• Proceedings of the ESK Conference
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• 1993.04a
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• pp.111-123
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• 1993

TravTek이란 Travel Technology의 합성어이며, 미래의 여행자들이 차를 사용하여 여행할 때, 보다 편리하고 쾌적한 여행이 될 수 있도록 차에 컴퓨터단말기를 부착하여 현재 주행하고 있는 도로 를 차안에서 화면을 통하여 볼 수 있고, 컴퓨터가 최단경로를 운전자에게 제시하고, 도로의 상황과 여 행정보(즉 호텔, 관공명소, 행사안내등)를 컴퓨터화면으로 나타내고, 그 정보를 컴퓨터음성으로 들려 주는 것등이 TravTek 시스템의 주된 요소들이다. 현재 미국 플로리다주의 올란도시에서 GM회사가 TravTek 시스템을 설치한 차 100대를 가지고 10개의 기관이 합작으로 평가연구중에 있으며, 이 논문에서 다루고자 하는 것은 그중에서 이차의 인간공학적 평가로 제한하고자 한다. 이러한 시스템을 설치한 차 를 타고 여행할 때, 여행자가 과연 얼마나 안락하게 여행할 수 있으며, 도로의 체증현상을 줄이고, 사 고를 예방하며, 차의 설계와 목적이 인간공학적으로 합당한 가를 알아보고자 하는 연구이다. 인간공학적 평가 인자들은 (1) 운전자의 수행도, (2) 사용자 선호도, (3) 사용자 인식, (4) 운행정보등이다. 그리고 컴퓨터음성을 사용하였을 때와 사용하지 않았을 때의 두가지 경우와 (1) 움직이는 컴퓨터 지도를 사용하 였을 때, (2) 단순화 한 도로안내를 사용하였을 때, (3) 컴퓨터 지도를 사용하지 않았을 경우(종이지도 사용)에 관해 위의 4가지 인간공학적 인자들을 평가하고자 한다. 이 연구는 아직도 진행중이라 발표하 고자 하는 논문역시 현재까지의 연구결과를 토대로 발표하는 것이므로 완전한 결론을 내릴 수는 없고, 진행과정의 내용과 토의사항과 잠정적인 결론을 제시하고자 한다.기서 사용된 아이콘에 대해서만 타당한 것으로 이것을 모든 아이콘에 대해 일반화시키기는 어려우나 이후에 행해질 Icon-based User Interface 분야의 많은 연구들의 기초가 될 것이다. 더불어 아이콘과 관련된 많은 요인들(문화적 영향, 아이콘 색깔, 크기, 아이콘의 위치등이 인식에 미치는 영향)에 대해서도 연구가 행해져야 할 것이다. 확인하고 각각의 기능을 분명히 했다.가 수월하게 하였고 메모리를 동적으로 관리할 수 있게 하였다. 또한 기존의 smpl에 디버깅용 함수 및 설비(facility) 제어용 함수를 추가하여 시뮬레이션 프로그램 작성을 용이하게 하였다. 예를 들면 who_server(), who_queue(), pop_Q(), push_Q(), pop_server(), push_server(), we(), wf(), printfct() 같은 함수들이다. 또한 동시에 발생되는 사건들의 순서를 조종하기 위해, 동시에 발생할 수 있는 각각의 사건에 우선순위를 두어 이 우선 순위에 의하여 사건 리스트(event list)에서 자동적으로 사건들의 순서가 결정되도록 확장하였으며, 설비 제어방식에 있어서도 FIFO, LIFO, 우선 순위 방식등을 선택할 수 있도록 확장하였다. SIMPLE는 자료구조 및 프로그램이 공개되어 있으므로 프로그래머가 원하는 기능을 쉽게 추가할 수 있는 장점도 있다. 아울러 SMPLE에서 새로이 추가된 자료구조와 함수 및 설비제어 방식등을 활용하여 실제 중형급 시스템에 대한 시뮬레이션 구현과 시스템 분석의 예를 보인다._3$", chain segment, with the activation energy of carriers from the shal

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.127-136
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• 2005

This paper measured a proper ratio of the size of demand fetch cache $L_1$ to that of prefetch cache $L_P$ by imulation when the size of $L_1$ and $L_P$ are constant which organize space-limited level 1 cache of a single microprocessor chip. The analysis of our experiment showed that in the condition of the sum of the size of $L_1$ and $L_P$ are 16 KB, the level 1 cache organization by constituting $L_P$ with 4 KB and employing OBL and FIFO as a prefetch technique and a cache replacement policy respectively resulted in the best performance. Also, this analysis showed that in the condition of the sum of the size of $L_1$ and $L_P$ are over 32 KB, employing dynamic filtering as prefetch technique of $L_P$ are more advantageous and splitting level 1 cache by constituting $L_1$ with 28 KB and $L_P$ with 4 KB in the case of 32 KB of space are available, by constituting $L_1$ with 48 KB and $L_P$ with 16 KB in the case of 64 KB elicited the best performance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.1-10
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• 2010

Recently heterogeneous storage system such as hybrid hard disk drive (H-HDD) combining flash memory and magnetic disk is launched, according as the read performance of NAND flash memory is enhanced as similar to that of hard disk drive (HDD) and the power consumption of NAND flash memory is reduced less than that of HDD. However, the read and write operations of NAND flash memory are slower than those of rotational disk. Besides, serious overheads are incurred on CPU and main memory in the case that intensive write requests to flash memory are repeatedly occurred. In this paper, we propose the Least Frequently Used-Hot scheme that replaces the data blocks whose reference frequency of read operation is low and update frequency of write operation is high, and the data flushing scheme that rearranges the data blocks into the multi-zone of the rotation disk. Experimental results show that the execution time of the proposed method is 38% faster than those of conventional LRU and LFU block replacement schemes in I/O performance aspect and the proposed method increases the life span of Non-Volatile Cache 40% higher than those of conventional LRU, LFU, FIFO block replacement schemes.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1993.05a
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• pp.97-97
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• 1993

Primary tumor arises infrequently in the parotid gland and generally, only about 20 to 40 percent of which prove to be malignant. They are characterized by histopathologic diversity, slow tumor growth, significant proportion of patients who have received previous treatment elsewhere. We have reviewed retrospectively 101 cases of parotid gland tumors which were treated for the recent eight years (1985-1992), Non-neoplastic tumor-like lesions were all excluded.

• 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.