Abstract: A disclosed notification and response system enables applications to communicate with recipients using a number of different media. The notification and response system (i) sends requests to one or more recipients, using the medium specified by each individual recipient; (ii) collects and processes responses; and (iii) forwards the responses to their final destination by means of the medium specified by the final destination. Applications frame requests in at least one supported human language and media format, and the request is delivered to the appropriate recipient(s), according to their preferences Communication flow expressions specify the recipients for a given request, and how, when and where each recipient shall receive the request. Requests are dynamically updated, and the parameters of a communication flow expression are not evaluated, until the request is delivered.

Abstract: Methods and apparatus are provided for analyzing user activity and for recording such user activity in an entity relationship model. A network is searched for materials associated with a user and content analysis techniques are performed on the associated materials to extract one or more characteristics from the associated materials based on predefined criteria. The extracted information is stored in an entity relationship model that relates the user to the extracted characteristics. The characteristics may include activity information, expertise information and location information. The entity relationship model can thereafter be searched to identify and optionally contact one or more users having desired characteristics.

Abstract: A computer-based system and method for ascertaining anomalies in an electric motor, includes computing and processing a set of Fast Fourier Transforms (FFT's) of the supply current waveforms for a motor known to be in a normal condition to create a feature set of input vectors. The input vector assignments for all possible cluster groupings 1 through n are derived using Ward's method. The method includes computing the Approximate Weight of Evidence (AWE) for each cluster grouping then selecting that count associated with a maximum AWE and designating this count as s. For each cluster in each grouping 1 through s, the method finds the member vector farthest from the cluster's centroid and defines this as the cluster's radius.

Abstract: A method for blade temp estimation in a steam turbine utilizes measurement values including pressure and temperature at locations other than directly at the blades, principally at the input and output stages. Initially, blade temperature is simulated by using a water/steam cycle analysis program as well as by directed experiments. An artificial neural network (ANN) is trained by presenting the measurement values and the blade temp values. In a hybrid approach, 5 measured values are utilized. A subset of 4 parameter values is used for training the ANN and another subset of 3 values is used for performing a calculation for another intermediate parameter. Using the intermediate parameter and one of the 5 measured values, a blade temperature is calculated.

Abstract: A method for detecting a departure from normal operation of an electric motor comprises the steps of modelling a set of normal current measurements and a set of operational current measurements for the motor being monitored. The modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output. A potential failure is indicated whenever the set of normal current measurements and the set of operational current measurements differ by more than a predetermined criterion.

Abstract: A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; forming clusters of the normal current measurements; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the input and output of the auto-associator; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is "trained"--using clusters of current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output.

Abstract: A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the current measurements with the normal current measurements; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is "trained"--using current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output.

Abstract: A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; forming clusters of the normal current measurements; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the input and output of the auto-associator; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is "trained"--using clusters of current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output.

Abstract: A self-repairing trellis network includes a plurality of stages (11, 12 and 13) wherein a first set of N processing elements and a second set of M processing elements are situated. The N processing elements are normally active while the M processing elements are designated as spares. The stages are connected together by paths through variable weighing circuits which are legal or illegal. In response to a combination of input bits, an arrangement maps bit groupings onto the stages to turn on selective cells which form nodes coupled by paths. Only certain paths (legal paths) are tolerated in accordance with convolution coding of the input bit combination. If an error is present, an illegal path attempts to form from the legal path which starts a process of error detection and correction wherein a spare cell may be substituted for a failed cell. Since the substitution serves to maintain the redundancy status of the network, subsequent failures will be detected and corrected.