Abstract:
Embodiments of the present invention are directed to novel techniques for showing the progress of an automated computer process, particularly through a graphical user interface (GUI). Graphical status displays are employed which show graphical time-based information, such as rate and estimated time to completion, as well as a completion portion of an automated computer process. A remaining time indicator can be shown as a time scale for the graphical completion indicator. Instantaneous and historical rate information may be graphically displayed in various novel displays.
Abstract:
An apparatus, system, and method are disclosed for providing a multi-dimensional weighted propagated status. The multi-dimensional weighted propagated status is provided by establishing a system including one or more system entities; establishing at least two status values for representing a condition of the system entities; assigning a status weight to each of the status values; assigning one of the status values to each of the system entities; and determining a propagated status for the system based on the status values assigned to the system entities, the status weights assigned to the status values, and the entity weights assigned to the system entities.
Abstract:
Embodiments of the present invention are directed to novel techniques for showing the progress of an automated computer process, particularly through a graphical user interface (GUI). Graphical status displays are employed which show graphical time-based information, such as rate and estimated time to completion, as well as a completion portion of an automated computer process. A remaining time indicator can be shown as a time scale for the graphical completion indicator. Instantaneous and historical rate information may be graphically displayed in various novel displays.
Abstract:
The present invention concerns methods and apparatus for implementing an interactive graphical user interface having the ability to graphically depict the relationships between or among multiple portlets. In one aspect, the present invention relates a child sub-portlet to a parent portlet with at least one graphical indicator when the child sub-portlet is surfaced from the parent portlet. Other aspects of the present invention concern in-line tools integrated in the graphical indicators for managing portlet operations. The graphical indicators and inline controls of the present invention allow a user to accurately and precisely control portlets.
Abstract:
Example methods of modeling a nonlinear dynamical system such as a vehicle engine include providing a model using linear programming support vector regression (LP-SVR) having an asymmetric wavelet kernel, such as derived from a raised-cosine wavelet function. The model may be trained to determine parallel model parameters while in a series-parallel configuration, and operated in the parallel configuration allowing improved and more flexible model performance. An improved engine control unit may use an LP-SVR with an asymmetric wavelet kernel.
Abstract:
A chromatographic immunoassay test strip comprising of a solid support having the portions with said portions being in a strip so as to permit capillary flow communication with each other based on the principle of “Bridge-Sanwhich”. In this chromatographic immunoassay test strip herein the analyte in the sample reacts with the ligand to form “ligand A-analyte-ligand B/tracer” first, then is captured by a bridge immobilized on the test zone of solid phase to form a complex of “bridge-ligand A-analyte-ligand B/tracer” which can be detected by vision or equipment.
Abstract:
Embodiments of the present invention are directed to novel techniques for showing the progress of an automated computer process, particularly through a graphical user interface (GUI). Graphical status displays are employed which show graphical time-based information, such as rate and estimated time to completion, as well as a completion portion of an automated computer process. A remaining time indicator can be shown as a time scale for the graphical completion indicator. Instantaneous and historical rate information may be graphically displayed in various novel displays.
Abstract:
A support vector machine with wavelet kernel was developed for accurate modeling of nonlinear systems. A method of providing an optimized model of a nonlinear system includes using a support vector machine (SVM) having a wavelet kernel, where support vectors include a family of multidimensional wavelets. Training the SVM allows optimization of the number of support vectors, the weights of the support vectors, and the translation factors of the support vectors. Use of a novel linear programming approach reduces computational demands required for training, allowing optimized support vectors to give an optimized model of the nonlinear system. Further, on-line retraining is possible, so that the model can be adapted to changing conditions.
Abstract:
Example methods of modeling a nonlinear dynamical system such as a vehicle engine include providing a model using linear programming support vector regression (LP-SVR) having an asymmetric wavelet kernel, such as derived from a raised-cosine wavelet function. The model may be trained to determine parallel model parameters while in a series-parallel configuration, and operated in the parallel configuration allowing improved and more flexible model performance. An improved engine control unit may use an LP-SVR with an asymmetric wavelet kernel.
Abstract:
A support vector machine with wavelet kernel was developed for accurate modeling of nonlinear systems. A method of providing an optimized model of a nonlinear system includes using a support vector machine (SVM) having a wavelet kernel, where support vectors include a family of multidimensional wavelets. Training the SVM allows optimization of the number of support vectors, the weights of the support vectors, and the translation factors of the support vectors. Use of a novel linear programming approach reduces computational demands required for training, allowing optimized support vectors to give an optimized model of the nonlinear system. Further, on-line retraining is possible, so that the model can be adapted to changing conditions.