Trade-off between resolution and interactivity in spatial taskperformance

Virtual reality displays usually lag far behind classical computer graphics displays in static image quality parameters, such as resolution. Both the popular press and scientific papers often stress that resolution will have to increase greatly before users can experience virtual environments as “the real thing”. Nevertheless, it is already possible to do some useful work in VR environments. The point we experimentally demonstrate here is that resolution is much less important for interactive tasks that employ immersive VR, where users can explore the environment by moving their heads and bodies, than it is in classical computer graphics applications, where users can only explore by gazing at a single picture. In the context of unmanned aerial vehicles, frame rate (read: passive camera movement) is more important than resolution for target detection, recognition, designation, and tracking. In the experiments reported here, we investigated the relative importance of various image parameters like spatial resolution (number of pixels per video frame), intensity resolution (number of gray levels per pixel), and temporal resolution (number of frame updates per second). Most experimental data concerning these resolutions come from classical psychophysics. However, experimental conditions in classical psychophysics feature stationary observers looking at short-term, pointlike flashes on stationary displays, and are thus far more representative of human interaction with pictures and photographs than with highly interactive systems like those employed in virtual reality     

Efficient tracking of the dominant eigenspace of a normalized kernel matrix

Various machine learning problems rely on kernel-based methods. The power of these methods resides in the ability to solve highly nonlinear problems by reformulating them in a linear context. The dominant eigenspace of a (normalized) kernel matrix is often required. Unfortunately, the computational requirements of the existing kernel methods are such that the applicability is restricted to relatively small data sets. This letter therefore focuses on a kernel-based method for large data sets. More specifically, a numerically stable tracking algorithm for the dominant eigenspace of a normalized kernel matrix is proposed, which proceeds by an updating (the addition of a new data point) followed by a downdating (the exclusion of an old data point) of the kernel matrix. Testing the algorithm on some representative case studies reveals that a very good approximation of the dominant eigenspace is obtained, while only a minimal amount of operations and memory space per iteration step is required.     

Whole-body vibration experienced by haulage truck operators in surface mining operations: A comparison of various analysis methods utilized in the prediction of health risks

Whole body vibration (WBV) was measured on eight surface haulage trucks in three size classes (35, 100, 150 ton haul capacities). Vibration was measured at the seat/operator interface in accordance with the ISO 2631-1 standard during 1 h of normal operation. Highest acceleration readings were observed in the z-axis (vertical). Estimated equivalent daily exposure values in the range of 0.44–0.82 ms^?2 were observed using the frequency-weighted r.m.s method and 8.7–16.4 ms^?1.75 using the vibration dose value method. Assessment was carried out using ISO 2631-1 and 2631-5. Operators of surface haulage trucks are regularly exposed to WBV levels that exceed safety limits as dictated by the ISO 2631-1 standard. However, according to ISO 2631-5 the probability of an adverse health effect remains low. These findings confirm an apparent disagreement between the two analysis methods.     

The importance of the number of degrees of freedom for rotation of objects

In an experiment input methods for object rotation with differing degrees of freedom were assessed. The results are relevant for human- computer interfacing, not only for the finger tip controlled interface proposed in this paper but also for evaluation of existing approaches to rotation. When designing an interface with finger tip controlled rotation of virtual objects, for technical reasons the number of finger tips to be registered should be minimized. Performance of subjects who rotated real objects with different numbers of finger tips was assessed. Subjects rotated a transparent sphere encasing an object according to their personal preference, with three, two or one finger, and restricted to three orthogonal axes. The latter reflects rotation in much current 3D software, whereby only one rotational degree of freedom (DOF) is accessible at a time. Performance in the three and two finger conditions did not differ significantly from the free condition, whilst performance with one finger and orthogonally restricted was significantly lower. However, only the three finger condition was rated as comfortable as the free condition, whilst the two finger, one finger and orthogonally restricted conditions were rated as less comfortable. It is argued that the number of DOFS which can be accessed simultaneously is an important design consideration when quick and intuitive rotation is to be achieved.     

