Abstract: Electric Grid Analytics Learning Machine, EGALM, is a machine learning based, “brutally empirical” analysis system for use in all energy operations. EGALM is applicable to all aspects of the electricity operations from power plants to homes and businesses. EGALM is a data-centric, computational learning and predictive analysis system that uses open source algorithms and unique techniques applicable to all electricity operations in the United States and other foreign countries.

Abstract: Electric Grid Analytics Learning Machine, EGALM, is a machine learning based, “brutally empirical” analysis system for use in all energy operations. EGALM is applicable to all aspects of the electricity operations from power plants to homes and businesses. EGALM is a data-centric, computational learning and predictive analysis system that uses open source algorithms and unique techniques applicable to all electricity operations in the United States and other foreign countries.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: Techniques for determining forecast information for a resource using learning algorithms are disclosed. The techniques can include an ensemble of machine learning algorithms. The techniques can also use latent states to generate training data. The techniques can identify actions for managing the resource based on the forecast information. The resource can include energy usage in buildings, distribution facilities, and resources such as Electric Delivery Vehicles. The resource can also include forecasting package volume for businesses.

Abstract: Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations.

Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: Techniques for generating a dynamic treatment control policy for a cyber-physical system having one or more components, including a data collector for collecting data representative of the cyber-physical system, and adaptive stochastic controller including one or more models for generating a predicted value corresponding to available actions based on an objective function, and an approximate dynamic programming element configured to receive actual operation metrics corresponding to the available actions. The approximate dynamic programming element can learn a state-action map and generate a dynamic treatment control policy using the one or more models.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data.

Abstract: Memory chips (15) are mounted perpendicularly on a memory module substrate (14) to achieve a close spacing between the chips. A plurality of memory chip signal lines (20) are located on the memory module substrate (14) and the memory chips (15) are electrically coupled to the memory chip signal lines at spaced apart chip coupling points (23). Digital signals are driven to the memory chip signal lines (20) through signal lines (21) having a first level impedance. The memory chip signal lines (20) have a second level impedance greater that the first level impedance. The spacing between the chip coupling points (23) is chosen such that the effective impedance level of the memory chip signal lines (20) substantially matches the lower, first level impedance.

Abstract: An improved packaging scheme for a CPU of a main frame computer improves the performance while at the same reduces the cost of manufacture of the main frame computer. A single packaging technology is used to package the whole CPU and eliminates cable connections inside the CPU. Surface power bus technology permits the fabrication of a module with chips mounted on both front and back sides of the substrate. The surface power bus is installed on one or both sides of the module surface and derives power directly from the power cable and distributes power to chip sites directly. In a specific implementation, a uni-processor CPU with chips mounted on both surfaces of the substrate and power fed from the surface power bus results in improved processor package density and system performance.

Abstract: A packaging substrate (10) is populated with memory chip cube(s) (40) and horizontally mounted interconnect chip(s) (19) mounted on the substrate which are joined during assembly using two kinds of lead tin solder alloys to form memory chip cube. One is a high melting point lead tin alloy (HMA), the other is a lower melting point lead tin alloy (LMA). The memory chip pairs (11) of the memory cube are formed by placing functional memory chips over another functional memory chips before they were diced. The chip pads of the individual memory chips and the lead tin pads of the memory chips within the wafer are aligned and the high melting point lead tin solder is reflowed, forming memory chip pairs. The memory cube (42) is formed by joining the memory chip pairs together in a boat (30) with a silicon bar (41) maintaining spacing during manufacture. The memory chip cube (42) as well as the supporting chips are then placed and joined to the packaging substrate.

Abstract: A variable frequency digital ring oscillator which can be formed in a small area for use in testing of chips employs a ring oscillator formed of CMOS inverters, transmission gates and capacitors and CMOS logic as a voltage controlled ring oscillator. A wide range of frequency of oscillation is achieved with small number of components. The ring oscillator circuit's oscillator frequency is controlled only by DC voltages, such as may be provided by (but not limited to) a manufacturing chip tester. The output signal of the oscillator swings between Vdd and Vss and does not need additional level translation circuits to drive CMOS logic. The ring oscillator can be composed of an odd number of CMOS inverters connected in cascade to form a loop. We provide a CMOS transmission gate with PMOS and NMOS transistor device inserted between each adjacent inverter and a MOS capacitor connected between the output of each transmission gate and the Vss supply of the ring oscillator circuit (conventionally ground).

Abstract: A packaging substrate (10) is populated with memory chip cube(s) (40) and horizontally mounted interconnect chip(s) (19)mounted on the substrate which are joined during assembly using two kinds of lead tin solder alloys to form the memory chip cube. One is a high melting point lead tin alloy (HMA), another is a lower melting point lead tin alloy (LMA). The memory chip pairs (11) of the memory cube are formed by placing functional memory chips over other functional memory chips before they were diced. The chip pads of the individual memory chips and the lead tin pads of the memory chips within the wafer are aligned and the high melting point lead tin solder is reflowed, forming memory chip pairs.