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The visual instruction software system was designed with typical industrial situations serving as guidelines. The programmer is assumed to have little knowledge of computers. Rather, he is expected to have a detailed knowledge of the task to be performed. The V/I system provides a safe and simple means to communicate this knowledge to the robot control computer. The design of a software system for programming industrial robots is presented. This software system allows the robot's task to be described through visual means. Television cameras and hand‐held arrays of small lights permit the time spent in programming a robot to be radically reduced. Programmers need only place an array of lights in the robot's field of view and press a button on a hand‐held keyboard to specify robot hand position and orientation. Hence, time delays usually encountered when co‐ordinating the movement of robot arm joints are eliminated.

Portfolios of mortgage loans played an important role in the Great Recession and continue to compose a material part of bank assets. This chapter investigates how cross-sectional dependence in the underlying properties flows through to the loan returns, and thus, the risk of the portfolio. At one extreme, a portfolio of foreclosed mortgage loans becomes a portfolio of real estate whose returns exhibit substantial cross-sectional and spatial dependence. Near the other extreme, almost all loans perform and yield constant returns, which do not correlate with other performing loan returns. This suggests that loan performance effectively censors the random returns of the underlying properties. Following the statistical properties of the correlations among censored variables, the authors build off this foundation and show how the loan return correlations will rise as economic conditions deteriorate and the defaulting loans reveal the underlying housing correlations. In this chapter, the authors (1) adapt tools from spatial statistics to document substantial cross-sectional dependence across house price returns and examine the spatial structure of this dependence, (2) investigate the nonlinear nature of correlations among loan returns as a function of the default rate and the underlying house price correlations, and (3) conduct a simulation exercise using parameters from the empirical data to show the implications for holding a portfolio of mortgages.

Despite its widespread acceptance and application in the psychology literature, exhaustion, the core dimension of job burnout, has only recently been examined in the domain of public accounting. These studies highlighted the problem of exhaustion within the profession and examined its causes relative to the environment of public accounting. Another factor, not previously addressed in the context of public accounting, is the role personality plays on public accountants’ exhaustion. The current study addressed this void by examining how the personality traits of hardiness, workaholism, neuroticism, and Type-A behavior in public accountants affect exhaustion. The results indicated that public accountants who were high in hardiness experienced significantly less exhaustion. The role stressors of overload and conflict were also significant contributors to public accountants’ exhaustion.

This paper aims to introduce and identify a new 3D handling operation (bin picking) for natural discrete food products using food categorisation.

The research shows a new food categorisation and the relation between food ordering processes and food categories. Bin picking in the food industry needs more flexible vision software compared to the manufacturing industry in order to decrease the degree of disarray of food products and transfer them into structure.

It has been shown that there are still manual operated ordering processes in food industry such as bin picking; it just needs new ideas of image processing algorithms such as active shape models (ASMs) on its development in order to recognise the highly varying shapes of food products.

This research was aimed at locating a new ordering process and proving a new principle, but for practical implementation this bin picking solution needs to be developed and tested further.

Identifying new ordering processes via food categorisation is unique and applying ASMs to bin picking opens a new industrial sector (food industry) for 3D handling.

Globalization has created an intense competitive environment. As a result, achieving competitive advantage has become the core argument in international management. Some scholars have argued that the development of global leaders is critical (Osland, Bird, & Mendenhall, 2006; Yukl, 2009), while others believe that the global mindset is the key to strategic advantage (Black, Morison, & Gregerson, 1999; Jeannet, 2000; Javidan, 2008). In this paper, we present a review of both literatures (i.e., global leadership and global mindset) to highlight that today's dynamic marketplace requires a shift in thinking. We conclude by drawing attention to existing gaps in these literatures, and shed light on an emerging integrative model of global leadership and mindset.

Automating domestic ironing is a challenge to the robotic community, particularly in terms of modelling and advanced mechanism design. This paper investigates the ironing process, its relevant folding algorithms and analysis techniques, presents the advanced mechanism synthesis and introduces cross‐disciplinary research. It summarises the second part of the results of a technology study carried out under an EPSRC grant “A Feasibility Study into Robotic Ironing”, and proposes new techniques in developing a folding and unfolding algorithm and in developing a task‐oriented mechanism synthesis for robotic ironing.

This paper aims to draw on a number of indicators of innovation to evaluate and compare two central regions – Hubei and Hunan – with three leading regions of China – Beijing, Shanghai and Guangdong.

The paper focuses on absorptive capacity at the level of domestic regions of China, using data on R&D expenditures, human resources in higher education and in high-tech industries. The paper uses social network analysis to investigate innovation networks based on patents from the SIPO and USPTO.

Results indicate that, in Hubei and Hunan, R&D expenditures do not translate into as many innovative outputs as in the coastal and Southern regions. Moreover, high-tech industries contribute towards a relatively low proportion of the regional outputs of Hubei and Hunan, where especially the electronics industry is very poorly represented. Findings also suggest that Hubei and Hunan have a relatively limited access to foreign technologies embedded in ties with international partners. By contrast, the more advanced three regions tend to develop collaborative activities across national boundaries to a higher extent than across regional boundaries.

The analysis of China's regional innovation system has implications for research on national innovation systems. Policy-makers can benefit from the comparative analysis of regions.

The study is primarily exploratory, and the findings contribute to the literature and ongoing discussion on data sources and methods for the analysis of regional innovation.

The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.

The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.

To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.

Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

The purpose of this research is to explore from a mass appraisal perspective how the effects of location are reflected within valuation models. The paper sets out to detail the various techniques and the efficacy of their application.

The approach adopted is analytical and based upon the development of locational attributes. An extensive literature base is synthesized with methods being evaluated in their application to mass appraisal.

This research has identified that the three main groups interested in residential property valuation, namely, academia, industry and commerce have to a certain extent been unfamiliar with the research developments occurring in the other groups. The impact of this is important, given the need for integration and collaboration in terms of future model development.

The research underpinning this paper will provide a solid basis for further research into this area. The importance of measuring the effect that location has on value is of major significance in the determination of objective estimates of property value.

Those within the assessment community could be described as pragmatists working in a situation that requires feasible and suitable solutions to the problem of measuring location value. It is our contention that the third generation techniques of spatially varying parameter models and spatial autocorrelation models will require greater industry verification before their use becomes more widely accepted.

This paper provides a detailed analysis of methodologies used to reflect the value of location over the last 50 years. The debate is taken forward by describing what will be the contribution to the development of the next generation of location‐specific modeling techniques.