Abstract:
A manipulator includes a mechanical arm, a cable positioned in the mechanical arm, at least one cable protection device, and a plurality of fasteners. The at least one cable protection device is fixed to the mechanical arm by the fasteners. The mechanical arm defines a receiving slot, in which the cable is partially received. The at least one cable protection device includes a fixing member and a resilient arm. The fixing member is connected to the mechanical arm, opposite to the receiving slot. The resilient arm is capable of swinging relative to the fixing member.
Abstract:
A robot arm assembly includes a support arm, a connecting arm, and an end arm. The support arm is rotatably assembled with the connecting arm along a first axis, and is located at one end of the connecting arm. The end arm is rotatably assembled to the other end of the connecting arm along a second axis substantially perpendicular to the first axis, such that the connecting arm is rotatably assembled between the support arm and the end arm.
Abstract:
Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such as Web mining, search marketing, bioinformatics, citation analysis, and epidemiology. A probabilistic model is presented for relational clustering, which also provides a principal framework to unify various important clustering tasks including traditional attributes-based clustering, semi-supervised clustering, co-clustering and graph clustering. The model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects. Under this model, parametric hard and soft relational clustering algorithms are provided under a large number of exponential family distributions. The algorithms are applicable to relational data of various structures and at the same time unify a number of state-of-the-art clustering algorithms: co-clustering algorithms, the k-partite graph clustering, and semi-supervised clustering based on hidden Markov random fields.
Abstract:
Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. Provided is a framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, an algorithm iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.
Abstract:
According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.
Abstract:
A focusing mechanism for focusing a lens module includes a base seat, a movable platform, a positioning assembly, a support bracket and a plurality of arms. The positioning assembly is fixed to the base seat and passes through a center of the movable platform, a lens of the lens module is detachably mounted on the positioning assembly. The support bracket is fixed to the movable platform. A sensor of the lens module is detachably mounted on the support bracket. Each of the plurality of arms rotatably interconnects the movable platform and the base seat, the movable platform drives the support bracket to rotate relative to the positioning assembly to enable the lens to rotate relative to the sensor via a drive of the arms.
Abstract:
A robot arm assembly includes a first robot arm and a second robot arm; the second robot arm is rotatably connected to the first robot arm. The first robot arm includes a first sleeve, a first input shaft, and a second input shaft. The first input shaft and the second input shaft are seated in the first sleeve. The second robot arm includes a second sleeve and an output shaft; the output shaft is received in the second sleeve. The first input shaft is connected to the second sleeve via a pair of bevel gears, and drives the second sleeve to swing relative to the first sleeve. The second input shaft is connected to the output shaft via a plurality of bevel gears meshing with each other, and drives the output shaft to rotate relative to the second sleeve.