• 한국분말재료학회지
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• 제27권5호
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• pp.365-372
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• 2020

Predicting the quality of materials after they are subjected to plasma sintering is a challenging task because of the non-linear relationships between the process variables and mechanical properties. Furthermore, the variables governing the sintering process affect the microstructure and the mechanical properties of the final product. Therefore, an artificial neural network modeling was carried out to correlate the parameters of the spark plasma sintering process with the densification and hardness values of Ti-6Al-4V alloys dispersed with nano-sized TiN particles. The relative density (%), effective density (g/㎤), and hardness (HV) were estimated as functions of sintering temperature (℃), time (min), and composition (change in % TiN). A total of 20 datasets were collected from the open literature to develop the model. The high-level accuracy in model predictions (>80%) discloses the complex relationships among the sintering process variables, product quality, and mechanical performance. Further, the effect of sintering temperature, time, and TiN percentage on the density and hardness values were quantitatively estimated with the help of the developed model.

• 한국정밀공학회지
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• 제31권3호
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• pp.189-197
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• 2014

Diffractive optical elements (DOEs) - in general a complex pattern of micro- and nano-scale structures - can modulate and transform light in a predetermined way. Their importance is being increased nowadays because they can be designed to handle a number of simultaneous tasks. In view point of machining DOEs, it is a big challenge to fabricate micro- and nano-scale structures on a free-form surfaces. In this paper, the state-of-the-art of the ultra-precision machine tools is reviewed. Also some technical issues which determine the machine tool accuracy are discussed.

• Advances in nano research
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• 제15권2호
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• pp.155-163
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• 2023

Exercise is beneficial to the body in some ways. It is vital for people who have heart problems to perform exercise according to their condition. This paper describes how an Android platform can provide early warnings of fatigue during wushu exercise using Photoplethysmography (PPG) signals. Using the data from a micro-electro-mechanical system (MEMS) gyroscope to detect heart rate, this study contributes an algorithm to determine a user's fatigue during wushu exercise. It sends vibration messages to the user's smartphone device when the heart rate exceeds the limit or is too fast during exercise. The heart rate monitoring system in the app records heart rate data in real-time while exercising. A simple pulse sensor and Android app can be used to monitor heart rate. This plug-in sensor measures heart rate based on photoplethysmography (PPG) signals during exercise. Pulse sensors can be easily inserted into the fingertip of the user. An embedded microcontroller detects the heart rate by connecting a pulse sensor transmitted via Bluetooth to the smartphone. In order to measure the impact of physical activity on heart rate, Wushu System tests are conducted using various factors, such as age, exercise speed, and duration. During testing, the Android app was found to detect heart rate with an accuracy of 95.3% and to warn the user when their heart rate rises to an abnormal level.

• 한국인공지능학회지
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• 제11권4호
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• 한국방사선학회논문지
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• 제7권1호
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• pp.93-98
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• 2013

이 연구의 목적은 6 MV 광자 빔에 대한 광자극형광나노닷선량계(OSLnD)의 교정을 조사하는 데 있다. OSLnD의 선량반응 분석으로부터 선형 및 비선형 교정의 선량범위들은 결정을 하였다. 교정 및 교정식의 정확성을 평가하기 위하여 교정 및 품질관리선량계의 세트들은 만들어서 사용하였다. 교정들은 각각 0~300 cGy 및 20~1300 cGy 선량범위에서 선형 및 비선형적으로 수행하였다. 교정의 오차들은 선형 및 비선형 교정에 대하여 품질관리선량계들의 측정으로부터 각각 0.1% 이하로 얻었다. 이 연구는 6 MV 광자 빔에 대한 OSLnD의 교정식을 제공한다.

• 대한기계학회논문집A
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• 제30권11호
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• pp.1425-1432
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• 2006

Coating layers on a coated sheet steel frequently affect distributions of strain rate of sheets and deteriorate the frictional characteristics between sheets and tools in sheet metal forming. Thus, it is important to identify the deformation behavior of these coatings to ensure the success of the sheet forming operation. In this study, the technique using nano-indentation test, FE-simulation and Artificial Neural Network(ANN) were proposed to determine the power law stress-strain behavior of coating layer and the power law behavior of extracted coating layers was examined using FE-simulation of drawing and nano-indentation process. Also, deep drawing test was performed to estimate the formability and frictional characteristic of coated sheet, which was calculated using the linear relationship between drawing force and blank holding force obtained from the deep drawing test. FE-simulations of the drawing process were respectively carried out for single-behavior FE-model having one stress-strain behavior and for layer-behavior FE-model which consist of coating and substrate separately. The results of simulations showed that layer-behavior model can predict drawing forces with more accuracy in comparison with single-behavior model. Also, mean friction coefficients used in FE-simulation signify the value that can occur maximum drawing force in a drawing test.

• 한국정밀공학회지
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• 제23권6호
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• pp.96-103
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• 2006

To establish of standard technique of nano-length measurement in 2D plane, new AFM system has been designed. In the long range (about several tens of ${\mu}m$), measurement uncertainty is dominantly affected by the Abbe error of XY scanning stage. No linear stage is perfectly straight; in other words, every scanning stage is subject to tilting, pitch and yaw motion. In this paper, an AFM system with minimum offset of XY sensing is designed. And XY scanning stage is designed to minimize rotation angle because Abbe errors occur through the multiply of offset and rotation angle. To minimize the rotation angle optimal design has performed by maximizing the stiffness ratio of motion direction to the parasitic motion direction of each stage. This paper describes the design scheme of full AFM system, especially about XY stage. Full range of fabricated XY scanner is $100{\mu}m\times100{\mu}m$. And tilting, pitch and yaw motion are measured by autocollimator to evaluate the performance of XY stage. As a result, XY scanner can have good performance. Using this AFM system, 3um pitch specimen was measured. The uncertainty of total system has been evaluated. X and Y direction performance is different. X-direction measuring performance is better. So to evaluate only ID pitch length, X-direction scanning is preferable. Its expanded uncertainty(k=2) is $\sqrt{(3.96)^2+(4.10\times10^{-5}{\times}p)^2}$ measured length in nm.

• 전력전자학회논문지
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• 제22권4호
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• pp.360-368
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• 2017

This paper proposes the 80-kW high-efficiency bidirectional hybrid SiC boost/buck converter using droop control for DC nano-grid. The proposed converter consists of four 20-kW modules to achieve fault tolerance, ease of thermal management, and reduced component stress. Each module is constructed as a cascaded structure of the two basic bi-directional converters, namely, interleaved boost and buck converters. A six-pack hybrid SiC intelligent power module (IPM) suitable for the proposed cascaded structure is adopted for high-efficiency and compactness. The proposed converter with hybrid switching method reduces the switching loss by minimizing switching of insulated gate bipolar transistor (IGBT). Each module control achieves smooth transfer from buck to boost operation and vice versa, since current controller switchover is not necessary. Furthermore, the proposed parallel control using DC droop with secondary control, enhances the current sharing accuracy while well regulating the DC bus voltage. A 20-kW prototype of the proposed converter has been developed and verified with experiments and indicates a 99.3% maximum efficiency and 98.8% rated efficiency.

• Nutrition Research and Practice
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• 제13권6호
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

• 농업과학연구
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• 제50권3호
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• pp.373-382
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.