• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.45-52
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• 2023

The power generation method using expensive diesel has operation problems such as high cost diesel generator and a lack of reserved power due to increase of power demand in some islands, requiring expansion of power generation facilities. To solve this problems, it is necessary to improve the efficiency of power generation facilities through an ORC(Organic Rankin Cycle) system application that uses waste heat as a heat source. Therefore, localized application technology of price competitive and highly reliable ORC power generation system is needed, and optimization technology of generators is having great effect, so this study performed two generator designs to get a high-efficiency generator with an optimized 30kW output. The comparison of simulation data for two designed models showed that a generator with SPM factor of 46.2% had an efficiency of 92.1% and a power ouput of about 23.2kW based on 12,000rpm, a generator with SPM factor of 44.46%, had a power output of 27.9kW and efficiency of 93.6% based on above rpm. For the verification of improved design model with SPM factor of 44.46%, the prototype test system with 110kW motor dynamometer was installed and got to the efficiency of 92.08% with conditions of the rated capacity 25kW at 12,000rpm, the test results of prototype generator showed the validity of generator design.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.117-124
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• 2023

Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1231-1237
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• 2022

In the present study, the optimization of the main particulars of a ship using AI-based design search techniques was investigated. For the design search techniques, the SHERPA algorithm by HEEDS was applied, and CFD analysis using STAR-CCM+ was applied for the calculation of resistance performance. Main particulars were automatically transformed by modifying the main particulars of the ship at the stage of preprocessing using JAVA script and Python. Small catamaran was chosen for the present study, and the main dimensions of the length, breadth, draft of demi-hull, and distance between demi-hulls were considered as design variables. Total resistance was considered as an objective function, and the range of displaced volume considering the arrangement of the outfitting system was chosen as the constraint. As a result, the changes in the individual design variables were within ±5%, and the total resistance of the optimized hull form was decreased by 11% compared with that of the existing hull form. Throughout the present study, the resistance performance of small catamaran could be improved by the optimization of the main dimensions without direct modification of the hull shape. In addition, the application of optimization using design search techniques is expected for the improvement in the resistance performance of a ship.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.115-131
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• 2022

Carbon dioxide (CO₂) emissions from the transportation sector in South Korea accounts for 16.5% of all CO₂ emissions, and road transportation accounts for 96.5% of this sector's emissions in South Korea. Hence, constant research is being carried out on methods to reduce CO₂ emissions from this sector. With the emerging use of smart crossings, attempts to monitor individual vehicles are increasing. Moreover, the potential commercial deployment of autonomous vehicles increases the possibility of obtaining individual vehicle data. As such, CO₂ emission research was conducted at five signalized intersections in the Gangnam District, Seoul, using data such as vehicle type, speed, acceleration, etc., obtained from image detectors located at each intersection. The collected data were then applied to the MOtor Vehicle Emission Simulator (MOVES)-Matrix model-which was developed to obtain second-by-second vehicle activity data and analyze daily CO₂ emissions from the studied intersections. After analyzing two large and three small intersections, the results indicated that 3.1 metric tons of CO₂ were emitted per day at each intersection. This study reveals a new possibility of analyzing CO₂ emissions using actual individual vehicle data using an improved analysis model. This study also emphasizes the importance of more accurate CO₂ emission analyses.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.70-77
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• 2023

Fine dust is a cause of serious ecological problems, and fugitive dust generated from construction sites is a major source of fine dust in Korea. However, at construction sites, including concrete manufacturing industry sites, measurements are rarely made at the fugitive dust generation stage, and passive removal methods are the majority. Therefore, in this study, a fugitive dust measurement method suitable for managing fugitive dust generated during aggregate unloading in the concrete manufacturing industry sites was selected. In addition, the purpose was to analyze the amount of fugitive dust reduction according to the operation of the reduction system by applying the automatic fugitive dust reduction system to the aggregate unloading site. As a result, the reliability of the light scattering method was secured through the comparative measurement of the beta-ray absorption method and the light scattering method, and the light scattering method correction coefficient was calculated and applied to the measured value of the fugitive dust particle mass concentration at the concrete manufacturing industry sites. In addition, the fugitive dust reduction rate according to the operation of the automatic fugitive dust reduction system was derived.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.382-390
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• 2023

The Korea market for LUAV (Light Unmanned Aerial Vehicle) weighing less than 150 kg is growing rapidly. As a result, the market for manufacturing and operating LUAV is expanding, and domestic development of parts and finished products is actively taking place. However, the flight control system and onboard software, which are key components of domestic LUAV, are largely dependent on overseas products due to the excessive cost and period required for development. This paper presented a domestic software development and certification procedure using DO-178C, a guideline for aircraft software development, and the Model-based Development method, and conducted a survey of those involved in the development, manufacturing, and certification of LUAV and analyzed the results. In addition, a case study was conducted to apply the software development plan to the helicopter FCC (Flight Control Computer).

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.