Abstract: A composite floor covering having an upper layer of a decorative fabric, a dimensionally stabilizing intermediate layer, and an optional lower cushioning layer has at least the upper surface of the decorative fabric coated with a protective polymeric coating in a manner such that the hand of the decorative fabric layer is substantially retained. The materials of the fabric layer, stabilizing layer, and cushioning layer (if provided) as well as the form of attachment therebetween, cooperate to render the floor covering substantially impervious to liquid.

Abstract: An on-line training neural network for controlling a process for producing a product having at least one product property that trains by retrieving training sets from a stream of process data. The neural network detects the availability of new training data, and constructs a training set by retrieving the corresponding input data. The neural network is trained using the training set. Over time, many training sets are presented to the neural network. When multiple presentations are needed to effectively train, a buffer of training sets is filled and updated as new training data becomes available. The size of the buffer is selected in accordance with the training needs of the neural network. Once the buffer is full, a new training set bumps the oldest training set off the top of the buffer stack. The training sets in the buffer stack can be presented one or more times each time a new training set is constructed.

Abstract: The present invention is a system and method for improved processing of flow data signals to provide output flow data signals which better approximate the true flows being measured. The general approach on which the invention is based is commonly known as flow data reconciliation. A dynamic simulation is run in parallel with a flow data reconciliation, and model predicted flows are used to complete balances wherever sensor measurements are unavailable. Model predicted compositions are also used to allow the computation of stream enthalpies so that enthalpy balances can be used in the reconciliation. The use of model predicted values of changes in inventory allow dynamic material balances to be used, making the reconciliation much more effective for processes with slow dynamic behavior. Weighting factors on sensors, computed based on the current value of the error of the sensor, reduce the impact of sensors with high errors.

Abstract: The present invention relates to an improved catalyst for the selective synthesis of monomethylamine (MMA) and dimethylamine (DMA) at the expense of trimethylamine (TMA) for a starting feed of methanol and/or dimethyl ether and ammonia. The current industrial catalyst for this process is a standard SiO.sub.2 /Al.sub.2 O.sub.3 material. The present invention combines this standard catalyst with microporous carbon molecular sieves (CMS) to form a composite material (i.e., a CMS/SiO.sub.2 /Al.sub.2 O.sub.3 material) with higher selectivity for the desired products MMA and DMA. The invention also relates to methods of making the improved catalyst and a process of using the improved catalyst material in the production of MMA and DMA.

Abstract: The present invention relates to an improved catalyst for the selective synthesis of monomethylamine (MMA) and dimethylamine (DMA) at the expense of trimethylamine (TMA) for a starting feed of methanol and/or dimethyl ether and ammonia. The current industrial catalyst for this process is a standard SiO.sub.2 /Al.sub.2 O.sub.3 material. The present invention combines this standard catalyst with microporous carbon molecular sieves (CMS) to form a composite material (i.e., a CMS/SiO.sub.2 /Al.sub.2 O.sub.3 material) with higher selectivity for the desired products MMA and DMA. The invention also relates to methods of making the improved catalyst and a process of using the improved catalyst material in the production of MMA and DMA.

Abstract: An on-line training neural network for process control system and method trains by retrieving training sets from the stream of process data. The neural network detects the availability of new training data, and constructs a training set by retrieving the corresponding input data. The neural network is trained using the training set. Over time, many training sets are presented to the neural network.When multiple presentations are needed to effectively train, a buffer of training sets is filled and updated as new training data becomes available. The size of the buffer is selected in accordance with the training needs of the neural network. Once the buffer is full, a new training set bumps the oldest training set off the top of the buffer stack. The training sets in the buffer stack can be presented one or more times each time a new training set is constructed.

Abstract: The present invention is a system and method for improving stream composition and/or product property estimates from process models, which better reflect the true conditions of the process. One or more process simulation models are run on a computer in parallel with the actual process to provide estimates of stream composition and/or product properties to be used to control the process. Adjustments are made to the models to maintain them in alignment with continuously measured key process variables that are closely related to stream composition and/or product properties where such a relationship exists. This greatly improves the ability of the model to track the actual process. Additional adjustments are made to both the models and to the model estimates based on differences between measured and calculated stream composition/product properties.

