Abstract: A system for monitoring thermal comfort of a room. Sensor data concerning temperature, relative humidity, air speed and air flow may be collected from the room. The sensor data may provide thermal comfort level information about the room. The level information may be quantified in terms of a thermal comfort level index, such as a predicted mean vote, which can be used to identify one or more thermal comfort levels in the room with a numerical measure. The one or more levels may be portrayed as a visualization in terms of a 3D plot of temperature, humidity and air speed, several 2D plots, a dashboard, or other items. The visualization may easily enable one to see where setpoint adjustment is possible in a heating, ventilation and air conditioning system to save energy while maintaining thermal comfort acceptable to occupants in the room, whether during a heating season or a cooling season.

Abstract: An approach for providing setpoint optimization for air handling units. An area for such optimization may incorporate choosing a thermal comfort level index, finding a zone air properties target area and computing a supply air properties target area. Beyond the present approach, an outdoor air portion may be optimized, a feasible psychrometric path found, supply airflow versus temperature optimized, and a controller run to meet setpoints.

Abstract: A method (400) for automatically checking proposed adjustments by an individual to at least one parameter at a manufacturing plant includes (401) creating a proposed vector including at least one proposed entry from the individual. The proposed vector represents a proposed adjustment to the parameter. A database of historical vectors including the parameter is searched relative to the proposed vector, wherein a number ?0 of similar stored vectors is identified in the searching based on respective distances (d) relative to the proposed vector (402). If the number ?1, a distance measure R is calculated from the (d) for the similar stored vector(s) to indicate of how far or close the said proposed vector is to the similar stored vector (403). An alert is provided if the number=0 or R is above a predetermined threshold value (405). The method can be used for validation of proposed production planning parameters or process control parameters.

Abstract: A method for automatically monitoring the performance of equipment includes compiling current operating conditions associated with current conditions (PMc). A historical database including a plurality of stored operating conditions and associated stored performance measure (PM*) is searched, each stored operating condition including at least one stored sensor reading, wherein at least one similar operating condition is identified in the search using distances between the current operating conditions and the stored operating conditions. The performance measure (PM*) associated with the similar operating condition is fit to generate a regression model. The regression model is applied to the current operating condition to generate an estimate for the performance measure for the current operating condition (PMe). A difference between PMc and PMe is computed.

Abstract: Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103).

Abstract: A system that transforms information into fuzzy observable states. These states may be matched against a mapping table which indicates which observable state admits or excludes particular faults. This information may be processed over time when in each time instant the admitted or excluded faults are used for updating the rate for each fault.

Abstract: A method for automatically monitoring the performance of equipment (100) includes the step (101) of compiling current operating conditions including at least one current sensor reading and at least one current performance measurement associated with the current conditions (PMc). A historical database including a plurality of stored operating conditions is searched (102), each stored operating condition including at least one stored sensor reading and at least one associated stored performance measure (PM*), wherein at least one similar operating condition is identified in the search using distances between the current operating conditions and the plurality of stored operating conditions. The performance measure associated with the similar operating condition is fit to generate a regression model (103). The regression model is applied to the current operating condition (104) to generate a model estimate for the performance measure for the current operating condition (PMe).

Abstract: A method (400) for automatically checking proposed adjustments by an individual to at least one parameter at a manufacturing plant includes (401) creating a proposed vector including at least one proposed entry from the individual. The proposed vector represents a proposed adjustment to the parameter. A database of historical vectors including the parameter is searched relative to the proposed vector, wherein a number ?0 of similar stored vectors is identified in the searching based on respective distances (d) relative to the proposed vector (402). If the number ?1, a distance measure R is calculated from the (d) for the similar stored vector(s) to indicate of how far or close the said proposed vector is to the similar stored vector (403). An alert is provided if the number=0 or R is above a predetermined threshold value (405). The method can be used for validation of proposed production planning parameters or process control parameters.

Abstract: Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103).

Abstract: A process includes providing a plurality of forecasts from a plurality of forecasting models. The plurality of forecasts each includes a mean and a variance. A model weight is calculated for each forecasting model. The model weight is proportional to the ability of that model to successfully forecast a queried situation. The plurality of forecasts are combined using an aggregate mean and an aggregate variance of the plurality of forecasts.

Abstract: When modeling an object in a visual input image, a model likelihood function for generating a predicted model based on the input data is moderated as a function of the portion of the object so as to less penalize regions of the model that tend to be more noisy. For instance, background subtraction algorithms often do not accurately interpret shadows of objects because the characteristics of shadows depend on so many factors, such as cloudiness, angle of the sun's rays, topography of the background upon which the shadow is cast, etc. Accordingly, the model likelihood function applied to a predicted shadow region of the model will be moderated so as to less penalize differences between the model and the input image with respect to shadows then with respect to other more reliably predictable portions of the image.

Abstract: A trend plot showing the trend of one variable or characteristic as a function of a second variable or characteristic on an trend graph is enhanced by displaying information about a third variable of a continuous nature by means of continuously varying the shade or color of the background on which the trend graph is displayed. In one embodiment of the invention, this third variable is represented by chart background color gradient.