• Journal of KIBIM
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• v.11 no.3
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• pp.45-54
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• 2021

The augmented reality (AR) has recently became an attractive technology in construction industry, which can play a critical role in realizing smart construction concepts. The AR has a great potential to help construction workers access digitalized information about design and construction more flexibly and efficiently. Though several AR applications have been introduced for on-site made to enhance on-site and off-site tasks, few are utilized in actual construction fields. This paper proposes a new AR framework that provides on-site managers with an opportunity to easily access the information about construction resources such as workers and equipment. The framework records videos with the camera installed on a wearable AR device and streams the video in a server equipped with high-performance processors, which runs an object detection algorithm on the streamed video in real time. The detection results are sent back to the AR device so that menu buttons are visualized on the detected objects in the user's view. A user is allowed to access the information about a worker or equipment appeared in one's view, by touching the menu button visualized on the resource. This paper details implementing parts of the framework, which requires the data transmission between the AR device and the server. It also discusses thoroughly about accompanied issues and the feasibility of the proposed framework.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.45-51
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• 2019

The use of the Internet of things plays a major role in the Fourth Industrial Revolution, and a series of tasks of accumulating, converging, analyzing and reusing various data and services becomes very important. Because the pace and scope if the paradigm shift in Fourth Industrial Revolution is so rapid and unpredictable, the development and utilization of a system to fulfill this role for IOT are urgently required. In this paper, we introduce the Web-based IOT management system, which connects the IOT with OKMindmap, which is a domestic open source software and service, and the Node-RED service. This system combines the advantages of OKMindmap with the advantages of Node-RED, which is capable of visual component based programming, so that it can easily and flexibly connect the IOT based on Web browsers, and various data and services can be integrated and linked. We developed a camera module, a temperature and humidity sensor module, and the motor control module in Raspberry PI basically, and tested the operation successfully. We plan to extend the IOT component gradually by using Arduino and System On Chip.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.174-179
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• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.

• Advances in Computational Design
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• v.4 no.2
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• pp.73-90
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• 2019

The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1039-1045
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• 2021

In modern society, due to the influence of the 4th industrial revolution, camera sensors and image-based automation systems are being used in various fields, and interest in image and signal processing is increasing. In this paper, we propose a digital filter algorithm for image reconstruction in an impulse noise environment. The proposed algorithm divides the image into eight masks in vertical, horizontal, and diagonal directions based on the local mask set in the image, and compares the standard deviation of each segmentation mask to obtain a reference value. The final output is calculated by applying the weight according to the spatial distance and the weight using the reference value to the local mask. To evaluate the performance of the proposed algorithm, it was simulated with the existing algorithm, and the performance was compared using enlarged images and PSNR.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.117-124
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• 2021

Among growth disorders, 'Short Stature' can be improved through rapid diagnosis and treatment, and for that, it is important to detect early'Short Stature'. It is recommended to measure the height steadily for early detection of 'Short Stature' and checking the kid's growth process, but existing height measurement methods have problems such as time and space limitations, cost occurrence, and difficulty in keeping records. So in this paper, we proposed an 'Development of Kid Height Measurement Application based on Image using computer vision' method using smart phones, a medium that is highly accessible to people. In images taken through a smartphone camera, the kid's height is measured using algorithms from OpenCV, a computer vision library, and the measured heights were printed on the screen through 'a comparison graph with the standard height by gender and age' and 'list by date', made possible to check the kid's growth process. It is expected to measure height anytime, anywhere without time and space limitations and costs through this proposed method, and it is expected to help early detection of 'Short Stature' and other disorder through steady height measurement and confirmation of growth process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.656-661
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• 2022

Password with a combination of 4-digit numbers has been widely adopted for various authentication systems (such as credit card authentication, digital door lock systems and so on). However, this system could not be safe because the 4-digit password can easily be stolen by predicting it from the fingermarks on the keypad or display screen. Furthermore, due to the prolonged COVID-19 pandemic, contactless method has been preferred over contact method in authentication. This paper suggests a new password input method based on eyeblink pattern analysis in video sequence. In the proposed method, when someone stands in front of a camera, the sequence of eyeblink motions is captured (according to counting signal from 0 to 9 or 9 to 0), analyzed and encoded, producing the desired 4-digit decimal numbers. One does not need to touch something like keypad or perform an exaggerated action, which can become a very important clue for intruders to predict the password.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.117-123
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• 2023

In the fintech environment, Smart phones are mainly used for various service. User authentication technology is required to use safe services. Authentication is performed by transmitting authentication information to the server when the PIN or password is entered and touch the button completing authentication. But A post-attack is possible because the smudge which is the trace of using screen remains instead of recording attack with a camera or SSA(Shoulder Surfing Attack). To prevent smudge attacks, users must erase their fingerprints after authentication. In this study, we proposed a technique to determine whether to erase fingerprints. The proposed method performed erasing fingerprint which is the trace of touching after entering PIN and designed the security keypads that processes instead of entering completion button automatically when determined whether the fingerprint has been erased or not. This method suggests action that must erase the fingerprint when entering password. By this method, A user must erase the fingerprint to complete service request and can block smudge attack.