• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.290-297
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• 2022

During surgery, Surgical instruments are often left behind due to accidents. Most of these are surgical gauze, so radioactive non-permeable gauze (X-ray gauze) is used for preventing of accidents which gauze is left in the body. This gauze is divided into wire and pad type. If it is confirmed that the gauze remains in the body, gauze must be detected by radiologist's reading by imaging using a mobile X-ray device. But most of operating rooms are not equipped with a mobile X-ray device, but equipped C-Arm equipment, which is of poorer quality than mobile X-ray equipment and furthermore it takes time to read them. In this study, Use C-Arm equipment to acquire gauze image for detection and Build dataset using artificial intelligence and select a detection model to Assist with the relatively low image quality and the reading of radiology specialists. mAP@50 and detection time are used as indicators for performance evaluation. The result is that two-class gauze detection dataset is more accurate and YOLOv5 model mAP@50 is 93.4% and detection time is 11.7 ms.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.901_902
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• 2009

This paper describes the development of a three-phase Line-Interactive DVR(Dynamic Voltage Restorer), which is applied to Hybrid detection method and super-capacitor. The operational feasibility was verified through computer simulations with PSCAD/EMTDC software, and experimental will be work with 3kVA prototype. The developed system can compensates the input voltage sag and interruption within 2ms, in which the maximum allowable sensitive load, such as computer, communication equipment, automation equipment, and medical equipment. The developed system has a simple structure to be easily implemented with commercially available components and to be highly reliable in operation.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.347-353
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• 2012

In this paper, authors will develop the quick and precise remote controller of automatic fire detection equipment (P-type one-class receiver) based on information communication technology (IT). The remote controller detects the fire and disaster in the building automatically and quickly and then activates the facilities to extinguish the fire and disaster, monitoring such situation in a real time through wire-wireless communication network. The proposed remote controller is applied a programmable logic device (PLD) micom. of one-chip type which is small size and lightweight and also has highly sensitive-precise reliabilities. The one-chip type PLD micom. analyzes digital signals from sensors, then activates fire extinguishing facilities for alarm and rapid suppression in a case of fire and disaster. The detected data is also transferred to a remote situation room through wire-wireless network of RS232c and bluetooth communication, and then the situation room sends an emergency alarm signal. The automatic fire detection equipment (AFDE) based on IT will minimize the life and wealth loss while prevents fire and disaster.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.30-35
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• 2008

This paper describes a fault detection and diagnosis (FDD) system developed for the heat source apparatus in building air-conditioning system. As HVAC&R systems in building become complex and instrumented with highly automated controllers, the processes and systems get more difficult for the operator to understand and detect the mal-functions. Poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of energy used in commercial building. When operating a complex facility, FDD system is beneficial in equipment management to provide the operator with tools which can help in decision making for recovery from a failure of the system. Automated FDD for HVAC&R system has the potential to reduce energy and maintenance costs and improves comfort and reliability. Over the last decade there has been considerable research for developing FDD system for HVAC&R equipment. However, they are being made too much of a theoretical study, so only a small of FDD methods are deployed in the field. This study deduced an actual defect source for the heat source apparatus and suggested a low price FDD method which is ready to be deployed in the field.

• Current Optics and Photonics
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• v.6 no.2
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• pp.151-160
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• 2022

Fundus images can reflect ocular diseases and systemic diseases such as glaucoma, diabetes mellitus, and hypertension. Thus, research on fundus-detection equipment is of great importance. The fundus camera has been widely used as a kind of noninvasive detection equipment. Most existing devices can only obtain two-dimensional (2D) retinal-image information, yet the fundus of the human eye also has spectral characteristics. The fundus has many pigments, and their different distributions in the eye lead to dissimilar tissue penetration for light waves, which can reflect the corresponding fundus structure. To obtain more abundant information and improve the detection level of equipment, a snapshot nonmydriatic fundus imaging spectral system, including fundus-imaging spectrometer and illumination system, is studied in this paper. The system uses a microlens array to realize snapshot technology; information can be obtained from only a single exposure. The system does not need to dilate the pupil. Hence, the operation is simple, which reduces its influence on the detected object. The system works in the visible and near-infrared bands (550-800 nm), with a volume less than 400 mm × 120 mm × 75 mm and a spectral resolution better than 6 nm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.100-113
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• 2010

Manufacturing companies' cost competitiveness with respect to equipment management can be achieved by satisfying additional market demands by their own capacity without purchasing additional equipments. In essence, it can be accomplished by making continuous investigation into bottlenecks and improvement on them. Therefore, equipment performance measure systems should be designed so that they can support manufacturing companies' such endeavors. With the purpose of establishing an effective equipment performance measurement system for detecting and improving bottlenecks, this study (1) suggests some desirable features that such a system should have, (2) evaluates conventional equipment performance measurement systems in terms of their usefulness for the detection and improvement of bottlenecks, and (3) proposes an improved system. We also perform a simulation experiment to demonstrate the limitations of the conventional systems and show how the proposed system can resolve the problems.