• Journal of KSNVE
• /
• v.4 no.3
• /
• pp.337-343
• /
• 1994

A method of the leak detection from the pipe system by using accelerometer is proposed. The signal detected from accelerometer is proved experimentally to be a dispersive wave. Based on the experiments, a method using the narrow band pass filter and the unit impulse response function is analyzed. The method uses the characteristics of the unit impulse response function, that the function is available evenin the narrow band signal because, unlike the cross correlation, it is normalized by the auto spectrum. The accelerometer is quite easier to use than the hydrophone in adapting to the pipe system.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.91-98
• /
• 2022

Lifelog data continuously collected through a wearable device may contain many outliers, so in order to improve data quality, it is necessary to find and remove outliers. In general, since the number of outliers is less than the number of normal data, a class imbalance problem occurs. To solve this imbalance problem, we propose a method that applies Variational AutoEncoder to outliers. After preprocessing the outlier data with proposed method, it is verified through a number of machine learning models(classification). As a result of verification using body weight data, it was confirmed that the performance was improved in all classification models. Based on the experimental results, when analyzing lifelog body weight data, we propose to apply the LightGBM model with the best performance after preprocessing the data using the outlier processing method proposed in this study.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.1
• /
• pp.51-56
• /
• 2021

As hydrogen is classified as an eco-friendly fuel, vehicles using hydrogen fuel are being developed worldwide. Vehicle fuel hydrogen is stored in cylinders at 70 MPa, so there is a high risk of explosion. Therefore, it is important to inspect hydrogen cylinders in used-vehicles. This study was conducted to improve the inspection method of the cylinders currently mounted on used-hydrogen buses. The inspection method is an image analysis method using a camera. Calcaulation algorithm was developed to quantitatively chech the amount of hydrogen leakage by the image method. As a result of adding a contact angle element to the calculation algorithm suggested by the GTR regulation and comparing it with the experimental data of the GTR regulation, the algorithm reliability was 94%, which secured similarity.

• Journal of Auto-vehicle Safety Association
• /
• v.12 no.4
• /
• pp.13-22
• /
• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• Journal of information and communication convergence engineering
• /
• v.13 no.3
• /
• pp.189-196
• /
• 2015

In this paper, we present a rotating auto-zeroing offset cancellation technique, which can improve the performance of touch screen sensing circuits. Our target touch screen detection method employs multiple continuous sine waves to achieve a high speed for large touch screens. While conventional auto-zeroing schemes cannot handle such continuous signals properly, the proposed scheme does not suffer from switching noise and provides effective offset cancellation for continuous signals. Experimental results show that the proposed technique improves the signal-to-noise ratio by 14 dB compared to a conventional offset cancellation scheme. For the realistic simulation results, we used Cadence SPECTRE with an accurate TSP model and noise source. We also applied an asymmetric device size (10% MOS size mismatch) to the OP Amp design in order to measure the effectiveness of offset cancellation. We implemented the proposed circuit as part of a touch screen controller system-on-chip by using a Magnachip/SK Hynix 0.18-µm complementary metal-oxide semiconductor (CMOS) process.

• Journal of Korea Multimedia Society
• /
• v.21 no.3
• /
• pp.333-341
• /
• 2018

In this paper, we propose an algorithm to automatically detect gait's joints. The proposed method classifies gait's types into front gait and flank gait so as to automatically detect gait's joints. And then according to classified types, the proposed applies joint extracting algorithm to input images. Firstly, we split input images into foreground image using difference images of Hue and gray-scale image of input and background one and extract gait's object. The proposed method classifies gaits into front gait and flank gait using ratio of Face's width to torso's width. Then classified gait's type, joints are detected 10 at front gait and detected 7~8 at flank gait. The proposed method is applied to the camera's input and the result shows that the proposed method automatically extracts joints.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.1
• /
• pp.30-34
• /
• 2006

Recently, there has been an increasing demand for computer-vision-based inspection and/or measurement system as a part of factory automation equipment. In general, it is almost impossible to check the fault of all parts, coming from part-feeding system, with only manual inspection because of time limitation. Therefore, most of manual inspection is applied to specific samples, not all coming parts, and manual inspection neither guarantee consistent measuring accuracy nor decrease working time. Thus, in order to improve the measuring speed and accuracy of the inspection, a computer-aided measuring and analysis method is highly needed. In this paper, a computer-vision-based pipe inspection system is proposed, where the front and side-view profiles of three different kinds of pipes, coming from a forming line, are acquired by computer vision. And the edge detection is processed by using Laplace operator. To reduce the vision processing time, modified Hough transform is used with clustering method for straight line detection. And the center points and diameters of inner and outer circle are found to determine eccentricity of the parts. Also, an inspection system has been built so that the data and images of faulted parts are stored as files and transferred to the server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.321-325
• /
• 2009

The detection process of defective tags in most of Korean domestic RFID manufacturing companies is handled or treated by on-hand processing after the job of chip bonding, so it has been requesting to reduce the time and cost for manufacturing of RFID tags. Therefore, in this paper, we design and implement the system to perform the functionality of detection of defective tags after the process of chip bonding, and so provide the basis of a related software to establish the foundation of a automation system for the detection of defected RFID tags which is requested in the related Korean domestic industrial field. The developed system in this paper shows the enhancement of 700% in processing speed and 100% in detection rate of defective tags, comparing to the method of on-hand processing.

• Korean Chemical Engineering Research
• /
• v.58 no.3
• /
• pp.408-415
• /
• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• Journal of Convergence for Information Technology
• /
• v.12 no.1
• /
• pp.31-38
• /
• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.