METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

Characterization of multilayer stack parameters from X-ray reflectivity data using the PPM program: measurements and comparison with TEM results

Future hard (10 -100 keV) X-ray telescopes (SIMBOL-X, Con-X, HEXIT-SAT, XEUS) will implement focusing optics with multilayer coatings: in view of the production of these optics we are exploring several deposition techniques for the reflective coatings. In order to evaluate the achievable optical performance X-Ray Reflectivity (XRR) measurements are performed, which are powerful tools for the in-depth characterization of multilayer properties (roughness, thickness and density distribution). An exact extraction of the stack parameters is however difficult because the XRR scans depend on them in a complex way. The PPM code, developed at ERSF in the past years, is able to derive the layer-by-layer properties of multilayer structures from semi-automatic XRR scan fittings by means of a global minimization procedure in the parameters space. In this work we will present the PPM modeling of some multilayer stacks (Pt/C and Ni/C) deposited by simple e-beam evaporation. Moreover, in order to verify the predictions of PPM, the obtained results are compared with TEM profiles taken on the same set of samples. As we will show, PPM results are in good agreement with the TEM findings. In addition, we show that the accurate fitting returns a physically correct evaluation of the variation of layers thickness through the stack, whereas the thickness trend derived from TEM profiles can be altered by the superposition of roughness profiles in the sample image

Towards Runtime Verification via Event Stream Processing in Cloud Computing Infrastructures

Software bugs in cloud management systems often cause erratic behavior, hindering detection, and recovery of failures. As a consequence, the failures are not timely detected and notified, and can silently propagate through the system. To face these issues, we propose a lightweight approach to runtime verification, for monitoring and failure detection of cloud computing systems. We performed a preliminary evaluation of the proposed approach in the OpenStack cloud management platform, an “off-the-shelf” distributed system, showing that the approach can be applied with high failure detection coverage

Maximizing Compressor Efficiency While Maintaining Reliability.

LecturePg. 91-100The natural gas and chemical processing industries have historically and necessarily demanded high reliability from their centrifugal compressors, which has led to a significant emphasis on field experience of designs. This emphasis has sometimes resulted in new units which reflect design and manufacturing practices which can be improved upon. Users can, in many cases, significantly increase efficiencies by considering designs which use more recently developed technologies that create only refinements of machines historically used in their applications. Accurately machined three-dimensional impellers are an example of an under-utilized available technology for multistage compressors. These designs used in several stages can provide significant efficiency gains, particularly at higher flows. At lower flows, impeller efficiencies can be improved by a process called abrasive flow machining. This process can provide similar benefits in process compressors by improving surface finish in areas that cannot be reached with conventional metal finishing techniques. Advancements in machine tool technology have also allowed changes in compressor casing designs. Numerical control (NC) machine tools can be used to machine inlets and variable area discharge volutes in the same axial casing space, thereby improving efficiencies through generous volute sizing without requiring additional diameter and bearing span. Specific examples of uses of these design and manufacturing technologies and comparisons to alternative designs are detailed. The data presented show that these technologies can be used with confidence to provide high compressor efficiencies while maintaining reliability

Systems-theoretic Safety Assessment of Robotic Telesurgical Systems

Robotic telesurgical systems are one of the most complex medical cyber-physical systems on the market, and have been used in over 1.75 million procedures during the last decade. Despite significant improvements in design of robotic surgical systems through the years, there have been ongoing occurrences of safety incidents during procedures that negatively impact patients. This paper presents an approach for systems-theoretic safety assessment of robotic telesurgical systems using software-implemented fault-injection. We used a systemstheoretic hazard analysis technique (STPA) to identify the potential safety hazard scenarios and their contributing causes in RAVEN II robot, an open-source robotic surgical platform. We integrated the robot control software with a softwareimplemented fault-injection engine which measures the resilience of the system to the identified safety hazard scenarios by automatically inserting faults into different parts of the robot control software. Representative hazard scenarios from real robotic surgery incidents reported to the U.S. Food and Drug Administration (FDA) MAUDE database were used to demonstrate the feasibility of the proposed approach for safety-based design of robotic telesurgical systems.Comment: Revise based on reviewers feedback. To appear in the the International Conference on Computer Safety, Reliability, and Security (SAFECOMP) 201