The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach for large scale streaming data. We developed a novel monitoring method named Adaptive Principal Component monitoring (APC) which adaptively chooses PCs that are most likely to vary due to the change for early detection. Importantly, we integrate a novel diagnostic approach, Principal Component Signal Recovery (PCSR), to enable a streamlined SPC. This diagnostics approach draws inspiration from Compressed Sensing and uses Adaptive Lasso for identifying the sparse change in the process. We theoretically motivate our approaches and do a performance evaluation of our integrated M&D method through simulations and case studies.

We consider the torpedo scheduling problem in steel production, which is concerned with the transport of hot metal from a blast furnace to an oxygen converter. A schedule must satisfy, amongst other considerations, resource capacity constraints along the path and the locations traversed as well as the sulfur level of the hot metal. The goal is first to minimize the number of torpedo cars used during the planning horizon and second to minimize the time spent desulfurizing the hot metal. We propose an exact solution method based on Logic based Benders Decomposition using Mixed-Integer and Constraint Programming, which optimally solves and proves, for the first time, the optimality of all instances from the ACP Challenge 2016 within 10 minutes. In addition, we adapted our method to handle large-scale instances and instances with a more general rail network. This adaptation optimally solved all challenge instances within one minute and was able to solve instances of up to 100,000 hot metal pickups.

You are free to share this article under the Attribution 4.0 International license. A new system combines artificial neural networks with infrared thermal imaging to control and interpret chemical reactions with precision and speed that far outpace conventional methods. Machine learning algorithms can predict stock market fluctuations, control complex manufacturing processes, enable navigation for robots and driverless vehicles, and much more. Now, researchers are tapping a new set of capabilities in this field of artificial intelligence with their new technique. "This system can reduce the decision-making process about certain chemical manufacturing processes from one year to a matter of weeks…" The researchers developed and tested the new method on microreactors that allow chemical discoveries to take place quickly and with far less environmental waste than standard large-scale reactions.

Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. Moreover, the proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach. Keywords: Sensor drift; Electronic nose; Subspace projection method; Domain adaptation; Transfer learning.

The vastness of the archival chemistry literature is both a blessing and a curse. The reaction that you're looking for is probably in there, provided you take enough time to search for it. Gao et al. trained a neural network model on 10 million known reactions to speed up this process. Specifically, the model was charged with predicting a catalyst, reagents, solvents, and temperature to achieve a given transformation. When tested, the model's top-10 list of suggestions produced a close match to actual conditions nearly 70% of the time, with a 20 C error margin in temperature.

Vigilante citizens in a town in Arizona have slashed tyres, thrown rocks and even pointed guns at self-driving vehicles being tested in their neighbourhood, an investigation has revealed. Police in Chandler recorded 21 incidents over the past two years in which the autonomous vehicles and their test drivers were targeted by local residents. One incident on 24 October saw a man emerge from a park and slash the tyres of a Waymo vehicle stopped at an intersection. Earlier this year a Waymo test driver reported a man in shorts aiming a gun at his car when it passed the man's driveway. Police reports also show that rocks were thrown at Waymo's fleet on at least four separate occasions, according to The Arizona Republic, while other incidents include people yelling at the vehicles, chasing them and forcing them off the road.

When you're trying to come up with gift ideas for someone who likes to cook, you want to find something that's both personal and practical. But finding a gift for a home cook that strikes that balance can be hard, especially if you're the kind of person whose fridge is filled with takeout containers. That's why we've gathered 35 of the best gifts for every type of home cook on your list--from the newbie who just wants to make a good grilled cheese to the home cook who has it all--all of them are available on Amazon, most of them with two-day Prime shipping. ChefSteps Joule Sous Vide, 1100 Watts, All White ($179) They might not think they need a sous vide machine, but that's exactly what makes it a great gift for an experienced chef, who can use it to make always-tender steaks, never-overcooked fish, and even soft-scrambled eggs. Echo Show (Second Generation) ($230) The new generation of the Echo Show has louder speakers and a bigger screen than before, so they can follow along with recipe videos and tutorials from any one of Amazon's partners, or ask Alexa to set a timer.

China is just days away from kicking off a historic mission to explore the dark side of the moon. The Chang'e-4, which launches on December 8th, will be the first ever rover to land on the far side of the lunar surface. The spacecraft is slated to leave from the Xichang Satellite Launch Center in Sichuan province, before entering the moon's orbit and hopefully touching down in the massive, 186-kilometer-wide Von Karman crater on the moon's south pole. The Chang'e-4, which launches on December 8th, will be the first ever rover land on the far side of the lunar surface. It will visit an unexplored region of the lunar surfacecalled the South Pole-Aitken Basin, located in the southern hemisphere of the dark side of the moon, which includes the Von Karman crater, among many others.

Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials. In this work, we introduce MOlecular SEtS (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and includes a set of metrics that evaluate the diversity and quality of generated molecules. MOSES is meant to standardize the research on the molecular generation and facilitate the sharing and comparison of new models. Additionally, we provide a large-scale comparison of existing state of the art models and elaborate on current challenges for generative models that might prove fertile ground for new research. Our platform and source code are freely available at https://github.com/molecularsets/

Nov 28, 2018 Ginny Engholm Danny Iny, on How to Future-Proof Your Job Digital disruption is transforming the world of work. The skills that workers will need in the long term are changing at a rapid pace, and this is causing employees -- and employers -- a lot of anxiety. Workers fear being replaced by tech for good reason -- a 2017 report from PricewaterhouseCoopers estimated that 38 percent of U.S. jobs are at risk of being replaced by automation by the early 2030s. Some industries, such as transportation, wholesale, manufacturing and retail, are particularly vulnerable to automation. But no industry is safe.