsorbent 5% (100:1), 2.31, 2.47, 3.84, 3.90, 3.61 and 4.42 in case of Al pre-mordanting/prasma 80W, 20min.1 UV absorbent 5% (o.w.f.). The lightfastness decreased when UV absorbent increased. 3. Dyeability of the samples pre-treated with five different methods was in the following order: plasma processing for 20minutes at 60W/Al pre-mordanting > Al pre-mordanting > plasma processing for 20minutes at 60W > Al after-mordanting. non mordanting Plasma treatment had superior effect on dyeability. 4. When UV absorbent was applied in fabric, the sample under higher electric discharge out puts showed more effective in improving lightfastness.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.206-211
• /
• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• KCI Concrete Journal
• /
• v.12 no.2
• /
• pp.3-10
• /
• 2000

A computer program, named NEO-PCBRG, for the analysis and design of prestressed con-crete(PSC) bridges was developed using the finite element method. NEO-PCBRG can predict the response of PSC bridges throughout the various stages of construction and service. NEO-PCBRG has both pre- and post-processing capabilities. Pre-processing refers to all the neces- sary steps required to prepare a virtual prototype, more commonly termed a varied model for analysis. Post-processing here stands for the step in which the results from the analysis are reviewed and interpreted. In order to allow for the easy and convenient execution of the entire procedure, NEO-PCBRG was developed using computer graphics in the Visual Basic pro- gramming language. In conclusion, this study presents a new software architecture for analy-sis using the user-oriented design technique.

• Journal of Korea Multimedia Society
• /
• v.21 no.3
• /
• pp.391-399
• /
• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.