Abstract: A computer neural network process measurement and control system and method uses real-time output data from a neural network to replace a sensor or laboratory input to a controller. The neural network can use readily available, inexpensive and reliable measurements from sensors as inputs, and produce predicted values of product properties as output data for input to the controller. The system and method overcome process deadtime, measurement deadtime, infrequent measurements, and measurement variability in laboratory data, thus providing improved control. An historical database can be used to provide a history of sensor and laboratory measurements to the neural network. The neural network can detect the appearance of new laboratory measurements in the history and automatically initiate retraining, on-line and in real-time. The system and method can use either a regulatory controller or a supervisory control architecture.

Abstract: A system and method stores fonts and generates characters. Instead of the fonts containing bit maps, the storage area (116) for each character contains the addresses (GPCR Addresses) of instructions to be used to form the character, and the required parameters for those instructions. In a preferred embodiment, a lookup table (114) for the font contains, for each character, the address of the code to start executing and details of how much more information is stored for this character and where to find it. For each character, the stored information includes addresses of microcode instructions (126) followed by the required number of parameters to define the actions necessary for character generation.

Abstract: An on-line process control neural network using data pointers allows the neural network to be easily configured to use data in a process control environment. The inputs, outputs, training inputs and errors can be retrieved and/or stored from any available data source without programming. The user of the neural network specifies data pointers indicating the particular computer system in which the data resides or will be stored; the type of data to be retrieved and/or stored; and the specific data value or storage location to be used. The data pointers include maximum, minimum, and maximum change limits, which can also serve as scaling limits for the neural network. Data pointers indicating time-dependent data, such as time averages, also include time boundary specifiers. The data pointers are entered by the user of the neural network using pop-up menus and by completing fields in a template. An historical database provides both a source of input data and a storage function for output and error data.

Abstract: An on-line training neural network for process control system and method trains by retrieving training sets from the stream of process data. The neural network detects the availability of new training data, and constructs a training set by retrieving the corresponding input data. The neural network is trained using the training set. Over time, many training sets are presented to the neural network. When multiple presentations are needed to effectively train, a buffer of training sets is filled and updated as new training data becomes available. The size of the buffer is selected in accordance with the training needs of the neural network. Once the buffer is full, a new training set bumps the oldest training set off the top of the buffer stack. The training sets in the buffer stack can be presented one or more times each time a new training set is constructed.

Abstract: A computer neural network regulatory process control system and method allows for the elimination of a human operator from real time control of the process. The present invention operates in three modes: training, operation (prediction), and retraining. In the training mode, training input data is produced by the control adjustment made to the process by the human operator. The neural network of the present invention is trained by producing output data using input data for prediction. The output data is compared with the training input data to produce error data, which is used to adjust the weight(s) of the neural network. When the error data is less than a preselected criterion, training has been completed. In the operation mode, the neutral network of the present invention provides output data based upon predictions using the input data. The output data is used to control a state of the process via an actuator.

Abstract: An on-line process control neural network using data pointers allows the neural network to be easily configured to use data in a process control environment. The inputs, outputs, training inputs and errors can be retrieved and/or stored from any available data source without programming. The user of the neural network specifies data pointers indicating the particular computer system in which the data resides or will be stored; the type of data to be retrieved and/or stored; and the specific data value or storage location to be used. The data pointers include maximum, minimum, and maximum change limits, which can also serve as scaling limits for the neural network. Data pointers indicating time-dependent data, such as time averages, also include time boundary specifiers. The data pointers are entered by the user of the neural network using pop-up menus and by completing fields in a template. An historical database provides both a source of input data and a storage function for output and error data.

Abstract: A neural network for adjusting a setpoint in process control replaces a human operator. The neural network operates in three modes: training, operation, and retraining. In operation, the neural network is trained using training input data along with input data. The input data is from the sensor(s) monitoring the process. The input data is used by the neural network to develop output data. The training input data are the setpoint adjustments made by a human operator. The output data is compared with the training input data to produce error data, which is used to adjust the weights of the neural network so as to train it. After training has been completed, the neural network enters the operation mode. In this mode, the present invention uses the input data to predict output data used to adjust the setpoint supplied to the regulatory controller. Thus, the operator is effectively replaced.

