Park Chan-kuk;Chu Byeong-gil;Kim chol;Jung Jai-hwan
Journal of the Korean Institute of Gas
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v.2
no.1
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pp.28-39
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1998
This study investigated the natural convection and radiation in a rectangular enclosure with ceiling vent experimentally and numerically. A heat source is located on the center of the bottom surface. The analysis was peformed a pure convection and is combination of natural convection and radiation. The shape of the considered two dimensional model is a square whose center of ceiling($30\%$) is opened. The numerical simulations are carried out for the pure natural convection case and the combined heat transfer case by using the SIMPLE algorithm. For the turbulent flow, Reynolds stresses are closed by the standard $k-{\epsilon}$ model and the wall function is used to determine the wall boundary conditions. The experiment was performed on the same geometrical shape as the computations. The radiative heat transfer is analized by the S-N discrete ordinates method. The results of pure natural convection are compared with those of combined heat transfer by the velocity vectors, stream lines, isothermal lines. The results obtained are as follows 1. Comparing the results of pure convection with those of the combined convection-radiation through the shape of stream lines, isothermal lines are similar to each other. 2. The temperature fields obtained by numerical method are compared to those obtained by experimental one, and it is found that they are showed mean relative error $8.5\%$. 3. Visualization bt smoke is similar to computational results.
Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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2009.05a
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pp.253-256
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2009
This paper describes a core generator (FFT_Core_Gen) which generates Verilog HDL models of 8 different FFT/IFFT cores with $N=64{\times}2^k$($0{\leq}k{\leq}7$ for OFDM-based communication systems. The generated FFT/IFFT cores are based on in-place single memory architecture, and use a hybrid structure of radix-4 and radix-2 DIF algorithm to accommodate various FFT lengths. To achieve both memory reduction and the improved SQNR, a conditional scaling technique is adopted, which conditionally scales the intermediate results of each computational stage, and the internal data and twiddle factor has 14 bits. The generated FFT/IFFT cores have the SQNR of 58-dB for N=8,192 and 63-dB for N=64. The cores synthesized with a $0.35-{\mu}m$ CMOS standard cell library can operate with 75-MHz@3.3-V, and a 8,192-point FFT can be computed in $762.7-{\mu}s$, thus the cores satisfy the specifications of wireless LAN, DMB, and DVB systems.
Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.
Continuous wave (CW) Doppler radar has the advantage of being able to solve the privacy problem unlike camera and obtains signals in a non-contact manner. Therefore, this paper proposes a human activity recognition (HAR) system using CW Doppler radar, and presents the hardware design and implementation results for acceleration. CW Doppler radar measures signals for continuous operation of human. In order to obtain a single motion spectrogram from continuous signals, an algorithm for counting the number of movements is proposed. In addition, in order to minimize the computational complexity and memory usage, binarized neural network (BNN) was used to classify human motions, and the accuracy of 94% was shown. To accelerate the complex operations of BNN, the FPGA-based BNN accelerator was designed and implemented. The proposed HAR system was implemented using 7,673 logics, 12,105 registers, 10,211 combinational ALUTs, and 18.7 Kb of block memory. As a result of performance evaluation, the operation speed was improved by 99.97% compared to the software implementation.
Journal of the Korean Society of Marine Environment & Safety
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v.23
no.7
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pp.918-925
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2017
Multiple reflections on exterior equipment with complex shape on naval ships cause unexpectedly high Radar Cross Section (RCS) distributions, and the directions of reradiated electromagnetic waves are hard to predict. Therefore, the optimum arrangement of exterior equipments should be considered according to the Radar Absorbing Structure (RAS) method. In this paper, the optimum arrangement for exterior equipments was determined to reduce multiple reflections and RCS even with complex shapes. The sequential descending arrangement method was used to establish an optimum arrangement algorithm. An LCS-2 type model was selected for optimum exterior equipment arrangements. In order to reduce computational cost, RCS distributions and multiple reflection path analysis of exterior equipments was carried out to select exterior equipments for optimum arrangement, and an optimum arrangement was determined to find positions with minimum RCS values. Also, the RCS reduction effect was analyzed using detectable radar range.
