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Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

This paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots.

Dynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control.

The model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag.

The entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well.

A full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.

The purpose is to show that it is necessary for a company to choose a business strategy that relies on a thorough understanding of the company setting.

The Strategic States Model is presented and illustrated by the Ericsson case. Advices on application of the model are presented.

The case shows that a search for an optimum business strategy means competitiveness.

In applying the Strategic States Model, management would be advised to formulate business strategy step‐by‐step.

The article turns theoretical research into advices that are valuable for companies when they choose business strategies. The case shows that practical company situations can be generalized in accordance with strategy models.

Shaft orbit is an important characteristic for vibration monitoring and diagnosing system of hydroelectric generating set. Because of the low accuracy and poor reliability of traditional methods in identifying the shaft orbit moving direction (MD), the purpose of this paper is to present a novel automatic identification method based on trigonometric function and polygon vector (TFPV).

First, some points on shaft orbit were selected with inter‐period acquisition method and joined together orderly to form a complex plane polygon. Second, by using the coordinate transformation and rotation theory, TFPV were applied comprehensively to judge the concavity or convexity of the polygon vertices. Finally, the shaft orbit MD is identified.

The simulation and experiment demonstrate that the method proposed can effectively identify the common shaft orbit MD.

In order to identity the shaft orbit MD effectively, a novel automatic identification method based on TFPV is proposed in this paper. The problem of identifying the shaft orbit MD is transformed into the problem about orientation of complex polygons, which are formed orderly by points on orbit shaft, and TFPV are applied comprehensively to judge the concavity or convexity of the polygon vertices.

The purpose of this paper is to apply the novel instantaneous power flow balance (IPFB)-based identification strategy to a specific practical situation like nonlinear lap joints having single and double bolts. The paper also investigates the identification performance of the proposed power flow method over conventional acceleration-matching (AM) methods and other methods in the literature for nonlinear identification.

A parametric model of the joint assembly formulated using generic beam element is used for numerically simulating the experimental response under sinusoidal excitations. The proposed method uses the concept of substructure IPFB criteria, whereby the algebraic sum of power flow components within a substructure is equal to zero, for the formulation of an objective function. The joint parameter identification problem was treated as an inverse formulation by minimizing the objective function using the Particle Swarm Optimization (PSO) algorithm, with the unknown parameters as the optimization variables.

The errors associated with identified numerical results through the instantaneous power flow approach have been compared with the conventional AM method using the same model and are found to be more accurate. The outcome of the proposed method is also compared with other nonlinear time-domain structural identification (SI) methods from the literature to show the acceptability of the results.

In this paper, the concept of IPFB-based identification method was extended to a more specific practical application of nonlinear joints which is not reported in the literature. Identification studies were carried out for both single-bolted and double-bolted lap joints with noise-free and noise-contamination cases. In the current study, only the zone of interest (substructure) needs to be modelled, thus reducing computational complexity, and only interface sensors are required in this method. If the force application point is outside the substructure, there is no need to measure the forcing response also.

Suggests that, without question, while every step in a systematic approach to the design of parts for assembly using integral snap‐fit features is important, none is more important than selecting locking features. After all, it is these features that hold the assembly together. While quite different in appearance and details of their operation, all integral locking features comprise a latch and a catch component to create a locking pair. Proper, no less optimum, function requires that such locking pairs be selected using a systematic approach. Presents that approach as a six‐step methodology, but first, defines and describes latch and catch components, bringing order to their apparent boundless variety. Demonstrates the utility of the methodology with a real‐life case study.

While applications of TQM tools and techniques in health care service industry are widely advocated, determination of customer satisfaction and factors of dissatisfaction in the hospitals has become enormously important as the main ingredient of TQM. This paper aims at determining the elements of customer satisfaction, by collecting information through survey, using both written questionnaire and interview, and then statistically determining correlation between factors and elements of dissatisfaction. The study is performed at the Muang Petch Thonburi Private Hospital, located in Petchaburi province of Thailand. The aim of the management is to gather information on customer satisfaction levels and factors of dissatisfaction that need to be addressed and subsequently eliminated in the near future.

This paper presents a methodology to nominate and select improvement projects that are perceived as adding value to customers (both internal and external). The structure of the methodology can be explained in three “stages”. First, the methodology suggests a new way of categorizing improvement opportunities, i.e. reactive‐proactive, to “upgrade” the little Q ‐ big Q categorisation. Then, it develops a roadmap that links performance indicators and improvement projects for both reactive and proactive improvements. Finally, it suggests an algorithm to select the improvement project, where the assessment of to what extent the nominated improvement projects add value to customers relies on the comparison between Overall Perceived Benefits (OPB) and Overall Perceived Efforts (OPE). The improvement project perceived as having the largest impact on adding value to customers receives the highest priority.

A model is constructed describing the interaction of systems of carbohydrate and fatty‐lipoid metabolism. The model equations can be presented in the form of a system of linear differential equations which combines 36 velocities of different metabolic and hormonal variances in different tissues into a system of combined feedback loops. A simplified variant is given of the model of a system of regulating sugar in blood. The mathematical model being available, it is possible to choose proper dosage. Mathematical models are used for practical medical applications.

The purpose of this study is to present the performance evaluation of three shooting methods typically applied to obtain the periodic steady state of electric power systems, with the aim to check the benefits of the use of cloud computing regarding relative efficiency and computation time.

The mathematical formulation of the methods is presented, and their parallelization potential is explained. Two case studies are addressed, and the solution is computed with the shooting methods using multiple computer cores through cloud computing.

The results obtained show a reduction in the computation time and increase in the relative efficiency by the application of these methods with parallel cloud computing, in the problem of obtainment of the periodic steady state of electric power systems in an efficient way. Additionally, the characteristics of the methods, when parallel cloud computing is used, are shown and comparisons among them are presented.

The main advantage of employment of parallel cloud computing is a significant reduction of the computation time in the solution of the problem of a heavy computational load caused by the application of the shooting methods.