• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.2
• /
• pp.219-228
• /
• 2022

One of the most pressing and demanding issues today in the conditions of widespread transformation and digitalization of spheres of human activity is information security and ensuring the integrity of data. The main research and development in the field of information security is aimed at improving efficiency and rationalization. One of the main means of data transmission and operation of information complexes are fiber-optic systems. To date, there have been incidents of illegal intrusion and theft of information, passing through this type of communication. Thus, today there is a problem associated with insufficient information security in fiber-optic data transmission systems. One of the most effective tools to counter acts of illegal interference in systems are artificial intelligence and cryptographic algorithms of information protection. It is the symbiosis of these two tools that can qualitatively improve the level of information security in fiber-optic data transmission systems. Thus, the authors of this article pursue the goal associated with the description of an innovative system for protecting information from violations in fiber-optic data transmission systems based on the integration of intelligent cryptographic algorithms.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.845-850
• /
• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4A
• /
• pp.587-596
• /
• 2006

A half portion of Korean railway bridges depends on the type of steel plate girder bridge. Since these bridges have been built in the early stage of Korean economical boom, numerous maintenance effort suffers from aging and progressive degradation issues at present. In accordance with these efforts, this paper would like to address the detailed analyses of thin steel plates with bolts in order to simulate the connection regions of steel plate girder bridge. The fundamental modal analysis, transient dynamic analysis with 3D piezoelectric element in open circuit loop and signal process with aids of TOF(time of flight) and WC(wavelet coefficient) are extensively discussed.

• Annual Conference of KIPS
• /
• 2008.11a
• /
• pp.882-884
• /
• 2008

통상 하드웨어 캐시의 크기보다 수십에서 수백배 큰 크기의 데이타를 랜덤하게 접근하는 경우 낮은 메모리 접근 지역성(locality)에 기인하여 캐시 메모리 성능이 급격히 저하되는 문제를 야기한다. 예를 들면, 현재 보편적으로 사용되고 있는 차량용 General Positioning System (GPS) 프로그램의 경우 최대 32개의 위성으로부터 데이터를 받아 수신단의 위치를 계산하는 부분이 핵심 모듈중의 하나 이며, 이는 전체 성능의 50% 이상을 차지한다. 이러한 모듈에서는 위성 신호를 실시간으로 받아 버퍼 메모리에 저장하며, 이때 필요한 데이터가 순차적으로 저장되지 못하기 때문에 랜덤하게 데이터를 읽어 사용하게 된다. 결과적으로 낮은 지역성에 기인하여 실시간 (realtime)안에 데이터 처리를 하기 어려운 문제에 직면하게 된다. 통상의 통신 응용의 알고리즘 상에 내재된(inherited) 낮은 메모리 접근 지역성을 개선하는 것은 알고리즘 상에서의 접근을 요구한다. 이는 높은 비용이 필요함으로 본 연구에서는 사용되는 데이터 구조를 변환하여 지역성을 높이는 방향으로 접근하였다. 결과적으로 핵심 모듈에서 2배, 전체 시스템 성능에서 14%를 개선할 수 있었다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.1
• /
• pp.38-46
• /
• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.801-807
• /
• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• Journal of Radiation Protection and Research
• /
• v.33 no.1
• /
• pp.13-20
• /
• 2008

The flow rate measurements in a multi-phase flow pipeline were evaluated quantitatively by means of a clamp-on sealed radioisotope based on a cross correlation signal processing technique. The flow rates were calculated by a determination of the transit time between two sealed gamma sources by using a cross correlation function following FFT filtering, then corrected with vapor fraction in the pipeline which was measured by the ${\gamma}$-ray attenuation method. The pipeline model was manufactured by acrylic resin(ID. 8 cm, L=3.5 m, t=10 mm), and the multi-phase flow patterns were realized by an injection of compressed $N_2$ gas. Two sealed gamma sources of $^{137}Cs$ (E=0.662 MeV, ${\Gamma}$ $factor=0.326\;R{\cdot}h^{-1}{\cdot}m^2{\cdot}Ci^{-1}$) of 20 mCi and 17 mCi, and radiation detectors of $2"{\times}2"$ NaI(Tl) scintillation counter (Eberline, SP-3) were used for this study. Under the given conditions(the distance between two sources: 4D(D; inner diameter), N/S ratio: $0.12{\sim}0.15$, sampling time ${\Delta}t$: 4msec), the measured flow rates showed the maximum. relative error of 1.7 % when compared to the real ones through the vapor content corrections($6.1\;%{\sim}9.2\;%$). From a subsequent experiment, it was proven that the closer the distance between the two sealed sources is, the more precise the measured flow rates are. Provided additional studies related to the selection of radioisotopes their activity, and an optimization of the experimental geometry are carried out, it is anticipated that a radioisotope application for flow rate measurements can be used as an important tool for monitoring multi-phase facilities belonging to petrochemical and refinery industries and contributes economically in the light of maintenance and control of them.

