Search results

Non‐linear transformation of freeform curves and surfaces is useful in computer‐aided design and computer graphics. It is highly desirable that the original and transformed curves or surfaces are defined using the same representation. But freeform curves and surfaces defined by control points are invariant only under affine transformations, and not so under non‐linear transformations. This paper develops a method that can perform non‐linear transformations of freeform curves to specific accuracies, while retaining the same representation. It involves first applying the transformation to the control points and then modifying them so that the resulting curve and the exact transformed curve are equal at a specific number of points, which is the number of control points. Refinement to the approximation is made by increasing the number of control points. A method for measuring the maximum positional error has been implemented and this is used to facilitate an algorithm for automatic refinement. Extension of the method for surfaces is also given.

Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications. In the literature, several approaches have been proposed to solve this problem. However, previous works lack formal characterization of the curve fitting problem and assessment on the effect of several parameters (i.e. scalars that remain constant in the optimization problem), such as control points number (m), curve degree (b), knot vector composition (U), norm degree (k), and point sample size (r) on the optimized curve reconstruction measured by a penalty function (f). The paper aims to discuss these issues.

A numerical sensitivity analysis of the effect of m, b, k and r on f and a characterization of the fitting procedure from the mathematical viewpoint are performed. Also, the spectral (frequency) analysis of the derivative of the angle of the fitted curve with respect to u as a means to detect spurious curls and peaks is explored.

It is more effective to find optimum values for m than k or b in order to obtain good results because the topological faithfulness of the resulting curve strongly depends on m. Furthermore, when an exaggerate number of control points is used the resulting curve presents spurious curls and peaks. The authors were able to detect the presence of such spurious features with spectral analysis. Also, the authors found that the method for curve fitting is robust to significant decimation of the point sample.

The authors have addressed important voids of previous works in this field. The authors determined, among the curve fitting parameters m, b and k, which of them influenced the most the results and how. Also, the authors performed a characterization of the curve fitting problem from the optimization perspective. And finally, the authors devised a method to detect spurious features in the fitting curve.

This paper provides a methodology to select the important tuning parameters in a formal manner.

Up to the best of the knowledge, no previous work has been conducted in the formal mathematical evaluation of the sensitivity of the goodness of the curve fit with respect to different possible tuning parameters (curve degree, number of control points, norm degree, etc.).

Using the backdrop of an (apparently) extended visit to the West Indies, analogies with key concerns of internal audit are drawn. An unusual and refreshing way of exploring the main themes ‐ a discussion between Bill and Jack on tour in the islands ‐ forms the debate. Explores the concepts of control, necessary procedures, fraud and corruption, supporting systems, creativity and chaos, and building a corporate control facility.

Using the backdrop of an (apparently) extended visit to the West Indies, analogies with key concerns of internal audit are drawn. An unusual and refreshing way of exploring the main themes ‐ a discussion between Bill and Jack on tour in the islands ‐ forms the debate. Explores the concepts of control, necessary procedures, fraud and corruption, supporting systems, creativity and chaos, and building a corporate control facility.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

The paper's purpose is to provide a method of reducing inventory costs in multi‐product and multi‐nodes supply chains (SC).

The proposed approach is based on applying the classical inventory control models and simulation. This is a two‐stage approach in which inventory cost reduction in the SC occurs as a result of the appropriate selection of inventory control policies by its members.

Cost reduction in the whole SC may occur as a result of an appropriate choice of inventory control policies in every node. SC members can learn which inventory control policies should be applied. Results of the learning process are closely connected with a kind of collaboration between chain nodes. The best results of the whole system can happen with a simultaneous deterioration of operation of its component elements. Therefore, losses which occur in some chain members as a result of collaboration should be compensated.

This approach does not offer an optimal solution. The authors do not know how far they are from an optimal solution. The approach should be tested for more complicated SC. The authors have not studied the strategy in which SC cells negotiate the level of their inventories.

SC cells should apply a local cooperation strategy. When the cost of the whole SC decreases, the cost in particular nodes may significantly increase. The start of collaboration in the SC can cause a deterioration of results in some companies; therefore this loss should be compensated.

The paper presents the development of a framework in which an application of different policies of inventory control and application of different collaboration strategies in the SC are studied. The framework enables the use of classical inventory control models in a coordinated manner, and SC members decide which policies should be applied only on the basis of earlier collected experiences.

The purpose of this paper is to propose two control charts to monitor multi-attribute processes and then a maximum likelihood estimator for the change point of the parameter vector (process fraction non-conforming) of multivariate binomial processes.

The performance of the proposed estimator is evaluated for both control charts using some simulation experiments. At the end, the applicability of the proposed method is illustrated using a real case.

The proposed estimator provides accurate and useful estimation of the change point for almost all of the shift magnitudes, regardless of the process dimension. Moreover, based on the results obtained the estimator is robust with regard to different correlation values.

To the best of authors’ knowledge, there are no work available in the literature to estimate the change-point of multivariate binomial processes.

Based on detailed analysis of seven case studies, involving consideration of approximately 300 parameters and face to face interviews with senior managers, identifies key characteristics which should be taken into account during the selection and effective implementation of different types of manufacturing control systems in individual manufacturing environments. These key characteristics help identify the need for particular functions of manufacturing control systems, as well as the impact on effective implementation and operation. They are grouped under the headings of complexity, uncertainty and flexibility. Concludes with a discussion of a structured approach which may be used to take account of key characteristics during the selection of a manufacturing control system.

This paper aims to present a unified motion control scheme for quadcopters which not only solves the point stabilization and trajectory tracking problems but also the path following problem.

The control problem is solved based on the kinematic model of the unmanned aerial vehicles (UAV). Next, a dynamic compensation controller is considered through of a quadcopter-inner-loop system to independently track four velocity commands: forward, lateral, up/downward and heading rate. Stability and robustness of the whole control system are proved through the Lyapunov’s method. To evaluate the controller’s performance, a multi-user application which allows bilateral communication between a ground station and the Phantom 3 PRO quadrotor is developed.

The performance of the proposed unified controller is shown through real experiments for the different motion control objectives: point stabilization, trajectory tracking and path following. The experiments confirm the capability of the unified controller to solve different motion problems by an adequate selection of the control references.

This work proposes the design of three types of motion controllers, which can be switched to comply a task in outdoor. Based on the software development kit provided by the company DJI, an application to get and send data to the UAV is developed. By means of this application, the three tasks are tested and the robustness of the controllers is proved.