• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.104-108
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• 2023

Because of recent expansion of the electric car market. it is highly growing that should be supplemented its performance and safely issue. The EMI problem due to the interlocking of electrical components that causes various safety problems such as fire in electric vehicles is emerging every time. We strive to achieve optimal charging efficiency by combining various technologies and reduce radioactive noise among the EMI noise of a weirless charging control module, one of the important parts of an electric vehicle was designed and tested. In order to analyze the EMI problems occurring in the wireless charging control module, the optimized wireless charging control module by applying the optimization design technology by learning the accumulated test data for critical factors by utilizing the Python-based script function in the Ansys simulation tool. It showed an EMI noise improvement effect of 25 dBu V/m compared to the charge control module. These results not only contribute to the development of a more stable and reliable weirless charging function in electric vehicles, but also increase the usability and efficiency of electric vehicles. This allows electric vehicles to be more usable and efficient, making them an environmentally friendly alternative.

• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.633-640
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• 2023

In this study, numerical analysis was performed based on field investigation to derive an appropriate reinforcement method by analyzing the displacement behavior characteristics of concrete blocks generated in the direct fixation track on the bridges of the serviced light rail transit. The track of this study was a direct fixation track on a sharp curved track, and the problem of movement of the concrete blocks installed on the bridge deck in the longitudinal and lateral directions occurred. In this study, based on the finite element model using 3D solid elements, the behavior of the direct fixation track that could be occurred under operating load conditions was analyzed. In addition, the reinforcement effect of various reinforcement methods was analyzed. As a result of analyzing the lateral displacement before and after reinforcement, it was analyzed that the maximum lateral displacement after reinforcement under the extreme lateral wheel loads significantly decreased to about 3% (about 0.1mm) compared to before reinforcement. In addition, as a result of examining the generated stress of the filling mortar, bridge decks, and reinforcing bar, it was analyzed that all of them secured a sufficient safety factor of 2.6 or higher, and the optimal conditions for the reinforcement method were derived. Therefore, it is judged that the number of anchoring reinforcements and symmetrical anchor placement reviewed in this study will be effective in controlling the occurrence of lateral displacement of concrete blocks and securing the structural integrity of bridges and concrete blocks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.63-72
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• 2009

This study aims at establishing the decision-making support system for the highway assets, long-term performance presumption and evaluation of asset value, which are appropriate for Korea, and proposing the methods of the optimal engineering method and the timing decision for the preventive maintenance through the project evaluation, the optimization method and life-cycle analysis related to the highways. In order to supplement the current problem of the near-sighted budget management system, which chooses the maintenance place of the highway, depending on the level of the budget with fixed amount, the long-term required budget prediction system and the economy principle were introduced, so that the pavement agency can predict the level of the required budget, and it was aimed to develop the pavement asset evaluation system to maintain the performance of the highway with the minimum of the cost. In the use of the highway pavement asset evaluation system, to maintain the appropriate level of the pavement evaluation index, when the budget was efficiently established in the reference of the required maintenance budget for the chosen section of the highway in the year concerned, it was possible to analyze the most rational pavement maintenance budget. With this result, it is estimated to prevent the unnecessary waste of budget in advance, and through the development of the decision-making system for the long-term performance presumption and the asset value estimation of the pavement, it is expected to able to analyze the previous evaluation of the project related to the highway and the feasibility of introduction.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.52-52
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• 2022

Recently, PE (polyethylene) film has been used increasingly in com cultivation. However, PE films often cause soil and environment contamination. In order to reduce this problem, many researches have been carrying out studies on biodegradable films (BF) that are easily decomposed by soil microorganisms. Therefore, this study was conducted to determine which BF is optimal for growth and yield of com crops while also having the highest rates of film decomposition. BFs Farmsbio (Farm Hannong), Heulgro Film (Sejin Bio), Vonto Film (Eco-Hansung) as well as a selected PE film were used in this study. For the control, we used crops grown without any kind of mulching. Experimental fields were fertilized according to conventional cultivation methods, tilled, and then covered by either BF or PE. After 1 week, com (cv. MIBECK2ho) at the 3-leaf stage (16 days after seeding) was transplanted. Plant height was measured at 18 and 32 days after transplanting and heading stages. Yield components and yield were also measured at harvest. In addition, pH, EC, and decomposition and light transmittance levels of films were investigated during the experimental period. Daily average temperature, relative humidity and organic matter in soils were also measured during the experimental period. There was no significant difference in plant height, heading date, and silking between crops with BFs and PE, but the crops grown with BFs and PE films reached higher growth parameters in a shorter amount of time than the crops in the non-mulching control. Additionally, there were no significant differences in yield components such as length of ears, ear width, ear weight, and yield in crops that were grown using films or crops in the control plot. Light transmittance and decomposition levels of films generally increased with time after transplanting, and was highest in the Heulgro film than other BFs. Soil pH and organic matter in crops using BFs and PE films were significantly higher than in the control plot at 99 and 113 days after transplanting. In general, the EC contents in the control plot was lower than in crops using BFs and PE films. The average daily moisture in soil was higher when BFs and PE films were used than in the control plot. However, the daily average soil temperature was higher in crops using BFs and PE films than in the control plots at the beginning of the experimental period, but there was no consistent difference in soil temperature towards the later part of the experimental period. Therefore, the BFs used in this study were shown to be helpful without causing negative impacts on the growth and yield of com.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.27-33
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• 2023

Recently, water shortages have occurred due to climate change, and the need for water management of agricultural water has increased due to the occurrence of algal blooms in reservoirs. Existing algae prevention is operated by putting many people on site and misses the optimal spraying time due to movement through boats. In order to solve this problem, it is necessary to block contamination in advance and move within time to uniformly spray complex microorganisms uniformly. Control drones are used for pesticide spraying and can be applied to algae prevention work by utilizing control drones. In this paper, basic research for the establishment of a marine control system was conducted for application to the reservoir environment, and as one of the results, the characteristics of a drone nozzle, a core technology that can be used for control drones, were calculated. In particular, it was found that the existing agricultural control drones had a disadvantage that the concentration was non-uniform within the suggested spraying interval, and to compensate for this, nozzle positioning and nozzle spraying uniformity were calculated. Based on the experimental results, we develop a core algorithm for establishing an algal bloom monitoring system in the reservoir environment and propose a precision control technology that can be used for marine control work in the future.