• Journal of Multimedia Information System
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• v.2 no.2
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• pp.207-214
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• 2015

In this paper, we proposed new software for 3D rendering of MR images in the medical domain using C# wrapper of Visualization Toolkit (VTK) and Microsoft .NET framework. Our objective in developing this software was to provide medical image segmentation, 3D rendering and visualization of hippocampus for diagnosis of Alzheimer disease patients using DICOM Images. Such three dimensional visualization can play an important role in the diagnosis of Alzheimer disease. Segmented images can be used to reconstruct the 3D volume of the hippocampus, and it can be used for the feature extraction, measure the surface area and volume of hippocampus to assist the diagnosis process. This software has been designed with interactive user interfaces and graphic kernels based on Microsoft.NET framework to get benefited from C# programming techniques, in particular to design pattern and rapid application development nature, a preliminary interactive window is functioning by invoking C#, and the kernel of VTK is simultaneously embedded in to the window, where the graphics resources are then allocated. Representation of visualization is through an interactive window so that the data could be rendered according to user's preference.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.79-92
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• 2009

With the rising reliance on market estimation through customer analysis in customer-centered marketing, there is a rapid increase in the amount of personal data owned by corporations. There has been a corresponding rise in the customers' interest in personal information protection, and the problem of personal information leakage has risen as a serious issue. The purpose of this research is to develop a diagnosis model for personal information protection that is suited to our country's corporate environment, and on this basis, to present diagnostic instruments that can be applied to domestic corporations. This diagnosis model is a structural equation model that schematizes the degree of synthetic effect that administration factors and estimation items have on the protection of personal information owned by corporations. We develop the model- consisting of the administration factors for personal information protection and the measurement items of each factor- using the development method of standardized structural equation model. We then present a tool through which the administration factors and estimation items verified through this model can be used in the diagnosis for personal information protection in corporations. This diagnostic tool can be utilized as a useful instrument to prevent in advance the leakage of personal information in corporations.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.241-245
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• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• Research in Plant Disease
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• v.20 no.3
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• pp.173-181
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• 2014

The early and accurate detection of plant viruses is an essential component to control those. Because the globalization of trade by free trade agreement (FTA) and the rapid climate change promote the country-to-country transfer of viruses and their hosts and vectors, diagnosis of viral diseases is getting more important. Because symptoms of viral diseases are not distinct with great variety and are confused with those of abiotic stresses, symptomatic diagnosis may not be appropriate. From the last three decades, enzyme-linked immunosorbent assays (ELISAs), developed based on serological principle, have been widely used. However, ELISAs to detect plant viruses decrease due to some limitations such as availability of antibody for target virus, cost to produce antibody, requirement of large volume of sample, and time to complete ELISAs. Many advanced techniques allow overcoming demerits of ELISAs. Since the polymerase chain reaction (PCR) developed as a technique to amplify target DNA, PCR evolved to many variants with greater sensitivity than ELISAs. Many systems of plant virus detection are reviewed here, which includes immunological-based detection system, PCR techniques, and hybridization-based methods such as microarray. Some of techniques have been used in practical, while some are still under developing to get the level of confidence for actual use.

• Tuberculosis and Respiratory Diseases
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• v.83 no.2
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• pp.132-140
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• 2020

In human immunodeficiency virus (HIV)-infected patients, Pneumocystis jirovecii pneumonia (PCP) is a well-known opportunistic infection and its management has been established. However, PCP is an emerging threat to immunocompromised patients without HIV infection, such as those receiving novel immunosuppressive therapeutics for malignancy, organ transplantation, or connective tissue diseases. Clinical manifestations of PCP are quite different between patients with and without HIV infections. In patients without HIV infection, PCP rapidly progresses, is difficult to diagnose correctly, and causes severe respiratory failure with a poor prognosis. High-resolution computed tomography findings are different between PCP patients with HIV infection and those without. These differences in clinical and radiological features are due to severe or dysregulated inflammatory responses that are evoked by a relatively small number of Pneumocystis organisms in patients without HIV infection. In recent years, the usefulness of polymerase chain reaction and serum β-D-glucan assay for rapid and non-invasive diagnosis of PCP has been revealed. Although corticosteroid adjunctive to anti-Pneumocystis agents has been shown to be beneficial in some populations, the optimal dose and duration remain to be determined. Recent investigations revealed that Pneumocystis colonization is prevalent and that asymptomatic carriers are at risk for developing PCP and can serve as the reservoir for the spread of Pneumocystis by airborne transmission. These findings suggest the need for chemoprophylaxis in immunocompromised patients as well as infection control measures, although the indications remain controversial. Because a variety of novel immunosuppressive therapeutics have been emerging in medical practice, further innovations in the diagnosis and treatment of PCP are needed.

• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Parasites, Hosts and Diseases
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• v.51 no.1
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• pp.1-8
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• 2013

Taenia solium, T. saginata, and T. asiatica are taeniid tapeworms that cause taeniasis in humans and cysticercosis in intermediate host animals. Taeniases remain an important public health concerns in the world. Molecular diagnostic methods using PCR assays have been developed for rapid and accurate detection of human infecting taeniid tapeworms, including the use of sequence-specific DNA probes, PCR-RFLP, and multiplex PCR. More recently, DNA diagnosis using PCR based on histopathological specimens such as 10% formalin-fixed paraffin-embedded and stained sections mounted on slides has been applied to cestode infections. The mitochondrial gene sequence is believed to be a very useful molecular marker for not only studying evolutionary relationships among distantly related taxa, but also for investigating the phylo-biogeography of closely related species. The complete sequence of the human Taenia tapeworms mitochondrial genomes were determined, and its organization and structure were compared to other human-tropic Taenia tapeworms for which complete mitochondrial sequence data were available. The multiplex PCR assay with the Ta4978F, Ts5058F, Tso7421F, and Rev7915 primers will be useful for differential diagnosis, molecular characterization, and epidemiological surveys of human Taenia tapeworms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of Korean Society on Water Environment
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• v.38 no.5
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• pp.231-239
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• 2022

In this study, we applied ENERWATER to evaluate the energy consumption labeling of wastewater treatment plants in Korea using the Korea sewerage statistics data. The results showed that the energy label status was excellent in the SBR process for small and medium-scale wastewater treatment plants and the A2O process for large-scale wastewater treatment plants. The energy labeling of wastewater treatment plants of 50,000 tons capacity was excellent. The statuses of metropolitan cities and Jeollanam-do province were excellent. We analyzed the effects of renewable energy on wastewater treatment plants' energy consumption and found out that digestion gas for large-scale plants and photovoltaic energy for small-scale plants were effective in improving energy labeling. In addition, we compared the energy labels of four wastewater treatment plants in "Z" city and wastewater treatment plant "X" had the best energy label, and the wastewater treatment plants "V" and "Y" had to be selected as priorities for the energy diagnosis and improvement project. In a comprehensive conclusion, the applicability of ENERWATER was confirmed based on sewage statistics data and labeling can be used to set priorities for the energy diagnosis and improvement project.

• Biomedical Science Letters
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• v.29 no.2
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• pp.53-57
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• 2023

The main objective of pathologists is to achieve accurate lesion diagnoses, which has become increasingly challenging due to the growing number of pathological slides that need to be examined. However, using digital technology has made it easier to complete this task compared to older methods. Digital pathology is a specialized field that manages data from digitized specimen slides, utilizing image processing technology to automate and improve analysis. It aims to enhance the precision, reproducibility, and standardization of pathology-based researches, preclinical, and clinical trials through the sophisticated techniques it employs. The advent of whole slide imaging (WSI) technology is revolutionizing the pathology field by replacing glass slides as the primary method of pathology evaluation. Image processing technology that utilizes WSI is being implemented to automate and enhance analysis. Artificial intelligence (AI) algorithms are being developed to assist pathologic diagnosis and detection and segmentation of specific objects. Application of AI-based digital pathology in biomedical researches is classified into four areas: diagnosis and rapid peer review, quantification, prognosis prediction, and education. AI-based digital pathology can result in a higher accuracy rate for lesion diagnosis than using either a pathologist or AI alone. Combining AI with pathologists can enhance and standardize pathology-based investigations, reducing the time and cost required for pathologists to screen tissue slides for abnormalities. And AI-based digital pathology can identify and quantify structures in tissues. Lastly, it can help predict and monitor disease progression and response to therapy, contributing to personalized medicine.