• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.502-507
• /
• 2003

Toward the development of practical methods for observed data oriented bispectral estimation, an automatic means for determining the smoothing bandwidth of bispectral windows is proposed, that can also provide an associated optimum bispectral estimate of stationary non-Gaussian signals, systematically only from an observed time series datum of finite length. For the conventional non-parametric bispectral estimation, the MSE (mean squared error) of the normalized estimate is reviewed under a certain mixing condition and sufficient data length, mainly from the viewpoint of the inverse relation between its bias and variance with respect to the smoothing bandwidth. Based on the fundamental relation, a systematic method not only for determining the bandwidth, but also for obtaining the optimum bispectral estimate is presented by newly introducing a MSE evaluation index of the estimate only from an observed time series datum of finite length. The effectiveness and fundamental features of the proposed method are illustrated by the basic results of numerical experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.450-454
• /
• 1997

This system is the automatic boiler tube bending equipment which has four heads for bending the tube and the carriage for moving the tube. The system consists of two frames for transporting each moving parts, high-frequency heating equipment for heating the tube in hot bending, control panel for inputting the job data operating, remote control unit for concetration and distribution of input/output, and the monitoring system which can establish unmanned operationby receiving the bending job data via LAN form a design teamwhich produces the job data and schedule based on master production plan and diagnoses bending data change, input, whole system status, and system malfunctions. By employing this system, 30% of production improvement was achieved was achieved comparing to the existing bending system.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.241-246
• /
• 2020

Hadoop is a framework to process large data sets in a distributed way across clusters of nodes. It has been a popular platform to process big data, but in recent years, other platforms became competitive ones depending on the characteristics of the application. Spark is one of distributed platforms to enable real-time data processing and improve overall processing performance over Hadoop by introducing in-memory processing instead of disk I/O. Whereas Hadoop is designed to work on Java and data analysis is processed using Java API, Spark provides a variety of APIs with Scala, Python, Java and R. In this paper, the goal is to find out whether the APIs of different programming languages af ect the performances in Spark. We chose two popular APIs: Python and Scala. Python is easy to learn and is used in AI domain in a wide range. Scala is a programming language with advantages of parallelism. Our experiment shows much faster processing with Scala API than Python API. For the performance issues on AI-based analysis, further study is needed.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.5
• /
• pp.787-795
• /
• 1994

In this paper, we have proposed a bit synchronization algorithm which extracts the synchronized clock for random NRZ signal and designed a circuit followed by its performance analysis. The synchronization circuit consists of the Data Transition Detector and Mod 64 Counter, Phase Comparison and Controller, 64 Divider. The data input rate and master clock rate are 16 Kbps and 4.096MHz, respectively. The phase is compensated by 1/64 of the data signal period for every data bit. Through a series of experiments, the maximum immunity of phase jiter for input signal and the deviation of the recovered clock are measured 23.8% and 1.6%, respectively. The fully digital synchronization circuit is simple to implement into signal IC chip and also effective for the low speed digital mobile communications.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.6
• /
• pp.1122-1128
• /
• 2006

In this paper, we implemented an wireless data logger system for checking gas volume data using microcontroller Atmega128. The system used wireless communication between master and slave, and operated by socket communication under TCP/IP protocol between server and client. And monitoring interface program was implemented as a software adoptable for GUI environment using Visual C++. The system is constructed server program and client program in order to display gas volume data at a real time, and is expensive, tiny, and easy in implementation. It allows the system to be useful as a pilot project for microcontroller experiment.

• Korean Journal of Metals and Materials
• /
• v.50 no.10
• /
• pp.721-728
• /
• 2012

This paper evaluates the reliability of creep rupture life under service conditions of Alloy 617, which is considered as one of the candidate materials for use in a very high temperature reactor (VHTR) system. A Z-parameter, which represents the deviation of creep rupture data from the master curve, was used for the reliability analysis of the creep rupture data of Alloy 617. A Service-condition Creep Rupture Interference (SCRI) model, which can consider both the scattering of the creep rupture data and the fluctuations of temperature and stress under any service conditions, was also used for evaluating the reliability of creep rupture life. The statistical analysis showed that the scattering of creep rupture data based on Z-parameter was supported by normal distribution. The values of reliability decreased rapidly with increasing amplitudes of temperature and stress fluctuations. The results established that the reliability decreased with an increasing service time.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.1
• /
• pp.61-70
• /
• 2024

As users use various services along with the rapid increase in Internet services, it may be difficult to manage accounts. To solve these difficulties, various password management applications are emerging. From a forensic point of view, password management applications can provide clues to obtain criminal evidence. The purpose of this paper is to acquire the data stored by the user in the password management application. To this end, we propose a better way to decrypt the encrypted data through reverse engineering, evaluate the security of the application to be analyzed, and safely store the data.

• Geophysics and Geophysical Exploration
• /
• v.13 no.1
• /
• pp.40-50
• /
• 2010

The building process of any geological map involves linking sparse lithological outcrop information with equally sparse geometrical measurements, all in a single entity which is the preferred interpretation of the field geologist. The actual veracity of this interpretative map is partially dependent upon the frequency and distribution of geological outcrops compounded by the complexity of the local geology. Geophysics is commonly used as a tool to augment the distribution of data points, however it normally does not have sufficient geometrical constraints due to: a) all geophysical inversion models being inherently non-unique; and b) the lack of knowledge of the physical property contrasts associated with specific lithologies. This contribution proposes the combined use of geophysical edge detection routines and 'three point' solutions from topographic data as a possible approach to obtaining geological contact geometry information (strike and dip), which can be used in the construction of a preliminary geological model. This derived geological information should first be assessed for its compatibility with the scale of the problem, and any directly observed geological data. Once verified it can be used to help constrain the preferred geological map interpretation being developed by the field geologist. The method models the contacts as planar surfaces. Therefore, it must be ensured that this assumption fits the scale and geometry of the problem. Two examples are shown from folded sequences at the Bathurst Mining Camp, New Brunswick, Canada.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.6
• /
• pp.1433-1442
• /
• 1996

Data distribution of SPMD(Single Program Multiple Data) pattern is one of main features of HPF (High Performance Fortran). This paper describes design is sues for such data distribution and its efficient execution model on TICOM IV computer, named SPAX(Scalable Parallel Architecture computer based on X-bar network). SPAX has a hierarchical clustering structure that uses distributed shared memory(DSM). In such memory structure, it cannot make a full system utilization to apply unanimously either SMDD(shared Memory Data Distribution) or DMDD(Distributed Memory Data Distribution). Here we propose another data distribution model, called DSMDD(Distributed Shared Memory Data Distribution), a data distribution model based on hierarchical masters-slaves scheme. In this model, a remote master and slaves are designated in each node, shared address scheme is used within a node and message passing scheme between nodes. In our simulation, assuming a node size in which system performance degradation is minimized,DSMDD is more effective than SMDD and DMDD. Especially,the larger number of logical processors and the less data dependency between distributed data,the better performace is obtained.

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