An object can be replicated by creating its 3D model and then using a 3D printer. Another important application can be found in manufacturing industries for inspection purposes. Minute cracks, faults or problems can be detected easily by comparing the reconstructed 3D model with the known model. Simultaneous Localization and Mapping (SLAM): SLAM is a technique used to create map of the surrounding environment. It is very useful in robotics and self-driving cars.

Dimensionality reduction is the process of expressing high-dimensional data in a reduced number of dimensions such that each one contains the most amount of information. Dimensionality reduction may be used for visualization of high-dimensional data or to speed up machine learning models by removing low-information or correlated features. Principal Component Analysis, or PCA, is a popular method of reducing the dimensionality of data by drawing several orthogonal (perpendicular) vectors in the feature space to represent the reduced number of dimensions. The variable number represents the number of dimensions the reduced data will have. In the case of visualization, for example, it would be two dimensions.

The popular show'Black Mirror' may be on hold due to the pandemic spreading across the globe, but fans of the dystopian world can create a part of the sci-fi series on their own. A Stanford student built a robot dog similar to that used in the episode titled'Metalhead' that hunts and kills humans in an apocalyptic setting. The miniature version, called the Stanford Pupper, was developed using a 3D printer, a PlayStation controller and other common pieces - and the team has shared all the details for the public to use. It has 12 degrees of freedom, meaning it can goes backwards, forwards, side-to-side and also features a'sneaky mode' that mimics the movement of a real canine creeping on the floor. A Stanford student built a robot dog similar to that used in the episode titled'Metalhead' that hunts and kills humans in an apocalyptic setting.

An injured hornbill that was found in Thailand with part of its beak snapped off can now eat again after vets fitted it with a replacement made using a 3D printer. The adult bird -- dubbed'Coco' -- was found sprawled on the ground with a broken wing and its lower bill missing in Kanchanaburi, western Thailand, on April 18. Wildlife officers are unsure how Coco was injured, but believe that she may have been shot or attacked by hunters or poachers and then left for dead in the forest. Although veterinarians were able to give urgent care and stabilise the bird, they were sadly unable to find its missing bill in order to reattach it. Realising it would be impossible for Coco to eat without her signature long bill, they scanned her body and used 3D printing technology to create plastic replacements.

Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrices can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time.

Increased Integration of Different Solutions to Provide Improved Performance 5.2.3.3 Rapid Industrial Growth in Emerging Economies 5.2.4 Challenges 5.2.4.1 Threats Related to Cybersecurity 5.2.4.2 Complexity in Implementation of Smart Manufacturing Technology Systems 5.2.4.3 Lack of Awareness About Benefits of Adopting Information and Enabling Technologies 5.2.4.4 Lack of Skilled Workforce 5.3 Industrial Wearable Devices Trends in Smart Manufacturing 5.3.1 By Device 5.3.1.1

However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.

A new primary torque control concept for hydrostatics mobile machines was introduced in 2018. The mentioned concept controls the pressure in a closed circuit by changing the angle of the hydraulic pump to achieve the desired pressure based on a feedback system. Thanks to this concept, a series of advantages are expected. However, while working in a Y cycle, the primary torque-controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover due to lack of recuperation ability. Alternatively, we use deep learning algorithms to improve machines' regeneration performance. In this paper, we firstly make a potential analysis to show the benefit by utilizing the regeneration process, followed by proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable performance but much fewer training parameters. Based on our dataset including 119 truck loading cycles, our best neural network shows a 98.2% test accuracy. Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.

The amount of digital data that currently exists is now growing at a rapid pace. The number is doubling every two years and it is completely transforming our basic mode of existence. According to a paper from IBM, about 2.5 billion gigabytes of data had been generated on a daily basis in the year 2012. Another article from Forbes informs us that data is growing at a pace which is faster than ever. The same article suggests that this year, 2020, about 1.7 billion of new information will be developed per second for all the human inhabitants on this planet.

Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.