LA HAGUE, France – At a nuclear waste site in Normandy, robotic arms guided by technicians behind a protective shield maneuver a pipe that will turn radioactive chemicals into glass as France seeks to make safe the byproducts of its growing reliance on atomic power. The fuel-cooling pools in La Hague, on the country's northwestern tip, could be full by the end of the decade, and state-owned Orano, which runs them, says the government needs to outline a long-term strategy to modernize its ageing facilities no later than 2025. While more nuclear energy can help France and other countries to reduce planet-warming emissions, environmental campaigners say it replaces one problem with another. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Using NIR Spectroscopy and don't want to pay for a calibration abo or a subscription based software/service? If you would like Pay per calibration, then CalibrationModel is the solution for you. "Near infrared spectroscopy for blend uniformity monitoring: An innovative qualitative application based on the coefficient of determination" LINK "Research on the secondary structure and hydration water around human serum albumin induced by ethanol with infrared and near-infrared spectroscopy" LINK "Point-of-Care Using Vis-NIR Spectroscopy for White Blood Cell Count Analysis" LINK "Rapid determination of viscosity and viscosity index of lube base oil based on near-infrared spectroscopy and new transformation formula" LINK "A recognition method of mushroom mycelium varieties based on near-infrared spectroscopy and deep learning model" LINK "Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods" LINK "Detection of early collision and compression bruises for pears based on hyperspectral imaging technology" LINK "Hyperspectral Imaging based Detection of PVC during Sellafield Repackaging Procedures" LINK "Study on the detection of apple soluble solids based on fractal theory and hyperspectral imaging technology" LINK "Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques" LINK "Sensors: Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression" LINK "Foods: Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method" LINK "Analysis of physio-chemical properties of solution grown third order nonlinear optical single crystal: 1, 4-oxazinanium nitrate for photonic applications" LINK "A novel composite colorimetric sensor array for quality characterization of shrimp paste based on indicator displacement assay and etching of silver nanoprisms" LINK "Research on weed identification method in rice fields based on UAV remote sensing" LINK "Flexible Microspectrometers Based on Printed Perovskite Pixels with Graded Bandgaps" spectrometers miniaturization LINK "Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification" LINK "Biomedicines: Fourier Transform Infrared Spectroscopy Reveals Molecular Changes in Blood Vessels of Rats Treated with Pentadecapeptide BPC 157" LINK "Electrochromic Tungsten Oxide Nanofilms and Ionic Liquid Based Ion Conductor for Smart Windows Development: Preparation, Characterization and …" LINK

The forward model, usually defined through partial differential equations (PDEs), describes certain physical phenomena with parameters as inputs. Generally, solving the forward model is computationally expensive but well-defined. In contrast, the related inverse problem which aims at inferring hidden parameters that cannot be directly observed from limited and noisy observations is typically ill-posed: different sets of parameters can result in similar sensor measurements, and there may be no feasible solution to fit the observed data, or minor errors can render unpredictable changes in the forward model. The Bayesian methods [5, 6], by viewing the unknown parameters as random variables, formulate the inverse problem into a probabilistic problem to capture the uncertainty in observations, forward models, and prior knowledge. One can assign a prior distribution to reflect our knowledge of the parameters before any measurements are made. The likelihood function is characterized through the forward model.

Airborne topographic LiDAR is an active remote sensing technology that emits near-infrared light to map objects on the Earth's surface. Derived products of LiDAR are suitable to service a wide range of applications because of their rich three-dimensional spatial information and their capacity to obtain multiple returns. However, processing point cloud data still requires a significant effort in manual editing. Certain human-made objects are difficult to detect because of their variety of shapes, irregularly-distributed point clouds, and low number of class samples. In this work, we propose an efficient end-to-end deep learning framework to automatize the detection and segmentation of objects defined by an arbitrary number of LiDAR points surrounded by clutter. Our method is based on a light version of PointNet that achieves good performance on both object recognition and segmentation tasks. The results are tested against manually delineated power transmission towers and show promising accuracy.

Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set. We also show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception.

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer We combine statistical methods and deep learning-based forecasting methods to enhance probabilistic forecasts. We evaluate ProbPNN empirically on more than 1000 time series from an Electricity and a Traffic data set. On these datasets, the proposed ProbPNN outperforms existing state-of-the-art methods. Abstract Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts.

Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air. For propulsion, the operating range is switched for the different mediums by the dual-speed propulsion unit, providing sufficient thrust and also ensuring output efficiency. For thruster configuration, thrust vectoring is realized by the rotation of the propulsion unit around the mount arm, thus enhancing the underwater maneuverability. This paper presents a quadrotor prototype of this concept and the design details and realization in practice.

Power capacitor device is a widely used reactive power compensation equipment in power transmission and distribution system which can easily have internal fault and therefore affects the safe operation of the power system. An intelligent non-destructive testing (I-NDT) method based on ICT is proposed to test the quality of power capacitors automatically in this study. The internal structure of power capacitors would be scanned by the ICT device and then defects could be recognized by the SSD algorithm. Moreover, the data data augmentation algorithm is used to extend the image set to improve the stability and accuracy of the trained SSD model.

Building structures detection and information about these buildings in aerial images is an important solution for city planning and management, land use analysis. It can be the center piece to answer important questions such as planning evacuation routes in case of an earthquake, flood management, etc. These applications rely on being able to accurately retrieve up-to-date information. Being able to accurately detect buildings in a bounding box centered on a specific latitude-longitude value can help greatly. The key challenge is to be able to detect buildings which can be commercial, industrial, hut settlements, or skyscrapers. Once we are able to detect such buildings, our goal will be to cluster and categorize similar types of buildings together.

The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the predictive accuracy of TSF. Furthermore, model structures have been proposed to combine time-series decomposition methods, such as seasonal-trend decomposition using Loess (STL) to ensure improved predictive accuracy. However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using five univariate time-series datasets and four multivariate time-series data. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results show that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.