In this paper, implications for future curriculum compilation were sought by analyzing the process and results of compiling books for elementary school mathematics textbooks government published according to the 2015 revised curriculum. The 2015 revised elementary mathematics textbooks government published was operated with a systematic compilation system so that academia and school field experts across the country could demonstrate their expertise. As improvements in content, the unit and time to strengthen basic computational skills were increased, and the mathematical concept and principle introduction method and algorithm presentation method were improved, and the internal connection between contents was strengthened. The learning period was adjusted, such as moving and arranging contents that are difficult for students to understand to the upper semester or the upper grade. In the 1st and 2nd graders, the amount of reading was drastically reduced to suit the students' level of Korean, and sentences and vocabulary were improved, and instructions were briefly revised. As for editing and design improvements, illustrations of each unit's introduction and contextual pictures were presented in detail, and the characters in the textbook were consistently presented across all grades, giving children characters a role to actively participate in learning in the textbook. In the process of compiling, the media, the National Assembly, and civic groups raised opinions that sentences and vocabulary in first-year textbooks are more difficult than students' level of Hangeul education, that reducing textbooks makes it difficult for students to understand. Accordingly, efforts to improve textbook compilation and the results were viewed. Through the overall analysis as above, for future compilation of state-authored textbooks and certified textbooks, a plan to improve textbook compilation for students and teachers and a plan to operate compilation was proposed.
With the growth of the packaging industry, demand on the packaging printing comes in various forms. Customers' orders are diversifying and the standards for quality are increasing. Offset printing is mainly used in the packaging printing since it is easy to print in large quantities. However, productivity of the offset printing decreases when printing various order. This is because it takes time to change colors for each printing unit. Therefore, scheduling that minimizes the color replacement time and shortens the overall makespan is required. By the existing manual method based on workers' experience or intuition, scheduling results may vary for workers and this uncertainty increase the production cost. In this study, we propose an automated scheduling method of parallel offset printing process for packaging printing. We decompose the original problem into assigning and sequencing orders, and ink arrangement for printing problems. Vehicle routing problem and assignment problem are applied to each part. Mixed integer programming is used to model the problem mathematically. But it needs a lot of computational time to solve as the size of the problem grows. So guided local search algorithm is used to solve the problem. Through actual data experiments, we reviewed our method's applicability and role in the field.
Journal of the Korean Society of Marine Environment & Safety
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v.28
no.7
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pp.1231-1237
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2022
In the present study, the optimization of the main particulars of a ship using AI-based design search techniques was investigated. For the design search techniques, the SHERPA algorithm by HEEDS was applied, and CFD analysis using STAR-CCM+ was applied for the calculation of resistance performance. Main particulars were automatically transformed by modifying the main particulars of the ship at the stage of preprocessing using JAVA script and Python. Small catamaran was chosen for the present study, and the main dimensions of the length, breadth, draft of demi-hull, and distance between demi-hulls were considered as design variables. Total resistance was considered as an objective function, and the range of displaced volume considering the arrangement of the outfitting system was chosen as the constraint. As a result, the changes in the individual design variables were within ±5%, and the total resistance of the optimized hull form was decreased by 11% compared with that of the existing hull form. Throughout the present study, the resistance performance of small catamaran could be improved by the optimization of the main dimensions without direct modification of the hull shape. In addition, the application of optimization using design search techniques is expected for the improvement in the resistance performance of a ship.
Kim, Yeonjoo;Kim, Siyeon;Hwang, Sungjoo;Hong, Seok Hwan
International conference on construction engineering and project management
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2022.06a
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pp.1243-1244
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2022
In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.
Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.
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