齿轮式金刚石修整滚轮在齿轮精加工中的应用研究

齿轮式金刚石修整滚轮是多种齿轮精加工工艺的修整工具，本文探讨了它的制作方法和注意事项，并用自制的金刚石修整滚轮在内啮合珩齿机、蜗杆砂轮磨齿机上对内珩轮、蜗杆砂轮进行了修磨，试验证明，自制的金刚石修整滚轮精度完全符合使用要求。     

Exploiting User Interests to Characterize Navigational Patterns in Web Browsing Assistance

In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements. In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals user assistance is effectively enhanced.

An architecture framework for an adaptive extensible processor

To improve the performance of embedded processors, an effective technique is collapsing critical computation subgraphs as application-specific instruction set extensions and executing them on custom functional units. The problem with this approach is the immense cost and the long times required to design a new processor for each application. As a solution to this issue, we propose an adaptive extensible processor in which custom instructions (CIs) are generated and added after chip-fabrication. To support this feature, custom functional units are replaced by a reconfigurable matrix of functional units (FUs). A systematic quantitative approach is used for determining the appropriate structure of the reconfigurable functional unit (RFU). We also introduce an integrated framework for generating mappable CIs on the RFU. Using this architecture, performance is improved by up to 1.33, with an average improvement of 1.16, compared to a 4-issue in-order RISC processor. By partitioning the configuration memory, detecting similar/subset CIs and merging small CIs, the size of the configuration memory is reduced by 40%.     

Systematic review on privacy categorisation

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people’s decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorisation, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorisation involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorisation has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorisation as a research attempt is still meaningful and may have a future.     

A modeling approach on the TelosB WSN platform power consumption

Substantial characteristics of wireless sensor networks, such as autonomy and miniature size, are achieved at the expense of restricted energy resources. Optimal resource management is thus among the most important challenges in WSNs development and its success requires accurate and practical models based on detailed insight concerning the factors contributing to the overall power consumption of a WSN mote. To achieve such awareness, that will enable models development, appropriate measuring test-beds and methodologies are needed, facilitating reliable and accurate power consumption measurements of critical functionalities.To cover the need for energy models that precisely define the power consumption behavior of WSN hardware platforms, this paper contributes with a measuring methodology including three steps: the design and implementation of a measuring system for a wide range of power consumption thresholds, the identification, isolation and measurement of elementary functionalities of a WSN platform with respect to their contribution to the overall mote power consumption, and the extraction of valuable conclusions based on the respective measurements resulting in the composition of a practical, yet accurate power consumption model.     

Non-targeted analysis of tea by hydrophilic interaction liquid chromatography and high resolution mass spectrometry

Tea is the second most consumed beverage in the world and its consumption has been associated with numerous potential health benefits. Factors such as fermentation methods, geographical origin and season can affect the primary and secondary metabolite composition of tea. In this study, a hydrophilic interaction liquid chromatography (HILIC) method coupled to high resolution mass spectrometry in both positive and negative ionisation modes was developed and optimised. The method when combined with principal component analysis to analyse three different types of tea, successfully distinguished samples into different categories, and provided evidence of the metabolites which differed between them. The accurate mass and high resolution attributes of the mass spectrometric data were utilised and relative quantification data were extracted post-data acquisition on 18 amino acids, showing significant differences in amino acid concentrations between tea types and countries. This study highlights the potential of HILIC chromatography combined with non-targeted mass spectrometric methods to provide a comprehensive understanding of polar metabolites in plant extracts.     

