Abstract: The present disclosure relates to a shaped charge. The shaped charge includes an explosive component and a liner member coupled to the explosive component. The explosive component and the liner member emit a perforating jet based on ignition of the explosive component. The liner member has a planar symmetric portion that is planar symmetric along an axial length of the planar symmetric portion relative to planes perpendicular to a direction of the emitted perforating jet.

Abstract: The present disclosure relates to a shaped charge liner. The shaped charge liner includes a first liner portion formed a first material. The first liner portion has an apex and a skirt section that define an interior volume of the first liner portion. The shaped charge liner also includes a second liner portion formed of a second material. The second liner portion is coupled to the first liner portion such that the second liner portion is an edge of the interior volume.

Abstract: Methods and apparatus to analyze valve characteristics are disclosed. A disclosed example apparatus to determine at least one characteristic of a valve includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to partition a valve stroke curve of the valve to define bins thereof, the valve stroke curve corresponding to stroke data of the valve, filter data points associated with a seat contact portion of the valve stroke curve, fit curves of the bins to define fitted curves, and characterize a seat contact of the valve based on the fitted curves.

Abstract: Methods and apparatus to analyze valve characteristics are disclosed. A disclosed example apparatus to determine at least one characteristic of a valve includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to partition a valve stroke curve of the valve to define bins thereof, the valve stroke curve corresponding to stroke data of the valve, filter data points associated with a seat contact portion of the valve stroke curve, fit curves of the bins to define fitted curves, and characterize a seat contact of the valve based on the fitted curves.

Abstract: The present disclosure relates to a shaped charge liner. The shaped charge liner includes a first liner portion formed a first material. The first liner portion has an apex and a skirt section that define an interior volume of the first liner portion. The shaped charge liner also includes a second liner portion formed of a second material. The second liner portion is coupled to the first liner portion such that the second liner portion is an edge of the interior volume.

Abstract: The present disclosure relates to shaped charge. The shaped charge includes an explosive component having a first density and a first mechanical strength. The shaped charge also includes a liner member having a second density and a second mechanical strength. Further, the shaped charge includes an intervening liner member having a third density and a third mechanical strength. The intervening liner member is disposed between the explosive component and the liner member. The intervening liner member has one or both of: the third density being between the first density and the second density; and the third mechanical strength being between the first mechanical strength and the second mechanical strength.

Abstract: The present disclosure relates to a shaped charge. The shaped charge includes an explosive component and a liner member coupled to the explosive component. The explosive component and the liner member emit a perforating jet based on ignition of the explosive component. The liner member has a planar symmetric portion that is planar symmetric along an axial length of the planar symmetric portion relative to planes perpendicular to a direction of the emitted perforating jet.

Abstract: Methods and apparatus to analyze valve characteristics are disclosed. A disclosed example apparatus to determine at least one characteristic of a valve includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to: partition a valve stroke curve of the valve to define bins thereof, the valve stroke curve corresponding to stroke data of the valve, determine travel edges based on the valve stroke curve, filter data points corresponding to the travel edges, define fitted curves based on data corresponding to the bins, and determine the at least one characteristic of the valve based on the fitted curves.

Abstract: Methods and apparatus to analyze valve characteristics are disclosed. A disclosed example apparatus to determine at least one characteristic of a valve includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to partition a valve stroke curve of the valve to define bins thereof, the valve stroke curve corresponding to stroke data of the valve, filter data points associated with a seat contact portion of the valve stroke curve, fit curves of the bins to define fitted curves, and characterize a seat contact of the valve based on the fitted curves.

Abstract: In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system supports a user interface with icons that describe original (e.g., out-of-the-box or factory) KPIs and user-modified KPIs. When a user modifies an original KPI object to create a customized KPI, its icon is changed to visibly indicate that the user has modified the KPI. The customized KPI can be used within KPI decks, cards, dashboards, or other types of visualizations; while retaining a lineage to the original KPI object.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system enables creation of a customized KPI, by layering variations of the KPI information on an original (e.g., out-of-the-box or factory) KPI object which are merged at runtime to create the final customized KPI. Each delta-KPI can itself also support multiple, e.g., site/user levels/layers.

Abstract: In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system enables creation of a customized KPI, by layering variations of the KPI information on an original (e.g., out-of-the-box or factory) KPI object which are merged at runtime to create the final customized KPI. Each delta-KPI can itself also support multiple, e.g., site/user levels/layers.

Abstract: In accordance with an embodiment, described herein is a system and method for providing key performance indicator (KPI) customization in an analytic applications environment, which enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud or computing environment. The system supports customization derived from multiple layers which, in aggregate, can yield a customized performance metric or KPI object. In accordance with an embodiment, the system supports a user interface with icons that describe original (e.g., out-of-the-box or factory) KPIs and user-modified KPIs. When a user modifies an original KPI object to create a customized KPI, its icon is changed to visibly indicate that the user has modified the KPI. The customized KPI can be used within KPI decks, cards, dashboards, or other types of visualizations; while retaining a lineage to the original KPI object.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more segments within a data set, associated with a target attribute value, based on, for example, the use of a classification and regression tree and a combination of different driving factors, or same driving factors with different values. Information describing segments associated with the data set can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: A variator for a mechanical transmission system is disclosed. Transfer means are in rolling contact with input and output members of the variator to transfer rotary motion between them. The input member is coupled to the variator input through a first biasing device arranged to exert a first biasing force on the variator according to a first, input gain which relates input torque acting on the input member and the first biasing force. The output member is coupled to the variator output through a second biasing device arranged to exert a second biasing force on the variator according to a second, output gain which relates output torque acting on the output member and the second biasing force. The first and second biasing forces clamp the variator to provide traction. The first, input gain and second, output gain are different, which, at least in specific variator applications, optimises the traction coefficient.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

Abstract: In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.