Prediction of part orientation error tolerance of a robotic gripper

This paper presents a model which predicts the part orientation error tolerance of a three-fingered robotic gripper. The concept of “self-alignment” is introduced, where the gripper uses the grasping process to bring the workpiece into its final state of orientation. The gripper and part are represented mathematically, and initial contact locations upon grasp closure determined. This information is used to solve for the contact forces present, and criteria are developed to determine if beneficial part motion resulting in self-alignment is expected. The results are visualized via a boundary projected on a reference plane below the part. The model is validated experimentally with a number of part configurations with favorable results. This method presents a useful tool by which the mechanical designer can quantitatively predict the performance of an intuitively designed gripping system.     

Foot motions in manual material handling transfer tasks: a taxonomy and data from an automotive assembly plant

Ergonomic job analysis commonly applies static postural and biomechanical analysis tools to particular postures observed during manual material handling (MMH) tasks, usually focusing on the most extreme postures or those involving the highest loads. When these analyses are conducted prospectively using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses. In automotive assembly jobs, workers frequently take several steps between task elements, for example, picking up a part at one location and moving to another location to place it on the vehicle. A detailed understanding of the influence of task type and task sequence on the stepping pattern is necessary to accurately predict the foot placements associated with MMH tasks. The current study examined the patterns of foot motions observed during automotive assembly tasks. Video data for 529 pickup and delivery tasks from 32 automotive assembly jobs were analysed. A minimum of five cycles was analysed for each task. The approach angle, departure angle, hand(s) used, manipulation height and patterns of footsteps were coded from the video. Object mass was identified from the job information sheet provided by the assembly plant. Three independent raters coded each video and demonstrated an intraclass correlation coefficient of 0.54 for identification of the configuration of the lower extremities during terminal stance. Based on an analysis of the distribution of stepping behaviours during object transitions (pickups or deliveries), a transition classification system (TRACS) was developed. TRACS uses a compact notation to quantify the sequence of steps associated with a MMH transition. Five TRACS behaviour groups accounted for over 90% of the transition stepping behaviours observed in the assembly plant. Approximately two-thirds (68.4%) of the object transfers observed were performed with only one foot in contact with the ground during the terminal posture. The results from this paper suggest that a predictive model for choosing a transition stepping behaviour, coupled with a model to scale the selected foot behaviours, is needed to facilitate accurate prospective ergonomic analyses. This study proposes a method for categorising the stepping patterns associated with MMH tasks. The influence of task type and task sequence on the stepping patterns observed during several automotive assembly tasks is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses.     

Critical success factors revisited: success and failure cases of information systems for senior executives

The literature suggests the existence of critical success factors (CSFs) for the development of information systems that support senior executives. Our study of six organizations gives evidence for this notion of CSFs. The study further shows an interesting pattern, namely that companies either “get it right”, and essentially succeed on all CSFs, or “get it completely wrong”, that is, fall short on each of the CSFs. Among the six cases for which data were collected through in-depth interviews with company executives, three organizations seemed to manage all the CSFs properly, while two others managed all CSFs poorly. Only one organization showed a mixed scorecard, managing some factors well and some not so well. At the completion of the study, this organization could neither be judged as a success, nor as a failure. This dichotomy between success and failure cases suggests the existence of an even smaller set of “meta-success” factors. Based on our findings, we speculate that these “meta-success” factors are “championship”, “availability of resources”, and “link to organization objectives”.     

Application of autoregressive spectral analysis to cepstral estimation of mean scatterer spacing

The problem of estimation of mean scatterer spacing in an object containing regularly spaced structures is addressed. An autoregressive (AR) spectral estimation method is compared with a conventional fast Fourier transform (FFT)-based approach for this task. Regularly spaced structures produce a periodicity in the power spectrum of ultrasonic backscatter. This periodicity is manifested as a peak in the cepstrum. A phantom was constructed for comparison of the two methods. It contained regularly spaced nylon filaments. It also contained randomly positioned glass spheres that produced incoherent backscatter. In an experiment in which this target was interrogated using broadband ultrasound, the AR spectral estimate offered considerable improvement over the FFT when the analysis gate length was on the order of the structural dimension. Advantages included improved resolution, reduction in bias and variance of scatterer spacing estimates, and greater resistance to ringing artifacts. Data were also acquired from human liver in vivo. AR spectral estimates on human data exhibited a decreased dependence on gate length. These results offer promise for enhanced spatial resolution and accuracy in ultrasonic tissue characterization and nondestructive evaluation of materials.     

Will virtual education initiatives succeed?

The use of information technology to enhance teaching and learning processes has been practiced for a number of years now. However, the rapid growth in the use of the Internet has led to a new dimension in interactive and collaborative learning anytime and anyplace dynamically. With the explosion of “virtual education initiatives”, the question of the feasibility and success criteria for such projects quickly arises. To address the question in an organized way, we propose a project assessment based on critical success factors. Hence, in this article, we draw on a widely recognized critical success factor framework. We (slightly) adjust the framework to fit the special characteristics of virtual education initiatives, and apply to one case study, namely the virtual education initiative at the Faculty of Business at the City University of Hong Kong. The results suggest that the past success of the case is due to the adherence to the large majority of critical success factors. However, it also outlines some areas of concern. The paper concludes with a discussion on the strength and limitations of virtual learning environment as well as future directions. This revised version was published online in July 2006 with corrections to the Cover Date.     

An educational strategy for occupational therapy community service

In response to the increasing trend toward community health care, a model of training that prepares students for community practice was incorporated into the occupational therapy curriculum at the University of Southern California. During academic training students are placed in a part-time community assignment where no occupational therapy services are offered. Training students for the role of community health specialist produces a dilemma for curriculum design. A balance must be achieved between providing traditional clinical content and providing the knowledge and expertise necessary for community practice. However, this training is considered necessary for maintaining the viability of the profession in a changing health system. Significantly, during the four-year period using this model, many graduates have sought employment in "nontraditional" community roles.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

Predictive neuro-control of uncertain systems: design and use of a neuro-optimizer

We consider the problem of predictive control of uncertain stochastic discrete I/O systems. Given a model identification procedure able to give accurate output system estimates, e.g. a neural network approximation, we use another feedforward neural network to generate at each time step a constrained optimal control. Dynamic backpropagation is used to improve when necessary the controller network parameters. Both system and controller neural structures are first selected off-line by a statistical Bayesian procedure in order to make the predictive control minimizing process more efficient. The issue of stochastic stability of the closed-loop is considered. We developed this approach for the tracking control of such uncertain systems as biotechnological processes. Actual and simulated predictive neuro-control case studies in this field of application are proposed as illustrations. A comparison with a more classic quasi-Newton-based approach is also proposed, showing the interest of this neuro-control approach.