• Journal of Digital Convergence
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• v.15 no.12
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• pp.323-331
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• 2017

In this paper, four types of deposition control processes such as temperature, pressure, input/output(I/O), and gas were replaced by the Internet of Things(IoT) to analyze the data load and sequence procedure before and after the application of it. Through this analysis, we designed the load balancing in the sensing area of the deposition process by creating the sequence diagram of the deposition process. In order to do this, we were modeling of the sensor I/O according to the arrival process and derived the result of measuring the load of CPU and memory. As a result, it was confirmed that the reliability on the deposition processes were improved through performing some functions of the equipment controllers by the IoT. As confirmed through this paper, by applying the IoT to the deposition process, it is expected that the stability of the equipment will be improved by minimizing the load on the equipment controller even when the equipment is expanded.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.76-95
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• 2019

An on-board truck scale is an essential technical solution for preventing overload, which makes the driver aware of the commercial vehicle weight. This study analyzed the effects of the driving pattern and transportation efficiency by the IoT Platform service for an on-board truck scale. A comparison of before and after installation using the long-term monitored data confirmed the reduction effects both of the overload ratio and overweight value, and their effects on increasing the transportation efficiency. In addition, the analysis result of the driving route showed that the installation of an on-board truck scale could be a more effective way of preventing overload than increasing the weighing checkpoints.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.465-475
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• 2018

Recently, as the use of social media and IoT has increased, large graph streams has been generating and studies on real-time processing for them have been actively carrying out. In this paper we propose a incremental graph stream processing scheme that reuses previous result data when the graph changes continuously. We also propose a cost model to selectively perform incremental processing and static processing. The proposed cost model computes the predicted value of the detection cost and the processing cost of the recalculation area based on the actually processed history and performs the incremental processing when the incremental processing is more profit than the static processing. The proposed incremental processing increases the efficiency by processing only the part that changes when the graph update occurs. Also, by collecting only the previous result data of the changed part and performing the incremental processing, the disk I/O costs are reduced. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.482-482
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• 2021

자동유량측정시스템은 하천 유량을 실시간으로 측정하기 위한 수문조사시설로 전국 하천 64개 주요 지점에 설치·운영 중이며 실시간으로 운영되는 시스템의 특성상 내·외부 요인으로 인해 자료의 결측이 발생할 수 있다. 주요 결측 요인으로는 계측장비의 고장 및 오작동, 낙뢰로 인한 전원부문제, 시스템 컨트롤러 단순 오류 등이며, 현재 이로 인해 결측이 발생한 경우 현장 방문을 통한 조치 외에는 복구가 불가한 실정이다. 본 연구에서는 현장 유지관리 개선을 통해 자료 결측 최소화 방안을 마련하고자 최근 5년간(2015년~2019년) 수행된 유지관리 점검내역을 검토하였다. 현장 유지관리는 정기적으로 수행되는 정기점검, 수중점검과 장애 발생 시 수행되는 현장점검으로 구분되며 최근 5년간 수행된 점검(1,735회) 중 정기적인 점검을 제외한 현장점검의 경우 총 764회(46%) 수행된 것으로 나타났으며 현장점검 중 유지관리 방법 및 장비 개선 적용 사례를 통해 최소 1일~3일 소요되던 점검 시간이 즉시 조치가 가능한 것으로 나타났다. 시스템 확대 및 기존 장비의 노후로 현장점검은 지속적으로 증가하고 있으며 최근 사회적 상황을 고려하였을 때 사물인터넷(IoT)을 활용한 시설물 개선 등으로 비대면 점검 수행이 가능해짐에 따라 점검 소요시간을 단축하여 보다 효율적인 시설물 운영, 예산 절감, 자료의 연속성 확보 등이 가능할 것으로 판단된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.171-178
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• 2023

In this paper, we propose a smart automated warehouse to maximize space utilization. Previous elevator-type automatic warehouses were designed with a maximum payload of 100kg on trays, which has the problem of extremely limiting the number of pallets that can be loaded within the space. In this paper, we design a smart warehouse that can maximize space utilization with a maximum vertical stiffness of 300kg. As a result of the performance evaluation of the implemented warehouse, the maximum payload was 500.6kg, which satisfied the original design and requirements, the lifting speed was 0.5m/s, the operating noise of the device was 67.1dB, the receiving and forwarding time of the pallet was 36.92sec, the deflection amount was 4mm, and excellent performance was confirmed in all evaluation items. In addition, the PLC control method, which designs the control UI and control panel separately, was integrated into the PC system to improve interoperability and maintainability with various process management systems. In the future, we plan to develop it into a fully automatic smart warehouse by linking IoT sensor-based logistics robots.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.17-22
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• 2023

Brain-Machine Interfaces(BMI) are interfaces that control machines by decoding brainwaves, which are electrical signals generated from neural activities. Although BMIs can be applied in various fields, their widespread usage is hindered by the low portability of the hardware required for brainwave measurement and decoding. To address this issue, previous research proposed a brain-machine interface system based on the Internet of Things (IoT) using cloud computing. In this study, we developed and tested an application that uses brainwaves to control the Pong game, demonstrating the real-time usability of the system. The results showed that users of the proposed BMI achieved scores comparable to optimal control artificial intelligence in real-time Pong game matches. Thus, this research suggests that IoT-based brain-machine interfaces can be utilized in a variety of real-time applications in everyday life.