Otis Elevator Company

University of Connecticut

Sikorsky Aircraft Corporation

Mauro Jorge Atalla

Alan Matthew Finn

John P Wesson

Daryl J Marvin

William A Veronesi

Arthur Hsu

Pengju Kang

Peter B Luh

Gregory G Luther

Bo Xiong

Ziyou Xiong

Harold Terry

Randall Keith Roberts

Vladimir Blasko

Stella M Oggianu

Pei-Yuan Peng

Theresa M Christy

James P Cycon

Mark W Davis

Alexander I Khibnik

Jun Ma

Hansoo Shim

Christian Maria Netter

Robert K Thornton

Lin Lin

Meghna Misra

CheongSik Shin

Andreas Bernhard

Peter F Horbury

A near-optimal scheduling method for a group of elevators uses advance traffic information. More particularly, advance traffic information is used to define a snapshot problem () in which the objective is to improve performance for customers. To solve the snapshot problem (), the objective function is transformed into a form to facilitate the decomposition of the problem into individual car subproblems (). The subproblems () are independently solved using a two-level formulation, with passenger to car assignment () at the higher level, and the dispatching of individual cars () at the lower level. Near-optimal passenger selection and individual car routing () are obtained. The individual cars are then coordinated through an iterative process () to arrive at a group control solution that achieves a near-optimal result for passengers.

Group elevator scheduling with advance traffic information

A device () manages power source variations in an elevator system. The device includes a transformer () having a primary () and a secondary (). An input of the elevator system is connected to the secondary (). Tap switches () are connected to the transformer () such that each tap switch is connected to a tap point () on the transformer (). A controller () operates the tap switches () based on a sensed power source output to provide power on the secondary () within a tolerance band of the elevator system.

Elevator system with regulated input power

Controlling the movement of elevator cars () within a single hoistway () prevents the cars from becoming too close while servicing assigned stops. Example control techniques include controlling door operation of at least one of the elevator cars () to effectively slow down a follower car or speed up a leader car for increasing a distance between the cars in an area within the hoistway () where the cars would otherwise be too close to each other.

Method and system for maintaining distance between elevator cars in an elevator system with multiple cars in a single hoistway

An elevator control system () provides elevator dispatch and door control based on passenger data received from a video monitoring system. The video monitoring system includes a video processor () connected to receive video input from at least one video camera (). The video processor () tracks objects located within the field of view of the video camera, and calculates passenger data parameters associated with each tracked object. The elevator controller () provides elevator dispatch (), door control (), and security functions () based in part on passenger data provided by the video processor (). The security functions may also be based in part on data from access control systems ().

Video aided system for elevator control

A method and system estimates a desired output signal waveform based on measured state parameters. A model training step generates a model by empirically obtaining measured signal waveforms corresponding to measured state parameters. Features, such as mode shapes and mode amplitudes, are extracted from the measured signals and stored in an estimation model along with coefficients of the model that correlate measured state parameters with the mode amplitudes. During part operation, the state parameters measured during operation are entered into the estimation model to obtain estimated features in a estimated signal waveform. The estimated features are then used to synthesize the estimated signal reflecting the actual operation of the operating part. The estimation model therefore allows estimation of an entire signal waveform from the state parameters.

Virtual load monitoring system and method

Growing community of inventors

South Glastonbury, CT, United States of America

Mauro Jorge Atalla