Performance tests of a new non-invasive sensor unit and ultrasound electronics

Industrial applications involving pulsed ultrasound instrumentation require complete non-invasive setups due to high temperatures, pressures and possible abrasive fluids. Recently, new pulser-receiver electronics and a new sensor unit were developed by Flow-Viz. The complete sensor unit setup enables non-invasive Doppler measurements through high grade stainless steel. In this work a non-invasive sensor unit developed for one inch pipes (22.5 mm ID) and two inch pipes (48.4 mm ID) were evaluated. Performance tests were conducted using a Doppler string phantom setup and the Doppler velocity results were compared to the moving string target velocities. Eight different positions along the pipe internal diameter (22.5 mm) were investigated and at each position six speeds (0.1–0.6 m/s) were tested. Error differences ranged from 0.18 to 7.8% for the tested velocity range. The average accuracy of Doppler measurements for the 22.5 mm sensor unit decreased slightly from 1.3 to 2.3% across the ultrasound beam axis. Eleven positions were tested along the diameter of the 48.4 mm pipe (eight positions covered the pipe radius) and five speeds were tested (0.2–0.6 m/s). The average accuracy of Doppler measurements for the 48.4 mm sensor unit was between 2.4 and 5.9%, with the lowest accuracy at the point furthest away from the sensor unit. Error differences varied between 0.07 and 11.85% for the tested velocity range, where mostly overestimated velocities were recorded. This systematic error explains the higher average error difference percentage when comparing the 48.4 mm (2.4–5.9%) and 22.5 mm (1.3–2.3%) sensor unit performance. The overall performance of the combined Flow-Viz system (electronics, software, sensor) was excellent as similar or higher errors were typically reported in the medical field. This study has for the first time validated non-invasive Doppler measurements through high grade stainless steel pipes by using an advanced string phantom setup.     

Execution profiling blueprints

Although traditional approaches to code profiling help locate performance bottlenecks, they offer only limited support for removing these bottlenecks. The main reason is the lack of detailed visual runtime information to identify and eliminate computation redundancy. We provide three profiling blueprints that help identify and remove performance bottlenecks. The structural distribution blueprint graphically represents the CPU consumption share for each method and class of an application. The behavioral distribution blueprint depicts the distribution of CPU consumption along method invocations and hints at method candidates for caching optimizations. The behavioral evolution blueprint compares profiles of different versions of a software system and highlights performance‐critical changes in the system. These three blueprints helped us to significantly optimize Mondrian, an open source visualization engine. Our implementation is freely available for the Pharo development environment and has been evaluated in a number of different scenarios. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparing GC–MS,HPLC and H NMR analysis of beef longissimus dorsi tissue extracts to determine the effect of suspension technique and ageing

Beef longissimus dorsi muscle samples matured over a 21 day period were analysed using three different analytical techniques; ¹H NMR, GC–MS and HPLC. The data from the three experimental techniques were correlated with each other to determine if the results were statistically similar to each other. From our analysis we determined that the metabolites measured using ¹H NMR were statistically similar to the compounds quantified using the chromatography techniques (p < 0.001). In addition, using PCA, we were able to show that different metabolites, measured using the various analytical techniques produced very similar scores and loadings plots for all the analysis and extraction techniques undertaken across the 21 day time domain. Using a combination of these three different techniques provides a unique and holistic insight into the biochemistry behind the conversion of muscle to meat which would not be possible using any single technique alone.     

Applying pinwheel scheduling and compiler profiling for power-aware real-time scheduling

Energy consumption is a critical design issue in embedded systems, especially in battery-operated systems. Maintaining high performance while extending the battery life is an interesting challenge for system designers. Dynamic voltage scaling and dynamic frequency scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Because of the high complexity involved, most solutions depend on heuristics for online power-aware real-time scheduling or offline time-consuming scheduling. In this paper, we discuss how we can apply pinwheel model to power-aware real-time scheduling so that task information, including start times, finish times, preemption times, etc, can be efficiently derived using pinwheel model. System predictability is thus increased and under better control on power-awareness. However, job execution time may be only a small portion of its worst case execution time and can only be determined at runtime. We implement a profiling tool to insert codes for collecting runtime information of real-time tasks. Worst case execution time is updated online for scheduler to perform better rescheduling according to actual execution. Simulations have shown that at most 50% energy can be saved by the proposed scheduling algorithm. Moreover, at most additional 33% energy can be saved when the profiling technique is applied. This paper is an extended version of the paper Power-Aware Real-Time Scheduling using Pinwheel Model and Profiling Technique that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications.