Concept Of Multi-sensor Data Fusion Research Articles

Modelling of smart intelligent materials with PZT & PVDF sensors/actuators to control the active vibrations of flexible aluminum mechanical cantilever beams using proportional integral derivative (PID) techniques

Modelling of smart intelligent materials with PZT & PVDF sensors / actuators to control the active vibrations of flexible aluminum mechanical cantilever beams using PID techniques is presented in this research article. A multi input multi output beam (flexible cantilever beam) is considered as the single point of attraction and this is the plant, which is to be subjected to the vibrations, this could be treated as the wing of an aircraft attached to the hub. The flexible mechanical aluminium cantilever beam is subjected to vibrations, say an external force being acted upon by the wind pressure or an exciter. Smart materials have got some special properties that react to the changes in their environmental conditions. The beam is subjected to vibrations. These vibrations should be suppressed in no time, else, the plant may get damaged. Hence, we put piezoelectric (PZT) and poly vinyl derylene fluoride (PVDF) sensor and actuators at 2 different positions to sense the vibrations and act upon immediately to neutralize it. The PZT sensors sense the vibrations & the actuators give an actuating signal to the controller, which in turn generates anti signals to neutralize the effect of the primary source of vibration. We have used the multi-sensor data fusion concept as if we use the same sensor and actuator, we may not get good results. Mathematical modelling of sensor and actuators of the 2 types is done, this is used to design the PID controller to suppress the vibrations in no time. The paper shows the effect of the multi sensor actuator pair of smart intelligent materials used in the vibration suppression. The work done can also compared with others to show the authenticity or the profoundness of the methodology proposed with the help of materials used in the theory of material science.

Development of mathematical model for smart intelligent flexible structures using smart intelligent sensor & actuator materials using Timoshenko beam theory & its control using POF

In this research paper, the multivariable modelling of intelligent flexible aerospace structures using smart materials with Timoshenko beam theory & state space techniques with multi-sensor data fusion concept & its control using POF strategy is presented in n this research article. Finite Element Method is used in the analysis also. An aerospace aluminum cantilever beam of rectangular cross section with known specifications is considered for the modelling purposes. To start with from the fundamental principles of Timoshenko beam theory, a Lagrange equation of motion is obtained both for the regular beam element as well as for the PZT & PVDF sensor actuator pair. Following which, a dynamic equation of motion of the smart intelligent structure developed using the smart materials consisting of PZT & PVDF is obtained. Smart materials have got some special properties that react to the changes in their environmental conditions. This means that some of their special properties can be changed by using an external condition, such as temperature, light, pressure, electricity, voltage, pH, or chemical compounds, which could be considered as the special characteristics and this change is reversible, which can be repeated many times. The dynamic equation is transformed into a state space model with 2 inputs and 2 outputs using the concepts of state space theory in the advanced control engineering. The model is developed considering the first 2 modes of vibration, which are the most dominant modes in the vibration theory. The developed state space model is used for active vibration control. The effect of shear is considered in modelling here, which is neglected in case of Euler – Beam theory, thus giving an accurate beam model. An external force of 1 N is applied at the end of the smart flexible multivariable multimodel cantilever beam after which the beam is subjected to vibrations. A POF controller can be designed and put in loop with the plant, after which the plant vibrations decays to zero in no time. The mathematical model of the POF controller can be used to control the vibrations of the plant using the concept of destructive interference. The work done can also be compared with others to show the authenticity or the profoundness of the methodology that is being developed by us.

Multivariable modelling of intelligent flexible mechanical structures using smart materials with FEM, Euler-Bernoulli beam theory, state space & multi-sensor data fusion techniques

In this research paper, the multivariable modelling of intelligent flexible mechanical structures using smart materials with Euler-Bernoulli beam theory & state space techniques is being presented with multi-sensor data fusion concept. To start with from the fundamental principles of E-B theory, a Lagrange equation of motion is obtained both for the regular beam element as well as for the PZT & PVDF sensor actuator pair. Following which, a dynamic equation of motion of the smart intelligent structure developed using the smart materials consisting of PZT& PVDF is obtained. Smart materials have got some special properties that react to the changes in their environmental conditions. This means that some of their special properties can be changed by using an external condition, such as temperature, light, pressure, electricity, voltage, pH, or chemical compounds, which could be considered as the special characteristics and this change is reversible, which can be repeated many times. The dynamic equation is transformed into a state space model with 2 inputs and 2 outputs using the concepts of state space theory in the advanced control engineering. The model is developed considering the first 2 modes of vibration, which are the most dominant modes in the vibration theory. The developed state space model is used for active vibration control. An external force of 1 N is applied at the end of the smart flexible multivariable multimodel cantilever beam after which the beam is subjected to vibrations. A Periodic Output Feedback controller can be designed and put in loop with the plant, after which the plant vibrations decays to zero in no time. The work done can also compared with others to show the authenticity or the profoundness of the methodology that is being developed by us.

Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture

Abstract Three mechanical-based techniques, including falling impact (FAI), forced impact (FOI), and acoustic impulse-response (AIR), were implemented for the nondestructive prediction of the apparent modulus of elasticity (E a ) and Magness-Taylor firmness (MTf) of kiwifruit, cv. Hayward . Considering the merits and limitations of each method in estimating the texture parameters, this study tried to improve the performance of the predictive models by using the concept of multi-sensor data fusion. Two different fusion strategies, including low-level and mid-level fusions, were accordingly applied using partial least square regression (PLSR) and principal component analysis combined with artificial neural network (PCA-ANN), respectively. Better predictions of E a were obtained, as compared to those of MTf, in using each method, as well as their combinations in both fusion strategies, thereby demonstrating the better fitness of E a with the nondestructive data. Moreover, both fusion strategies enhanced the performance of E a and, in most cases, MTf predictive models, as compared with using each technique individually. Comparing two fused systems showed that the mid-level fusion was more effective than the low-level one, where in the best fused systems (integration of all three sensors), the standard deviation ratio (SDR) values for E a and MTf were improved by 11.2% and 9.1%, respectively, and the satisfactory results for both E a ( R 2 p = 0.926, SDR = 3.07) and MTf ( R 2 p = 0.841, SDR = 2.51) were obtained. This study revealed that compared to the implementation of mechanical-based methods individually, and also the low-level fusion of them, their mid-level fusion using PCA-ANN algorithm could be an effective approach for providing more detailed and complementary information about kiwifruit texture.

Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content

Four nondestructive technologies (i.e., acoustic firmness, bioyield firmness, visible and shortwave near infrared (Vis-SWNIR) spectroscopy, and spectral scattering) have been developed in recent years for assessing the firmness and/or soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, the four sensing systems were evaluated and combined for nondestructive prediction of the firmness and SSC of ‘Jonagold’ (JG), ‘Golden Delicious’ (GD), and ‘Delicious’ (RD) apples. A total of 6535 apples harvested in 2009 and 2010 were used for analysis. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by correlation coefficient and standard error of prediction (SEP). The SEPs for the firmness of JG, GD and RD using the best combination of two-sensor data were reduced by 13.5%, 20.0% and 7.3% for the 2009 data and 14.6%, 14.2% and 6.2% for the 2010 data; and using the best three or four fused sensor data by 19.1%, 24.9% and 13.9% in 2009, and 15.7%, 23.6%, and 8.9% in 2010, respectively. The combination of Vis-SWNIR and scattering data improved SSC predictions for RD apples, with the SEP values being reduced by 5.8% and 6.0% for 2009 and 2010, respectively. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more effective than individual sensors in prediction of apple quality.

Acoustic data fusion devoted to underwater vegetation mapping

This paper presents research tasks conducted by SEMANTIC TS, in collaboration with GESMA, aimed to develop a mapping method for underwater vegetation lying on seabed. First stage is to develop a method for detecting and characterizing vegetation on the seabed using the acoustic response from a conventional single beam echo sounder. This new method is then operated simultaneously with multibeam sonar producing micro-relief information and side scan sonar providing gray scale levels associated with bottom reflectivity. Then fusion of these three data is processed. We show efficiency of these multisensor data fusion concept to get very precise seabed vegetation mapping in a way reducing truth control (video and diving investigations). Sensors and method accuracy allow obtaining, like in biomedical field, real 3D scan pictures of seabed vegetation. This study is first applied to posidonia and cymodocea, which play a key role in Mediterranean's echosystem. Then, extension of the method is investigated to address laminaria which may significantly affect the performance of acoustic and optical sensors used for sea-mines detection and this paper presents results of data fusion mapping on an Atlantic sea area covered by luminaria, studied and well known by the CEVA.

An Expandable Multi-Sensor Data-Fusion Concept for Autonomous Driving in Urban Environments

We introduce a multi-target, multi-sensor data fusion concept for automotive applications in urban environments. Based on a sensor system consisting of laser scanners, radar and LIDAR sensors, a system architecture has been created which is capable of tracking objects ofarbitrarycontours.Thefusionsystemisindependentfromthesensortypesusedandcanbe set up in a distributed architecture based on Ethernet communication.The classical Kalman filter algorithm has been extended to realize contour-functionality, removing the necessity to define a static object reference point. A specialized data association and pretracking stage effectively uses intentional sensor redundancy in order to suppress false alarms and increase trackquality.Thefusionsystemisimplementedinthe2007DARPAUrbanChallengevehicle of Team CarOLO, Technische Universitat Braunschweig.

The role of multisensor data fusion in neuromuscular control of a sagittal arm with a pair of muscles using actor-critic reinforcement learning method

In this study, we consider the role of multisensor data fusion in neuromuscular control using an actor-critic reinforcement learning method. The model we use is a single link system actuated by a pair of muscles that are excited with alpha and gamma signals. Various physiological sensor information such as proprioception, spindle sensors, and Golgi tendon organs have been integrated to achieve an oscillatory movement with variable amplitude and frequency, while achieving a stable movement with minimum metabolic cost and coactivation. The system is highly nonlinear in all its physical and physiological attributes. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex loops. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by the State-Action-Reward-State-Action (SARSA) method. The Critic will train the Actor by supervisory learning based on previous experiences. The reinforcement signal in SARSA is evaluated based on available alternatives concerning the concept of multisensor data fusion. The effectiveness and the biological plausibility of the present model are demonstrated by several simulations. The system showed excellent tracking capability when we integrated the available sensor information. Addition of a penalty for activation of muscles resulted in much lower muscle coactivation while keeping the movement stable.

Artificial senses for characterization of food quality

Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch. In the characterization of food quality, people assess the samples sensorially and differentiate “good” from “bad” on a continuum. However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pattern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual systems in differentiation of food samples.