Browsing by Subject "Intelligent control systems."

Now showing 1 - 7 of 7

Open Access
A comparative study on human activity classification with miniature inertial and magnetic sensors
(2011) Yüksek, Murat Cihan
This study provides a comparative assessment on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques compared in this study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). The algorithms for these techniques are provided on two commonly used open source environments: Waikato environment for knowledge analysis (WEKA), a Java-based software; and pattern recognition toolbox (PRTools), a MATLAB toolbox. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost. The methods that result in the highest correct differentiation rates are found to be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the best type of sensor to be used in classification whereas gyroscope is the least useful. Considering the locations of the sensor units on body, the sensors worn on the legs seem to provide the most valuable information.
Open Access
Development of a modular control algorithm for high precision positioning systems
(2012) Ulu, Nurcan Geçer
In the last decade, micro/nano-technology has been improved significantly. Micro/nano-technology related products started to be used in consumer market in addition to their applications in the science and technology world. These developments resulted in a growing interest for high precision positioning systems since precision positioning is crucial for micro/nano-technology related applications. With the rise of more complex and advanced applications requiring smaller parts and higher precision performance, demand for new control techniques that can meet these expectations is increased. The goal of this work is developing a new control technique that can meet increased expectations of precision positioning systems. For this purpose, control of a modular multi-axis positioning system is studied in this thesis. The multiaxis precision positioning system is constructed by assembling modular single-axis stages. Therefore, a single-axis stage can be used in several configurations. Model parameters of a single-axis stage change depending on which axis it is used for. For this purpose, an iterative learning controller is designed to improve tracking performance of a modular single-axis stage to help modular sliders adapting to repeated disturbances and nonlinearities of the axis they are used for. When modular single-axis stages are assembled to form multi-axis systems, the interaction between the axes should be considered to operate stages simultaneously. In order to compensate for these interactions, a multi input multi output (MIMO) controller can be used such as cross-coupled controller (CCC). Cross-coupled controller examines the effects between axes by controlling the contour error resulting in an improved contour tracking. In this thesis, a controller featuring cross-coupled control and iterative learning control schemes is presented to improve contour and tracking accuracy at the same time. Instead of using the standard contour estimation technique proposed with the variable gain cross-coupled control, presented control design incorporates a computationally efficient contour estimation technique. In addition to that, implemented contour estimation technique makes the presented control scheme more suitable for arbitrary nonlinear contours and multi-axis systems. Also, using the zero-phase filtering based iterative learning control results in a practical design and an increased applicability to modular systems. Stability and convergence of the proposed controller has been shown with the necessary theoretical analysis. Effectiveness of the control design is verified with simulations and experiments on two-axis and three-axis positioning systems. The resulting controller is shown to achieve nanometer level contouring and tracking performance.
Open Access
Fuzzy controller design for parametric controllers
(1996) Akgül, Murat
In this thesis, fuzzy logic controller (FLC) design for tuning some parametric controller is investigated. The objective in designing an FLC is to determine the rule bcise of the system and the data base which includes membership functions, set operations, and inference engine. Two designs have been realized using heuristic rule generation; one for a PID controller and one for a lead-lag type controller. The FTCs in these designs set the parcimeters of the PID and leadlag controller on-line. The rules and the corresponding membership functions are constructed by observing the effect of the changes of the parameters on the overall performance. One other design is performed using the desired inputoutput data pairs. In this design Fuzzy c-Means clustering algorithm is used to e.xtract the rules and the membership functions from the input-output data of the system. Simulation results showed that better controller performance can be cichieved by FLCs in comparison with the classical design methods.
