Joint stiffness identification of six-revolute industrial serial robots

Although robots tend to be as competitive as CNC machines for some operations, they are not yet widely used for machining operations. This may be due to the lack of certain technical information that is required for satisfactory machining operation. For instance, it is very difficult to get information about the stiffness of industrial robots from robot manufacturers. As a consequence, this paper introduces a robust and fast procedure that can be used to identify the joint stiffness values of any six-revolute serial robot. This procedure aims to evaluate joint stiffness values considering both translational and rotational displacements of the robot end-effector for a given applied wrench (force and torque). In this paper, the links of the robot are assumed to be much stiffer than its actuated joints. The robustness of the identification method and the sensitivity of the results to measurement errors and the number of experimental tests are also analyzed. Finally, the actual Cartesian stiffness matrix of the robot is obtained from the joint stiffness values and can be used for motion planning and to optimize machining operations.     

Podcasting: A new technological tool to facilitate good practice in higher education

The literature has frequently highlighted the usefulness of podcasting in higher education; however, there is an important gap between the theory on good practice in higher education and empirical studies about podcasting. With this in mind, we carried out an empirical study on an undergraduate degree course in Information Systems Management. The study consisted of the creation and broadcast of 13 podcasts, distributed over four months in which ninety distance students took part. The analysis follows the suggestions proposed in previous literature about the evaluation of technologies in a university learning environment. The findings, discussed within the framework of principles for good practice in higher education, suggest some interesting issues in distance courses, such as: (1) podcasting is a powerful tool as a complement to the traditional resources on a course, but not a substitute for them; (2) the characteristics of podcasting increase the impression of permanent contact between students and teachers, increasing students’ motivation; (3) the use of podcasting allows for a diverse range of student skills and learning methods. Other secondary findings are discussed and some suggestions for future research are proposed at the end of this paper.     

Leveraging technological knowledge transfer by using fuzzy linear programming technique for multiattribute group decision making with fuzzy decision variables

Despite the importance of knowledge transfer for firms involved in foreign direct investment activities, this area has not received appropriate attention from the perspectives of both the knowledge transferor (i.e., MNC parent) and the knowledge recipient. To fill in the gap in the current literature we propose a model to understand the links between criteria complicating the transfer of knowledge and preferences that the company has to focus. This model is based on both the existing literature as well as views of company representatives and provides a useful methodology for identifying decision making problems on the transfer of knowledge. In this paper, we investigate the fuzzy linear programming technique (FLP) to analyze these links and for multiple attribute group decision making (MAGDM) problems with preference information on criteria. To reflect the decision maker’s subjective preference information and to determine the weight vector of attributes, the technique for order preference by similarity to ideal solution (TOPSIS) developed by Hwang and Yoon (1995) and the linear programming technique for multidimensional analysis of preference (LINMAP) developed by Sirinivasan and Shocker (Psychometrica 38:337–369, 1973) are used.     

GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development

This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.     

A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners

Research in psychology, psycholinguistics, and cognitive science has discovered and examined numerous psychological constraints on human information processing. Short term memory limitations, a focus of attention bias, and a preference for the use of temporally recent information are three examples. This paper shows that psychological constraints such as these can be used effectively as domain-independent sources of bias to guide feature set selection and weighting for case-based learning algorithms.We first show that cognitive biases can be automatically and explicitly encoded into the baseline instance representation: each bias modifies the representation by changing features, deleting features, or modifying feature weights. Next, we investigate the related problems of cognitive bias selection and cognitive bias interaction for the feature weighting approach. In particular, we compare two cross-validation algorithms for bias selection that make different assumptions about the independence of individual component biases. In evaluations on four natural language learning tasks, we show that the bias selection algorithms can determine which cognitive bias or biases are relevant for each learning task and that the accuracy of the case-based learning algorithm improves significantly when the selected bias(es) are incorporated into the baseline instance representation.     

