Patent application number | Description | Published |
20080197000 | Guide For Conveyor Of Articles - A guide for a conveyor of articles, comprising: a guide element elongated in a longitudinal direction and intended in use to be arranged along a transport path of a conveyor of articles for the containment and/or the routing of the transported articles, said guide element comprising a substantially planar portion and first and second edges opposed to each other in a first transversal direction, transversal to said longitudinal direction; a clamp is associable to said guide element, the clamp comprising a back portion and two clamp appendixes opposed to each other in said first transversal direction and adapted to clamp said guide element along said first and second edges. The clamp is adapted to the coupling to a support bar of the conveyor of articles intended to support the guide element to a frame of the conveyor; tightening means are provided, associable to said clamp and actuatable to exert an action of traction on the appendixes of the clamp to urge them one towards the other in said first transversal direction, so as to tighten said clamp appendixes against said first and second edges of the guide element. The clamp appendixes of the clamp and the first and second edges of the guide element are shaped in such a way as to at least partly transform the action of traction exerted by said tightening means into a urging action of the guide element toward the back portion of the clamp, the urging action being adapted to bring the planar portion of the guide element in abutment against at least one among said support bar or said tightening means. | 08-21-2008 |
20100222920 | SYSTEM AND A METHOD FOR REMOTELY MONITORING THE OPERATIONAL LIFE OF CONVEYORS OF ARTICLES - The invention relates to a monitoring system for a conveyor ( | 09-02-2010 |
20110247923 | Chain for Articles Conveyor - A conveyor chain ( | 10-13-2011 |
20120090964 | SYSTEM FOR POSITIONING GUIDES OF A CONVEYOR - A positioning unit ( | 04-19-2012 |
20120152700 | BEND SEGMENT AND METHOD FOR MANUFACTURING A BEND SEGMENT - A bend segment ( | 06-21-2012 |
20140110223 | ARRANGEMENT OF TRANSFER MODULES - A transfer modules arrangement ( | 04-24-2014 |
20140346014 | MODULAR IDLE ROLLER BELT FOR CONVEYOR OF ARTICLES - A modular roller belt ( | 11-27-2014 |
20150008097 | MONITORING SYSTEM FOR MEASURING SPEED AND ELONGATION OF TRANSPORT CHAINS - A monitoring system for a conveyor of articles is provided. The conveyor of articles comprises a static portion and at least one respective endless transport chain adapted to be moved with respect to the static portion when the conveyor of articles is in operation. The system includes a reference element located on the transport chain, a first sensor integral with the static portion and a second sensor integral with the static portion. Said first and second sensors are distant to each other by a first distance; each sensor is configured for sensing the passage of the reference element close to the sensor itself during the operation of the conveyor. The system further includes counting means coupled with the sensors and configured to measure a first time corresponding to the time elapsed between a first passage of the reference element close to the first sensor and a first passage of the reference element close to the second sensor. The counting means are further configured to measure a second time corresponding to the time elapsed between the first passage of the reference element close to the first sensor and a second passage of the reference element close to the first sensor, or to the time elapsed between the first passage of the reference element close to the second sensor and a second passage of the reference element close to the second sensor. Said second passage is subsequent to the first passage. The system further comprises computing means configured to determine the transport chain movement speed with respect to the static portion based on the first measured time and the first distance, and determine the length of the chain based on the determined movement speed and based on the second measured time. | 01-08-2015 |
Patent application number | Description | Published |
20080300879 | FACTORIAL HIDDEN MARKOV MODEL WITH DISCRETE OBSERVATIONS - A method for analyzing hidden dynamics, includes acquiring discrete observations, each discrete observation having an observed value selected from two or more allowed discrete values. A factorial hidden Markov model (FHMM) relating the discrete observations with a plurality of hidden dynamics is constructed. A contribution of the state of each hidden dynamic to the discrete observation may be represented in the FHMM as a parameter of a nominal distribution which is scaled by a function of the state of the hidden dynamic. States of the hidden dynamics are inferred from the discrete observations based on the FHMM. Information corresponding to at least one inferred state of at least one of the hidden dynamics is output. The parameters of the contribution of each dynamic to the hidden states may be learnt from a large number of observations. An example of a networked printing system is used to demonstrate the applicability of the method. | 12-04-2008 |
