PulseNet International is a global network dedicated to laboratory-based surveillance for food-borne diseases. The network comprises the national and regional laboratory networks of Africa, Asia ...Pacific, Canada, Europe, Latin America and the Caribbean, the Middle East, and the United States. The PulseNet International vision is the standardised use of whole genome sequencing (WGS) to identify and subtype food-borne bacterial pathogens worldwide, replacing traditional methods to strengthen preparedness and response, reduce global social and economic disease burden, and save lives. To meet the needs of real-time surveillance, the PulseNet International network will standardise subtyping via WGS using whole genome multilocus sequence typing (wgMLST), which delivers sufficiently high resolution and epidemiological concordance, plus unambiguous nomenclature for the purposes of surveillance. Standardised protocols, validation studies, quality control programmes, database and nomenclature development, and training should support the implementation and decentralisation of WGS. Ideally, WGS data collected for surveillance purposes should be publicly available, in real time where possible, respecting data protection policies. WGS data are suitable for surveillance and outbreak purposes and for answering scientific questions pertaining to source attribution, antimicrobial resistance, transmission patterns, and virulence, which will further enable the protection and improvement of public health with respect to food-borne disease.
We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">>30</tex-math> ...<mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3014297.gif"/> </inline-formula> fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU . We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
A two-step strategy is developed for real-time trajectory planning of a hypersonic vehicle (HV) in the reentry phase. The first step generates the optimal trajectory for the HV using a recently ...proposed fuzzy multiobjective transcription method. In the second step, the optimally generated trajectories are utilized to train a deep neural network (DNN), which is then acted as the optimal command generator in real time. A detailed simulation study is carried out to verify the effectiveness and real-time applicability of the proposed integrated design. The DNN-driven controller is further compared against other optimization-based techniques existing in relative works. Moreover, extension works on the real-time trajectory planning of a six-degree-of-freedom HV model are performed. The results confirm the feasibility and reliability of applying the proposed method for the planning of the HV entry flight path in real time.
The IEEE Transactions on Nuclear Science (TNS) is pleased to announce that Dr. Hideya Nakanishi has accepted an appointment as an Associate Editor for papers originating in the Real Time Conference.
The recent development of the International Global Navigation Satellite Systems Service Real‐Time Pilot Project and the enormous progress in precise point positioning (PPP) techniques provide a ...promising opportunity for real‐time determination of Integrated Water Vapor (IWV) using GPS ground networks for various geodetic and meteorological applications. In this study, we develop a new real‐time GPS water vapor processing system based on the PPP ambiguity fixing technique with real‐time satellite orbit, clock, and phase delay corrections. We demonstrate the performance of the new real‐time water vapor estimates using the currently operationally used near‐real‐time GPS atmospheric data and collocated microwave radiometer measurements as an independent reference. The results show that an accuracy of 1.0 ~ 2.0 mm is achievable for the new real‐time GPS based IWV value. Data of such accuracy might be highly valuable for time‐critical geodetic (positioning) and meteorological applications.
Key Points
We develop a new RT GPS water vapor processing system
PPP ambiguity fixing with RT satellite orbit, clock, and phase delays
Our results are very promising and demonstrate RT ZTD/IWV accuracy
Real‐time release testing (RTRT) is defined as “the ability to evaluate and ensure the quality of in‐process and/or final drug product based on process data, which typically includes a valid ...combination of measured material attributes and process controls” (ICH Q8R2). This article discusses sensors (process analytical technology, PAT) and control strategies that enable RTRT for the spectrum of critical quality attributes (CQAs) in biopharmaceutical manufacturing. Case studies from the small‐molecule and biologic pharmaceutical industry are described to demonstrate how RTRT can be facilitated by integrated manufacturing and multivariable control strategies to ensure the quality of products. RTRT can enable increased assurance of product safety, efficacy, and quality—with improved productivity including faster release and potentially decreased costs—all of which improve the value to patients. To implement a complete RTRT solution, biologic drug manufacturers need to consider the special attributes of their industry, particularly sterility and the measurement of viral and microbial contamination. Continued advances in on‐line and in‐line sensor technologies are key for the biopharmaceutical manufacturing industry to achieve the potential of RTRT.
Related article: http://onlinelibrary.wiley.com/doi/10.1002/bit.26378/full
Sensors (process analytical technology, PAT) and control strategies are reviewed that enable real‐time release testing (RTRT) for the spectrum of critical quality attributes (CQAs) in biopharmaceutical manufacturing. To implement a complete RTRT solution, biologic drug manufacturers need to consider the special attributes of their industry, particularly sterility and the measurement of viral and microbial contamination. Continued advances in on‐line and in‐line sensor technologies are key for the biopharmaceutical manufacturing industry to achieve the potential of RTRT.
Energy storage and virtual power plant technologies have been developed and become important technical means to enhance power system stability and reduce real‐time dispatching costs. In this study, ...the dispatching capability and dispatching cost characteristics of the virtual power plants are analysed firstly in detail, as well as the dispatching difficulties under the traditional market modes. Hence, virtual power plant real‐time bidding package model and virtual auction‐based real‐time power market mechanism are proposed. Data‐driven virtual power plant real‐time packing method and bidding package model integrated virtual Vickrey–Clarke–Groves auction model are put forward. Finally, the feasibility and validity of the proposed mechanism and method are verified by case studies and result in analyses of the IEEE‐30 bus test system with multiple virtual power plants, providing a scientific foundation and a practical solution to the real‐time power market.
In the field of 3D face alignment, most researchers have focused on improving the prediction accuracy of algorithms and ignored the portability for practical applications. To this end, this study ...presents a real-time 3D face-alignment method that uses an encoder-decoder network with an efficient deconvolution layer. The fusion of the encoding and decoding feature adds more abundant features to this network. An efficient deconvolution layer at the decoding stage applies the L1 norm to select useful features and generate abundant ones through linear operations. Experimental results using the standard AFLW2000-3D and AFLW-LFPA datasets show that our algorithm has low prediction errors with real-time applicability.
Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods ...because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning.
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