• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.107-113
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• 2023

This paper proposes a method for detecting smoking behavior in images using artificial intelligence technology. Since smoking is not a static phenomenon but an action, the object detection technology was combined with the posture estimation technology that can detect the action. A smoker detection learning model was developed to detect smokers in images, and the characteristics of smoking behaviors were applied to posture estimation technology to detect smoking behaviors in images. YOLOv8 was used for object detection, and OpenPose was used for posture estimation. In addition, when smokers and non-smokers are included in the image, a method of separating only people was applied. The proposed method was implemented using Google Colab NVIDEA Tesla T4 GPU in Python, and it was found that the smoking behavior was perfectly detected in the given video as a result of the test.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.80-86
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• 2019

As computer performance improves, research using deep learning are being actively carried out in various fields. Recently, deep learning technology has been applying to the safety evaluation for structures. In particular, the internal blades of a turbine structure requires experienced experts and considerable time to detect surface damages because of the difficulty of separation of the blades from the structure and the dark environmental condition. This study proposes a Faster R-CNN deep learning model that can detect surface damages on the internal blades, which is one of the primary elements of the turbine structure. The deep learning model was trained using image data with dent and punch damages. The image data was also expanded using image filtering and image data generator techniques. As a result, the deep learning model showed 96.1% accuracy, 95.3% recall, and 96% precision. The value of the recall means that the proposed deep learning model could not detect the blade damages for 4.7%. The performance of the proposed damage detection system can be further improved by collecting and extending damage images in various environments, and finally it can be applicable for turbine engine maintenance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.115-122
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• 2016

This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.242-247
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• 2003

Murine encephalomyocarditis virus (EMCV) has been used as a surrogate for hepatitis A virus (HAV) for the validation of virus removal and/or inactivation during the manufacturing process of biopharmaceuticals. Recently international regulation for the validation of HAV safety has been reinforced because of the reported cases of HAV transmission to hemophiliac patients who had received ntihemophilic factors prepared from human plasma. The purpose of the present study was to compare the resistance of HAV and EMCV to various viral inactivation processes and then to standardize the HAV validation method. HAV was more resistant than EMCV to pasteurization (60oC heat treatment for 10 hr), low pH incubation (pH 3.9 at 25oC for 14 days), 0.1 M NaOH treatment, and lyophilization. EMCV was completely inactivated to undetectable levels within 2 hr of pasteurization, however, HAV was completely inactivated to undetectable levels after 5 hr treatment. EMCV was completely inactivated to undetectable levels within 15 min of 0.1 M NaOH treatment, however, residual infectivity of HAV still remained even after 120 min of treatment. The log reduction factors achieved during low pH incubation were 1.63 for HAV and 3.84 for EMCV. Also the log reduction factors achieved during a lyophilization process of antihemophilic factor VIII were 1.21 for HAV and 4.57 for EMCV. These results indicate that HAV rather than EMCV should be used for the virus validation study and the validation results obtained using EMCV should be precisely reviewed.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.422-427
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• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.1-7
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• 2012

The brake-by-wire technology is a new automotive chassis system that allows standard braking operations by electronic components with lighter weights and faster response. The brake-by-wire units such as EMB (Electro-Mechanical Brake) are controlled by electronic sensors and actuators and, thus, the fault diagnosis is essential for implementation. In this study, a model-based fault diagnosis system is developed for the sensors based on the analytical redundancy method. The fault detection algorithm is verified in simulations for various faulty cases. A test bench is built including the EMB unit and the performance of the proposed fault diagnosis system is evaluated through the experiment.