• Applied Chemistry for Engineering
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• v.28 no.6
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• pp.607-618
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• 2017

Harmful air pollutants are exhausted from the various industrial facilities including the coal-fired thermal power plants and these substances affects on the human health as well as the nature environment. In particular, nitrogen oxides ($NO_x$) and sulfur dioxide ($SO_2$) are known to be causative substances to form fine particles ($PM_{2.5}$), which are also deleterious to human health. The integrated system composed of selective catalytic reduction (SCR) and wet flue gas desulfurization (WFGD) have been widely applied in order to control $NO_x$ and $SO_2$ emissions, resulting in high investment and operational costs, maintenance problems, and technical limitations. Recently, new technologies for the simultaneous removal of $NO_x$ and $SO_2$ from the flue gas, such as absorption, advanced oxidation processes (AOPs), non-thermal plasma (NTP), and electron beam (EB), are investigated in order to replace current integrated systems. The proposed technologies are based on the oxidation of $NO_x$ and $SO_2$ to $HNO_3$ and $H_2SO_4$ by using strong aqueous oxidants or oxidative radicals, the absorption of $HNO_3$ and $H_2SO_4$ into water at the gas-liquid interface, and the neutralization with additive reagents. In this paper, we summarize the technical improvements of each simultaneous abatement processes and the future prospect of technologies for demonstrating large-scaled applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.845-848
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• 2013

Due to intense climate changes and extreme weather conditions a noticeable decrease has been observed in the growth of certain plants. The indoor plant factories would have certain benefits including increase in crop yield, reduction in distribution cost, and maintains the healthy freshness level of the agricultural product. Recently, an artificial light source with optimum wavelength is spot lighted to fulfill the need of light for the indoor plant factories. The energy efficient light emitting diodes (LED) provide the essential light energy for the proper growth of indoor cultivated plants. This work focuses to utilize ubiquitous sensors network(USN) in providing suitable environment for the proper growth of agricultural product inside the indoor plant factory. The proposed system makes use of sensors and actuators, communicating each other through WPAN, ZigBee network. The proposed system obscured the traditional indoor plant factories with easy installation and wireless connectivity of the sensors and actuators along with eliminating the web of wires reducing the initial installation and maintenance cost.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.720-731
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• 2001

Complexity metrics have been developed for the structured paradigm of software development are not suitable for use with the object-oriented(OO) paradigm, because they do not support key object-oriented concepts such as inheritance, polymorphism. message passing and encapsulation. There are many researches on OO software metrics such as program complexity or design metrics. But metrics measuring the complexity of classes at the OO analysis phase are needed because they provide earlier feedback to the development project. and earlier feedback means more effective developing and less costly maintenance. In this paper, we propose the new metrics to measure the complexity of analysis classes which draw out in the analysis based on RUP(Rational Unified Process). By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and determine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We verify theoretically the suggested metrics for Weyuker's nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the user using the text mining technique. As a result of the comparison of CC and CBO and WMC suggested by Chidamber and Kemerer, the class that have highly the proposed metric value maintain the high complexity at the design phase too. And the complexity can be represented by CC and IC more than CBO and WMC. We can expect that our metrics may provide us the earlier feedback and hence possible to predict the efforts, costs and time required to remainder processes. As a result, we expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC(Software Development Life Cycle).

• Clean Technology
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• v.17 no.3
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• pp.215-224
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• 2011

It is an important issue to develop quality assessing system for biofuel for the purpose of accelerating the mass production of biofuel. It is particularly challenging to conduct testing method in the mass production of bioethanol while meeting quality specifications such as ASTM (American Society for Testing & Materials) D4806-10. In order to address this challenge, this paper proposes on-line spectroscopic quality assesment system based on Near Infrared spectrum and Partial Least Squares method in Chemometrics. As a result of testing a number of preprocessing methods and Partial Least Squares, it was found out that Savitzky-Golay method showed the best performance in terms of spectrum correction, noise reduction, and model maintenance. The proposed system allows us to assess multiple quality components continuously using spectroscopic facilities with the cheap cost. Since the value of R2 is more than 0.99, it is possible to replace the laboratory analysis.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.194-203
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• 2021

With the recent increase in maintenance works on water and sewer pipes as well as district heating supply pipes, pavement cutting work using pavement cutter is on the rise. The pavement cutting operation generates considerable dust (cutting sludge) as well as noise; therefore, it is necessary to apply eco-friendly technologies that have low noise and dust recovery capability. Thus far, various equipment for recovering dust have been developed; however, there is a limitation in that the environmental friendliness is not quantified. Therefore, in this study, we developed a low-noise, dust-recovery type pavement cutter that can fundamentally remove the causes of environmental hazards such as noise and dust and evaluated the eco-friendliness of the pavement cutting process performed by this cutter. To this end, an integrated water cooling-sludge recovery system composed of a vacuum device and a sludge suction unit was developed, and the developed system was applied to a pavement cutter. Subsequently, the developed equipment was applied to the test bed, and data related to its eco-friendliness were collected and evaluated. The results showed that the cutting sludge recovery rate of the developed equipment was greater than 83%, the noise level was approximately 82 - 83 dB, and the sound power level was 115 dB. The results of this study will be used as basic data to develop improved pavement cutters in the future with improved cutting sludge recovery performance and lower noise.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.1
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• pp.75-84
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• 2021

In this study, to evaluate deterioration propagation life and deterioration curve of the shop painted and field re-painted steel bridges, accelerated corrosion tests were carried out on 4 types of heavy-duty painting systems with different surface treatments. The surface treatments prior to painting were examined by hand tool(SSPC SP-2), power tool(SP-3,) or blast cleaning(SP-10) considering shop painting and field re-painting. The paint deterioration curves for each painting system and surface treatment were evaluated based on corrosion propagation from the initial paint defects. From the test results, the paint deterioration life of shop painted and field re-painted system was evaluated and compared by using corrosivity categories and durability performance evaluation of structural steel. The deterioration propagation life of shop and field paint was estimated in 18 to 21 years and 5.3 to 8.0 years with atmospheric corrosion category C4.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• The Journal of Engineering Geology
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• v.12 no.1
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• pp.35-51
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• 2002

This study aims to establish the methodology for design of an optimum water curtain system of the unlined underground oil storage cavern satisfying the requirements of hydrodynamic performance in a volcanic terrain of the south coastal area. For the optimum water curtain system in the storage facility, the general characteristics of groundwater flow system in the site are quantitatively described, i.e. distribution of hydraulic gradients, groundwater inflow rate into the storage caverns, and hydrogeologic influence area of the cavern. In this study, numerical models such as MODFLOW, FracMan/MAFIC and CONNECTFLOW are used for calculating the hydrogeological stability parameters. The design of a horizontal water curtain system requires considering the distance between water curtain and storage cavern, spacing of the water curtain boreholes, and injection pressure. From the numerical simulations at different scales, the optimum water curtain systems satisfying the containment criteria are obtained. The inflow rates into storage caverns estimated by a continuum model ranged from about 120 m$^3$/day during the operation stage to 130~140m$^3$/day during the construction stage, whereas the inflow rates by a fracture network model are 80~175m$^3$/day. The excavation works in the site will generate the excessive decline of groundwater level in a main fracture zone adjacent to the cavern. Therefore, the vertical water curtain system is necessary for sustaining the safe groundwater level in the fracture zone.