• Clean Technology
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• v.28 no.4
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• pp.316-322
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• 2022

The flow pattern at the inlet of the catalyst layer in a selective catalytic reduction (SCR) system is one of the key parameters influencing the performance of the denitrification process. In the curved diffusing parts between the ammonia injection grids and the catalyst layers, guide vanes are installed to improve flow uniformity. In the present study, a numerical simulation has been performed to investigate the effect of the geometrical configuration of the guide vanes on the aerodynamic characteristics of a denitrification facility. This application has been made to the existing SCR process in a large-scaled coal-fired power plant. The flow domain to be solved covers the whole region of the flow passages from the exit of the ammonia injection gun to the exit of the catalyst layers. ANSYS-Fluent was used to calculate the three-dimensional steady viscous flow fields with the proper turbulence model fitted to the flow characteristics. The root mean square of velocity and the pressure drop inside the flow passages were chosen as the key performance parameters. Four types of guides vanes were proposed to improve the flow quality compared to the current configuration. The numerical results showed that the type 4 configuration was the most effective at improving the aerodynamic performance in terms of flow uniformity and pressure loss.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.689-700
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• 2022

Layout work for building structures requires high precision to construct structural elements in the correct location. However, the accuracy and precision of the layout position are affected by the worker's skill, and productivity can be reduced when there is information loss and error. To solve this problem, it is necessary to automate the overall layout operation and introduce information technology, and layout process automation using construction robots can be an effective means of doing this. This study develops a prototype of an automated layout robot for building structures and evaluates its basic performance. The developed robot is largely composed of driving, marking, sensing, and control units, and is designed to enable various driving methods, and movement and rotation of the marking unit in consideration of the environment on structural work. The driving and marking performance experiments showed satisfactory performance in terms of driving distance error and marking quality, while the need for improvement in terms of some driving methods and marking precision was confirmed. Based on the results of this study, we intend to continuously improve the robot's performance and establish an automation system for overall layout work process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.343-350
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• 2022

The computerization project for underground facilities in Korea began in earnest after the city gas explosion in Seoul in 1994, and the Daegu subway explosion in 1995. As such a large-scale gas explosion accident caused enormous economic loss including human casualties and potential benefits, the need for computerized for efficient management of underground facilities was greatly emphasized in society. Meantime, computerization of underground facilities has been carried out according to the basic plan for building national geographic information system. In this study, problems were identified based on the current status of construction and management of underground facility information, as well as laws and regulations, and directions for establishing quality standards were presented. In addition, construction work standards such as 「Public Survey Work Regulations」, design standards, standard specifications, and technical specifications, gas technology standards, design standards, and communication works so that underground facility information can be linked and utilized in construction work by examining the linkage of the underground facilities, the targets that can be used for quality level information on underground facilities were derived, and a proposal to revise the construction standards was presented. In the future, if the quality standards are established, it is expected that the accuracy and utilization in the construction field will be increased.

• CELLMED
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• v.11 no.4
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• pp.21.1-21.9
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• 2021

Background: Unani System of Medicine (USM) has its origin to Greece. To ensure and develop the quality, authenticity of Unani drugs, standardization on modern analytical parameter is essential requirement for drugs. Objectives: The aimed of the present study was to develop a standard profile of "Qurṣ-e-Mafasil" by systematic study through authenticated ingredients, pharmacognostic identification followed by physicochemical, TLC, HPTLC fingerprinting analysis as per standard protocol. Material and Methods: In this study three batches of "Qurṣ-e-Mafasil" QM were prepared by standard method as per UPI had been followed by organoleptic properties of formulation such as appearance, color, odor, taste. Powder Microscopy and physicochemical studies were carried out such as Uniformity of weight, Friability, Disintegration time, hardness, LOD, ash vales and extractive values in like aqueous, alcohol & hexane. Further qualitative tests such as Thin-Layer Chromatography (TLC), and High-Performance Thin Layer Chromatography (HPTLC) studies were also carried out to develop fingerprint pattern of the alcoholic solvent extract of QM. Phytochemical screening was carried out in different solvent extracts such as alcoholic, aqueous and chloroform extracts to detect the presence phytoconstituents in the formulation QM. Heavy metals, Microbial Load Contamination and pesticidal residues were also determined. Results: Qurṣ-e-Mafasil showed tablet-like appearance, light brown colour, mild pungent odour and acrid taste. Uniformity of weight (mg), friability (rpm), and hardness (kg/cm) and disintegration time was ranged between (500 to 503), (0.0340 to 0.038), (8.40 to 8.67) and (4-5 minutes) respectively for the three batches. Loss in weight on drying at 105℃ was ranged between (8.3425 to 8.7346). Extracted values were calculated in distilled water ranged between (30.9091 to 31.4358), hexane (1.1419 to 1.4281), and alcohol (3.3352 to 3.3962). The ash values recorded were ranged between (3.7336 to 3.8378), and acid insoluble ash (0.5859 to 0.6112).

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Korean Journal of Biological Psychiatry
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• v.29 no.1
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• pp.22-31
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• 2022

Objectives Recently, weight loss has emerged as a national concern in South Korea, and this has resulted in an increase in the frequency of use of central nervous system (CNS)-stimulating appetite suppressants. This study aimed to collect cases of psychotic disorders and bipolar disorders triggered by phentermine and phendimetrazine and explore the clinical features and courses. Methods In this retrospective study, we analyzed the electronic medical records of patients and selected eight patients who developed psychotic symptoms and manic symptoms for the first time after taking phentermine and phendimetrazine. All cases were reviewed, and their clinical features and course were summarized. Results All eight patients developed psychotic symptoms, and one had accompanying manic symptoms. The final diagnosis was appetite-suppressant-induced psychotic disorder in four patients, schizophrenia in three, and appetite-suppressant-induced bipolar disorder in one. In addition, three patients were diagnosed as having substance-use disorder. The key psychotic symptoms of these patients were hallucinations and paranoia. Conclusions These case findings suggest that phentermine and phendimetrazine can cause psychotic disorder, bipolar disorder, or substance use disorder and that medical professionals and the public should practice caution when prescribing and using these drugs.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.73-82
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• 2019

The satirical content of the absurd play, centered on Lee Keun-sam's play, represents the family image of a modern capitalist society where only duty is emphasized while the characters are lost in love with the family. They show humans becoming subordinate to economic logic as traditional relationships and family relationships change into material ones due to the rapid development of the economy. The narrator expresses the roles of the performer and the narrator together. It also presents the plot as a characteristic element of epic and absurd dramas, and directs actors as directors. It also foretells the events that will take place in the future, presents the inner consciousness of the characters in the play, and reduces and expands events and times. In terms of conflict, in order to fulfill the financial responsibility of their children, the professor translates them like a machine and the wife distributes the money they earn as they demand. The middle-aged professor and his wife are not willing to make a difference in the real world, so specific conflicts are not revealed. Therefore, no concrete conflict appears within this work. The plot of consisted of 22 epicentre compartments, consisting of a time frame from evening to the next morning. And no special events happen and show only one family's daily life. In addition, materials that show simple repetition of daily life such as newspapers, rice, birthdays, etc. are effectively showing the character of absurdity through repeated structure. The linguistic features of the absurd play focus on expressing anxiety, despair, fantasy and the sense of loss that the object's purpose has disappeared. The stage system avoids detailed portrayals of naturalist plays and creates a thoroughly simplified image that the theme of the play demands, which shows that the stage unit is also an important element that characterizes the absurdity of reflexes.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.