• Journal of Digital Convergence
• /
• v.16 no.7
• /
• pp.223-229
• /
• 2018

Recently, the necessity of safety diagnosis of electrical devices has been increasing as the fire caused by electric devices has increased rapidly. This study is concerned with the safety diagnosis of electric equipment using intelligent Fuzzy technology. It is used as a diagnostic input for the multiple electrical safety factors such as the use current, cumulative use time, deterioration and arc characteristics inherent to the equipment. In order to extract these information in real time, a device composed of various sensor circuits, DSP signal processing, and communication circuit is implemented. The fuzzy logic algorithm using the Gaussian function for each information is designed and compiled to be implemented on a small DSP board. The fuzzy logic receives the four diagnostic information, deduces it by the fuzzy engine, and outputs the overall safety status of the device as a 100-step analog fuzzy value familiar to human sensibility. By experiments of a device that combines hardware and fuzzy algorithm implemented in this study, it is verified that it can be implemented in a small DSP board with human-friendly fuzzy value, diagnosing real-time safety conditions during operation of electric equipment. In the future, we expect to be able to study more intelligent diagnostic systems based on artificial intelligent with AI dedicated Micom.

• Parasites, Hosts and Diseases
• /
• v.54 no.3
• /
• pp.375-380
• /
• 2016

Angiostrongyliasis is difficult to be diagnosed for the reason that no ideal method can be used. Serologic tests require specific equipment and are not always available in poverty-stricken zone and are time-consuming. A lateral flow immunoassay (LFIA) may be useful for angiostrongyliasis control. We established a LFIA for the diagnosis of angiostrongyliasis based on 2 monoclonal antibodies (mAbs) against antigens of Angiostrongylus cantonensis adults. The sensitivity and specificity were 91.1% and 100% in LFIA, while those of commercial ELISA kit was 97.8% and 86.3%, respectively. Youden index was 0.91 in LFIA and 0.84 in commercial ELISA kit. LFIA showed detection limit of 1 ng/ml of A. cantonensis ES antigens. This LFIA was simple, rapid, highly sensitive and specific, which opened an alternative approach for the diagnosis of human angiostrongyliasis.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.4
• /
• pp.226-231
• /
• 2019

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

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

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.4
• /
• pp.77-89
• /
• 2024

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.7
• /
• pp.233-240
• /
• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.3
• /
• pp.245-251
• /
• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.6
• /
• pp.2209-2213
• /
• 2015

DNA methylation of tumor suppressor gene promoters is the most frequent phenomenon leading to inactivation of function, consequently driving malignant cell transformation. Cyclin D2 is implicated in tumor suppression. In our study, we carried out the MSP assay to evaluation the methylation status at CpG islands in the cyclin D2 promoter in breast cancer cases from the Vietnamese population. The results showed that the frequency of methylation reached 62.1% (59 of 95 breast cancer tumors), but was low in non-cancer specimens at 10% (2 of 20 non-cancer specimens). Additionally, with an RR (relative risk) and OR (odd ratios) of 6.21 and 14.8, DNA hypermethylation of cyclin D2 increased the possibility of malignant transformation. Our results confirmed the cyclin D2 hypermethylation could be used as the potential biomarker which could be applied in prognosis and early diagnosis of Vietnamese breast cancer patients.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.35 no.1
• /
• pp.11-19
• /
• 2019

Recently, with the development of the fourth industrial revolution that has been achieved through the fusion of information and communication technology (ICT), the technologies of AI, IOT, BIG-DATA, it is increasing utilization rate by industry and research and development of application technologies are being actively carried out. Especially, in the case of unmanned aerial vehicles, the construction market is expected to be one of the most commercialized areas in the world for the next decade. However, research on utilization of unmanned aerial vehicles in the construction field in Korea is insufficient. In this study, We have developed a quantitative building inspection method using the unmanned aerial vehicle and presented the protocol for it. The proposed protocol was verified by applying it to existing old buildings, and defect information could be quantified by calculating length, width, and area for each defect. Through this technical research, the final goal is to contribute to the development of safety diagnosis technology using unmanned aerial vehicle and risk assessment technology of buildings in case of disaster such as earthquake.

• Clinical and Experimental Pediatrics
• /
• v.46 no.4
• /
• pp.389-392
• /
• 2003

Netherton's syndrome is an unusual disorder which consists of triad of ichtyosiform dermatosis, multiple defects of hair shaft and an atopic diathesis. The finding of bamboo hair is pathognomic in Netherton's syndrome and the ichthyosiform dermatosis may consist of either ichtyosis linearis circumflexa or congenital ichthyosiform erythroderma. Often, variability in the clinical features leads to a delay in diagnosis in many cases. We report a case of Netherton's syndrome diagnosed in the neonatal period. The patient presented with severe ichthyosis and confirmed microscopically distinctive bamboo hair.