Sap Se

Wenjun Zhou

Wen-Syan Li

Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving a first current detected data value for a first sensor of a sensor network including multiple sensors, determining a first predicted data value based on historical data values of the first sensor, and a second predicted data value based on a second current detected data value for a second sensor, providing a combined predicted data value based on the first predicted data value, and the second predicted data value, comparing the first current detected data value and the combined predicted data value to provide a comparison, and determining a first corrected data value for the first sensor.

Online self-correction on multiple data streams in sensor networks

A non-transitory computer-readable storage medium may comprise instructions stored thereon for controlling operation of a vehicle travelling from a starting location to a target location. When executed by at least one processor, the instructions may be configured to cause a computing system to generate a travel itinerary for the vehicle to travel over different road segments at respective speeds between the starting location and the target location subject to an arrival time deadline for arriving at the target location, the different road segments being selected on a map to minimize a total fuel consumption of the vehicle in travelling from the starting location to the target location, and send the travel itinerary to a vehicle operator to operate the vehicle to travel over different road segments at respective speeds between the starting location and the target location.

System and method for vehicle fuel consumption optimization

The present disclosure involves systems, software, and computer implemented methods for mutual reinforcement of edge devices with dynamic triggering conditions and/or computation frequencies. In one example, a first edge device in multiple edge devices in an Internet of Things (IoT) system monitors at least a first parameter. The first edge device determines whether a first condition from one set of conditions is satisfied based on at least the monitored first parameter. In response to determining that the first condition is satisfied, the first edge device automatically transmits a signal to a second edge device in the multiple edge devices to initiate or stop monitoring of a second parameter by the second edge device.

Mutual reinforcement of edge devices with dynamic triggering conditions

A multi-depot delivery schedule optimizer includes an iteration controller to initialize scheduling iterations for determining an optimized route schedule for a plurality of demands and a plurality of vehicles, in which at least one of the route schedules includes a first depot as a start location, at least one delivery location, and a second depot as an end location. The multi-depot delivery schedule optimizer further includes a particle handler to define a plurality of particles within a solution space of allowable route schedules, each particle representing an allowable route schedule and represented by a corresponding position within the solution space.

Data analysis for optimizations of scheduling with multiple location variables

A method of manufacturing at least a first product and a second product with at least a first machine and a second machine at minimum cost in an environment in which a cost of energy used by the first machine and the second machine varies as a function of time may include generating multiple chromosomes, determining fitness scores of each of the chromosomes, randomly generating, with probabilities based on the fitness scores, new chromosomes, determining fitness scores of the new chromosomes, selecting one of the new chromosomes with an optimal fitness score, and manufacturing at least the first product and the second product with at least the first machine and the second machine according to a schedule based on the selected new chromosome.

Optimizing manufacturing schedule with time-dependent energy cost

The present description provides data analysis for machine maintenance scheduling. For example, dynamic maintenance intervals are assigned for each machine being scheduled. Then, a system is provided for implementing a particle swarm optimization for finding an optimized maintenance schedule. In the optimization, an objective function is defined for maximizing production while minimizing relative maintenance cost.

Machine maintenance optimization with dynamic maintenance intervals

A computer system for controlling operation of a vehicle travelling from a starting location to a target location includes a memory and a semiconductor-based processor forming one or more logic circuits configured to generate a travel itinerary by selecting different highway segments on a map for the vehicle to drive over at respective speeds from the starting location to the target location subject to an arrival time deadline for arriving at the target location. Each of the highway segments has a distance that can be driven at the respective speed in less than a continuous driving time limit. The computer system sends the travel itinerary to a vehicle operator to drive the vehicle over the different highway segments with instructions to make a stop at the end of each highway segment to stay below the continuous driving time limit.

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Shanghai, China

Wenjun Zhou