Real-time Generation Research Articles

Real-time trajectory generation for dual-stage feed drive systems

Machine tools are designed using dual-stage feed drives with slow and fast stages when speed and accuracy must be delivered over large work-volume. The slow stage provides large-stroke positioning whereas the fast stage executes short-stroke accurate tool-tip placement. This paper presents a real-time trajectory generator (governor) that distributes reference motion commands so that coordinated high-speed dual-stage contouring motion can be realized. A novel dynamic command scaling (DCS) approach is developed to generate trajectories in real-time that fully utilize the stroke (range), velocity, acceleration and dynamic limits of the dual-stages drives. Demonstration in 2D laser patterning shows >2 times increase in productivity.

Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

Under-oil open microfluidic systems for rapid phenotypic antimicrobial susceptibility testing.

Antimicrobial susceptibility testing (AST) remains the cornerstone of effective antimicrobial selection and optimization in patients. Despite recent advances in rapid pathogen identification and resistance marker detection with molecular diagnostics (e.g., qPCR, MALDI-TOF MS), phenotypic (i.e., microbial culture-based) AST methods - the gold standard in hospitals/clinics - remain relatively unchanged over the last few decades. Microfluidics-based phenotypic AST has been growing fast in recent years, aiming for rapid (i.e., turnaround time <8 h), high-throughput, and automated species identification, resistance detection, and antibiotics screening. In this pilot study, we describe the application of a multi-liquid-phase open microfluidic system, named under-oil open microfluidic systems (UOMS), to achieve a rapid phenotypic AST. UOMS provides an open microfluidics-based solution for rapid phenotypic AST (UOMS-AST) by implementing and recording a pathogen's antimicrobial activity in micro-volume testing units under an oil overlay. UOMS-AST allows free physical access (e.g., by standard pipetting) to the system and label-free, single-cell resolution optical access. UOMS-AST can accurately and rapidly determine antimicrobial activities [including susceptibility/resistance breakpoint and minimum inhibitory concentration (MIC)] from nominal sample/bacterial cells in a system aligned with clinical laboratory standards where open systems and optical microscopy are predominantly adopted. Further, we combine UOMS-AST with a cloud lab data analytic technique for real-time image analysis and report generation to provide a rapid (<4 h) sample-to-report turnaround time, shedding light on its utility as a versatile (e.g., low-resource setting and manual laboratory operation, or high-throughput automated system) phenotypic AST platform for hospital/clinic use.

Real-time dense point cloud generation and digital model construction of surface environment based on UAV platform

Real-time dense point cloud generation and digital model construction of surface environment based on UAV platform

A Near Real-Time Big Data Provenance Generation Method Based on the Conjoint Analysis of Heterogeneous Logs

A Near Real-Time Big Data Provenance Generation Method Based on the Conjoint Analysis of Heterogeneous Logs

Online and Real-Time Trajectory Generation Method for Unforeseen Events Using a Modified Spline Approach

This letter focuses on the problem of online and instantaneous generation of smooth trajectories and their analytical time derivatives, where future reference signals provided by a trajectory planner or a user are a-priori unknown. For this purpose, we propose a modified cubic spline polynomial which transforms the instantaneous piecewise references into smooth and continuous functions for digital control systems. The resulting reference signals are not only smooth and continuous in time but also analytically differentiable. The inferred derivative values could then be used in a variety of linear and non-linear controllers such as, Proportional-Integral-Derivative (PID), sliding mode, and backstepping. We also provide optimum and boundary values for the parameters used in the proposed technique. In addition, we present a lightweight implementation (a few lines of code) of the algorithm by which real-time computations are rapidly performed using a microcontroller. Both simulation and real experiments demonstrate that for the plants having different degrees of complexities, the proposed approach with a PD / PID controller outperforms the conventional filtered derivative-based one in terms of overshoot in the step response, robustness against noise, and trajectory tracking error.

FEASIBLE AND OPTIMAL TRAJECTORIES GENERATION FOR AUTONOMOUS DRIVING VEHICLES, 11-24.

Real-time feasible and optimal trajectory generation subject tothe vehicle physical constraints, the surrounding obstacles, andthe environmental conditions is one of the most important partsof autonomous driving vehicles.

A review of forest visualization and forest fire simulation technology research

A review of forest visualization and forest fire simulation technology research

Near real-time generation of a country-level burned area database for Italy from Sentinel-2 data and active fire detections

The first national database of burned areas in Italy at 20 m spatial resolution is presented. It was generated in near real-time by processing, as soon as they were available, all the Sentinel-2 images covering the Italian territory throughout the fire seasons of years 2019–2021. An updated version of the AUTOmatic Burned Area Mapper (AUTOBAM), originally developed in a previous study, was used to produce the database. Active fire data and a feature extraction procedure were used by AUTOBAM to associate a set of quality flags to each burned area.The AUTOBAM product was evaluated by comparing it with reference polygons derived from ground surveys. Even the database derived from the European Forest Fire Information System (EFFIS) was compared to reference polygons to assess the utility of the AUTOBAM product with respect to an already existing one. Good results were obtained by AUTOBAM in terms of detection capability (the Correctness parameter) and overlap factor (both larger than 60%). However, quite high values of the commission error were obtained, especially in 2019. Through a per land cover analysis, it was found that this error mostly occurred in cultivated land. Excluding the latter target, the commission error was always less than 35%, the omission error was less than 27% and the Dice Coefficient was larger than 69%.

Active and Reactive Power Control in Transmission Line with PSO Optimized PI controlled Five Level UPFC

Abstract: In this paper, particle swarm optimization (PSO) optimized PI controlled five level UPFC is proposed for a double circuit transmission line. To enhance UPFC performance, a different approach is considered here. It uses simplified power system models to derive the decoupled power controllers, but detailed modeling of the UPFC power converters improves their ride-through capability. This paper introduces decoupled linear UPFC power controllers to obtain the reference ac voltages and currents for the two back-to-back-connected three-phase five-level NPC converters that enforce active and reactive power control in the transmission line. This paper proposes three main contributions to increase the dc-link voltage steadiness of multilevel UPFCs under line faults: 1) decoupled active and reactive linear power controllers; 2) real-time PWM generation; and 3) balancing of dc capacitor voltages. The MATLAB/SIMULINK model for the proposed circuit with PSO-optimized PI controlled five level UPFC is shown here with the results.

Active and reactive power control in transmission line with PSO optimized PI controlled five level UPFC

In this paper, particle swarm optimization (PSO) optimized PI controlled five level UPFC is proposed for a double circuit transmission line. To enhance UPFC performance, a different approach is considered here. It uses simplified power system models to derive the decoupled power controllers, but detailed modeling of the UPFC power converters improves their ride-through capability. This paper introduces decoupled linear UPFC power controllers to obtain the reference ac voltages and currents for the two back-to-back-connected three-phase five-level NPC converters that enforce active and reactive power control in the transmission line. This paper proposes three main contributions to increase the dc-link voltage steadiness of multilevel UPFCs under line faults: 1) decoupled active and reactive linear power controllers; 2) real-time PWM generation; and 3) balancing of dc capacitor voltages. The MATLAB/SIMULINK model for the proposed circuit with PSO-optimized PI controlled five level UPFC is shown here with the results.

Programmable CRISPR-Cas12a and self-recruiting crRNA assisted dual biosensing platform for simultaneous detection of lung cancer biomarkers hOGG1 and FEN1

Human 8-oxoguanine DNA glycosylase (hOGG1) and flap endonuclease 1 (FEN1) are recognized as potential biomarkers in lung cancer investigations. Developing analytical platforms of simultaneously targeting hOGG1 and FEN1 with high selectivity, sensitivity, especially programmability and universality is highly valuable for clinical research. Herein, we established a signal-amplified platform for simultaneously detecting hOGG1 and FEN1 on the basis of cleavage-induced ligation of DNA dumbbell probes, rolling circle transcription (RCT) and CRISPR-Cas12a. A hOGG1 cleavable site and FEN1 cleavable flap were dexterously designed at the 5’ end of DNA flapped dumbbell probes (FDP) for hOGG1 and FEN1. After cleavage, the resulting nick sites with juxtaposition of 5′ phosphate and 3′ hydroxyl terminus could be linked to closed DNA dumbbell probes (CDP) by DNA ligase. The CDP served as a template for RCT, producing plentiful crRNA repeats to activate the trans-cleavage activity of CRISPR-Cas12a which could cleave fluorophores (TAMRA and FAM) and quenchers (BHQ2 and BHQ1) double-labeled ssDNA reporters. Then, hOGG1 and FEN1 could be detected by the recovered fluorescence signal, allowing for the highly sensitive calculated detection limits of 0.0013 and 0.0052 U/mL, respectively. Additionally, this method made it possible to evaluate the inhibitory effects, even to measure hOGG1 and FEN1 activities at the single-cell level. This novel target enzyme-initiated, circles-transcription without promoters, real-time generation, and self-assembly features of FDP-RCT-Cas12a system suppressed nonspecific background remarkably and relieved rigorous requirement of protospacer adjacent motif site. Hence, the universality of FDP-RCT-Cas12a system toward various disease-related non-nucleic acid targets which are tested without using aptamers was extremely improved.

