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PURPOSE: This prospective study evaluates whether sonoelastography can improve B-mode ultrasonographic sizing of breast tumors. Precise measuring is important for effective therapy planning for breast cancer patients. MATERIALS AND METHODS: The size of 100 surgically excised breast lesions (92 patients: 77 malignant, 23 benign) was compared to preoperative measurements. Lesions were imaged with both ultrasonographic techniques in identical planes. The largest sizes measured with each modality were compared to the largest histopathological measurements. The interobserver variability was also computed from measurements made by two examiners assessing identical planes. RESULTS: Both ultrasonographic measuring techniques underestimate lesion size. The sonoelastography measurements were within ± 5 mm of the histological size in 70.1 % of malignant lesions, and the B-mode measurements in 57.1 % of cases. Sonoelastography leads to more accurate measurements of 13.0 % of cases (statistically not significant). A total of 22 lesions were also imaged by a second examiner. Sonoelastography had 27.3 % less interobserver variability (examiners agreed in 36.4 % of sonoelastography and in 9.1 % of B-mode results). CONCLUSION: In this study there is no significant advantage of sonoelastography, although a tendency is apparent. The low interobserver variability also favors sonoelastography for preoperative diagnostics, since it may be less dependent on the observer than conventional B-mode imaging. The results of this prospective study require validation in a prospective multicenter study with larger case numbers.

The estimation of the height of the centre of gravity of vehicle is significantly important for various applications in modern car management. A theoretical model of a full - scale half car including suspension and a crank axle at the front and the rear axle is developed. A longitudinal model of the vehicle is described to compute the forces at the wheels curing braking and accelerating manoeuvres. Based on nonlinear spring curves and measured deflection at the front and the rear axle the centroidal height of the vehicle is estimated. Some experimental results with a space wagon are shown.

PURPOSE: This prospective study aimed to compare sonoelastography, B-mode ultrasonography, and mammography in terms of their ability to distinguish benign from malignant breast lesions. We also assessed how the diagnostic value of sonoelastography differs between palpable and clinically occult lesions. MATERIALS AND METHODS: Evaluation revealed a total of 97 lesions (66 benign; 31 malignant) without histological confirmation at the time of the initial examination. The sensitivity, specificity, positive (PPV) and negative predictive value (NPV) as well as efficiency were calculated. These parameters were separately assessed for palpable lesions and for non-palpable lesions. We subsequently compared these results. RESULTS: Sonography had a sensitivity of 97 % and a specificity of 82 % (PPV: 71 %, NPV: 98 %, efficiency: 87 %). For mammography, the respective figures were 84 % and 89 % (PPV: 79 %, NPV: 92 %, efficiency: 88 %). Sonoelastography had a sensitivity of 71 % and a specificity of 48 % (PPV: 39 %, NPV: 78 %, efficiency: 56 %). The combination of sonography and sonoelastography yielded a sensitivity of 100 % and a specificity of 38 % (PPV: 43 %, NPV: 100 %, efficiency: 58 %). The sensitivity and specificity were not statistically different between the groups of palpable and non-palpable lesions. CONCLUSION: Sonoelastography is easily performed and not very time-consuming. Used by itself, the method is not more efficacious than alternative techniques. When used in conjunction with B-mode ultrasonography, the latter’s sensitivity was increased, albeit at the expense of specificity.

Mechatronics combines the main disciplines electrical- and mechanical engineering and informatics. Its increasing importance occurs in all kind of on-line control systems, process control, sensor guided robots, magnetic bearings, automobile engineering etc. This book addresses students in all this fields. The main topics are: actuators, sensors, signal processing, data processing, simulation of multibody systems, planning of trajectories, control of mechatronic systems and as an attachment mathematical fundamentals. (GL) [German] Mechatronische Systeme entstehen durch Integration von vorwiegend mechanischen und elektronischen Systemen sowie zugehoeriger Informationsverarbeitung. Wesentlich ist dabei die Integration der mechanischen und elektronischen Elemente durch ihre raeumliche Anordnung und durch ihre Funktionen sowie die Erzielung synergetischer Effekte. Die oertliche Integration erfolgt durch den konstruktiven Entwurf, die funktionelle Integration durch die Informationsverarbeitung und damit durch die Gestaltung der Software. Das vorliegende Buch fuehrt in den Aufbau und die Modellbildung mechatronischer Systeme in einer einheitlichen Form ein und stellt das Verhalten von mechanischen Bauelementen, elektrischen Antrieben, Maschinen, Sensoren, Aktoren und Mikrorechnern dar. Ziel dabei ist, ein bestimmtes Systemverhalten zu erreichen. Die zweite Auflage enthaelt wesentliche Erweiterungen bei der Entwicklungsmethodik, bei mechanischen Komponenten, elektrischen Antrieben, Beispielen von Maschinenmodellen, Sensoren, hydraulischen und pneumatischen Aktoren und fehlertoleranten Systemen. Aufgabensammlungen ergaenzen die einzelnen Kapitel.

