• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.200-208
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• 2009

JBIG2, as the next generation standard for binary image compression, must be designed in hardware modules for the JBIG2 FAX to be implemented in an embedded equipment. This paper proposes a hardware module of the high-speed Huffman coder for JBIG2. The Huffman coder of JBIG2 uses selectively 15 Huffman tables. As the Huffman coder is designed to use minimal data and have an efficient memory usage, high speed processing is possible. The designed Huffman coder is ported to Virtex-4 FPGA and co-operating with a software modules on the embedded development board using Microblaze core. The designed IP was successfully verified using the simulation function test and hardware-software co-operating test. Experimental results shows the processing time is 10 times faster than that of software only on embedded system, because of hardware design using an efficient memory usage.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.718-723
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• 2011

In this paper, we propose a visual multi-touch air input device using vision cameras. The implemented device provides a barehanded interface which copes with the multi-touch operation. The proposed device is easy to apply to the real-time systems because of its low computational load and is cheaper than the existing methods using glove data or 3-dimensional data because any additional equipment is not required. To do this, first, we propose an image processing algorithm based on the HSV color model and the labeling from obtained images. Also, to improve the accuracy of the recognition of hand gestures, we propose a motion recognition algorithm based on the geometric feature points, the skeleton model, and the Kalman filter. Finally, the experiments show that the proposed device is applicable to remote controllers for video games, smart TVs and any computer applications.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.101-104
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• 2010

Purpose: Raynaud scan is divided to flow, blood pool and local-delay image. Usually, we evaluate comparison through blood pool and local-delay image. We will evaluate about usability when comparative observe blood image and local-delay image in Raynaud scan that used $^{201}Tl$ as making flow image to one sheet of images. Materials and Methods: We have selected 29 Raynaud phenomenon patients aged 14~68 years who visited department of vascular surgery between Feb. 2008 and Aug. 2009. An intravenous injection $^{201}Tl$ of 111 MBq (3 mCi) to opposite side diagonal line limbs above an internal auditing department. Equipment used Philips gamma camera forte A-Z, and collimator used LEHR. Matrix size set up to each $64{\times}64$, $128{\times}128$, $256{\times}256$ and zoom factor used to full field. Protocol of dynamic is 2 second to 155 frames. Blood pool and delay count to 300 second. We set up ROI by a foundation to data acquired in PEGASYS processing program. Each results were analyzed with the SPSS 12.0 statistical software. Results: Each averages of count ratio (Rt / Lt) to have been given at composite image, a blood pool image, delay images analyzed at Raynaud phenomenon patients is $1.25{\pm}0.39$, $1.20{\pm}0.33$, $1.11{\pm}0.17$. The sample analysis results of blood pool image and delay image contented itself with p<0.029. Also, there don't have been each difference, and blood pool image, delay image regarding composite image was able to know. Conclusion: We were able to give help for comparison to evaluate a blood pool image and a local delay image at the Raynaud scan which used $^{201}Tl$ while making a flow image to one sheet image. Identification to be visual too was possible. If you are proceeded a researcher that there was further depth, you are more appropriate for, and you may get useful information.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.317-324
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• 1994

This paper proposed a new method in the implementation of ISDN (integrated services digital network) LAPD (link access procedure on the D-channel) and LAPB (link access procedure on the B-channel) protocols. The proposed method in this paper implement ISDW LAPD protocol through multi-tasking operating system and adopt a kernel part that is changed operating system to target board. The features of implemented system are (1) the para.llel processing of the events generated at each layer, as follows (2) the supporting necessary timers for the implementation of ISDW LAPD protocol from the kernel part by using software, (3) the recommanded SAP (Service Access Point) from CCITT was composed by using port function in the operating system. With the proposed method, the protocols of ISDH layerl, layer2 and layer3 (call control) were implemented by using the kernel part and related tests were carried out by connecting the ISDH terminal simulator to ISDN S-interface system using the ISDN LAPD protocol The results showed that ISDW S-interface terminals could be discriminated by TEI (Terminal Equipment Identifier) assignment in layer 2 (LAPD) and the message transmission of layer 3 was verified by establishing the multi-frame transmission and then through the path established by the LAPD protocol, a user data was tranfered and received on B-channel with LAPB protocol Thererfore, as new efficient ISDN S-interface environment was implemented in the thesis, it was verified that the implemented system can be utilized by connecting ISDW in the future to transfer a medical image data.

• The Journal of Engineering Geology
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• v.30 no.1
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• pp.17-30
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• 2020

An optical televiewer is a geophysical logging device that produces continuous high-resolution full-azimuth images of a borehole wall using a light-emitting-diode and a complementary metal-oxide semiconductor image sensor to provide valuable information on subsurface discontinuities. Recently, borehole imaging logging has been applied in many fields, including ground subsidence monitoring, rock mass integrity evaluation, stress-induced fracture detection, and glacial annual-layer measurements in polar regions. Widely used commercial borehole imaging logging systems typically have limitations depending on equipment specifications, meaning that it is necessary to clearly verify the scope of applications while maintaining appropriate quality control for various borehole conditions. However, it is difficult to directly check the accuracy, implementation, and reliability for outcomes, as images derived from an optical televiewer constitute in situ data. In this study, we designed and constructed a modular fractured borehole model having similar conditions to a borehole environment to report unprecedented results regarding reliable data acquisition and processing. We investigate sonde magnetometer accuracy, color realization, and fracture resolution, and suggest data processing methods to obtain accurate aperture measurements. The experiment involving the fractured borehole model should enhance not only measurement quality but also interpretations of high-resolution and reliable optical imaging logs.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.77-83
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• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.