Requirements for a virtual environment to support the social participation education of low-literates

People of low literacy experience difficulties while participating in society. Learning support software could help alleviate these difficulties. However, there is currently no overview of theoretically and empirically sound requirements for this kind of support. This paper uses the situated cognitive engineering method to create a requirements baseline for a virtual environment to support the societal participation education of low-literates (VESSEL), based on an analysis of the domain, human factors, and current applications. Four major outcomes are presented. First, a comprehensive overview is collected of the operational demands and human factors knowledge relevant to societal participation learning for low-literate citizens. Second, this overview is translated into a list of eight functional requirements: focused on low-literate learners, set in the context of societal participation, and supported by claims of cognitive, affective, and social benefits to learning. Third, a sample of Dutch societal participation learning support programs is assessed using these requirements, to highlight both current technology best practices and discrepancies between theory and practice. Fourth, virtual learning environment technology is suggested as an ‘enabling’ technology; an overview is shown of how virtual environments, actors, and objects can beneficially enable meeting the requirements baseline. Finally, directions for future study are discussed.

     

Improved process control of an industrial sludge centrifuge-dryer installation through binary logistic regression modeling of the fouling issues

Biological wastewater treatment generates huge amounts of waste sludge which need to be dewatered and eventually dried to minimize transportation and incineration costs. A characteristic feature of sludge in this context is that it turns into a sticky substance during its drying process inducing fouling problems in the drying installation. At the wastewater treatment plant of Monsanto in Antwerp, Belgium, one enclosed centrifuge-dryer system is used to dry the sludge. In the past, this installation had to be shut down regularly due to dryer fouling problems. To avoid these operational problems, a binary logistic regression analysis is presented in this research based on a 5-year database, resulting in an empirical model for the evaluation of the dryer fouling risk as a function of the sludge feed characteristics. The model inputs are the sludge volume index (SVI) and the dosing of clay additive and tertiary (flotation) sludge, the latter containing polyaluminumchloride (PACl), to the sludge feed of this particular system.By exploiting the knowledge captured by this model, the derived control strategy is based on the value of the SVI. Whenever the SVI is high the original high clay dosing to the feed needs to be maintained. At moderate SVI values, implying an intrinsically better sludge dewaterability, the strategy dictates a reduction in the clay dosing to the sludge feed to have a reduced sludge solids dryness after dewatering, thereby avoiding that the sludge exhibits its most sticky phase when passing the most fouling sensitive part of the dryer. When the SVI is lower than 50 mL/g the control strategy states that conditioning of the sludge with PACl is required to mask the stickiness instead of postponing it, avoiding that the stickiness of the sludge already hampers the dewatering stage of the process.     

Optimal design of dispersive tubular reactors at steady-state using optimal control theory

This paper studies the design of optimal temperature profiles for a class of exothermic, jacketed dispersive tubular reactors under steady-state conditions and subject to maximum temperature constraints. The studied class ranges from perfectly mixed continuous stirred tank reactors to plug flow reactors. The aim is to derive the Pareto optimal set of temperature profiles for conflicting conversion and energy costs, while extracting generic features from the obtained solutions. Hereto, a four step procedure which is based on a weighted sum of both costs and which combines indirect, analytical and direct, numerical optimal control techniques, is employed. The generic features are studied (i) along the Pareto set by varying the weights and (ii) along the reactor class by adapting the dispersion level.     

厌氧氨氧化技术用于高氨废水脱氮

在简要介绍厌氧氨氧化菌生理生化特性的基础上,总结了目前应用厌氧氨氧化的主要工艺及其工程实例,重点分析了厌氧氨氧化技术在处理高氨废水实际工程应用过程中需要解决的关键技术,为厌氧氨氧化技术在污水处理工程中的推广提供技术借鉴.     

Classification using belief functions: relationship between case-based and model-based approaches.

The transferable belief model (TBM) is a model to represent quantified uncertainties based on belief functions, unrelated to any underlying probability model. In this framework, two main approaches to pattern classification have been developed: the TBM model-based classifier, relying on the general Bayesian theorem (GBT), and the TBM case-based classifier, built on the concept of similarity of a pattern to be classified with training patterns. Until now, these two methods seemed unrelated, and their connection with standard classification methods was unclear. This paper shows that both methods actually proceed from the same underlying principle, i.e., the GBT, and that they essentially differ by the nature of the assumed available information. This paper also shows that both methods collapse to a kernel rule in the case of precise and categorical learning data and for certain initial assumptions, and a simple relationship between basic belief assignments produced by the two methods is exhibited in a special case. These results shed new light on the issues of classification and supervised learning in the TBM. They also suggest new research directions and may help users in selecting the most appropriate method for each particular application, depending on the nature of the information at hand.