Open Access
Intelligent sensing for robot mapping and simultaneous human localization and activity recognition
(2011) Altun, Kerem
We consider three different problems in two different sensing domains, namely ultrasonic sensing and inertial sensing. Since the applications considered in each domain are inherently different, this thesis is composed of two main parts. The approach common to the two parts is that raw data acquired from simple sensors is processed intelligently to extract useful information about the environment. In the first part, we employ active snake contours and Kohonen’s selforganizing feature maps (SOMs) for representing and evaluating discrete point maps of indoor environments efficiently and compactly. We develop a generic error criterion for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. We optimize the parameters of active snake contours and SOMs using uniform sampling of the parameter space and particle swarm optimization. A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities. In the second part, we use body-worn inertial/magnetic sensor units for recognition of daily and sports activities, as well as for human localization in GPSdenied environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors are modeled using the Allan variance technique, and the parameters of lowand high-frequency error components are estimated. Then, we provide a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. We compute a large number of features extracted from the sensor data, and reduce these features using both Principal Components Analysis (PCA) and sequential forward feature selection (SFFS). We consider eight different pattern recognition techniques and provide a comparison in terms of the correct classification rates, computational costs, and their training and storage requirements. Results with sensors mounted on various locations on the body are also provided. The results indicate that if the system is trained by the data of an individual person, it is possible to obtain over 99% correct classification rates with a simple quadratic classifier such as the Bayesian decision method. However, if the training data of that person are not available beforehand, one has to resort to more complex classifiers with an expected correct classification rate of about 85%. We also consider the human localization problem using body-worn inertial/ magnetic sensors. Inertial sensors are characterized by drift error caused by the integration of their rate output to get position information. Because of this drift, the position and orientation data obtained from inertial sensor signals are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user provides information about position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing activity recognition simultaneously with localization, one can detect the activity context switches and use the corresponding position information as position updates in the localization filter. The localization filter also involves a smoother, which combines the two estimates obtained by running the zero-velocity update (ZUPT) algorithm both forward and backward in time. We performed experiments with eight subjects in an indoor and an outdoor environment involving “walking,” “turning,” and “standing” activities. Using the error criterion in the first part of the thesis, we show that the position errors can be decreased by about 85% on the average. We also present the results of a 3-D experiment performed in a realistic indoor environment and demonstrate that it is possible to achieve over 90% error reduction in position by performing activity recognition simultaneously with localization.
Open Access
Mechatronic design of a modular three-axis slider system for high-precision positioning applications
(2012) Ulu, Erva
Following the recent improvements in precision engineering related technology, interest for micro/nano-engineering applications are increased and various micro/nano-scale operations and products are developed. For micro/nano-scale applications, high-precision equipment including micro/nano-positioning devices with high accuracy and precision are required. In this thesis, mechatronic design of a three axes micro/nano-positioning device is discussed in detail. In order to satisfy nanometer level precision, an adaptive method to increase the available measurement resolution of quadrature encoders is presented. Performance characteristics of micro/nano-positioning devices usually include positioning accuracy of their each individual axis, operation range, maximum velocity and maximum acceleration. For this reason, permanent magnet linear motors (PMLM) are chosen as actuators in the presented design due to their outstanding characteristics. Moreover, in order to provide high-flexibility in terms of applications and simplify the control of the system, modularity is one of the main concerns while designing the micro/nano-positioning system presented here. Building the modular single axis slider system, three axes positioning device is constructed by assembling three of them perpendicularly. In this design, linear optical encoders are used as feedback sensors. Movement range of the designed system is 120mm in each direction. Since the available linear optical encoders have measurement resolution of 1µm, resolution of them is to be improved in software for sub-micron level positioning applications. For this purpose, a new method to increase the available measurement resolution of quadrature encoders is presented in this thesis. This method features an adaptive signal correction phase and an interpolation phase. Imperfections in the encoder signals including amplitude differences, mean offsets and quadrature phase shift errors are corrected by using recursive least squares (RLS) with exponential forgetting and resetting. Interpolation of the corrected signals is accomplished by a quick access look-up table calculated offline to satisfy linear mapping from available sinusoidal signals to higher order ones. With the conversion of the high-order sinusoids to binary pulses, position information is derived. By using the presented method, 10nm measurement resolution is achieved with an encoder with 1µm off-the-shelf resolution. Experiment results demonstrating the effectiveness of the proposed method are presented. Validation of the method is accomplished for several cases including the best resolution obtained. Practical constraints limiting the maximum interpolation number are also discussed in detail.
