Wearable cognitive assistants in a factory setting: a critical review of a promising way of enhancing cognitive performance and well-being

Cognition, Technology & Work - Rapid technological innovations are constantly influencing the complexification and automatization of the work lines pushing human operators to use diverse...     

Guided architecture trade space exploration: fusing model-based engineering and design by shopping

Software and Systems Modeling - Advances in model-based system engineering have greatly increased the predictive power of models and the analyses that can be run on them. At the same time, designs...     

纵向旋转切削加工对环形零件畸变的影响

通过旋转试验和有限元分析介绍了工件在切削加工过程中产生的畸变情况，分析了工件的装夹方式、切削速度、切削深度和进刀量对100Cr6钢环圆度的影响。通过去应力退火释放冷加工诱发的残余应力后工件的圆度与切削参数有关。另外测试了被试验环的表面残余应力，其表面残余应力与装夹方式有关。将测量的装夹力作为计算参数输入，通过有限元分析方法测试了装夹方式对工件变形的影响。协同测量结果示出了装夹方式影响工件变形的一个主要因素，表面残余应力与工件的径向变形有关，最大的拉伸应力位于夹口位置。旋转切削试验结果表明，提高切削速度圆度会稍有增加；随着切削深度的加大，圆度呈下降趋势，尽管切削力增加了；进给量的增加会导致更高的切削力，因此圆度值也增加；常规的去应力退火可使被加工环的圆度值增加。     

Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors

Tracking the spatio-temporal activity is highly relevant for domains like security, health, and quality management. Since animal welfare became a topic in politics and legislation locomotion patterns of livestock have received increasing interest. In contrast to the monitoring of pedestrians cattle activity tracking poses special challenges to both sensors and data analysis. Interesting states are not directly observable by a single sensor. In addition, sensors must be accepted by cattle and need to be robust enough to cope with a rough environment. In this article, we introduce the novel combination of heart rate and positioning sensors. Attached to neck and chest they are less interfering than accelerometers at the ankles. Exploiting the potential of such combined sensor system that records locomotion and non-spatial information from the heart rate sensor however is challenging. We introduce a novel two level method for the activity tracking focused on the duration and sequence of activity states. We combine Support Vector Machine (SVM) with Conditional Random Field (CRF) and extend Conditional Random fields by an explicit representation of duration. The SVM characterizes local activity states, whereas the CRF addresses sequences of local states to sequences incorporating spatial and non-spatial contextual knowledge. This combination provides a reliable and comprehensive identification of defined activity patterns, as well as their chronology and durations, suitable for the integration in an activity data base. This data base is used to extract physiological parameters and promises insights into internal states such as fitness, well-being and stress. Interestingly we were able to demonstrate a significant correlation between resting pulse rate and the day of pregnancy.     

Electrochemical Behavior of Single CuO Nanoparticles: Implications for the Assessment of their Environmental Fate

The electrochemical behavior of copper oxide nanoparticles is investigated at both the single particle and at the ensemble level in neutral aqueous solutions through the electrode‐particle collision method and cyclic voltammetry, respectively. The influence of Cl^? and NO₃^? anions on the electrochemical processes occurring at the nanoparticles is further evaluated. The electroactivity of CuO nanoparticles is found to differ between the two types of experiments. At the single‐particle scale, the reduction of the CuO nanoparticles proceeds to a higher extent in the presence of chloride ion than of nitrate ion containing solutions. However, at the multiparticle scale the CuO reduction proceeds to the same extent regardless of the type of anions present in solution. The implications for assessing realistically the environmental fate and therefore the toxicity of metal‐based nanoparticles in general, and copper‐based nanoparticles in particular, are discussed.     

The search for a research method for studying OSS process innovation

Medium-sized, open-participation Open Source Software (OSS) projects do not usually perform explicit software process improvement on any routine basis. It would be useful to understand how to get such a project to accept a process improvement proposal and hence to perform process innovation. We want to determine an effective and feasible qualitative research method for studying the above question. We present (narratively) a case study of how we worked towards and eventually found such a research method. The case involves four attempts at collecting suitable data about innovation episodes (direct participation (twice), polling developers for episodes, manually finding episodes in mailing list archives) and the adaptation of the Grounded Theory data analysis methodology. Direct participation allows gathering rather rich data, but does not allow for observing a sufficiently large number of innovation episodes. Polling developers for episodes did not prove to be useful. Using mailing list archives to find data to be analyzed is both feasible and effective. We also describe how the data thus found can be analyzed based on the Grounded Theory Method with suitable adjustments. By-and-large, our findings ought to apply to studying various phenomena in OSS development processes that are similarly heavyweight and infrequent. However, specific details may block this possibility and we cannot predict which details that might be. The amount of effort involved in direct participation approaches to qualitative research can easily be underestimated. Also, survey approaches are not well-suited for many process issues in OSS, because too few developers are sufficiently process-conscious. An approach based on passive observation is a viable alternative in the OSS context due to the availability of large amounts of fairly complete archival data.     

From monolithic to component-based performance evaluation of software architectures

Model-based performance evaluation methods for software architectures can help architects to assess design alternatives and save costs for late life-cycle performance fixes. A recent trend is component-based performance modelling, which aims at creating reusable performance models; a number of such methods have been proposed during the last decade. Their accuracy and the needed effort for modelling are heavily influenced by human factors, which are so far hardly understood empirically. Do component-based methods allow to make performance predictions with a comparable accuracy while saving effort in a reuse scenario? We examined three monolithic methods (SPE, umlPSI, Capacity Planning (CP)) and one component-based performance evaluation method (PCM) with regard to their accuracy and effort from the viewpoint of method users. We conducted a series of three experiments (with different levels of control) involving 47 computer science students. In the first experiment, we compared the applicability of the monolithic methods in order to choose one of them for comparison. In the second experiment, we compared the accuracy and effort of this monolithic and the component-based method for the model creation case. In the third, we studied the effort reduction from reusing component-based models. Data were collected based on the resulting artefacts, questionnaires and screen recording. They were analysed using hypothesis testing, linear models, and analysis of variance. For the monolithic methods, we found that using SPE and CP resulted in accurate predictions, while umlPSI produced over-estimates. Comparing the component-based method PCM with SPE, we found that creating reusable models using PCM takes more (but not drastically more) time than using SPE and that participants can create accurate models with both techniques. Finally, we found that reusing PCM models can save time, because effort to reuse can be explained by a model that is independent of the inner complexity of a component. The tasks performed in our experiments reflect only a subset of the actual activities when applying model-based performance evaluation methods in a software development process. Our results indicate that sufficient prediction accuracy can be achieved with both monolithic and component-based methods, and that the higher effort for component-based performance modelling will indeed pay off when the component models incorporate and hide a sufficient amount of complexity.     

Modular architecture of the microfactories for automatic micro-assembly

The construction of a new generation of MEMS which includes micro-assembly steps in the current microfabrication process is a big challenge. It is necessary to develop new production means named micromanufacturing systems in order to perform these new assembly steps. The classical approach called “top-down” which consists in a functional analysis and a definition of the tasks sequences is insufficient for micromanufacturing systems. Indeed, the technical and physical constraints of the microworld (e.g. the adhesion phenomenon) must be taken into account in order to design reliable micromanufacturing systems. A new method of designing micromanufacturing systems is presented in this paper. Our approach combines the general “top-down” approach with a “bottom-up” approach which takes into account technical constraints. The method enables to build a modular architecture for micromanufacturing systems. In order to obtain this modular architecture, we have devised an original identification technique of modules and an association technique of modules. This work has been used to design the controller of an experimental robotic micro-assembly station.