VStyclone: Real-time Chinese voice style clone

This paper proposes a novel Chinese speech cloning model named VStyclone, which consists of three stages: multi-speaker training, target speaker encoding, and target speaker synthesis. In this work, we design an efficient tone extractor, which can reallocate resources to the sequences of log-mel spectrogram frames obtained from multiple speakers’ speech, thus allowing the network to learn multiple speakers’ features differently. This approach allows the network to focus more on the voice features of the target speaker and extract the target features accurately. To cluster the voices of the same speaker and sparse the voices of different speakers, we build an optimal softmax loss to optimize the model. Then, we develop a style synthesizer, which adopts the idea of transformer instead of recurrent neural network, so that the model can not only process text information in parallel, but also improve the model's ability to process long-distance contextual information. Meanwhile, we embed a style extraction module in the style synthesizer to dynamically capture style ranges in an unsupervised manner. In addition, the VStyclone model uses generative adversarial networks as the base generation model of the vocoder to improve the generation speed, which runs 1.2 times faster than the real-time generation speed on CPU, and finally the VStyclone model obtains the SOTA effect.

Great new generation carbon microsphere-based composites: Facile synthesis, properties and their application in piezo-electric energy harvesting

In this work, new generation carbon microspheres (CMS) based rubber composites have been obtained for flexible electronics applications such as stretchable piezo-electric energy harvesting devices. The rubber matrix used was room-temperature-vulcanized silicone rubber (RTV-SR). Herein, the CMS was exploited as a source of reinforcement and conductive material for improving mechanical and electrical properties. For example, the compressive modulus was 2.1 MPa (virgin) and increased to as high as 3.3 MPa (5 phr CMS). Similarly, the Shore A hardness was 26 (virgin) and increased to 40 (5 phr CMS). The electrical properties were also influenced significantly with electro-chemical resistance of 22.05 kΩ (virgin) and decreased to 15.91 kΩ (5 phr CMS). CMS's XPS spectrum revealed a high carbon-to-oxygen atomic ratio (74.47 and 25.53 %). The carbon and oxygen-containing functional groups of CMS acted as a catalyst to improve interfacial interaction of the composites thereby leading to higher mechanical properties. These improved properties were explored in the real-time device and output voltage generation with high durability was tested. From measurements, it was found that the output voltage from the piezo-electric energy harvesting device was around 1.25 V and high durability of 0.5 million cycles was reported.

LIGAA: Generative adversarial attack method based on low-frequency information

Recent studies have shown that the performance of deep neural network is extremely susceptible to adversarial examples. Slightly perturbation of the input that are not perceptible to the model may generate incorrect prediction results. Most existing attack methods with gradient or optimization operation cannot generate adversarial examples immediately, the robustness and imperceptibility of the adversarial examples are also not very good. To address this limitation, we propose a novel generative adversarial attack method with low-frequency information called LIGAA, which can achieve end-to-end real-time generation of adversarial examples. It mainly consists of a low-frequency information extractors and two symmetric decoders including a noise decoder and a saliency map decoder. The low-frequency information extractor eliminates the “useless” features in the original input. The noise decoder is used to generate perturbation noise for the entire image region that can be misclassified. The saliency map decoder limits the added noise to the specific areas that have a strong impact on classification, which can effectively enhance the imperceptibility of adversarial examples. Experimental results on CIFAR-10, Imagenette and CIFAR-100 of MobileNetv2 illustrate that the proposed LIGAA has obtained an attack success rate of 86.51%, 88.72% and 60.38%, and the time consumption is 0.001107s, 0.001706s and 0.004125s respectively, which are the best performance in all comparable methods. Particularly, the classification accuracy rate of LIGAA on ResNet-18 doesn’t increase even with the JPEG compress defense, but decreases to 3.11%, which also significantly verifies the attack performance and robustness against the defense method.

Reactive Power Compensation Configuration of Offshore Wind Power Based on Economic Differential Pressure Theory

The conventional reactive power balance analysis method for offshore wind power transmission projects can be used to obtain many feasible solutions for reactive power compensation configuration, which requires a large number of simulation calculations to eliminate unreasonable schemes. The work is tedious and lacks theoretical guidance. In this paper, the main influencing factors, such as the current carrying capacity of offshore wind power and submarine cable, the mode of real-time power generation, and the reactive power output regulation of the wind turbine itself, are considered. The reactive power balance of offshore wind power transmission projects is derived. Combined with the economic differential pressure theory, the sea-side and land-side configuration methods of high reactance and dynamic reactive power compensation are put forward. On the basis of realizing real-time reactive power balance, the recommended reactive power compensation configuration scheme effectively reduces the active power loss of the transmission project, and at the same time, lowers the bus voltage of each node at sea, thus reducing the high risk of the excessive voltage of offshore wind farm. This project is supported by the Science and Technology Project of State Grid Corporation of China under “Research on multiple temporal and spatial coordinated optimal control theory for medium and long-distance large-scale offshore wind power transmission system” (No. 5100-202155305A-0-0-00).