20090216698 | TEMPORAL EVENTS ANALYSIS EMPLOYING TREE INDUCTION - An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k′ in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process. | 08-27-2009 |
20090216700 | TEMPORAL EVENTS ANALYSIS EMPLOYING TREE INDUCTION - An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k′ in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process. | 08-27-2009 |
20110055122 | MONITORING WITH ADAPTIVE DYNAMIC CLASSIFICATION - In a monitoring method, a time sequence of information pertaining to a monitored device, network, or system is recorded, comprising observations of the monitored device, network, or system and known prior correct action recommendations for the monitored device, network, or system. A hidden Markov model (HMM) operating on the time sequence of information is maintained. The HMM comprises a hidden state of the monitored device, network, or system. A current state of the monitored device, network, or system is classified using a classification value comprising an emission of the HMM that depends on an estimate of the distribution of the hidden state and on a selected portion of the time sequence of information. An action recommendation is generated for the current state of the monitored device, network, or system based on the classification value. | 03-03-2011 |
20110265086 | USER AND DEVICE LOCALIZATION USING PROBABILISTIC DEVICE LOG TRILATERATION - A system and method of localizing elements (shared devices and/or their users) in a device infrastructure, such as a printing network, are provided. The method includes mapping a structure in which the elements of a device infrastructure are located, the elements comprising shared devices and users of the shared devices. Probable locations of fewer than all of the elements in the structure are mapped, with at least some of the elements being initially assigned to an unknown location. Usage logs for a plurality of the shared devices are acquired. The acquired usage log for each device includes a user identifier for each of a set of uses of the device, each of the uses being initiated from a respective location within the mapped structure by one of the users. Based on the acquired usage logs and the input probable locations of some of the elements, locations of at least some of the elements initially assigned to an unknown location are predicted. The prediction is based a model which infers that for each of a plurality of the users, a usage of at least some of the shared devices by the user is a function of respective distances between the user and each of those devices. | 10-27-2011 |
20110270796 | ON-LINE AUTOREGRESSIVE PREDICTION IN TIME SERIES WITH DELAYED DISCLOSURE AND MONITOR SYSTEMS USING SAME - An apparatus operating on a time sequence of events comprises an event handling module configured to generate a predicted label for a current observed event of the time sequence of events, and a true label handling module configured to process a true label revealed for an observed event of the time sequence of events. The event handling module and the true label handling module cooperatively model stochastic dependence of a true label for the current observed event based on observed events of the time sequence of events and revealed true labels for past observed events of the time sequence of events. The event handling module and the true label handling module operate asynchronously. The event handling module and the true response handling module are suitably embodied by one or more digital processors. | 11-03-2011 |
20130145187 | MULTI-DEVICE POWERSAVING - A control system reduces energy consumption in a multi-device system comprising a plurality of devices. The control system includes at least one processor. The processor is programmed to receive a job to be executed, as well as a selection of one of the plurality of devices for executing the job and a transfer cost for transferring the job from the selected device to each of the plurality of devices. A device to execute the job is determined through optimization of a first cost function. The first cost function is based on the device selection and the transfer costs. The job is assigned to the determined device and a time-out for each device in the multi-device system is determined through optimization of a second cost function. The second cost function is based on an expected energy consumption by the multi-device system. The devices are provided with the determined time-outs. | 06-06-2013 |
20130151441 | MULTI-TASK LEARNING USING BAYESIAN MODEL WITH ENFORCED SPARSITY AND LEVERAGING OF TASK CORRELATIONS - Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D≧1 and P≧1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks. | 06-13-2013 |
20140181552 | MULTI-MODE DEVICE POWER-SAVING OPTIMIZATION - Methods and systems input an energy consumption profile for each of a plurality of different sleep modes available for a device, and input a probability distribution of interjob times for the device. The methods and systems then compute the optimal time-out period for each sleep mode based on the energy consumption profile of each sleep mode and the probability distribution of interjob times. Further, such methods and systems monitor the usage of the device to determine the current interjob time, and switch between sleep modes to relatively lower power sleep modes as the current interjob time becomes larger. | 06-26-2014 |