Open Access
Multi-sensor based ambient assisted living system
(2013) Yazar, Ahmet
An important goal of Ambient Assisted Living (AAL) research is to contribute to the quality of life of the elderly and handicapped people and help them to maintain an independent lifestyle with the use of sensors, signal processing and the available telecommunications infrastructure. From this perspective, detection of unusual human activities such as falling person detection has practical applications. In this thesis, a low-cost AAL system using vibration and passive infrared (PIR) sensors is proposed for falling person detection, human footstep detection, human motion detection, unusual inactivity detection, and indoor flooding detection applications. For the vibration sensor signal processing, various frequency analysis methods which consist of the discrete Fourier transform (DFT), mel-frequency cepstral coefficients (MFCC), discrete wavelet transform (DWT) with different filter-banks, dual-tree complex wavelet transform (DT-CWT), and single-tree complex wavelet transform (ST-CWT) are compared to each other to obtain the best possible classification result in our dataset. Adaptive-threshold based Markov model (MM) classifier is preferred for the human footstep detection. Vibration sensor based falling person detection system employs Euclidean distance and support vector machine (SVM) classifiers and these classifiers are compared to each other. PIR sensors are also used for falling person detection and this system employs two PIR sensors. To achieve the most reliable system, a multi-sensor based falling person detection system which employs one vibration and two PIR sensors is developed. PIR sensor based system has also the capability of detecting uncontrolled flames and this system is integrated to the overall system. The proposed AAL system works in real-time on a standard personal computer or chipKIT Uno32 microprocessors without computers. A network is setup for the communication of the Uno32 boards which are connected to different sensors. The main processor gives final decisions and emergency alarms are transmitted to outside of the smart home using the auto-dial alarm system via telephone lines. The resulting AAL system is a low-cost and privacy-friendly system thanks to the types of sensors used.
Open Access
Recognition and classification of human activities using wearable sensors
(2012) Yurtman, Aras
We address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization is provided. Position data of multiple RF tags worn on the human body are acquired asynchronously and non-uniformly. Curves fitted to the data are re-sampled uniformly and then segmented. The effect of varying the relevant system parameters on the system accuracy is investigated. Various curve-fitting, segmentation, and classification techniques are compared and the combination resulting in the best performance is presented. The classifiers are validated through the use of two different cross-validation methods. For the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30% for 5-fold and subject-based leave-one-out (L1O) cross validation, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%. The system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. In the second part, data acquired from five sensory units worn on the human body, each containing a tri-axial accelerometer, a gyroscope, and a magnetometer, during 19 different human activities are used to calculate inter-subject and interactivity variations in the data with different methods. Absolute, Euclidean, and dynamic time-warping (DTW) distances are used to assess the similarity of the signals. The comparisons are made using time-domain data and feature vectors. Different normalization methods are used and compared. The “best” subject is defined and identified according to his/her average distance to the other subjects.Based on one of the similarity criteria proposed here, an autonomous system that detects and evaluates physical therapy exercises using inertial sensors and magnetometers is developed. An algorithm that detects all the occurrences of one or more template signals (exercise movements) in a long signal (physical therapy session) while allowing some distortion is proposed based on DTW. The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, identifying the error type if there is any. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercise movements performed by five subjects is recorded, having totally 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58% of the executions are missed and 4.91% of the idle intervals are incorrectly detected as an execution. The accuracy is 93.46% for exercise classification and 88.65% for both exercise and execution type classification. The proposed system may be used to both estimate the intensity of the physical therapy session and evaluate the executions to provide feedback to the patient and the specialist.