• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.4
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• pp.67-74
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• 2016

Load Duration Curve (LDC) can be used as a method for load management of point and non-point pollution source because the LDC easily assesses the water quality corresponding to hydrological changes in a watershed. Recently, the application of LDC to total pollution load management is a growing interest in Korea. In this regard, A desktop-based LDC assessment system was developed in this study to provide convenience to users in water quality evaluation. The developed system can simply produce the LDC by using streamflow and water quality data involved in its database. Also, The system can quantitatively inform the success or failure of the achievement for a target water quality at monthly scale. Furthermore, seasonal water quality and point/non-point pollution load in a watershed can be estimated by this system. We expect that the developed system will contribute to establish local and national policies regarding water management and total pollution load management because of its advantages such as the pollution tracking investigation and the analysis of water quality and pollution loading amount in an ungauged watershed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.520-526
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• 2012

Mostly lung diseases by smoking and air pollution is increasing social interest one of 6 kinds of modern diseases which is difficult functional recovery of damaged lung as dangerous diseases of life extension. Therefore, to reduce suffering from respiratory diseases is usually non-smoking, to do strengthen behavior of lung function. In this paper, we would like to propose method to do investigation by voice analysis technology to apply when lung associated ear acupuncture point stimulus to help strengthen actually lung function. From this, we would like to consider the voice change of before/after in smoking to analyze the impact on the human body to the lungs. Based on this experiment, we would like to investigate numerically quantity data actual improved lung function to analyze of voice character difference of before/after in lung associated ear acupuncture point stimulating.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.79-85
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• 2020

Recently, the RPAS(Remote Piloted Aircraft System), by remote control and autonomous navigation, has been increasing in interest and utilization in various industries and public organizations along with delivery drones, fire drones, ambulances, agricultural drones, and others. The problems of the stability of unmanned drones, which can be self-controlled, are also the biggest challenge to be solved along the development of the drone industry. drones should be able to fly in the specified path the autonomous flight control system sets, and perform automatically an accurate landing at the destination. This study proposes a technique to check arrival by landing point images and control landing at the correct point, compensating for errors in location data of the drone sensors and GPS. Receiving from the Google Map API and learning from the destination video, taking images of the landing point with a drone equipped with a NAVIO2 and Raspberry Pi, camera, sending them to the server, adjusting the location of the drone in line with threshold, Drones can automatically land at the landing point.

• Transactions of the Society of Information Storage Systems
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• v.3 no.4
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• pp.196-201
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• 2007

As the demand for the lap-top computer has been increased, most users ask quiet environment to work comfortably. Therefore, noise problems of an ODD are of great interest. For the high speed ODD, the flow induced noise is caused by the turbulent flow[1], which is known to be a major source of overall noise of a slim type ODD. In this study, we introduce a new attempt to reduce the noise level using the concept of Helmholtz resonator[2].The experimental analysis is carried out for several cases at different resonance frequencies and different hole patterns. The results show reductions in the noise level from the acoustic noise absorption point of view.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.135-150
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• 2013

This study proposes a novel option strategy by using characteristic of volatility reversion and rough set algorithm in options market. Until now, various research has been conducted on stock and future markets, but minimal research has been done in options market. Particularly, research on the option trading strategy using high frequency data is limited. This study consists of two purposes. The first is to enjoy a profit using volatility reversion model when volatility gap is occurred. The second is to pursue a more stable profit by filtering inaccurate entry point through rough set algorithm. Since options market is affected by various elements like underlying assets, volatility and interest rate, the point of this study is to hedge elements except volatility and enjoy the profit following the volatility gap.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.123-128
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• 2020

Radiation Following the 2011 Fukushima nuclear accident in Japan, public interest and anxiety about radiation safety increased, and vague anxiety about commonly exposed living radiation was generated. The Atomic Energy Safety Commission has been conducting a survey of processed products that advertise "negative ions" and "far-infrared" emissions under the Living Radiation Safety Management Act. In this study, in-depth analysis was performed from a statistical point of view using the measurement data presented in the Nuclear Safety Committee's actual survey analysis report as secondary data. As a result, there was a statistically significant difference (p<0.005) between latex and civil affairs products. There were also statistically significant differences (p<0.05) in the results of testing whether there were significant differences in the annual exposure dose between groups after categorizing 71 civil products, including radon beds, into bed, bedding, and living and other categories. The correlation analysis results also confirm that, as is commonly known, the annual doses received from processed products are associated with radon derived from U-238 and Th-232.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.361-368
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• 2022

The purpose of this study was to develop a hand rehabilitation training system for hemiplegic patients. We also tried to find out five hand postures (WF: Wrist Flexion, WE: Wrist Extension, BG: Ball Grip, HG: Hook Grip, RE: Rest) in real-time using multi-channel EMG-based deep learning. We performed a pre-processing method that converts to Spider Chart image data for the classification of hand movement from five test subjects (total 1,500 data sets) using Convolution Neural Networks (CNN) deep learning with an 8-channel armband. As a result of this study, the recognition accuracy was 92% for WF, 94% for WE, 76% for BG, 82% for HG, and 88% for RE. Also, ten physical therapists participated for the usability evaluation. The questionnaire consisted of 7 items of acceptance, interest, and satisfaction, and the mean and standard deviation were calculated by dividing each into a 5-point scale. As a result, high scores were obtained in immersion and interest in game (4.6±0.43), convenience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Conformity of intention for treatment (3.90±0.49) was relatively low. This is thought to be because the game play may be difficult depending on the degree of spasticity of the hemiplegic patient, and compensation may occur in patient with weakened target muscles. Therefore, it is necessary to develop a rehabilitation program suitable for the degree of disability of the patient.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.75-80
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• 2017

Recently, there has been growing interest in spatial data that combines information and communication technology with smart cities. The high-precision LiDAR (Light Dectection and Ranging) equipment is mainly used to collect three-dimensional spatial data, and the acquired data is also used to model geographic features and to manage plant construction and cultural heritages which require precision. The LiDAR equipment can collect precise data, but also has limitations because they are expensive and take long time to collect data. On the other hand, in the field of computer vision, research is being conducted on the methods of acquiring image data and performing 3D reconstruction based on image data without expensive equipment. Thus, precise 3D spatial data can be constructed efficiently by collecting and processing image data using CCTVs which are installed as infrastructure facilities in smart cities. However, this method can have an accuracy problem compared to the existing equipment. In this study, experiments were conducted and the results were analyzed to increase the number of extracted matching points by applying the feature-based method and the area-based method in order to improve the precision of 3D spatial data built with image data acquired from stereo CCTVs. For techniques to extract matching points, SIFT algorithm and PATCH algorithm were used. If precise 3D reconstruction is possible using the image data from stereo CCTVs, it will be possible to collect 3D spatial data with low-cost equipment and to collect and build data in real time because image data can be easily acquired through the Web from smart-phones and drones.