A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area. UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the contextual outlier. Most existing works conduct experiments based on datasets with injected outliers. However, we find that the most widely-used outlier injection approach has a serious data leakage issue. By only utilizing such data leakage, a simple approach can achieve state-of-the-art performance in detecting outliers. In addition, we observe that existing algorithms have a performance drop with the mitigated data leakage issue. The other major issue is on balanced detection performance between the two types of outliers, which has not been considered by existing studies.In this paper, we analyze the cause of the data leakage issue in depth since the injection approach is a building block to advance UNOD. Moreover, we devise a novel variance-based model to detect structural outliers, which outperforms existing algorithms significantly and is more robust at kinds of injection settings. On top of this, we propose a new framework, Variance-based Graph Outlier Detection (VGOD), which combines our variance-based model and attribute reconstruction model to detect outliers in a balanced way. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of VGOD. The results on 5 real-world datasets validate that VGOD achieves not only the best performance in detecting outliers but also a balanced detection performance between structural and contextual outliers.

High contour accuracy is vital to the multi-axis motion system. Improvement in the contour accuracy of the parallel kinematic machine (PKM) has being a challenging issue in the process of its practical application. In analogy to the intelligent structure of the organisms, this paper proposes a smart structure based compensation (SSBC) method to improve the PKM's contour accuracy. Instead of adopting the advanced control strategies or improving the performances of machine's hardware, this method deals with the contouring issue through innovative mechanical design. The smart structures, which have the ability of perception and control, are introduced into the PKM's components to actively suppress their deformations and vibrations. Meanwhile, the accurate micro motions provided by the smart structure components can be used to compensate for the PKM's contour error caused by the uncoordinated control of the driving system just by using a simple control strategy. The implementation procedure of the SSBC method is first discussed in detail. A 2-DOF PKM is taken as the application object and the smart structures are introduced into its kinematic chains to control their axial deformations and internal forces. Then this PKM's kinematic error is reduced in advance to guarantee the result of the contour accuracy improvement. Finally, several experimental tests are performed to verify the effectiveness the proposed SSBC method in improving the PKM's contour accuracy. Except for the PKM, the SSBC method also provides a novel and effective way for the advanced multi-axis machines to improve their contour accuracies.

This paper introduces the mobility analysis of a 1-DOF (Degrees of Freedom) Sarrus Linkage like 7-R single closed loop mechanism. The analysis procedure implements both group theory based criterion and reciprocal screw theory. The analysis shows that such mechanism allows rectilinear motion with full-cycle mobility. Further, unlike the Sarrus Linkage, which is overconstrained, this 7-R linkage is non-overconstrained. This property makes the Sarrus Linkage-like mechanism more robust to manufacturing errors. Simulation has shown that even with large manufacturing errors, such mechanism is still able to realize desired rectilinear motion. One possible application for this mechanism is a durable automotive suspension.

Recently the parallel manipulators with less DOF have attracted the researchers, but works on their dynamics are relative few. In this paper, an inverse dynamic formulation is presented by the Newton-Euler approach for a spatial parallel manipulator, which has two translational degrees of freedom and one rotational degree of freedom. The inverse kinematics analysis is firstly performed in closed form. Then the force and moment equilibrium equations for the manipulator are presented. According to the kinematic constraints of the legs and the platform, some joint constraint forces are eliminated and an algorithm to solve the actuator forces is given. In addition, ADAMS is used to perform the kinematic and dynamic simulation for the manipulator. The simulation results are compared to those derived from algebraic formulae and the comparison shows the validity of the mathematical model.

Compared with serial Machine Center (MC), the Parallel Machine Tools (PMTs) have the advantages of high rigidity, low cost, high accuracy, high velocity, high acceleration and especially suitable for free surface cutting. The main kinematic part in PMTs is Parallel Manipulator (PM). PM includes a fixed base, a platform and several kinematic chains connected with fixed base and platform. The parallel structure characteristics of PM lead to non-linear between actuators and platform, and results in difficulty and complexity in PMT design and PMT control algorithm design. Now the popular method for kinematic analysis includes two steps: non-linear modeling and non-linear equations solving, some of which are difficult and inefficient. A hybrid computational intelligent method is proposed for kinematic analysis of PMTs. The approach includes three main steps: 1) artificial neural network (ANN) models and parameters selection; 2) artificial neural network learning and training; 3) kinematic analysis. The approach overcomes the shortages of ANN. The main processes and key techniques are illuminated for kinematic analysis of PMTs with several analysis samples finally.

Stiffness is one of the important considerations in the design of Stewart platform based parallel kinematic machine (PKM). In most stiffness models of PKMs, the machine frames are thought of rigid bodies. In this paper, an approach is presented to establish the stiffness model of a Stewart platform-based PKM, considering the deformation of the frame. The deformation of legs and the frame is considered as kinematic parameter errors of Stewart platform. According to the differential error model, the machine structure is decomposed into two subsystems: the parallel links subsystem and the machine frame subsystem. The stiffness matrix of each subsystem is established respectively, assuming that the other one is a rigid structure. By linear superposition of the two subsystems, the stiffness model of the machine structure is obtained. A finite element analysis (FEA) model is used to simulate the physical structure. The FEA results are compared to those derived from a mathematical model, and the comparison shows the validity of this approach.

Parallel manipulators are characterized by symmetry, high precision and stiffness, which allow them to be the mechanisms of micromanipulators. This paper deals with the optimum design of micromanipulator mechanisms. A useful design tool space, named the physical model of the solution space, is proposed. The design space for 6-DOF parallel micromanipulators is established. The relationships between the performance indices and link lengths of micromanipulators are presented in the established solution space. The method described leads to a new way for the computer-aided design of micromanipulators.

In this paper, a new spatial three-degrees-of-freedom (two degrees of translational freedom and one degree of rotational freedom) parallel manipulator is proposed. The parallel manipulator consists of a base plate, a movable platform, and three connecting legs. The inverse and forward kinematics problems are described in closed forms and the velocity equation of the new parallel manipulator is given. Three kinds of singularities are also presented. The workspace for the manipulator is analyzed systematically; in particular, indices to evaluate the mobility (in this paper, mobility means rotational capability) of the moving platform of the manipulator will be defined and discussed in detail. Finally, a topology architecture of the manipulator is introduced. The parallel manipulator has wide application in the fields of industrial robots, simulators, micromanipulators, and parallel machine tools.