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This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg- Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process

Magnetic Induction Tomography Using Magnetic Dipole and Lumped Parameter Model

This paper presents a novel approach to analyze the magnetic field of magnetic induction tomography (MIT) using magnetic dipoles and a lumped parameter model. The MIT is a next-generation medical imaging technique that can identify the conductivity of target objects and construct images. It is noninvasive and can be compact in design and, thus, used as a portable instrument. However, it still exhibits inferior performance due to the nonlinearity, low signal-to-noise ratio of the magnetic field, and ill-posed inverse problem. To overcome such difficulties, the magnetic field of the MIT system is first modeled using magnetic dipoles and a lumped parameter. In particular, the extended distributed multipole (eDMP) model is proposed to analyze the system, using magnetic dipoles. The method can dramatically reduce the computational efforts and improve the ill-posed condition. Hence, the forward and inverse problems of MIT are solved using the eDMP method. The modeling method can be validated by comparing with experiments, varying the modeling parameters. Finally, the image can be reconstructed, and then, the position and shape of the object can be characterized to develop the MIT

Multiple Sensor Linear Multi-Target Integrated Probabilistic Data Association for Ultra-Wide Band Radar

Ultra-Wide Band (UWB) radar has a number of advantages of resolving multipath, exceptional spatial resolution, and ranging performance. However, several difficulties are confronted for multiple target tracking using UWB radars such as clutter signals which contaminate target signals, and unidentified number and behavior of the targets. Hence, this paper presents to develop a multiple moving target tracking algorithm, consisting of preprocessing and multiple target tracking steps. In the preprocessing step, static clutter reduction and constant false alarm rate (CFAR) detection extract the target candidate range measurements from each UWB radar. Then, two multiple target tracking (MTT) steps are developed: range- based MTT and position-based MTT. The range-based MTT is mainly based on existing linear multi-target integrated probabilistic data association (LM-IPDA) from each UWB radar measurement. Then the outputs of each LM-IPDA are gathered in the positioning center to estimate the position of multiple targets. On the other hands, the position-based MTT is based on multiple sensor LM-IPDA (msLM-IPDA) as an accurate target tracking method for various uncertainties by improving the probabilistic model of LM-IPDA. The tracking performance of two MTT methods is investigated with both numerical simulation and experiments

Influence of substrate heating on hole geometry and spatter area in femtosecond laser drilling of silicon

The objective of this research is to evaluate the effects of the hole geometry and the spatter area around the drilled hole by femtosecond laser deep drilling on silicon with various temperatures. Deep through holes were produced on single crystal silicon wafer femtosecond laser at elevated temperatures ranging from 300K to 873K in a step of 100K. The laser drilling efficiency is increased by 56% when the temperature is elevated from 300K to 873K. The spatter area is found to continuously decrease with increasing substrate temperature. The reason for such changes is discussed based on the enhanced laser energy absorption at the elevated temperature.open0

Dynamics estimator based robust fault-tolerant control for VTOL UAVs trajectory tracking

This paper investigates the control issue of the trajectory tracking of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) in the presence of partial propeller fault and external disturbance. In particular, a robust passive fault-tolerant control strategy is proposed by introducing a first-order filter based dynamics estimator. First, a bounded force command is exploited by employing a new smooth saturation function in the output of the estimator. A sufficient condition in terms of a specified parameter selection criteria is provided to ensure the nonsingularity extraction of the command attitude. Then, a torque command is applied to the attitude loop tracking. Since there is merely one filter parameter involved in the dynamics estimator, the practical implementation and parameter tuning can be significantly simplified. Stability analysis indicates that the proposed control strategy guarantees the semi-globally ultimately bounded tracking of VTOL UAVs subject to partial propeller fault and external disturbance. Simulation and experiment results with comparison examples are performed to validate the effectiveness of the proposed strategy. Experimental results show that the proposed strategy achieves the trajectory tracking with a good performance (mean deviation 0.0074 m and standard deviation 0.1202 m) in the presence of 35% propeller fault and 4 m/s persistent wind disturbance

Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations

Orientation measurement based on magnetic inductance by the extended distributed multi-pole model

This paper presents a novel method to calculate magnetic inductance with a fast-computing magnetic field model referred to as the extended distributed multi-pole (eDMP) model. The concept of mutual inductance has been widely applied for position/orientation tracking systems and applications, yet it is still challenging due to the high demands in robust modeling and efficient computation in real-time applications. Recently, numerical methods have been utilized in design and analysis of magnetic fields, but this often requires heavy computation and its accuracy relies on geometric modeling and meshing that limit its usage. On the other hand, an analytical method provides simple and fast-computing solutions but is also flawed due to its difficulties in handling realistic and complex geometries such as complicated designs and boundary conditions, etc. In this paper, the extended distributed multi-pole model (eDMP) is developed to characterize a time-varying magnetic field based on an existing DMP model analyzing static magnetic fields. The method has been further exploited to compute the mutual inductance between coils at arbitrary locations and orientations. Simulation and experimental results of various configurations of the coils are presented. Comparison with the previously published data shows not only good performance in accuracy, but also effectiveness in computation.open0

Multifunctional Smart Ball Sensor for Wireless Structural Health Monitoring in a Fire Situation

A variety of sensor systems have been developed to monitor the structural health status of buildings and infrastructures. However, most sensor systems for structural health monitoring (SHM) are difficult to use in extreme conditions, such as a fire situation, because of their vulnerability to high temperature and physical shocks, as well as time-consuming installation process. Here, we present a smart ball sensor (SBS) that can be immediately installed on surfaces of structures, stably measure vital SHM data in real time and wirelessly transmit the data in a high-temperature fire situation. The smart ball sensor mainly consists of sensor and data transmission module, heat insulator and adhesive module. With the integrated device configuration, the SBS can be strongly attached to the target surface with maximum adhesion force of 233.7-N and stably detect acceleration and temperature of the structure without damaging the key modules of the systems even at high temperatures of up to 500 degrees C while ensuring wireless transmission of the data. Field tests for a model pre-engineered building (PEB) structure demonstrate the validity of the smart ball sensor as an instantly deployable, high-temperature SHM system. This SBS can be used for SHM of a wider variety of structures and buildings beyond PEB structures

Effects of magnetic field models on control of electromagnetic actuators

Many applications such as automobiles, gyroscopes, machine tools, and transfer systems require orientation control of a rotating shaft. Demands for multi-degree of freedom (DOF) actuators in modern industries have motivated this research to develop a ball-joint-like, brushless, direct-drive spherical wheel motor (SWM) that offers a means to control the orientation of its rotating shaft. This thesis presents a general method for deriving a closed-form magnetic field solution for precise torque calculation. The method, referred here as distributed multi pole (DMP) modeling, inherits many advantages of the dipole model originally conceptualized in the context of physics, but provides an effective means to account for the shape and magnetization of the physical magnet. The DMP modeling method has been validated by comparing simulated fields and calculated forces against data obtained experimentally and numerically; the comparisons show excellent agreement. The DMP models provide a basis to develop a non-contact magnetic sensor for orientation sensing and control of a rotating shaft. Three controllers have been designed and experimentally implemented for the SWM; open-loop and PD with/without an observer. The OL control system, which decouples the spin from the shaft inclination, provides the fundamental design structure for the SWM and serves as a basis for designing feedback controllers with/without an observer. While the observer and controller designs have been developed in the context of a spherical wheel motor, these techniques along with the models and analysis tools developed in this research can be applied to design, analysis and control of most electromagnetic devices. We expect that the analytical method along with the orientation sensor and spherical wheel motor will have broad spectrum of applications.Ph.D.Committee Chair: Lee, Kok-Meng; Committee Member: Bill Singhose; Committee Member: David G. Taylor; Committee Member: Eric Johnson; Committee Member: Nader Sadeg

Fault-tolerant Control of Multirotor UAVs by Control Variable Elimination

This article presents to develop a fault-tolerant control (FTC) to cope with defective thrust systems in multirotor unmanned aerial vehicles (UAV). Faults can occur for a number of reasons in flight, including defective motor performance and damaged propellers. The faults deform both the thrust and torque outputs that could result in the UAV crashing and causing secondary accidents. The FTC method in the article is developed to reconfigure thrust systems using the optimal control when significant degrees of failure occur in a number of defective motors. Orientation control could be abandoned to maintain the controllability of altitude when the control output loss cannot be compensated for because of motor saturation. Specifically, yaw motion control is sacrificed, resulting in a rapid rotation and divergence of position control. The divergence can be resolved by a feed forward control loop on the roll and pitch angles. Both simulations and experiments are implemented to demonstrate the performance of the FTC. The results indicate that the proposed FTC can be implemented to minimize accidents from failures of main thrust systems