• The Korean Journal of Food And Nutrition
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• v.37 no.4
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• pp.197-210
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• 2024

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.73-84
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• 2020

As the Internet began to become popular, there have been hacking and attacks on networks including systems, and as the techniques evolved day by day, it put risks and burdens on companies and society. In order to alleviate that risk and burden, it is necessary to detect hacking and attacks early and respond appropriately. Prior to that, it is necessary to increase the reliability in detecting network intrusion. This study was conducted on applying weight initialization and weight optimization to the KDD'99 dataset to improve the accuracy of detecting network intrusion. As for the weight initialization, it was found through experiments that the initialization method related to the weight learning structure, like Xavier and He method, affects the accuracy. In addition, the weight optimization was confirmed through the experiment of the network intrusion detection dataset that the Adam algorithm, which combines the advantages of the Momentum reflecting the previous change and RMSProp, which allows the current weight to be reflected in the learning rate, stands out in terms of accuracy.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.684-694
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• 2020

Objective: The purpose was to review the diagnostic performance of the length of tumor capsular contact (LCC) on magnetic resonance imaging (MRI) for detecting prostate cancer extraprostatic extension (EPE). Materials and Methods: PubMed and EMBASE databases were searched up to March 24, 2019. We included diagnostic accuracy studies that evaluated LCC on MRI for EPE detection using radical prostatectomy specimen histopathology as the reference standard. Quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and graphically presented using hierarchical summary receiver operating characteristic (HSROC) plots. Meta-regression and subgroup analyses were conducted to explore heterogeneity. Results: Thirteen articles with 2136 patients were included. Study quality was generally good. Summary sensitivity and specificity were 0.79 (95% confidence interval [CI] 0.73-0.83) and 0.67 (95% CI 0.60-0.74), respectively. Area under the HSROC was 0.81 (95% CI 0.77-0.84). Substantial heterogeneity was present among the included studies according to Cochran's Q-test (p < 0.01) and Higgins I² (62% and 86% for sensitivity and specificity, respectively). In terms of heterogeneity, measurement method (curvilinear vs. linear), prevalence of Gleason score ≥ 7, MRI readers' experience, and endorectal coils were significant factors (p ≤ 0.01), whereas method to determine the LCC threshold, cutoff value, magnet strength, and publication year were not (p = 0.14-0.93). Diagnostic test accuracy estimates were comparable across all assessed MRI sequences. Conclusion: Greater LCC on MRI is associated with a higher probability of prostate cancer EPE. Due to heterogeneity among the studies, further investigation is needed to establish the optimal cutoff value for each clinical setting.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.8
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• pp.29-36
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• 2008

An optical rotary encoder is easy to implement for automation system applications. In particular, the output of the encoder has a digital form pulse, which is also easy to be connected to a popular digital controller. By using an incremental encoder and a counting device, it is easy to measure angular displacement, as the number of the output pulses is proportional to the rotational displacement. This method can only detect the angular placement once a pulse signal comes out of the encoder. The angular displacement detection period is strongly subject to the change of the angular displacement in case of ultimate low velocity range. They have ultimate long detection period or cannot even detect angular displacement at near zero velocity. This paper proposes an algorithm for detecting angular displacement by using a dual encoder system with two encoders of normal resolution. The angular displacement detecting algorithm is able to keep detection period moderately at near zero velocity and even detect constant angular displacement within nominal period. It is useful for motion control applications in case of changing rotational direction at which there occurs zero velocity. In this paper, various experimental results are shown for the angular displacement detection algorithm.

• The KSFM Journal of Fluid Machinery
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• v.20 no.1
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• pp.59-64
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• 2017

Diverter is an essential element in gravimetric calibration method of flowmeter. Error of diverter are influenced by flow velocity profile of nozzle outlet, motion velocity of diverter and detecting location. That's why, time detection position of diverter is tuned through repetitive test for minimizing error of diverter. Further the diverter must be compared with the other institutions test since the influence on the accuracy of the flow meter used in the test. In this paper, errors (flow velocity profile of nozzle outlet, motion velocity of diverter and detecting location) of diverter are decreased by produced uni-direction diverter and error of gravimetric calibration system is decreased. Uni-direction diverter is calibrated by gravimetric calibration system with precision flowmeter, the flowmeter is calibrated by pipe prover and other institutions and uni-direction diverter is evaluated. Uni-direction diverter is not influenced by flow velocity profile of nozzle outlet, motion velocity of diverter and detecting location. As a result, Uni-direction diverter can calibrate in wider scope since increasing ratio of maximum and minimum flow rate of uni-direction diverter.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.192-198
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• 2023

Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.956-961
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• 2007

We propose a new distance measurement method and local positioning system for the autonomous mobile robots localization. The distance measurement method is able to measure long-range distances with a high accuracy by using ultrasonic sensors. The time of flight of the ultrasonic waves include various noises is calculated accurately by the proposed period detecting method. The proposed local positioning system is composed of four ultrasonic transmitters and one ultrasonic receiver. The ultrasonic transmitter and receiver are separated but they are synchronized by RF (Radio frequency) signal. The proposed system using ultrasonic waves is represented as USAT(Ultrasonic Satellite System). USAT is able to estimate the position using the least square estimation. The experimental results show that the proposed local positioning system enables to estimate the absolute position precisely.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.797-807
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• 2016

The discussion in this paper aims to introduce an approach to detect drowsiness with Android based smartphones using the OpenCV platform tools. OpenCV for Android actually provides powerful tools for real-time body's parts tracking. We discuss here about the maximization of the accuracy in real-time eye tracking. Then we try to develop an approach for detecting eye blink by analyzing the structure and color variations of human eyes. Finally, we introduce a time variable to capture drowsiness.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2002.11a
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• pp.325-328
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• 2002

The partial-discharge(PD) is accompanied by physical and chemical phenomena, such as heat, light, noise, gas, chemical transformation, electric current, and electromagnetic radiation. The PD can be detected by measuring one of these changes. Although some techniques are employed in this purpose, several obstacles interfere with an on-line measurement. Now a fiber-optic sensor for detecting ultrasonics is suggested for the on line measurement system with high accuracy. In this paper, an optical fiber sensor utilizing the principal of Mach-Zehnder interferometer was proposed to detect the discharge signal.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1765-1772
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• 2015

The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.