• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.146-146
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• 2020

최근 하천의 유사 중 소류사량을 계측하기 위해 사용된 기존의 물리적 소류사 샘플러를 이용한 직접계측방법은 홍수 시에 깊은 수위와 빠른 유속, 계측 절차상의 위험성 때문에 현장관측이 매우 어려운 한계를 극복하기 위해 현업에서는 소류사량을 간접적으로 추정하는 이론식에 의한 방법이 광범위하게 활용되고 있으나 이 방법 또한 추정이론식의 적용지역, 적용방법에 따라 결과가 수십배 이상 큰 차이를 나타나 실제 활용성에 대한 문제점이 있다. 이러한 기존의 소류사량 측정 방법의 문제점을 보완하기 위해 소류사량을 간접계측하는 방법이 활발히 제안되고 있다. 대표적인 방법으로 하상 이동 시 소류사의 충돌음을 음향센서로 계측하여 신호처리를 통해 소류사량을 추정하는 계측기기인 하이드로폰이 있다. 그러나 국외의 소류사량 간접계측 장치는 소류사량의 운송량이 많을 경우 음향신호 중접으로 인해 펄스 수의 감소, 감지 가능한 입경크기의 제한 등의 문제가 있다. 또한 국내의 백무평(2018)이 제안한 소류사 분석 방법인 대역통과방법(B-P Method)는 소류사량 추정에 있어서 기존의 방법과는 달리 주파수 특성을 반영하여 이전 연구들에 비하여 펄스 검출률을 향상시겼지만 이 방법은 극히 낮은 저유속과 작은 입경이라는 실험조건에서 이루어졌다는 제한사항이 있다. 따라서 본 연구는 다양한 입경과 고유속에 대하여 소류사량을 정량화할 수 있는 방법을 제시하기 위해 소류사 입경이 하이드로폰에 충돌할 때 발생하는 단독입자의 충돌음을 계측하기 위한 실외 수로실험장치를 구축하여 계측을 수행하였다. 실험은 현장에서 대표 시료로 분류된 몇 가지 입경에 대해서 유량 변화에 따른 충돌음향과 소류사량 그리고 소류사 입경크기에 따른 하이드로폰에서 인지되는 음향 특성을 계측 및 분석하였다. 연구결과 입경 크기 및 수리조건 변화에 따른 하이드로폰의 충돌음향 특성을 파악하여 단일 입경별 소류사량 추정관계식을 산출하였다. 또한 산출된 추정 관계식의 특성치와 공급 소류사량 간의 관계를 유도해 보았다. 향후 혼합입경에 대한 실험과 추정 관계식 신뢰성 검토 후 추가적으로 다양한 실험조건을 고려하여 실제 하천에 운송되는 소류사량과의 교정관계 확립을 진행한다면 국내 소류사량 데이터 수집을 위한 현장 설치까지 가능할 것으로 사료된다.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.315-324
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• 2023

A simple and accurate whole body rehabilitation interaction technology using immersive digital content is needed for elderly patients with steadily increasing age-related diseases. In this study, we introduce whole-body interaction technology using HoloLens and Kinect for this purpose. To achieve this, we propose three coordinate transformation methods: mesh feature point-based transformation, AR marker-based transformation, and body recognition-based transformation. The mesh feature point-based transformation aligns the coordinate system by designating three feature points on the spatial mesh and using a transform matrix. This method requires manual work and has lower usability, but has relatively high accuracy of 8.5mm. The AR marker-based method uses AR and QR markers recognized by HoloLens and Kinect simultaneously to achieve a compliant accuracy of 11.2mm. The body recognition-based transformation aligns the coordinate system by using the position of the head or HMD recognized by both devices and the position of both hands or controllers. This method has lower accuracy, but does not require additional tools or manual work, making it more user-friendly. Additionally, we reduced the error by more than 10% using RANSAC as a post-processing technique. These three methods can be selectively applied depending on the usability and accuracy required for the content. In this study, we validated this technology by applying it to the "Thunder Punch" and rehabilitation therapy content.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.

