• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.539-546
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• 2013

Surge phenomenon can be occurred in a compressor when compressor performance of turbofan engine for an aircraft is changed considerably in a short time on the cases like take-off phase and changing of RPM from idle to maximum, because performance of aircraft engine is changed suddenly. This study is aimed to avoid surge in a compressor. Dynamic simulation in a compressor is modeled by simulink in specific condition. Fuel flow is control input, rpm and air mass flow are expressed in terms of transfer function. Surge margin is obtained by using compressor performance map from NPSS. VIGV(Variable Inlet Guide Vane) is controlled by PD controller with difference between surge margin and reference. Finally this paper verifies IGV can prevent surge phenomenon in a compressor.

• Journal of Communications and Networks
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• v.14 no.1
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• pp.75-90
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• 2012

Thin client computing trades local processing for network bandwidth consumption by offloading application logic to remote servers. User input and display updates are exchanged between client and server through a thin client protocol. On wireless devices, the thin client protocol traffic can lead to a significantly higher power consumption of the radio interface. In this article, a cross-layer framework is presented that transitions the wireless network interface card (WNIC) to the energy-conserving sleep mode when no traffic from the server is expected. The approach is validated for different wireless channel conditions, such as path loss and available bandwidth, as well as for different network roundtrip time values. Using this cross-layer algorithm for sample scenario with a remote text editor, and through experiments based on actual user traces, a reduction of the WNIC energy consumption of up to 36.82% is obtained, without degrading the application's reactivity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.102-109
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• 2018

This paper seeks to present a multi-control method that can contribute to effective control of the production line with multiple bottleneck processes. The multi-control method is the production system that complements shortcomings of CONWIP and DBR, and it is designed to determine the raw material input according to the WIP level of two bottleneck processes and WIP level of total process. The effectiveness of the production system developed by applying the multi-control method was verified by the following three procedures. Raw material input conditions of the multi-control method are as follows. First, raw materials are go into the production line when the number of the total process WIP is lower than established number of WIP in total process and first process is idle. Second, raw materials are introduced when the number of WIP of two bottleneck processes is lower than the established number of WIP of each bottleneck process. Third, raw materials are introduced when the first process and in front of bottleneck process are idle even if the number of WIP in the total process is less than established number of WIP of the total process. The production line with two bottleneck processes was selected as the condition for production environment, and the production process modeling of CONWIP, DBR and multi-control production method was defined according to the production condition. And the optimum limited WIP level suitable for each system was obtained by applying a genetic algorithm to determine the total limited number of WIP of CONWIP, the limited number of WIP of DBR bottleneck process, the number of WIP in the total process of multi-control method and the limited number of WIP of bottleneck process. The limited number of WIP of CONWIP, DBR and multi-control method obtained by the genetic algorithm were applied to ARENA modeling, which is simulation software, and a simulation was conducted to derive result values on the basis of three criteria such as production volume, lead time and number of goods in-progress.

• Journal of the Korea Institute of Building Construction
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• v.10 no.1
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• pp.73-79
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• 2010

Deck plates have been widely used for steel framework due to their merits in terms of schedule reduction and work repetition. For this reason, most of the previous studies related to deck plates have focused on the development of form type and their constructability. In this study, through an actual case study and interviews with experts, a simulation model was developed using the CYCLONE method. Based on this model, this study not only analyzed the productivity of the work process of the deck plate in steel framework, but also identified the occurrence of idle time in the work process. In addition, using a sensitivity analysis, productivity and duration could be analyzed according to variation of input resources. Based on the results, this paper suggests a way to improve the productivity of deck plate work in steel frameworks. Using the model, it is expected that project managers would be able to predict the productivity and total duration of the deck plate work in the early project phase, which will enable managers to make an appropriate plan for input resources.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.50-57
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• 2013

This paper provides a design of high-throughput parallel turbo decoder that is able to decode several packets of various length simultaneously. For high-speed communications, designing of Turbo decoder as parallel structures reduces the long decoding time caused by iterative turbo decode way. Also, by employing the double buffer structure for input and output packets improves the decoder throughput by enabling continuous decoding. Because parallel turbo decoder is designed to be able to decode the packet of the longest length, there exist idle PE's(Processing Element) in the case of decoding packets of short length. The main idea of this paper is to increase the utilization of PE's in parallel Turbo decoder and to improve the decoder throughput by using the idle PE's immediately for the subsequent packets decoding. For this, the control is necessary to enable the concurrent decoding of several short packets and we propose the method of this control. Applying the proposed method, we implemented Turbo Decoder with 32 PE's that can decode packets of 6144 bits maximum. Compared to the conventional Turbo decoder, although the area was increased about 16%, the decoder throughput was improved 28 times for short packets.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.2 s.29
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• pp.141-146
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• 2007

In this study, we have investigated several input factors and output factors to maintain safety management of domestic shipping companies, and then have analyzed the efficiency of performance about each shipping companies' safety management system from 1998 year to 2004 year using DEA method. The result of analysis shows that the annual mean efficiency index of total companies tended downward every year. Analysis was that the cause was increase of the cost of repairing ship, the cost of ship's stores and idle day of ship while the number of marine accidents and sanctions of PSC, ship's insurances and P&I insurances was decreased.

• Journal of the Korea Computer Graphics Society
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• v.21 no.5
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• pp.29-36
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• 2015

Human fingers are essential parts of the body that perform complex and detailed motion. Expression of natural finger motion is one of the most important issues in character animation research. Especially, keyboard typing animation is hard to create through the existing animation pipeline because the keyboard typing typically requires a high level of dexterous motion that involves the movement of various joints in a natural way. In this paper, we suggest a method for the generation of realistic keyboard typing motion based on physics simulation. To generate typing motion properly using physics-based simulation, the hand and the keyboard models should be positioned in an allowed range of simulation space, and the typing has to occur at a precise key location according to the input signal. Based on the observation, we incorporate natural tendency that accompanies actual keyboard typing. For example, we found out that the positions of the hands and fingers always assume the default pose, and the idle fingers tend to minimize their motion. We handle these various constraints in one solver to achieve the results of real-time natural keyboard typing simulation. These results can be employed in various animation and virtual reality applications.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.