Fast generation of arbitrary optical focus array

We report a novel method to generate arbitrary optical focus arrays (OFAs). Our approach rapidly produces computer-generated holograms (CGHs) to precisely control the positions and the intensities of the foci. This is achieved by replacing the fast Fourier transform (FFT) operation in the conventional iterative Fourier-transform algorithm (IFTA) with a linear algebra one, identifying/removing zero elements from the matrices, and employing a generalized weighting strategy. On the premise of accelerating the calculation speed by >70 times, we demonstrate OFA with 99% intensity precision in the experiment. Our method proves effective and is applicable to systems in which real-time OFA generation is essential.

A Control Framework for Adaptation of Training Task and Robotic Assistance for Promoting Motor Learning With an Upper Limb Rehabilitation Robot

Robot-assisted rehabilitation has been a promising solution to improve motor learning of neurologically impaired patients. State-of-the-art control strategies are typically limited to the ignorance of heterogeneous motor capabilities of poststroke patients and therefore intervene suboptimally. In this article, we propose a control framework for robot-assisted motor learning, emphasizing the detection of human intention, generation of reference trajectories, and modification of robotic assistance. A real-time trajectory generation algorithm is presented to extract the high-level features in active arm movements using an adaptive frequency oscillator (AFO) and then integrate the movement rhythm with the minimum-jerk principle to generate an optimal reference trajectory, which synchronizes with the motion intention in the patient as well as the motion pattern in healthy humans. In addition, a subject-adaptive assistance modification algorithm is presented to model the patient’s residual motor capabilities employing spatially dependent radial basis function (RBF) networks and then combining the RBF-based feedforward controller with the impedance feedback controller to provide only necessary assistance while simultaneously regulating the maximum-tolerated error during trajectory tracking tasks. We conduct simulation and experimental studies based on an upper limb rehabilitation robot to evaluate the overall performance of the motor-learning framework. A series of results showed that the difficulty level of reference trajectories was modulated to meet the requirements of subjects’ intended motion, furthermore, the robotic assistance was compliantly optimized in response to the changing performance of subjects’ motor abilities, highlighting the potential of adopting our framework into clinical application to promote patient-led motor learning.

Neural Brushstroke Engine

We propose Neural Brushstroke Engine, the first method to apply deep generative models to learn a distribution of interactive drawing tools. Our conditional GAN model learns the latent space of drawing styles from a small set (about 200) of unlabeled images in different media. Once trained, a single model can texturize stroke patches drawn by the artist, emulating a diverse collection of brush styles in the latent space. In order to enable interactive painting on a canvas of arbitrary size, we design a painting engine able to support real-time seamless patch-based generation, while allowing artists direct control of stroke shape, color and thickness. We show that the latent space learned by our model generalizes to unseen drawing and more experimental styles (e.g. beads) by embedding real styles into the latent space. We explore other applications of the continuous latent space, such as optimizing brushes to enable painting in the style of an existing artwork, automatic line drawing stylization, brush interpolation, and even natural language search over a continuous space of drawing tools. Our prototype received positive feedback from a small group of digital artists.

Artificial Intelligence and the Indian Media Industry: the Future is Now

The Indian media industry is currently undergoing a significant transformation with the advent of Artificial Intelligence (AI). AI has the potential to revolutionize the way news stories are produced and consumed, providing new opportunities for innovation and growth. However, it is also important to consider the challenges that come with implementing AI in the Indian media industry. This research aims to provide a comprehensive examination of the future of the Indian media industry in the age of AI. The study will analyse the current state of AI in the Indian media industry, focusing on its impact on efficiency, cost-effectiveness, personalization, multilingual news production, real-time news generation, and combat against misinformation. Additionally, the research will also explore the challenges that come with the adoption and implementation of AI in the Indian media industry, such as job losses, ethical concerns, lack of understanding, data, and infrastructure. The study will use both primary and secondary data sources, including interviews with industry experts, case studies of media organizations that have implemented AI, and a review of relevant literature. The research will provide valuable insights for media organizations, policymakers, and researchers on the opportunities and challenges of AI in the Indian media industry, and how it is shaping the future of the industry.