• Structural Engineering and Mechanics
• /
• 제31권3호
• /
• pp.333-347
• /
• 2009

Due to structural complicacy, structural health monitoring for civil engineering needs more accurate and effectual methods of damage identification. This study aims to import multi-source information fusion (MSIF) into structural damage diagnosis to improve the validity of damage detection. Firstly, the essential theory and applied mathematic methods of MSIF are introduced. And then, the structural damage identification method based on multi-mode information fusion is put forward. Later, on the basis of a numerical simulation of a concrete continuous box beam bridge, it is obviously indicated that the improved modal strain energy method based on multi-mode information fusion has nicer sensitivity to structural initial damage and favorable robusticity to noise. Compared with the classical modal strain energy method, this damage identification method needs much less modal information to detect structural initial damage. When the noise intensity is less than or equal to 10%, this method can identify structural initial damage well and truly. In a word, this structural damage identification method based on multi-mode information fusion has better effects of structural damage identification and good practicability to actual structures.

• 제어로봇시스템학회논문지
• /
• 제8권1호
• /
• pp.5-14
• /
• 2002

This study presents a solution to control a Permanent Magnet Synchronous Motor without sensors based on the superposition principle. Because the proposed method of sensorless theory is very simple to compute the estimated angle, computing time to estimate the angle is shorter than other sensorless method. The use of this system yields enhanced operations, fewer system components, lower system cost, energy efficient control system design and increased efficiency. The performance of a sensorless architecture allows an intelligent approach to reduce the complete system costs of the digital motion control applications using cheaper electrical motors without sensors. This paper deals with an overview of sensorless solutions in PMSM control applications whereby the focus will be on the new controller without sensors and its applications.

• Smart Structures and Systems
• /
• 제3권4호
• /
• pp.439-454
• /
• 2007

In this paper a damage imaging technique using pre-stack migration is developed using Lamb (guided) wave propagation in composite structures for imaging multi damages by both numerical simulations and experimental studies. In particular, the paper focuses on the experimental study using a finite number of sensors for future practical applications. A composite laminate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is illustrated as an example. Two types of damages, one straight-crack damage and two simulated circular-shaped delamination damage, have been studied. First, Mindlin plate theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves in the composite laminate are also derived from dispersion relations and validated by experiments. Then the pre-stack migration technique is performed by using a two-dimensional explicit finite difference algorithm to back-propagate the scattered energy to the damages and damages are imaged together with the excitation-time imaging conditions. Stacking these images together deduces the resulting image of damages. Both simulations and experimental results show that the pre-stack migration method is a promising method for damage identification in composite structures.

• Journal of Power Electronics
• /
• 제11권4호
• /
• pp.456-463
• /
• 2011

Grid-connected converters have widely adopted LCL filters to acquire high harmonic suppression. However, the LCL filter increases the system order so that the design of the system stability would be complicated. Recently, sole-loop control strategies have been used for grid-connected converters with L or LC filters. But if the sole-loop control is directly transplanted to grid-connected converters with LCL filters, the systems may be unstable. This paper presents a novel dual-loop power control strategy composed of a power outer loop and a current inner loop. The outer loop regulates the grid-connected power. The inner loop improves the system stability margin and suppresses the resonant peak caused by the LCL filter. To obtain the control variables, a single-phase current detection is proposed based on PQ theory. The system transfer function is derived in detail and the influence of control gains on the system stability is analyzed with the root locus. Simulation and experimental results demonstrate the feasibility of the proposed control.

• Smart Structures and Systems
• /
• 제5권4호
• /
• pp.329-344
• /
• 2009

Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient damage (loss of preload) in a bolted joint. In this study we examine a physical experiment on a single-bolt aluminum lap joint as well as a three-dimensional physics-based simulation designed to model the behavior of guided ultrasonic waves through a similarly configured joint. A multiple bolt frame structure is also experimentally examined. In the physical experiment each signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the lap joint and sensed using an equivalent MFC patch on the opposite side of the joint. The model applies the waveform via direct nodal displacement and 'senses' the resulting displacement using an average of the nodal strain over an area equivalent to the MFC patch. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence. This damage detection algorithm is used to evaluate multiple damage levels and locations.

• 산업공학
• /
• 제24권1호
• /
• pp.8-14
• /
• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.127-134
• /
• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.

• Advances in nano research
• /
• 제12권2호
• /
• pp.223-239
• /
• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
• /
• pp.301-307
• /
• 1998

The goal of this paper is to describe an advanced method of the fault diagnois using Control Theory with reference to a crack detection, a new way to localize the crack position under infulence of the plant disturbance and white measurement noise on a rotating shaft. As a first step, the shaft is physically modelled with a finite element method as usual and the dynamic mathematical model is derived from it using the Hamilton - principle and in this way the system is modelled by various subsystems. The equations of motion with crack is established by adaption of the local stiffness change through breathing and gaping from the crack to the equation of motion with un-damaged shaft. This is supposed to be regarded as reference for the given system. Based on the fictitious model of the time behaviour induced from vibration phenomena measured at the bearings, a nonlinear State Observer is designed in order to detect the crack on the shaft. This is elementary NL- observer(EOB). Using the elementary observer, an Estimator(Observer) Bank is established and arranged at the certain position on the shaft. In case a crack is found and its position is known, the procedure for the estimation of the depth is going to begin.

• 전자공학회논문지
• /
• 제51권7호
• /
• pp.157-168
• /
• 2014

본 논문에서는 자연영상에 대한 돌출영역을 자동으로 검출하고 이를 분할하기 위한 새로운 인공시각집중모델을 제안한다. 제안된 모델은 인간의 생물학적 시각인지 기반이며 주된 특징은 다음과 같다. 먼저 영상의 강도특징과 색상특징을 사용하는 대립과정이론 기반의 새로운 인공시각집중모델의 구조를 제안하고, 돌출영역을 인지하기 위해 영상의 강도 및 색상 특징채널의 정보량을 고려하는 엔트로피 필터를 설계하였다. 엔트로피 필터는 높은 정확도와 정밀도로 돌출영역에 대해 검출 및 분할이 가능하다. 마지막으로 최종 돌출지도를 효율적으로 구성하기 위한 적응 조합 방법 또한 제안되었다. 이 방법은 각 인지 모델로부터 검출된 강도 및 색상 가시성지도에 대하여 평가하며 평가된 점수로부터 얻어진 가중치를 이용해 가시성 지도들을 조합한다. 돌출지도에 대해 ROC분석을 이용한 AUC를 측정한 결과 기존 최신의 모델들은 평균 0.7824의 성능을 나타낸 반면 제안된 모델의 AUC는 0.9256으로서 약 15%의 성능 개선을 보였다. 또한 돌출영역 분할에 대해 F-beta를 측정한 결과 기존 최신의 모델은 0.5178이고 제안된 모델은 0.7325로서 분할 성능 또한 약 22%의 성능 개선을 보였다.