Abstract: A neural network/expert system process control system and method combines the decision-making capabilities of expert systems with the predictive capabilities of neural networks for improved process control. Neural networks provide predictions of measurements which are difficult to make, or supervisory or regulatory control changes which are difficult to implement using classical control techniques. Expert systems make decisions automatically based on knowledge which is well-known and can be expressed in rules or other knowledge representation forms. Sensor and laboratory data is effictively used. In one approach, the output data from the neural network can be used by the controller in controlling the process, and the expert system can make a decision using sensor or lab data to control the controller(s). In another approach, the output data of the neural network can be used by the expert system in making its decision, and control of the process carried out using lab or sensor data.

Abstract: Batch process control is improved by defining a step endpoint condition in an expert system knowledge base; using the expert system to monitor for the occurrence of the endpoint in the batch process; and triggering a change in a batch process condition when the endpoint is found. Preferably, the expert system and the batch process condition change are implemented as modules which execute under control of timing and sequencing functions in a supervisory control system, and the change affects a setpoint (or other control objective) in a continuous control system. Multiple instances of modular expert systems allow parallel process units to be easily controlled.

Abstract: A variable TCE composition for making dielectric layers having low dielectric constant and low dielectric loss, the composition comprising an admixture of finely divided solids comprising amorphous crystallizable glass, amorphous borosilicate frit, ceramic expansion control additive, and crystallization control additive. The dielectric layers are useful in circuits especially for high density and high frequency applications.

Abstract: Dihydrobenz[c]acridine carboxylic acid derivatives are provided which are useful in treating tumors in mammals. These dihydrobenz[c]acridine carboxylic acid derivatives have the formula: ##STR1## or a pharmaceutically acceptable salt thereof, where R.sup.1, R.sup.2, R.sup.3, R.sup.4 and R.sup.5 are as defined in the specification. Also provided are pharmaceutical compositions of said compounds. In addition, processes for the preparation of these compounds are disclosed.

Abstract: Described is a process for preparing 2-(1-hydroxyalkyl)-5,5-diphenylhydantoin having the formula ##STR1## wherein R.sub.1, R.sub.2 and R.sub.3 are each independently hydrogen, C.sub.1 -C.sub.20 alkyl, C.sub.3 -C.sub.12 cycloalkyl, C.sub.7 -C.sub.15 aralkyl, unsubstituted or substituted aryl or heteroaryl or alternatively, or R.sub.1 and R.sub.2 together with the carbon atom form a 3 to 12 member cycloalkyl group, the process comprising: reacting diphenylhydantoin with an aldehyde or a ketone in the presence of a strong inorganic base; an alcohol was used as solvent to solubilize the reactants. The reaction is practically complete in 15 minutes.The compounds so prepared are intermediates in the preparation of phenyltoin prodrugs.

Abstract: Novel compounds of the general formula ##STR1## wherein R.sub.1 is lower alkyl, lower cycloalkyl, lower alkenyl, lower alkynyl, lower alkyl carboxymethyl, aryl carboxymethyl, aryl, or aralkyl; A is a direct bond, lower alkylene, or lower alkenylene; x is 1 or 2, provided that when x is greater than 1, different occurrences of the ##STR2## group may be the same or different; Ar is heterocyclic, unsubstituted aromatic or aromatic substituted with lower alkyl, lower alkenyl, lower alkynyl, lower alkoxy, halogen, acetamido, amino, nitro, lower alkylamino, hydroxy, lower hydroxyalkyl or cyano; W is alkylene containing from 1 to about 10 carbon atoms; and B is --NR.sub.2 COR.sub.3, --NR.sub.2 CONR.sub.3 R.sub.4, --NR.sub.2 SO.sub.2 R.sub.3, --NR.sub.2 SO.sub.2 NR.sub.3 R.sub.4, or --NR.sub.2 COOR.sub.5, wherein R.sub.2, R.sub.3, R.sub.4 and R.sub.5 may be the same or different and may be hydrogen, alkyl, alkoxyalkyl, alkoxyaryl, cycloalkyl, alkenyl, alkynyl, aryl, heteroaryl, or aralkyl, except that R.sub.3 and R.