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In this paper a heuristic method, called Moving Window K-Nearest Neighbors (MW-KNN), for detecting QRS complexes was developed. To achieve this, a new simple 2-D geometrical feature space (feature space dimension was equal to 2) was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the preprocessed ECG signal. During each forward sliding, an artificial image was generated from the excerpted segment allocated in the window. Each image estimated by a 300×300 pixels matrix. Then, a pictorial-geometrical feature extraction technique based on curve-length was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN) Classification method was designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se = 99.93% and positive predictivity P+ = 99.88% were obtained.

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The quality of knitted fabric in circular knitting machines is highly sensitive to any undesired changes in the mechanism and components involved. For instance, a broken needle causes defects on the surface of knitted fabric. Consequently in order to increase the quality and reduce production cost, rapid detection and diagnosis of defected needles on industrial circular weft knitting machines is a crucial need. In these machines when the yarn is pulled down by the needles to knit a loop the created yarn tension, causes fluctuations in the feeding yarn flow. The aim of present research is to identify broken needle defects and their numbers, during yarn feeding in a circular knitting machine, employing neural network analysis on yarn fluctuation signals. The experiments procedures were designed so that three needle defected conditions were implemented on an industrial circular knitting machine. The yarn fluctuation signals were captured and saved, then using wavelet the contaminated signal noise was removed. Statistical and wavelet analysis are implemented to produce the required features. Finally the capability of neuro network for classification of four groups of data including healthy, one, two and four broken needles were examined. The results show that 99.43 % accurate distinction of broken needles is achieved in 50 iterations.

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The necessity to meet ongoing needs of industry, considering theoretical progress achievements and availability of cost-effective equipment, has encouraged numerous researchers to investigate the application of monitoring systems. In this paper the sound localization is implemented to find the impact position on the surface of a plate. As an experimental example the sound caused by ball impact on a ping pong table is used. For this purpose, a database is gathered. These sound's signals were recorded 25 times at 5 different points along the length of the table by a low cost microphone, attached to the surface. In the proposed method, first the data related to the ball impacts are detected and isolated from the whole pc recorded signals sent by the microphone. Then, the above 125 impacts are clustered based on the impact point locations, using a 4 dimensional space feature extracted from statistical signal moments. Furthermore in order to specify sound localization, a second space feature based on energy of wavelet transform coefficient signals was extracted. Ultimately for clustering the impact point locations, an artificial neural network was designed and applied to the above data. The results show average values of sensitivity Se=91.20% and positive predictivity P+=91.18%. Also, sensitivity Se=91.97% and positive predictivity P+‌=‌93.45%, correspondingly for impact localization.

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In this study the sound localization is implemented to find the impact position on the surface of a glass plate using acoustical sensors. As an experimental example, the sound caused by ping pong ball impact on the glass plate is used. Most of the published paper algorithms are based on using large number of sensor with high sampling rates. In this study a new method is extended due to sound localization. In the proposed method, by reducing the number of sensors into two, a pattern for secondary points is extended. In the specified pattern, locations of points are restricted according to the sensors signal frequency specification. To achieve this goal, a database is gathered from sound caused by ball impact on the glass plate. Furthermore, in order to specify sound localization, space feature based on entropy of wavelet transform coefficient signals from frequency domain of impacts and geometrical specification was extracted. Finally by implementing signal processing into the data the location of impacts are specified. The results show average values of error and Standard deviation 17 centimeter and 1.34, respectively.

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Circular micro-plates are used in microelectromechanical systems (MEMS) such as micro-pumps and ultrasonic transducers due to their special geometry. One of the most important problems with electrostatic micro-actuators is pull-in instability which prevents large displacements. Stabilization in beyond pull-in displacements can be attained using an appropriate controller. This paper presents a position control problem for an electrostatic micro-actuator consisting two circular clamped micro-plates to enhance the stroke and speed up the input commands. To consider the modeling error and geometric uncertainties, a fuzzy controller is applied. First, the equation of the plates vibration is derived using Lagrange equation with single mode assumption. Fuzzy rule-base is constructed according to static and dynamic simulations. Genetic algorithm is utilized for finding the optimum parameters of the controller to accelerate accomplishing the commands. Finally, the maximum voltage of the plates is fitted with a function using the optimization results for full range gap commands. The performance of the fuzzy controller along with this function is depicted applying step, multiple step and chirp commands. The obtained results show that the objective has been met well.

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Design of fault detection and diagnosis systems (FDDS), although extending the control strategies, they are challenged by controller interferences in fault diagnosis. In this study, in order to improve performance and accuracy of FDDS in the fault detection process, considering influential parameters and the level of corresponding interferences is investigated. To achieve this enterprise, a powerful method in fault pattern recognition of industrial plants based on dynamic behavior and dynamic model by using soft computing is designed and tested on simulated suspension system of a vehicle. The suspension system is one the parts, most affecting reliability and safety of the vehicle. For investigating the level of interference caused by the control unite, the simulations of both passive and active (equipped with hydraulic actuator) suspension systems are utilized in association with the control unite. The results of tests under variable circumstances (using random values) demonstrate that the presence of control unite, strict the FDDS process and reduces the robustness of the system against disturbances and noise. Considering the way in which the control unite affects the process, application of suggested solutions in this research, have a considerable impact on amendment of the adverse effects.
Fault detection program which is provided by Matlab software benefits special possibilities to investigate and define the effect of controlling unite and can be considered as a useful device to facilitate and precipitate conduction of tests in different stages of the research.