• Journal of Wetlands Research
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• v.23 no.2
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• pp.122-132
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• 2021

We reviewed the methods employed in Korean tidal flat surveys to measure the local abundance of the endangered wildlife and marine protected species, the fiddler crab, Austruca lactea. A complete census for infinite population is impossible even in a limited habitat within a tidal flat, and density estimates from samples strongly vary due to diverse biological and ecological factors. The habitat boundaries and areas shift with periodicities or rhythmic activities of organisms as well as measurement errors. Hence the local abundance calculated from density and habitat areas should be regarded as transient. This conjecture was valid based on the spatio-temporal variations of the density averages, standard error ranges, and spatial distribution of the crab, A. lactea observed for 3 years (2015-2017) in Songdo tidal flat in Incheon. We proposed the potential habitat areas using the occurrence probability of 50% from logistic regression model, reflecting the importance of habitat conservation value as an alternative to local abundance. The spatial shape of potential habitat predicted from a generalized model would remain constant over time unless the species' critical environmental conditions change rapidly. The species-specific model is expected to be used for the introduction of desired species in future habitat restoration/creation projects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.590-596
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• 2020

In the island areas where system connection with the commercial power grid is difficult, it is quite important to find a method to efficiently manage energy produced with independent microgrids. In this paper, a prosumer management system for P2P power transaction was realized through the testing the power meter and the response rate of the collected data for the power produced in the small-scale microgrids in which hybrid models of solar power and wind power were implemented. The power network of the microgrid prosumer was composed of mesh structure and the P2P power transaction was tested through the power meter and DC power transmitter in the off-grid sites which were independently constructed in three places. The measurement values of the power meter showed significant results of voltage (average): 380V + 0.9V, current (average): + 0.01A, power: 1000W (-1W) with an error range within ±1%. Stabilization of the server was also confirmed with the response rate of 0.32 sec. for the main screen, 2.61 sec. for the cumulative power generation, and 0.11 sec for the power transaction through the transmission of 50 data in real time. Therefore, the proposed system was validated as a P2P power transaction system that can be used as an independent network without transmitted by Korea Electric Power Corporation (KEPCO).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.138-147
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• 2021

The purpose of this study was to develop a measuring tool for the health promotion behavior of Korean nurses. This would address the lack of a proven tool that reflects the nature of the nurses' nursing environment. This study was conducted on 530 nurses from January to December 2019. A literature review and focus group interview were conducted, data analysis was carried out to measure validity and reliability, and the conceptual framework was constructed by applying the IMB model. Five factors namely self-concept (2 questions), hospital life management (4 questions), knowledge and information regarding health (5 questions), physical and mental stress management (3 questions), and work adaptation (2 questions) were framed into 16 questions. The model fit was 346.23 (��<.001), Parsimonious Normed Fit Index (PNFI) was 0.60, and Parsimonious Comparative Fit Index (PCFI) was 0.63, which met the acceptance criteria, and the Root Mean Square Error of Approximation (RMSEA) was 0.10. Goodness of Fit Index (GFI) was 0.88, Comparative Fit Index (CFI) was 0.85, and Incremental Fit Index (IFI) was 0.85 which were found to be acceptable as per the applicable standards. All items had a Cronbach's �� score of .85, which ensured stable reliability. The nurse's health promotion behavior measurement tool developed in this study will be used to measure the nurse's health promotion behavior in terms of nursing practice which will help in understanding the broad contours of this behavior.

• Journal of IKEEE
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• v.25 no.4
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• pp.782-785
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• 2021

In this paper, we propose a personalized cooling management system with thermal imaging camera. The proposed equipment uses a thermal imaging camera to control the amount of cold air and the system according to the difference between the user's skin temperature before and after the procedure. When the skin temperature is abnormally low, the cold air supply is cut off to prevent the possibility of a safety accident. It is economical by replacing the skin temperature sensor with a thermal imaging camera temperature measurement, and it can be visualized because the temperature can be checked with the thermal image. In addition, the proposed equipment improves the sensitivity of the sensor that measures the distance to the skin by calculating the focal length by using a dual laser pointer for the safety of a personalized cooling management system to which a thermal imaging camera is applied. In order to evaluate the performance of the proposed equipment, it was tested in an externally accredited testing institute. The first measured temperature range was -100℃~-160℃, indicating a wider temperature range than -150~-160℃(cryo generation/USA), which is the highest level currently used in the field. In addition, the error was measured to be ±3.2%~±3.5%, which showed better results than ±5%(CRYOTOP/China), which is the highest level currently used in the field. The second measured distance accuracy was measured as below ±4.0%, which was superior to ±5%(CRYOTOP/China), which is the highest level currently used in the field. Third, the nitrogen consumption was confirmed to be less than 0.15 L/min at the maximum, which was superior to the highest level of 6 L/min(POLAR BEAR/USA) currently used in the field. Therefore, it was determined that the performance of the personalized cooling management system applied with the thermal imaging camera proposed in this paper was excellent.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.50-59
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• 2021

Currently, the response correction factor is calculated by comparing the response measured by the load test on a bridge with the response analyzed in the initial analytical model. Then the load rating and the load carrying capacity are evaluated. However, the response correction factor gives a value that fluctuates depending on the measurement location and load condition. In particular, when the initial analytical model is not suitable for representing the behavior of a bridge, the range of variation is large and the analysis response by the calibrated model may give a result that is different from the measured response. In this study, a pseudo-static load test was applied to obtain static response with dynamic components removed under various load conditions of a vehicle moving at a low speed. Static response was measured on two similar PSC-I girder bridges, and the response correction factors for displacement and strain were calculated for each of the two bridges. When the initial analysis model was not properly set up, it is verified that the response of the analytical model corrected by the average response correction factor does not fall within the margin of error with the measured response.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.171-177
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• 2022

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.222-230
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• 2023

In the present work, the scale effect on the Boil-Off Rate (BOR) was investigated based on an analytical method to systematically evaluate the thermal performance of a Liquefied Natural Gas (LNG) Cargo Containment System (CCS). A two-dimensional thermal resistance network model was developed to accurately estimate the heat ingress into the CCS from the outside. The analysis was performed for the KC-1 LNG membrane tank under the IGC and USCG design conditions. The ballast compartment of both the LNG tank and cofferdam was divided into six sections and a thermal resistance network model was made for each section. To check the validity of the developed model, the analysis results were compared with those from existing literature. It was shown that the BOR values under the IGC and USCG design conditions were agreed well with previous numerical results with a maximum error of 1.03% and 0.60%, respectively. A SDR, the scale factor of the LNG CCS was introduced and the BOR, air temperature of the ballast compartment, and the surface temperature of the inner hull were obtained to examine the influence of the SDR on the thermal performance. Finally, a correlation for the BOR was proposed, which could be expressed as a simple formula inversely proportional to the SDR. The proposed correlation could be utilized for predicting the BOR of a full-scale LNG tank based on the BOR measurement data of lab-scale model tanks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.