• Journal of Biomedical Engineering Research
• /
• v.25 no.5
• /
• pp.391-401
• /
• 2004

3D imaging systems using 2D phased arrays have a large number of active channels, compelling to use a very expensive and bulky beamforming hardware, and suffer from low volume rate because, in principle, at least one ultrasound transmit-receive event is necessary to construct each scanline. A high speed 3D imaging method using a cross array proposed previously to solve the above limitations can implement fast scanning and dynamic focusing in the lateral direction but suffer from low resolution except at the fixed transmit focusing along the elevational direction. To overcome these limitations, we propose a new real-time volumetric imaging method using a cross array based on the synthetic aperture technique. In the proposed method, ultrasound wave is transmitted successively using each elements of an 1D transmit array transducer, one at a time, which is placed along the elevational direction and for each firing, the returning pulse echoes are received using all elements of an 1D receive array transducer placed along the lateral direction. On receive, by employing the conventional dynamic focusing and synthetic aperture method along lateral and elevational directions, respectively, ultrasound waves can be focused effectively at all imaging points. In addition, in the proposed method, a volume of interest consisting of any required number of slice images, can be constructed with the same number of transmit-receive steps as the total number of transmit array elements. Computer simulation results show that the proposed method can provide the same and greatly improved resolutions in the lateral and elevational directions, respectively, compared with the 3D imaging method using a cross array based on the conventional fixed focusing. In the accompanying paper, we will also propose a new real-time 3D imaging method using a cross array for improving transmit power and elevational spatial resolution, which uses linear wave fronts on transmit.

• Analytical Science and Technology
• /
• v.25 no.2
• /
• pp.121-126
• /
• 2012

It is essential to evaluate the quality of Hanji record paper without damaging the record paper by previous destructive methods. The samples were Hanji record paper produced in the 1900s. Near-infrared (NIR) spectrometer was used as a non destructive method for evaluating the quality of record papers. Fourier transform (FT) spectrometer was used with 12,500 to 4,000 $cm^{-1}$ wavenumber range for quantitative analysis and it has high accuracy and good signal-to-noise ratio. The acidity and moisture content of Hanji record paper were measured by integrating sphere as diffuse reflectance type. The acidity (pH) of chemical factors as a quality evaluated factor of Hanji was correlated to NIR spectrum. The NIR spectrum was pretreated to obtain the coefficients of optimum correlation. Multiplicative scatter correction (MSC) and First derivative of Savitzky-Golay were used as pretreated methods. The coefficients of optimum correlation were calculated by PLSR (partial least square regression). The correlation coefficients ($R^2$) of acidity had 0.92 on NIR spectra without pretreatment. Also the standard error of prediction (SEP) of pH was 0.24. And then the NIR spectra with pretreatment would have better correlation coefficient ($R^2$ = 0.98) and 0.19 as SEP on pH. For moisture contents, the linearity correlation without pretreatment was higher than the case with pretreatment (MSC, $1^{st}$ derivative). As the best result, the $R^2$ was 0.99 and SEP was 0.45. This indicates that it is highly proper to evaluate the quality of Hanji record papers speedily with integrated sphere and FT NIR analyzer as a non-destructive method.

• Journal of Internet Computing and Services
• /
• v.23 no.5
• /
• pp.145-154
• /
• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..