This paper presents a new strategy of process control in cylindrical surface grinding. A self tuning control concept with a stop-measure and force cascade has been developped. Based on a mathematical model the on-line identification of grinding process parameters allows to design a control system, which adapts to the actual machining behavior. Based on data from a real process identification a simulation on a digital computer shows, that 25% of grinding time can be saved and the influence of abrasion and wrong process-adjustments can be eliminated.

An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, an on-board process parameter estimation was implemented which allows one to compute the physical coefficients of lateral vehicle models and their changes during operation. In addition, tire- and velocity-dependent look-up tables in presently used vehicle models were replaced by feedforward neural networks. In the phase of driving state monitoring, a set of these hybrid models-each of them trained for a special driving situation-predict the vehicle motion as a result of the actual steering angle and velocity. In a further step, suitable classification algorithms were used to detect the actual driving state by processing the residual output.

Discusses model based methods for controlling vehicle dynamics. A nonlinear model describing the longitudinal motion of a vehicle is presented. This model consists of a braking system, a powertrain and mass-damper system with damping from aerodynamical drag and rolling resistance. According to the most essential time constants, parameter estimation is used to determine the mass and the aerodynamical drag of the vehicle. As an example for controller design, a combination of fuzzy logic and a classical feedback linearization is presented. Furthermore, some examples of a velocity controller are shown using the described controlling techniques.

For the fault detection of technical processes different methods can be applied based on the information extracted from direct measured signals, from signal models and process models. Examples for signal model based fault detection methods are spectral analysis or parameter estimation of ARMA models, examples for process model based methods are parameter estimation, state estimation or parity equation approaches. A comparison of these methods shows that they have different properties with regard to the detection of faults in the process, the actuators and the sensors. By a proper integration of different fault detection methods mainly their advantages can be used to generate a number of different analytical symptoms. For fault diagnosis a knowledge based procedure is required, because also qualitative information in form of heuristic symptoms have to be taken into account. Based on heuristic process knowledge as fault-symptom causalities and a unified representation of all symptoms an integrated fault diagnosis can be performed. This comprises the treatment of the symptoms as uncertain facts and approximate diagnostic reasoning via if-then rules either in a probabilistic or a fuzzy-logic (possibilistic) frame. The described methodology was verified by experiments with several technical processes like electric motors, actuators, pumps, machine tools, robots, heat exchangers, combustion engines and vehicles.

The feasibility of exclusively utilizing cutting tool signals as an alternative feedback technique to enhance robotic machining of complex precision components is evaluated. A hybrid fine motion control scheme based on sophisticated models of the cutting tool dynamics and the nonlinear cutting process is proposed for accommodating the robot trajectory to positional inaccuracies in automated deburring and chamfering applications. Artificial neural networks as general approximators in conjunction with correlation analysis methods are employed to establish the nonlinear mapping of the measurable cutting tool quantities to the depth of engagement during the cutting operation. A superordinate adaptation strategy is developed to adjust the direction of motion correction and the nominal robot path to the local workpiece displacement. The effectiveness and performance of the proposed fine motion control scheme are demonstrated by some robotic deburring and chamfering experiments of fibre-reinforced plastics with a high-speed cutting tool.

The unknown parameters of physical laws which govern the dynamics of processes can be gained from measured input and output signals in a two step procedure. First the parameters of differential equations are estimated and then the physical parameters are calculated based on algebraic relations. After considering the model structures a suitable parameter estimation method for continuous time signals is described and the influence of a priori known parameters on the convergence is shown. Then the determination of physical parameters is considered based on nonlinear algebraic relations known from theoretically modeling. Applying the implicit function theorem identifiability conditions for the unique determination of physical parameters are given. Then applications are shown for the estimation of the ratios of inertia, d.c. motor damping coefficients and Coulomb friction coefficients of an 6-axis-industrial robot and the use of parameter changes for the detection of faults.