Time-correlated single photon counting FLIM: some considerations for physiologists

Recent developments in cellular imaging now permit the minimally invasive study of protein interactions in living cells. These advances are of enormous interest to cell biologists, as proteins rarely act in isolation, but rather in concert with others in forming cellular machinery. Up until recently, all protein interactions had to be determined in vitro using biochemical approaches. This biochemical legacy has provided cell biologists with the basis to test defined protein-protein interactions not only inside cells, but now also with spatial resolution. More recent developments in TCSPC imaging are now also driving towards being able to determine protein interaction rates with similar spatial resolution, and together, these experimental advances allow investigators to perform biochemical experiments inside living cells. Here, we discuss some findings we have made along the way which may be useful for physiologists to consider.     

Phytoplankton Productivity and Standing Crop in the Vicinity of the Lake Huron-Saginaw Bay Front

Continuous underway sampling was used in four research cruises near the mouth of Saginaw Bay in order to locate the bay-lake front, where eutrophic bay water mixes with oligotrophic Lake Huron water. Although mixing between these water masses normally occurs in outer Saginaw Bay, the distribution was variable, depending on wind force and direction as well as water temperature. Large algal crops in inner Saginaw Bay had reduced soluble nutrients, particularly silicate and nitrate-nitrogen, in the outer bay to levels lower than the adjacent lake water. Nonetheless, outer bay water supported high algal biomass (up to 14 μg/L chlorophyll a) and ¹⁴C uptake (up to 73 μg /L/h). The phytoplankton community at the nutrient front between bay and lake waters was characterized by high chlorophyll a biomass ($\overline{x}$ = 4.47 μg/L), low ¹⁴C uptake ($\overline{x}$ = 7.12 μg/L/h), and high bacterial activity (V_max$\overline{x}$ = 1.33) indicating the presence of a senescent assemblage. Decomposition and settling of phytoplankton in the vicinity of the front apparently served to remove much of the bay plankton from the water column before mixing into the open lake. But, during mid-summer, occasional large inputs of nutrients and/or phytoplankton from the bay into the open lake were observed.     

Olfactory and auditory event-related potentials in Huntington's disease.

The influence of Huntington's disease (HD) on the olfactory event-related potential (OERP), an electrophysiological measure of olfactory information processing, has not been reported to date. In the present study, olfactory and auditory event-related potentials (ERPs) were recorded monopolarly from Fz, Cz, and Pz electrode sites in 8 patients with HD and 8 age- and gender-matched control participants. Results demonstrated that individuals with HD were delayed compared with controls on the P3 component of the OERP (p     

A finite element formulation to identify damage fields: the equilibrium gap method

It is proposed to determine damage parameters in two dimensions (surface of a material) or three dimensions (in the bulk of a solid) by using full‐field displacement measurements. A finite‐element approach is developed to evaluate piece‐wise constant elastic parameters modeled by an isotropic damage variable. Two sets of examples are discussed. The first series deals with mechanical fields obtained by finite element simulations to assess the performance of the approach. The second series is concerned with displacement measurements performed during a biaxial test on a composite material. Copyright © 2004 John Wiley & Sons, Ltd.     

Successful adoption of macroergonomics in manufacturing: Using a multicriteria decision‐making methodology– analytic hierarchy process

The research presented in this paper aims to support the macroergonomics adoption improvement process by developing a broader understanding of relationships between key macroergonomics factors and management styles. The methodology involves knowledge acquisition, identifying, and categorizing a holistic set of key criteria about the macroergonomics adoption process. The Analytic Hierarchy Process is suggested as a multi‐attribute decision‐making methodology to effectively enhance adoption of macroergonomics and to improve management decision performance in measuring and comparing the overall performance of different management styles based on macroergonomical criteria. The study found that in terms of company culture, participation, human capability, and attitudes, the best management style in improving macroergonomics adoption is Management by Values. © 2004 Wiley Periodicals, Inc. Hum Factors Man 14: 353–377, 2004.