• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.31-40
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• 2022

This paper proposes an augmented QSBC(Quantum Short-Block Code) that preserves the function of the existing QSBC and adds a single bit-flip error correction function due to Pauli X and Y errors. The augmented QSBC provides the diagnosis and automatic correction of a single Pauli X error by inserting additional auxiliary qubits and Toffoli gates as many as the number of information words into the existing QSBC. In this paper, the general expansion method of the augmented QSBC using seed vector and the realization method of the Toffoli gate of the single bit-flip error automatic correction function reflecting the scalability are also presented. The augmented QSBC proposed in this paper has a trade-off with a coding rate of at least 1/3 and at most 1/2 due to the insertion of auxiliary qubits.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.102-108
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• 2010

The photovoltaic system changes solar energy into DC by solar cell and this DC is inverted into AC which is used in general houses by inverter. Recently, the use of power of the photovoltaic system is increased. Therefore, the study of 3 phase solar system to transmit large power is very important. This paper proposes a method that finds simply faults and diagnoses the switch open faults of 3-phase pulse width modulation (PWM) inverter of grid-connected photovoltaic system. The proposed method in $\alpha\beta$ plane uses the patterns of trajectory image as the characteristic parameters and differenciates a normal state and open states of switches. Then, the result is made into tree. The tree is composed of 21 fault patterns and the parameters to classify faults are a shape, a trajectory area, a distributed angle, and a typical vector angle. The result shows that the proposed method diagnosed fault diagnoses, classified correctly them, and made a pattern tree by fault patterns.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.563-570
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• 2018

This paper proposes an efficient dissolved gas analysis(DGA) and classification method of an oil-filled transformer using machine learning algorithms to solve problems inherent in IEC 60599. In IEC 60599, a certain diagnosis criteria do not exist, and duplication area is existed. Thus, it is difficult to make a decision without any experts since the IEC 60599 standard can not support analysis and classification of gas date of a power transformer in that criteria. To address these issue. we propose a dissolved gas analysis(DGA) and classification method using a machine learning algorithm. We evaluate the performance of the proposed method using support vector machines with dissolved gas dataset extracted from a power transformer in the real industry. To validate the performance of the proposed method, we compares the proposed method with the IEC 60599 standard. Experimental results show that the proposed method outperforms the IEC 60599 in the classification accuracy.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5551-5556
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• 2012

Cytological examination is widely used as a diagnostic tool because of the ease of collecting cells from the involved area. However, the diagnostic yield of cytological examination is unsatisfactory; the reasons include sampling error, poorly prepared samples, small numbers of malignant cells, and low grades of cellular atypia. In this study, we focused on the high infectivity of adenovirus towards epithelial cells and applied the luciferase-expressing adenoviral vector to a new cancer cell detection tool. In addition, adenoviral infectivity was enhanced by modifying viral fiber proteins. The sensitivity of the diagnostic tool was tested using the NCI-H1299 lung cancer cell line, and validated in body fluid samples from cancer patients with a variety of etiology. Results showed that the adenovirus efficiently transfected NCI-H1299 with high sensitivity. Only 10 cancer cells were sufficient for detection of luciferase signals. In body fluid samples, the adenovirus confirmed the diagnosis for malignant and benign cancer, but not in non-epithelial cell derived samples. This study provides proof-of-concept for a more reliable and sensitive diagnostic tool for epithelium-derived cancer.

• Journal of Life Science
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• v.28 no.1
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• pp.99-104
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• 2018

Streptococcus pneumoniae is one of the major pathogens in community-acquired diseases, and it contains several factors that promote its pathogenesis, including pneumolysin (PLY). PLY is a member of the cholesterol-dependent cytolysin family, which attacks cholesterol-containing membranes, thereby forming ring-shaped pores. Thus, it is a major key target for vaccines against pneumococcal disease. We cloned the PLY gene from S. pneumoniae D39 and inserted it into the pQE-30 vector. Recombinant PLY (rPLY) was overexpressed in Escherichia coli M15 and purified by $Ni^{2+}$ affinity chromatography. Similarly, a PLY-EGFP fusion gene was produced by inserting the EGFP gene at the 3' end of the PLY gene in the same vector, and the recombinant protein was purified. Sodium dodecyl sulfate - polyacrylamide gel electrophoresis (SDS-PAGE) showed that both recombinant proteins were purified. rPLY exhibited significant hemolytic activity against 1% human red blood cells (RBCs). Complete hemolysis was obtained at 500 ng/ml, and 50% hemolysis was found with a 240 ng/ml concentration. In contrast, rPLY-EGFP did not show hemolytic activity. However, rPLY-EGFP did bind the RBC membrane, indicating that rPLY-EGFP lost hemolytic activity via EGFP fusion, while retaining its membrane-binding ability. These data suggest that PLY's C terminus is important for its hemolytic activity. Therefore, these two recombinant proteins can be extremely useful for investigating the toxin mechanism of PLY and cell damage during pneumonia.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.