• Journal of the Korean Society of Earth Science Education
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• v.15 no.2
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• pp.132-141
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• 2022

The purpose of this study is to investigate the effect of science classes using elementary school distance science classes on science academic achievement and creative personality. The research group and non-research group were selected for 6th grade elementary school students. After 10 weeks of experimental treatment, science classes were conducted with the contents of the elementary school science section, 'Changes of the seasons'. In the three domains of 'knowledge', 'inquiry', and 'attitude', which are sub-domains of science academic achievement, as a result of the pre-post test, there was a positive effect in 'inquiry' and 'attitude', which are sub-domains of science academic achievement. However, it was found that there was no positive effect in 'knowledge', a sub-domain of academic achievement. However, it was found that there was a positive effect in the overall test result of academic achievement. Therefore, it is interpreted that science classes using elementary school distance science classes had an effect on academic achievement. There was a significant effect in the sub-domains of the creative personality test, 'persistence/obsession', 'self-confidence', 'humor', 'imagination', 'openness', 'adventure', and 'independence'. However, it was found that there was no effect in the sub-domain 'curiosity'. The overall test results of the creative personality test showed a significant effect. Therefore, it is interpreted that science classes using elementary school distance science classes are effective in cultivating creative personality. Students' perceptions of science classes using elementary school distance science classes also showed positive responses.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.7-15
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• 2023

Recently, as interest in air quality in vehicles increases, the use of fine dust detection sensors for air quality measurement is becoming common. An axial-flow fan is inserted in the fine dust sensor installed in the air conditioning system in the vehicle to prevent dust from sinking directly on the sensor. When the sensor operates, the flow noise caused by the rotation of the axial-flow fan acts as a major noise source of the fine dust sensor. flow noise is recognized as one of the product competitiveness of fine dust sensors. In this study, the noise was gradually reduced at the same flow rate by improving the flow performance of the small axial flow fan. First, a virtual fan performance tester consisting of about 20 million grids was developed to analyze the aerodynamic performance of the target small axial-flow fan. In addition, the flow field was simulated by using compressible Large Eddy Simulation for direct computation of flow noise as well as high-accurate prediction of flow rate. The validity of numerical method are confirmed through the comparison of predicted results with experimental ones. After the effects of pitch angle on flow performance were analyzed using the verified numerical method, the pitch angle was determined to maximize the flow rate. It was found that the flow rate was increased by 8.1 % and noise was reduced by 0.8 dBA when the axial-flow fan with the optimum pitch angle was used.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.697-702
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• 2023

Recently, with the introduction of drones, a core technology of the 4th industrial revolution, various convergence education using drones is being conducted in school education sites. In particular, drone theory and practice education is being conducted in connection with free semester classes and career exploration. The drone convergence education program has higher learner satisfaction than simple demonstration and practice education, and the learning effect is high due to direct practical experience. However, since practical education is being conducted for a large number of learners, it is impossible to restrict and control the flight of a large number of drones in a limited place. In this paper, we propose a monitoring system that allows the instructor to monitor multiple drones in real time and learners to recognize collisions between drones in advance when multiple drones are operated, focusing on education operated in schools. The communication module used in the experiment was equipped with GPS in Murata LoRa, and the server and client were configured to enable monitoring based on the location data received in real time. The performance of the proposed system was evaluated in an open space, and it was confirmed that the communication signal was good up to a distance of about 120m. In other words, it was confirmed that 25 educational drones can be controlled within a range of 240m and the instructor can monitor them.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.1
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• pp.5-14
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• 2023

In this study, LiDAR-reflective black hollow-structured silica/titania(B-HST) materials are successfully synthesized by employing the NaBH₄ reduction and etching method on silica/titania core/shell(STCS) materials, which also effectively enhance near-infrared(NIR) reflectance. Moreover, core-etched supernatant solutions are collected and recycled for the synthesis of extracted silica(e-SiO₂) process, which successfully applies as filler materials for semiconductor epoxy molding compound(EMC). In detail, B-HST materials, fabricated by the sequential experimental steps of sol-gel, reduction, and sonication-mediated etching method, manifest blackness(L^*) of 13.2 similar to black paint and excellent NIR reflectance(31.1%). Consequently, B-HST materials are successfully prepared as LiDAR-reflective black materials. Additionally, core-etched supernatant solution with silanol precursors are employed for synthesis of homogeneous silica filler materials via sol-gel method. As-synthesized silica fillers are incorporated with epoxy resin and carbon black for the preparation of semiconductor EMC. Experimentally synthesized EMC exhibits comparable mechanical-chemical properties to commercial EMC. Conclusively, this study successfully proposes designing procedure and practical experimental method for simultaneously synthesizing the NIR-reflective black materials for self-driving vehicles and EMC materials for semiconductors, which are materials suitable for the industrial 4.0 era, and presented their applicability in future industries.

• Journal of the Korean Geotechnical Society
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• v.25 no.5
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• pp.53-64
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• 2009

Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.595-604
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• 2022

It is necessary to pay attention to healthy eating habits and attitudes toward health functional foods at a time when the incidence of diseases increases exponentially due to side effects of westernized eating habits. The purpose of this study is to clarify the effect of healthy eating habits and attitudes toward health functional foods on mood states of college students and the elderly. As a result of the analysis, in the elderly, the explanatory power for vitality emotion was lower than that of college students, and only healthy eating habits were factors that significantly explained. For anxiety-depression, it was found that health eating habits could explain the decrease in anxiety-depression emotions even less only in the elderly, and for anger emotions, neither college students nor the elderly showed significant explanatory power. This suggests that it is necessary to further research and analyze the experimental and practical effects based on a wider group and various emotions on how health functional foods and healthy eating habits affect emotions.