• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.476-484
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• 2019

RFID yoking proof proves that a pair of tags is scanned at the same time. Since the tags scanned simultaneously by a single reader are adjacent to each other, the yoking proof is used in applications that need to check the physical proximity of tagged objects. Most of the yoking proof schemes require pre-knowledge on adjacent tags. If an error occurs in the process of collecting information about adjacent tags, all subsequent proofs will fail verification. However, there is no research that suggests specific methods for obtaining information about adjacent tags. In this study, I propose a tag proximity information acquisition scheme for a yoking proof. The proposed method consists of two steps: scanning area determination and scanning area verification. In the first step, the size and position of the area to scan tags is determined in consideration of position and transmission range of the tags. In the next step, whether tag scanning is performed within the scanning area or not is verified through reference tags of the fixed position. In analysis, I show that the determined scanning area assures acquisition of adjacent tag information and the scanning area verification detects deformation and deviation of the scanning area.

• Ocean Systems Engineering
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• v.12 no.2
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• pp.225-245
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• 2022

More and more shipping containers are falling into the sea due to bad weather. Containers lost at sea negatively affect the shipping line, the trader and the consumer, and the environment. The question of locating and recovering dropped containers is a challenging engineering problem. Model-testing of small-scaled container models is proposed as an efficient way to investigate their falling trajectories to salvage them. In this study, we first build a standard 20-ft container model in SOLIDWORKS. Then, a three-dimensional (3D) geometric model in the STL (Standard Tessellation Language) format is exported to a Stratasys F170 Fused Deposition Modeling (FDM) printer. In total, six models were made of acrylonitrile styrene acrylate (ASA) and printed for the purpose of testing. They represent three different loading conditions with different densities and center of gravity (COG). Two samples for each condition were tested. The physical models were dropped into the towing tank of University of New Orleans (UNO). From the experimental tests, it is found that the impact of the initial position after sinking can cause a certain initial rolling velocity, which may have a great impact on the lateral displacement, and subsequently affect the final landing position. This series of model tests not only provide experimental data for the study of the trajectory of box-shape objects but also provide a valuable reference for maritime salvage operations and for the pipeline layout design.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.235-244
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• 2017

This paper aims to design a system for psychological diagnosis in sandbox play by applying rule based expert system. Sandbox play is one of play therapy and it is a technique that can be combined with psychological diagnosis and treatment using sand and various kinds of figures. In this technique, we focus on psychological diagnostic factors and try to implement a system that automatically diagnoses psychological types when input values are given. Therefore, six kinds of language objects are set and the rules are created according to the types of figures, arrangement of figures, and production time in the sand box used as a reference element in the diagnosis method. In this system, it is assumed that the client recognizes the finished sandbox as a sensor device. Then, when the recognized state enters the input value, the rules based on the language object are ignited in order. Through this, the client is diagnosed with one of 26 types of psychology. As a result, the diagnostic process is simplified and automated. Accordingly, a more detailed psychological diagnosis and treatments are provided.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• Physical Therapy Korea
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• v.30 no.3
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• pp.221-229
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• 2023

Background: In modern society, the use of computers accounts for a large proportion of our daily lives. Although substantial research is being actively conducted on musculoskeletal diseases resulting from computer use, there has been a recent surge in interest in improving the working environment for prevention. Objects: This study aimed to examine the effects of posture correction feedback (PCF) on changes in neck posture and muscle activation during computer typing. Methods: The participants performed a computer typing task in two sessions, each lasting 16 minutes. The participant's dominant side was photographed and analyzed using ImageJ software to verify neck posture. Surface electromyography (EMG) was used to confirm the participant's cervical erector spinae (CES) and upper trapezius muscle activities. The EMG signal was analyzed using the percentage of reference voluntary contraction and amplitude probability distribution function (APDF). In the second session, visual and auditory feedback for posture correction was provided if the neck was flexed by more than 15° in the initial position during computer typing. A 20-minute rest period was provided between the two sessions. Results: The neck angle (p = 0.014), CES muscle activity (p = 0.008), and APDF (p = 0.015) showed significant differences depending on the presence of the PCF. Furthermore, significant differences were observed regarding the CES muscle activity (p = 0.001) and APDF (p = 0.002) over time. Conclusion: Our study showed that the feedback system can correct poor posture and reduces unnecessary muscle activation during computer work. The improved neck posture and reduced CES muscle activity observed in this study suggest that neck pain can be prevented. Based on these results, we suggest that the PCF system can be used to prevent neck pain.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.37-46
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• 2019

The PRODUCE 101 (Chinese version《创造101》) program is an "Idol Nurture" reality show bought by China's Tencent Video from the copyright of the Korean PRODUCE 101 program. The program's "Idol Nurture" pattern is concentrated in the four aspects of the "Semi-finished" training objects, the complementary nurturing goals, the key points of storytelling, and the extreme empowerment of training subjects. The "Idol Nurture" process is fully presented through the programming of the training processes inside entertainment companies, and the foregrounding of the back stages of the recording, trainees and entertainment companies. The "Idol Nurture" pattern of the program has attracted great attention and has been applauded by many because it better satisfies audiences' diversion utility, personal relation utility, and personal identity utility. This pattern of "Idol Nurture" program can provide insightful reference and valuable experience to the development of other reality talent shows.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.77-84
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• 2024

In this paper, we propose a method for accurately and rapidly detecting defects in wire harnesses by utilizing computer vision to calculate six crucial measurement values: the length of crimped terminals, the dimensions (width) of terminal ends, and the width of crimped sections (wire and core portions). We employ Harris corner detection to locate object positions from two types of data. Additionally, we generate reference points for extracting measurement values by utilizing features specific to each measurement area and exploiting the contrast in shading between the background and objects, thus reflecting the slope of each sample. Subsequently, we introduce a method using the Euclidean distance and correction coefficients to predict values, allowing for the prediction of measurements regardless of changes in the wire's position. We achieve high accuracy for each measurement type, 99.1%, 98.7%, 92.6%, 92.5%, 99.9%, and 99.7%, achieving outstanding overall average accuracy of 97% across all measurements. This inspection method not only addresses the limitations of conventional visual inspections but also yields excellent results with a small amount of data. Moreover, relying solely on image processing, it is expected to be more cost-effective and applicable with less data compared to deep learning methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.637-644
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• 2024

Building Information Modeling (BIM) technology has been widely adopted in the construction industry. However, a challenge encountered in the operational phase is that building object data cannot be updated in real time. The concept of Digital Twin is to digitally simulate objects, environments, and processes in the real world, employing real-time monitoring, simulation, and prediction to achieve dynamic integration between the virtual and the real. This research considers an example related to indoor air quality for realizing the concept of Digital Twin and solving the problem that the digital twin platform cannot be updated in real time. In indoor air quality monitoring, the ventilation rate and the presence of occupants significantly affects carbon dioxide concentration. This study uses the indoor carbon dioxide concentration recommended by the Taiwan Environmental Protection Agency as a reference standard for air quality measurement, providing a solution to the aforementioned challenges. The research develops a digital twin platform using Unity, which seamlessly integrates BIM and IoT technology to realize and synchronize virtual and real environments. Deep learning techniques are applied to process camera images and real-time monitoring data from IoT sensors. The camera images are utilized to detect the entry and exit of individuals indoors, while monitoring data to understand environmental conditions. These data serve as a basis for calculating carbon dioxide concentration and determining the optimal indoor air exchange volume. This platform not only simulates the air quality of the environment but also aids space managers in decision-making to optimize indoor environments. It enables real-time monitoring and contributes to energy conservation.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.