• Journal of The Korean Association For Science Education
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• v.27 no.1
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• pp.59-69
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• 2007

The aim of this study was to investigate teachers' perceptions on MBL (microcomputer-based laboratory) experiment training program for teachers, the expecting effects of MBL experiment and application of MBL experiment after conducting MBL experiment training for science classes in schools. This study showed that most of the teachers who participated in the training program thought that the MBL experiment training program was very useful and instructive. Many teachers considered that MBL experiments using a computer could decrease time spent in the experiment by accurate and fast data collection and analysis. They also thought that the reduced time could be used more effectively in the analysis of experimental data and discussion activities leading to correct concept formation as well as in the development of graphical analysis and science process skills. However, they thought that MBL experiments were ineffective in learning how to operate experiment apparatus. This study also revealed that most teachers intended to apply MBL experiments in real classrooms context right after the training course and they pointed out many obstacles in introducing MBL experiments into their classrooms such as a budget to purchase equipment, poor laboratory conditions, and few MBL experiment training opportunities. In order to apply MBL experiment into the real classrooms, further changes were suggested as follows; development of technologies to reduce unit cost of equipment for MBL experiments, production and supply of many kinds of sensors, development of MBL experiment materials, and expansion of the training program for teachers.

• 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.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.27-35
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• 2024

Fuel gases such as methane and propane are used in explosion hazardous area of domestic plants and can form non-uniform mixtures with the influence of process conditions due to leakage. The fire-explosion risk assessment using literature data measured under uniform mixtures, damage prediction can be obtained the different results from actual explosion accidents by gas leaks. An explosion characteristics such as explosion pressure and flame velocity of non-uniform gas mixtures with concentration change similar to that of facility leak were examined. The experiments were conducted in a closed 0.82 m long stainless steel duct with observation recorded by color high speed camera and piezo pressure sensor. Also we proposed the quantification method of non-uniform mixtures from a regression analysis model on the change of concentration difference with time in explosion duct. For the non-uniform condition of this study, the area of flame surface enlarged with increasing the concentration non-uniform in the flame propagation of methane and was similar to the wrinkled flame structure existing in a turbulent flame. The time to peak pressure of methane decreased as the non-uniform increased and the explosion pressure increased with increasing the non-uniform. The ranges of K_G (Deflagration index) of methane with the concentration non-uniform were 1.30 to 1.58 [MPa·m/s] and the increase rate of K_G was 17.7% in methane with changing from uniform to non-uniform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.27-34
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• 2024

The direct and indirect damages caused by fires in underground utility tunnels have a great impact on society as a whole, so efforts are needed to prevent and manage them in advance. To this end, research is ongoing to prevent disasters such as fire flooding by applying digital twin technology to underground utility tunnels. A network is required to transmit the sensed signals from each sensor to the platform. In essence, it is necessary to analyze the application of wireless networks in the underground utility tunnel environments because the tunnel lacks the reception range of external wireless communication systems. Within the underground utility tunnels, electromagnetic interference caused by transmission and distribution cables, and diffuse reflection of signals from internal structures, obstacles, and metallic pipes such as water pipes can cause distortion or size reduction of wireless signals. To ensure real-time connectivity for remote surveillance and monitoring tasks through sensing, it is necessary to measure and analyze the wireless coverage in underground utility tunnels. Therefore, in order to build a wireless network environment in the underground utility tunnels. this study minimized the shaded area and measured the actual cavity environment so that there is no problem in connecting to the wireless environment inside the underground utility tunnels. We analyzed the data transmission rate, signal strength, and signal-to-noise ratio for each section of the terrain of the underground utility tunnels. The obtained results provide an appropriate wireless planning approach for installing wireless networks in underground utility tunnels.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.119-128
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• 2024

Marine magnetic surveys provide a rapid and cost-effective method for pioneer geophysical survey for many purposes. Sea-surface magnetometers offer high accuracy but are limited to measuring the scalar total magnetic field and require dedicated cruise missions. Shipboard three-component magnetometers, on the other hand, can collect vector three components and applicable to any cruise missions. However, correcting for the ship's magnetic field, particularly viscous magnetization, still remains a challenge. This study proposes a new additional correction method for ship's viscous magnetization effect in vector data acquired by shipboard three-component magnetometer. This method utilizes magnetic data collected simultaneously with a sea-surface magnetometer providing total magnetic field measurements. Our method significantly reduces deviations between the two datasets, resulting in corrected vector anomalies with errors as low as 7-25 nT. These tiny errors are possibly caused by the vector magnetic anomaly and its related viscous magnetization. This method is expected to significantly improve the accuracy of shipborne magnetic surveys by providing corrected vector components. This will enhance magnetic interpretations and might be useful for understanding plate tectonics, geological structures, hydrothermal deposits, and more.