• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1103-1105
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• 2015

Leaked Radioactive source in nuclear power station, radiation related facilities and the aging nuclear power plant for the dismantling must need to detect and remove early to prevent major accidents. In this paper, we implemented a single sensor-based gamma-ray detectors stereo which can provide the distance to the radiation source, a direction and doserate information for fast and efficient decontamination work the radiation source. And we have carried out an algorithm development for high-speed detection of the detection equipment. Two detectors are required for stereo structure for obtaining the distance information of the radioactive source, but we designed the only sensor-based detection device for the weight reduction. We have extracted the region of interest and obtained the distance calculation result and distribution of radiation source in order to minimize a stereo image acquisition time. Detection time of the algorithm showed a shorter time of about 41%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.2061-2068
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• 1993

Condition monitoring technology is of great importance for the maintenance of complex machinery in view of its early monitoring of the abnormal condition and the protection against failure. Several methods have been used for the detection of failure of journal bearings, one of the main elements of mechanical system. The methods most frequently used are vibration and temperature monitoring, but these are unable to monitor the wear conditions exactly. In this study, an ultrasonic measument method, one of the non-destructive testing methods, was introduced as the monitoring technology. Furtermore a pattem recognition method was applied to analyze the ultrasonic signal. The monitoring system using the pattern recognition method is composed of digital signal processing units and uses Hamming net algorithm for the recognition of ultrasonic waves. From the journal bearing wear test, the occurrence of adhesive wear of the white metal in rubbing contact with the shaft was exactly detected by this system, and the wear status of the journal bearing was monitored by measuring the wear thickness.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Journal of the Korean institute of surface engineering
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• v.55 no.6
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• pp.398-402
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• 2022

Vacuum Insulation Panel (VIP) is an high energy efficient insulation system that facilitate slim but high insulation performance, based on based on a porous core material evacuated and encapsulated in a multi-barrier envelope. Although VIP has been on the market for decades now, it wasn't until recently that efforts have been initiated to propose a standard on aging testing. One of the issues regarding VIP is its durability and aging due to pressure and moisture dependent increase of the initial low thermal conductivity with time. It is hard to visually determine at an early stage. Recently, a method of analyzing the damage on the a material surface by applying image processing technology has been widely used. These techniques provide fast and accurate data with a non-destructive way. In this study, the surface VIP images were analyzed using the Harris corner detection algorithm. As a result, 171,333 corner points in the normal packaging were detected, whereas 32,895 of the defective packaging, which were less than the normal packaging. were detected. These results are considered to provide meaningful information for the determination of VIP condition.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.275-281
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• 2022

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.1-13
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• 2010

It is not easy to set RED(Random Early Detection) parameter according to environment in managing Network Device. Especially, it is more difficult to set parameter in the case of maintaining the constant service rate according to the change of environment. In this paper, we hypothesize the router that has Multi-core CPU in output queue and propose AI RED(Artificial Intelligence RED), which directly induces Genetic Algorithm of Artificial Intelligence in the output queue that is appropriate to the optimization of parameter according to RED environment, which is automatically adaptive to workload. As a result, AI RED Is simpler and finer than FuRED(Fuzzy-Logic-based RED), and RED parameter that AI RED searches through simulations is more adaptive to environment than standard RED parameter, providing the effective service. Consequently, the automation of management of RED parameter can provide a manager with the enhancement of efficiency in Network management.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.