Abstract: A parallel robot includes a base, a movable platform, an actuator, and four control arms. The actuator is positioned on the movable platform. The control arms are interconnected between the base and the movable platform. Each control arm includes a driving member connected to the base, an action pole driven by the driving member, and a four-rod linkage assembly interconnected between the action pole and the movable platform. One end of the four-rod linkage assembly is connected to the action pole via a first rotary joint, and the other end of the four-rod linkage assembly is connected to the movable platform via a second rotary joint. An axis of the first rotary joint is substantially parallel to an axis of the second rotary joint.

Abstract: Method, apparatus, and programs for customizing a web site are provided. In one example, a method for customizing a web site is provided. One or more representations corresponding to one or more customizable components of a web site are provided. The one or more representations are to be displayed on a display screen to a user. An input entered by the user and directed to a specific customizable component of the web site is received. How to customize the specific customizable component of the web site is determined based on the input. An instruction is generated with respect to the customizable component. The instruction is used to implement customization of the specific customizable component of the web site in accordance with the input from the user.

Abstract: A robot arm assembly includes a middle joint, an upper arm rotatably connected to the middle joint, a driver rotating the upper arm mounted on the middle joint, and a transmission mechanism transferring the power of the driver to the upper arm. The driver includes an output shaft. The transmission mechanism includes a first gear coupled to the output shaft of the driver, a second gear mounted on the outer circumference of the arm, a third gear meshed with the first gear, and a fourth gear coinciding with the third gear and meshed with the second gear.

Abstract: A multi-spindle gearbox system includes a gearbox, a gear assembly, an output sleeve spindle, and a cable receiving barrel. The gearbox includes a base and a sealing cover covering the base. The sealing cover forms a hollow box space together with the base receiving the gear assembly therein. The base defines an assembly hole therethrough. The sealing cover defines a guiding hole corresponding to the assembly hole of the base. The output sleeve spindle is hollow and cylindrical with one end thereof assembled to the assembly hole of the base corresponding to the gear assembly. The cable receiving barrel is assembled within the gearbox. One end of the cable receiving barrel connects with the assembly hole and the other end of the cable receiving barrel passes through the guiding hole and is exposed to an outer side of the sealing cover.

Abstract: A robot includes a base seat, a first arm fixed to the base seat, a second arm rotatably connected to the first arm, an output shaft rotatably connected to a distal end of the second arm, a first driving member, and a first transmission mechanism. The first transmission mechanism includes a first transmission belt and a reducer. The first driving member is located at one end of the first arm adjacent to the base seat, the reducer is located at the other end of the first arm away from the base seat, and connected to the second arm, the first driving member is capable of driving the reducer via the first transmission belt.

Abstract: A robot arm assembly includes a first arm, a second arm and a third arm. The first arm includes a main body, a pivotal portion perpendicularly connected to the main body, a first transmission mechanism and a first reducer. The second arm includes a fixing portion, a second transmission mechanism, a first connecting portion and a second connecting portion extending from opposite ends of the fixing portion. The first connecting portion and the second connecting portion are connected to opposite ends of the pivotal portion, the second transmission mechanism is connected to the first transmission mechanism. The third arm includes an output flange, a second reducer connected to the output flange and a transmission bevel gear. The transmission bevel gear is fixed to the input end and engages with the second transmission mechanism.

Abstract: A ball joint mechanism includes a ball joint member, a ball socket member and a plurality of elastic assemblies. The ball joint member has a head portion. The ball socket member defines a spherical engaging socket and a plurality of mounting holes communicating with the spherical engaging socket. The head portion of the ball joint member is assembled into and pivotally engages with the corresponding spherical engaging socket of the ball socket member. The plurality of elastic assemblies are assembled within the mounting holes of the ball socket member, respectively, and elastically resist against a periphery of the ball joint member. One or more kinematic chains and a parallel robot using the ball joint mechanism are also provided.

Abstract: A robot arm mechanism includes a drive arm, a support arm, a transmission assembly and three tool portions. The support arm is rotatably connected to the drive arm. The three tool portions are rotatably mounted on the support arm. The transmission assembly includes a first bevel gear, a second bevel gear and a transmission belt. The first bevel gear is mounted on the drive arm, the second bevel gear is mounted on the support arm and engages with the first bevel gear. The transmission belt is driven by the second bevel gear and imparts rotary motion to the plurality of tool portions simultaneously.

Abstract: A balancing mechanism for a robot configured for lifting heavy weights comprises a hollow balancing body comprising an opening; an elastic assembly received in the balancing body and a pulling rod assembly received in the balancing body and hinged to a robot arm of the robot. One end of the pulling rod assembly resists the elastic assembly, and another opposite end of the pulling rod assembly extends out from the balancing body through the opening, the pulling rod assembly is movably assembled with the balancing body via the elastic assembly to make the elastic assembly capable of producing a balancing moment against the gravity moment of the robot arm.

Abstract: An industrial robot with a gear transmission mechanism is disclosed. The industrial robot comprises a base; a robot arm assembly rotatably connected to the base; the robot arm assembly comprising a gear box; a first driving member positioned on the gear box, the first driving member having a first driving shaft; a first transmission mechanism positioned in the gear box, wherein the first transmission mechanism has at least two gears meshing with each other and a fixed shaft fixed to the base, one of the at least two gears is connected to the first driving shaft, and another one of the at least two gears is rotatably sleeved on the fixed shaft, and fixed to the gear box.

Abstract: A 3DOF universal joint includes a first connecting base, a second connecting base, a two-way hinged member, and a third connecting base. The first connecting base and the second connecting base are rotatably mounted together via the two-way hinged member along a first axis and a second axis substantially perpendicular to the first axis. The third connecting base is rotatably assembled to the second connecting base along a third axis substantially perpendicular to the first and second axes.

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.

Abstract: A general model is provided which provides collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Under this model, a spectral relational clustering algorithm is provided to cluster multiple types of interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects.

Abstract: A robot arm assembly includes a support member, a first rotating member rotatably connected to the support member, a second rotating member rotatably connected to the first rotating member, and a driving mechanism to drive the first rotating member. The driving mechanism includes a gear transmission mechanism directly coupled to the first rotating member, a driver mounted on the support member and provided with an output shaft coupled to the gear transmission mechanism, and a support mechanism supporting the gear transmission mechanism.

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.

Abstract: A robot arm includes a support, a wrist joint rotatably connected to the support, a distal joint rotatably connected to the wrist joint, a first driving mechanism driving the wrist joint, and a second driving mechanism driving the distal joint. The first driving mechanism comprises a first input shaft connected to the wrist joint via two bevel gears, and the first driving mechanism drives the wrist joint to rotate relative to the support. The second driving mechanism comprises a second input shaft connected to the distal joint via at least four bevel gears, and the second driving mechanism drives the distal joint to rotate relative to the wrist joint. The robot arm is sealed and defines a conduit for allowing a plurality of pipes and cables to pass through.

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: A general model is provided which provides collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Under this model, a spectral relational clustering algorithm is provided to cluster multiple types of interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects.