A self-learning optimal control algorithm for sequential manufacturing processes with time-discrete control actions is proposed and evaluated with simulated deep drawing processes. The necessary control model is built during consecutive process executions under optimal control via Reinforcement Learning, using the measured product quality as reward after each process execution. Prior model formation, which is required by state-of-the-art algorithms like Model Predictive Control and Approximate Dynamic Programming, is therefore obsolete. This avoids the difficulties in system identification and accurate modelling, which arise with processes subject to non-linear dynamics and stochastic influences. Also runtime complexity problems of these approaches are avoided, which arise when more complex models and larger control prediction horizons are employed. Instead of using pre-created process- and observation-models, Reinforcement Learning algorithms build functions of expected future reward during processing, which are then used for optimal process control decisions. The learning of such expectation functions is realized online by interacting with the process. The proposed algorithm also takes stochastic variations of the process conditions into consideration and is able to cope with partial observability. A method for the adaptive optimal control of partially observable fixed-horizon manufacturing processes, based on Q-learning is developed and studied. The resulting algorithm is instantiated and then evaluated by application to a time-stochastic optimal control problem in metal sheet deep drawing, where the experiments use FEM-simulated processes. The Reinforcement Learning based control shows superior results over the model-based Model Predictive Control and Approximate Dynamic Programming approaches.

Abstract--Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timingdependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and communicate through the NoC. This enables chip-level interconnection, scalability and reconfigurability necessary for deploying different applications. The hardware is applied to learn MNIST digits as an evaluation of its learning capability. We explore the design space to study the tradeoffs between speed, area and energy. How to use this procedure to find optimal architecture configuration is also discussed. In the field of deep learning, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are developed to perform a series of human-level cognitive applications [1] [2]. However, the tremendous computation and memory requirement have been seriously challenging the processing efficiency of deep learning systems [3] [4]. The limitations of Von Neumann architecture coupled with increasing power demands due to Dennard scaling and the approaching end of Moore's Law have motivated multiple research efforts into low-power, highly parallel and distributed computing architecture [5] [6] [7] [8] and brain-inspired computing architecture [9] [10]. Brain as a source of inspiration is not surprising given its ability to process massive amounts of real-time information while consuming less than 20 W of power [11]. The goal of neuromorphic hardware design is to explore the bio-inspired architecture to achieve cognitive functions in real time utilizing lower power and smaller footprint than the traditional Von Neumann architectures.

Satellite imagery, Light Detection and Ranging (LiDAR) data from aircraft, physical characterics of properties, building permit data and energy consumption data describe the property itself. Weather station data, topography, vegetation cover, and location of infrastructure assets provide additional detail on the surroundings of a property. Demographic, psychographic, credit, financial and social media data provide a holistic understanding of policy holders and residents.

This dictionary decodes abbreviations and acronyms found in various publications including maps and websites. January 7th 2015: The International Journal of Biology and Biomedical Engineering is now Indexed in Scopus. The artificial intelligence is used widely in CannyTEM. From over 50 industries, here are 1000 GIS applications to open your mind of our amazing planet, its interconnectivity with location intelligence in mind. Advanced Communications; Bioscience; Buildings and Construction BSC has launched a new spin-off, ELEM Biotech, which will allow companies of medical technologies, pharmaceuticals, CROs and doctors to perform virtual simulations.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

This paper addresses the question of identifying the time-window in short-term past from which the information regarding the future occupant's window opening actions and resulting window states in buildings can be predicted. The addressed sequence duration was in the range between 30 and 240 time-steps of indoor climate data, where the applied temporal discretization was one minute. For that purpose, a deep neural network is trained to predict the window states, where the input sequence duration is handled as an additional hyperparameter. Eventually, the relationship between the prediction accuracy and the time-lag of the predicted window state in future is analyzed. The results pointed out, that the optimal predictive performance was achieved for the case where 60 time-steps of the indoor climate data were used as input. Additionally, the results showed that very long sequences (120-240 time-steps) could be addressed efficiently, given the right hyperprameters. Hence, the use of the memory over previous hours of high-resolution indoor climate data did not improve the predictive performance, when compared to the case where 30/60 minutes indoor sequences were used. The analysis of the prediction accuracy in the form of F1 score for the different time-lag of future window states dropped from 0.51 to 0.27, when shifting the prediction target from 10 to 60 minutes in future.

Researchers are paving the way to total reliance on renewable energy as they study both large- and small-scale ways to replace fossil fuels. One promising avenue is converting simple chemicals into valuable ones using renewable electricity, including processes such as carbon dioxide reduction or water splitting. But to scale these processes up for widespread use, we need to discover new electrocatalysts--substances that increase the rate of an electrochemical reaction that occurs on an electrode surface. To do so, researchers at Carnegie Mellon University are looking to new methods to accelerate the discovery process: machine learning. Zack Ulissi, an assistant professor of chemical engineering (ChemE), and his group are using machine learning to guide electrocatalyst discovery.

As a geologist and professor I speak and write rather cavalierly about eras and eons. One of the courses I routinely teach is "History of Earth and Life," a survey of the 4.5-billion-year saga of the entire planet--in a 10-week trimester. But as a human, and more specifically as a daughter, mother, and widow, I struggle like everyone else to look Time honestly in the face. That is, I admit to some time hypocrisy. The now risible "Y2K" crisis that threatened to cripple global computer systems and the world economy at the turn of the millennium was caused by programmers in the 1960s and '70s who apparently didn't really think the year 2000 would ever arrive.

'Dragon eggs' lowered into the heart of volcanoes using drones could help monitor for clues of future eruptions with more precision, scientists have revealed. Such extreme, hazardous and unpredictable environments present a very difficult challenge to reliably record volcanic behaviour. For some volcanoes, it is simply too dangerous for humans to get close enough to take readings manually. However, scientists have got around this problem by creating highly sensitive pods that can be positioned in dangerous locations to provide real-time data on eruptions. Dubbed'dragon eggs', scientists say these devices could also monitor other natural phenomenon such as glaciers, geological faults and man-made hazards such as nuclear waste storage sites.

In this work, we propose a new Gaussian process regression (GPR) method: physics-informed Kriging (PhIK). In the standard data-driven Kriging, the unknown function of interest is usually treated as a Gaussian process with assumed stationary covariance with hyperparameters estimated from data. In PhIK, we compute the mean and covariance function from realizations of available stochastic models, e.g., from realizations of governing stochastic partial differential equations solutions. Such a constructed Gaussian process generally is non-stationary, and does not assume a specific form of the covariance function. Our approach avoids the costly optimization step in data-driven GPR methods to identify the hyperparameters. More importantly, we prove that the physical constraints in the form of a deterministic linear operator are guaranteed in the resulting prediction. We also provide an error estimate in preserving the physical constraints when errors are included in the stochastic model realizations. To reduce the computational cost of obtaining stochastic model realizations, we propose a multilevel Monte Carlo estimate of the mean and covariance functions. Further, we present an active learning algorithm that guides the selection of additional observation locations. The efficiency and accuracy of PhIK are demonstrated for reconstructing a partially known modified Branin function and learning a conservative tracer distribution from sparse concentration measurements.