Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Though it is an intuitive controller easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as artificial neural network-based (ANN-based) approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC with feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.

Traditional clustering algorithms such as K-means rely heavily on the nature of the chosen metric or data representation. To get meaningful clusters, these representations need to be tailored to the downstream task (e.g. cluster photos by object category, cluster faces by identity). Therefore, we frame clustering as a meta-learning task, few-shot clustering, which allows us to specify how to cluster the data at the meta-training level, despite the clustering algorithm itself being unsupervised. We propose Centroid Networks, a simple and efficient few-shot clustering method based on learning representations which are tailored both to the task to solve and to its internal clustering module. We also introduce unsupervised few-shot classification, which is conceptually similar to few-shot clustering, but is strictly harder than supervised* few-shot classification and therefore allows direct comparison with existing supervised few-shot classification methods. On Omniglot and miniImageNet, our method achieves accuracy competitive with popular supervised few-shot classification algorithms, despite using *no labels* from the support set. We also show performance competitive with state-of-the-art learning-to-cluster methods.

Statistical uncertainties are rarely incorporated in machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties, though with significantly increased computational cost. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating the ability of algorithms to detect anomalies.

Frank n Raf Market Research LLP As per Frank n Raf latest Research report, The global industrial robotics market size is expected to reach USD 41.23 billion by 2020., increasing at a CAGR of 7.0% through the forecast period. The accelerated expansion of the automotive industry worldwide and increasing adoption of robotics in the non-automotive industry including chemicals, food & beverage, rubber & plastics, and electronics/electrical are stoking the growth of the market. Companies executing industrial robots are frequently realizing substantial financial advantages, which is pointing to a surge in installation of robots in modern manufacturing plants. Combination of robots with production processes help increase productivity, reduces overheads, contributes a high degree of flexibility, improves quality, and reduces waste to a large range as compared to the outcome of manual labor, which consequently drives the market. Industrial robots have been effective for the formation of a new ecosystem distinguished by rewarding, lucrative, and high-paying jobs.

The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.

WINTER PARK, Fla.--(BUSINESS WIRE)--Feb 20, 2019--COPC Inc., a global consulting firm that helps companies improve operations to transform the customer experience, and Execs In The Know, a global community of customer experience professionals, have announced the release of the 2018 Corporate Edition of the Customer Experience Management Benchmark (CXMB) Series. The report, The CX Journey: Understanding Corporate Strategies and Best Practices, provides customer experience management insights from the corporate perspective. A key finding is that since 2017, companies have dramatically increased their use of artificial intelligence (AI)-powered solutions for customer service. "Our new corporate report shows that companies see tremendous potential in AI-powered solutions for customer care, both in applications that are customer-facing and in those that assist call center agents with their work. However, we also know from previous research that customers want a quick and easy way out of any AI-powered solution to reach a live person. Our findings overwhelmingly show that companies are keenly aware of this necessity in any customer-facing application. And while customers still want that personal interaction, we think that AI-powered solutions will find their appropriate place in the service journey," said Kyle Kennedy, president and chief operating officer, COPC Inc.

Traditionally undervalued in the tech industry, empathy -- which is the ability to read and respond to another person's feelings, thoughts and experiences -- is a trait hiring managers and C-level executives can no longer ignore. After all, in a world where artificial intelligence will take up to 5 million jobs away from humans by 2020, the McKinsey Global Institute predicts that up to 14% of human workers will need to adapt to new occupations to secure our future in the workforce. In other words, as we start sharing the workforce with more machines, human soft skills such as empathy will be at a premium. And, that premium is justified. Hiring employees who are empathetic helps companies increase productivity, develop strong leadership and retain high-performing talent.

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.

Artificial intelligence (AI) is a rapidly growing industry that's perpetually impressing people with what's possible. Those advancements wouldn't happen without the people working tirelessly to research innovations. Many of the people pushing artificial intelligence forward are male, and that's evidence of a known gender gap associated with the industry. Concentrated efforts are needed to tackle the problem, but it's a situation that could change. The five women here are among those leading the way in AI research and inspiring everyone by their dedication.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.