Towards Aggregating Time-Discounted Information in Sensor Networks

Sensor networks are deployed to monitor a seemingly endless list of events in a multitude of application domains. Through data collection and aggregation enhanced with data mining and machine learning techniques, many static and dynamic patterns can be found by sensor networks. The aggregation problem is complicated by the fact that the perceived value of the data collected by the sensors is affected by many factors such as time, location and user valuation. In addition, the value of information deteriorates often dramatically over time. Through our research, we already achieved some results: A formal algebraic analysis of information discounting, especially affected by time. A general model and two specific models are developed for information discounting. The two specific models formalize exponetial time-discount and linear time-discount. An algebraic analysis of aggregation of values that decay with time exponentially. Three types of aggregators that offset discounting effects are formalized and analyzed. A natural synthesis of these three aggregators is discovered and modeled. We apply our theoretical models to emergency response with thresholding and confirm with extensive simulation. For long-term monitoring tasks, we laid out a theoretical foundation for discovering an emergency through generations of sensors, analysed the achievability of a long-term task and found an optimum way to distribute sensors in a monitored area to maximize the achievability. We proposed an implementation for our alert system with state-of-art wireless microcontrollers, sensors, real-time operating systems and embedded internet protocols. By allowing aggregation of time-discounted information to proceed in an arbitrary, not necessarily pairwise manner, our results are also applicable to other similar homeland security and military application domains where there is a strong need to model not only timely aggregation of data collected by individual sensors, but also the dynamics of this aggregation. Our research can be applied to many real-world scenarios. A typical scenario is monitoring wildfire in the forest: A batch of first-generation sensors are deployed by UAVs to monitor a forest for possible wildfire. They monitor various weather quantities and recognize the area with the highest possibility of producing a fire --- the so-called area of interest (AoI). Since the environment changes dynamically, so after a certain time, the sensors re-identify the AoI. The value of the knowledge they learned about the previous AoI decays with time quickly, our methods of aggregation of time-discounted information can be applied to get update knowledge. Close to depletion of their energy of the current generation of sensors, a new generation of sensors are deployed and inherit the knowledge from the current generation. Through this way, monitoring long-term tasks becomes feasible. At the end of this thesis, we propose some extensions and directions from our current research: Generalize and extend the special classes of Type 1 and Type 2 aggregation operators; Analyze aggregation operator of Type 3 and Type 4, find some special applicable candidates; Data aggregation across consecutive generations of sensors in order to learn about events with discounting that take a long time to manifest themselves; Network implications of various aggregation strategies; Algorithms for implementation of some special classes of aggregators. Implement wireless sensor network that can autonomously learn and recognize patterns of emergencies, predict incidents and trigger alarms through machine learning

Finite element simulation for the effect of loading rate on visco-hyperelastic characterisation of soft materials by spherical nanoindentation

Nanoindentation test performed by atomic force microscopy is highly recommended for the characterisation of soft materials at nanoscale. The assumption proposed in the characterisation is that the material is pure elastic with no viscosity. However, this assumption does not represent the real characteristics of soft materials such as bio tissues or cells. Therefore, a parametric finite element simulation of nanoindentation by spherical tip was carried out to investigate the response of cells with different constitutive laws (elastic, hyperelastic and visco-hyperelastic). The investigation of the loading rate effect on the characterisation of cell mechanical properties was performed for different size of spherical tips. The selected dimensions of spherical tips cover commercially available products. The viscosity effects are insensitive to the varied dimensions of spherical tip in this study. A limit loading rate was found above which viscous effect has to be considered to correctly determine the mechanical properties. The method in this work can be implemented to propose a criterion for the threshold of loading rate when viscosity effect can be neglected for soft material characterisation

Correlation analysis of surface tilt effect on its mechanical properties by nano-indentation

In this study, finite element analysis and nano-indentation experiments were carried out to investigate the effect of surface tilt on the nanoindentation test results. This paper revealed that standard Oliver–Pharr method underestimated the contact area due to the influence of the tilt condition. Consequently, it is necessary to compensate this difference to ensure that the result is reliable. The finding was verified by the nano-indentation experiments on a sinusoidal surface sample, which is used for the study of correlation between surface topography and its mechanical properties. A corrective action was implemented to compensate the errors by finite element analysis. By eliminating such errors, the study of the relationship between surface topography and mechanical properties was performed and discussed

Strongly Enhanced Current-Carrying Performance in MgB2 Tape Conductors by Novel C60 Doping

MgB2 is a promising superconductor for important large-scale applications for both high field magnets and cryocooler-cooled magnet operated at temperatures around 20 K. In this work, by utilizing C60 as a viable alternative dopant, we demonstrate a simple and industrially scaleable rout that yields a 10-15-fold improvement in the in-high-field current densities of MgB2 tape conductors. For example, a Jc value higher than 4x10^4 A/cm^2 (4.2 K, 10 T), which exceeds that for NbTi superconductor, can be realized on the C60 doped MgB2 tapes. It is worth noting that this value is even higher than that fabricated using strict high energy ball milling technique under Ar atmosphere. At 20 K, Hirr was about 10 T for C60 doped MgB2 tapes. A large amount of nanometer-sized precipitates and grain boundaries were found in MgB2 matrix. The special physical and chemical characteristic of C60, in addition to its C containing intrinsic essence, is a key point in enhancing the superconducting performance of MgB2 tapes.Comment: 18 pages, 5 figure

Mask-guided Style Transfer Network for Purifying Real Images

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared with real images, the desired performance cannot be achieved. To solve this problem, the previous method learned a model to improve the realism of the synthetic images. Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images. In this paper, we first introduce the segmentation masks to construct RGB-mask pairs as inputs, then we design a mask-guided style transfer network to learn style features separately from the attention and bkgd(background) regions and learn content features from full and attention region. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on various public datasets, including LPW, COCO and MPIIGaze. Experimental results show that the proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582

Development of Powder-in-Tube Processed Iron Pnictide Wires and Tapes

The development of the PIT fabrication process of iron pnictide superconducting wires and tapes has been carried out in order to enhance their transport properties. Silver was found to be the best sheath material, since no reaction layer was observed between the silver sheath and the superconducting core. The grain connectivity of iron pnictide wires and tapes has been markedly improved by employing Ag or Pb as dopants. At present, critical current densities in excess of 3750 A/cm^2 (Ic = 37.5 A) at 4.2 K have been achieved on Ag-sheathed SrKFeAs wires prepared with the above techniques, which is the highest in iron-based wires and tapes so far. Moreover, Ag-sheathed Sm-1111 superconducting tapes were successfully prepared by PIT method at temperatures as low as 900C, instead of commonly used temperatures of 1200C. These results demonstrate the feasibility of producing superconducting pnictide composite wires, even grain boundary properties require much more attention.Comment: 4 pages, 6 figures. Submitted to ASC2010 proceeding