The use of a Personal Computer (PC) for the design and test of a digital selftuning combustion control system for a furnace with low power (< 120 kW) is presented. Therefore a mathematical model of the combustion was developed which describes the dynamics of fuel combustion in a furnace. The mathematical combustion model was used for the development and test of a combustion control system. While the parameters of the combustion controller depend on the size of the furnace, on the adjusted fire-rate and on the reference value, a selftuning algorithm was applied for the tuning of the used PI-controller. The selftuning algorithm consists of an estimation algorithm of the Least Square type (LS) for the estimation of the discrete time process parameters and a parameter optimization algorithm for the calculation of the controller parameters. The selftuning algorithm was at first implemented on a PC and tested per simulation with the mathematical process model and then it was tested with the furnace. By the use of a selftuning algorithm, the developed combustion control system can be easily implemented on other furnaces because only the controller parameters have to be adapted.

Fehler in technischen Prozessen sind mit der Messung leicht erfasbarer Signale und Nutzung ihrer kausalen Verknupfungen fruher zu erkennen und besser zu lokalisieren als durch Grenzwertkontrolle weniger, einzelner Signale. Mit Parameterschatzverfahren (fur Differentialgleichungen) lassen sich Parameteranderungen im Prozes wahrend des Betriebs feststellen. Hieraus wird die Anderung physikalischer Prozeskoeffizienten wie der Reihungswerte ermittelt. Sie dienen einem On-line-Expertensystem zur Fehlerdiagnose.

A software package OLIOPT was developed for the on-line optimization of the steady-state behaviour of slow dynamic processes in a relatively short time period. In the starting phase, the independently variable inputs are changed according to a special test signal. A nonlinear dynamic process model is identified on-line. Based on the static part of the model and the known inputs, the gradients of the performance index are calculated. An optimization algorithm changes the inputs towards their optimal values. On-line identification of the nonlinear model continues and the prediction of the optimum improves. In the last phase, the inputs take their optimal values and the process follows, feedforward controlled, to its optimal steady-state. The method is suited for industrial processes with one or more variable inputs, where a small gain in efficiency turns out to give a relatively large financial return. Results are shown for the on-line optimization of a thermal pilot process.

Es wird ein Identifikationsoerfahren beschrieben, das zuerst ein nichtparametrisches Modell (Kreuzkorrelationsfunktion, Gewichtsfunktion) ermittelt und dann die Parameter eines parametrischen Modells (Differenzengleichung) schätzt. An diese zweistufige Identifikation schließt sich ein Verfahren zur Schätzung der Totzeit und der Modellordnung an. Das vorgeschlagene Verfahren, das off-line und on-line eingesetzt werden kann, wurde mit den bekanntesten Parameterschätzverfahren verglichen. Es zeichnet sich durch kurze Rechenzeit, kleinen Speicherplatzbedarf,\ gute Genauigkeit und zuverlässige Konvergenz aus. In der rekursiven (on-line)-Version müssen keine Anfangswerte gewählt werden. Das Verfahren ist besonders für den Einsatz von Prozeßrechnern zur Prozeßidentifikation geeignet.

On presente une enquete d'introduction sur l'estimation des parametres: on considere d'abord les tâches d'identification des systemes dynamiques et les caracteristiques principales des differentes methodes. Puis, on s'interesse a l'estimation des parametres des systemes discrets ou continus, statiques, multivariables et non lineaires. On insiste sur les algorithmes d'estimation recursive compatibles avec des applications temps reel, en ligne

Solutions of test-case C [1] are presented for process IV (single-input single-output) and process V (two-input two-output). The applied on-line identification method first identifies recursively a nonparametric model by correlation analysis and then estimates the parameter of a parametric model (difference equation). In comparison to other methods [2], [3] (least squares, generalized least squares, instrumental variables, stochastic approximation) this method has shown different advantages, such as good convergence, short computation time and little effort for search of model order. For multivariable systems with simultaneous excitation of the inputs this method can be accompanied by a compensation technique [7] which decreases the noise-to-signal ratio and therefore leads to identified models which are almost as good as for identification of single-input single-output systems.

An identification method is described which first identifies a linear nonparametric model (crosscorrelation function, impulse response) by correlation analysis and then estimates the parameters of a parametric model (discrete transfer function) and also includes a method for the detection of the model order and the time delay. The performance, the computational expense and the overall reliability of this method is compared with five other identification methods. This two-step identification method, which can be applied off-line or on-line, is especially suited to identification by process computers, since it has the properties: Little a priori knowledge about the structure of the process model; very short computation time; small computer storage; no initial values of matrices and parameters are necessary and no divergence is possible for the on-line version. Results of an on-line identification of an industrial process with a process computer are shown.