It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.

The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.

The front end of recycling is familiar to the point of invisibility: Blue bins, clear bags, and barely comprehensible signs designating which material goes where. Once the right plastic or paper is put in the right place, most people forget all about it. For the actual recycled material, though, that's not the end of the journey but rather the beginning. Most of it gets trucked to a special recycling facility, where it is unceremoniously dumped on a concrete floor. Front-end loaders scoop bottles, papers, and myriad other materials onto conveyors, which zoom off in various directions, often climbing to different levels like staircases.

NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK Do you develop NIR / NIRS calibrations by yourself? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared "Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution" LINK "Advances in Near Infrared Spectroscopy and Related Computational Methods" MDPI Books – Pages: 496 OpenAccess NIRSpectroscopy NIRS NIR LINK " Ampliación de una librería espectral de mezclas unifeed analizadas en un instrumento NIRS de laboratorio" LINK "Applied Sciences, Vol. 9, Pages 5058: Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy" LINK " Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK "Non-invasive measurements of'Yunhe'pears by vis-NIRS technology coupled with deviation fusion modeling approach" LINK "Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties." LINK "Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis" LINK "Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "NIR spectroscopic determination of urine components in spot urine: preliminary investigation towards optical point-of-care test." LINK "O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression."

New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.

We've written a lot about how new technologies are building a better supply chain. But technologies such as AI, IoT and advanced analytics can only achieve their true potential if all parties within the supply chain network are working together. Even the smallest, most well-intentioned decisions made by individual stakeholders can cause catastrophic failures in the manufacturing and delivery of goods. Modern collaborative business planning lets partners on a supply chain work off shared data unspoiled by human misconceptions and misestimations. Automated integrated planning removes bias from supply chain data and creates a single, transparent source that engenders collaboration.

The paper announces the new long-term challenge for improving the performance of automatic speech recognition systems. The goal of the challenge is to investigate methods of correcting the recognition results on the basis of previously made errors by the speech processing system. The dataset prepared for the task is described and evaluation criteria are presented.

Sign in to report inappropriate content. Dorabot's Robot for recycling, can identify, pick, and sort recyclable items such as plastic bottles, glass bottles, paper, cartons, and aluminum cans. The robot has deep learning-based computer vision and dynamic planning to select items in a moving conveyor belt. It also includes customized and erosion resistant grippers to pick irregularly shaped items, which results in a cost-effective integrated solution. Follow us on Twitter: https://twitter.com/dorabot_inc

A BSTRACT A fundamental goal in deep learning is the characterization of trainability and generalization of neural networks as a function of their architecture and hyper-parameters. In this paper, we discuss these challenging issues in the context of wide neural networks at large depths where we will see that the situation simplifies considerably. To do this, we leverage recent advances that have separately shown: (1) that in the wide network limit, random networks before training are Gaussian Processes governed by a kernel known as the Neural Network Gaussian Process (NNGP) kernel, (2) that at large depths the spectrum of the NNGP kernel simplifies considerably and becomes "weakly data-dependent", and (3) that gradient descent training of wide neural networks is described by a kernel called the Neural Tangent Kernel (NTK) that is related to the NNGP . Here we show that in the large depth limit the spectrum of the NTK simplifies in much the same way as that of the NNGP kernel. By analyzing this spectrum, we arrive at a precise characterization of trainability and a necessary condition for generalization across a range of architectures including Fully Connected Networks (FCNs) and Con-volutional Neural Networks (CNNs). In particular, we find that there are large regions of hyperparameter space where networks can only memorize the training set in the sense they reach perfect training accuracy but completely fail to generalize outside the training set, in contrast with several recent results. By comparing CNNs with-and without-global average pooling, we show that CNNs without average pooling have very nearly identical learning dynamics to FCNs while CNNs with pooling contain a correction that alters its generalization performance. We perform a thorough empirical investigation of these theoretical results and finding excellent agreement on real datasets. Historically, the rampant success of deep learning models has lacked a sturdy theoretical foundation; architectures, hyperparameters, and learning algorithms are often selected by brute force search (Bergstra & Bengio, 2012) and heuristics (Glorot & Bengio, 2010). Recently, significant theoretical progress has been made on several fronts that have shown promise in making neural network design more systematic. In particular, in the infinite width (or channel) limit, the distribution of functions induced by neural networks with random weights and biases has been precisely characterized before, during, and after training. The study of infinite networks dates back to seminal work by Neal (1994) who showed that the distribution of functions given by single hidden-layer networks with random weights and biases in the infinite-width limit are Gaussian Processes (GPs). Recently, there has been renewed interest in studying random, infinite, networks starting with concurrent work on "conjugate kernels" (Daniely et al., 2016; Daniely, 2017) and "mean-field theory" (Poole et al., 2016; Schoenholz et al., 2017).

Sequential decision-making under uncertainty for optimal life-cycle control of deteriorating engineering systems and infrastructure entails two fundamental classes of decisions. The first class pertains to the various structural interventions, which can directly modify the existing properties of the system, while the second class refers to prescribing appropriate inspection and monitoring schemes, which are essential for updating our existing knowledge about the system states. The latter have to rely on quantifiable measures of efficiency, determined on the basis of objective criteria that, among others, consider the Value of Information (VoI) of different observational strategies, and the Value of Structural Health Monitoring (VoSHM) over the entire system life-cycle. In this work, we present general solutions for quantifying the VoI and VoSHM in partially observable stochastic domains, and although our definitions and methodology are general, we are particularly emphasizing and describing the role of Partially Observable Markov Decision Processes (POMDPs) in solving this problem, due to their advantageous theoretical and practical attributes in estimating arbitrarily well globally optimal policies. POMDP formulations are articulated for different structural environments having shared intervention actions but diversified inspection and monitoring options, thus enabling VoI and VoSHM estimation through their differentiated stochastic optimal control policies. POMDP solutions are derived using point-based solvers, which can efficiently approximate the POMDP value functions through Bellman backups at selected reachable points of the belief space. The suggested methodology is applied on stationary and non-stationary deteriorating environments, with both infinite and finite planning horizons, featuring single- or multi-component engineering systems.