Real-time Generation Research Articles

Cybersecurity Vulnerabilities in Smart Grids with Solar Photovoltaic: A Threat Modelling and Risk Assessment Approach

Cybersecurity is a growing concern for smart grids, especially with the integration of solar photovoltaics (PVs). With the installation of more solar and the advancement of inverters, utilities are provided with real-time solar power generation and other information through various tools. However, these tools must be properly secured to prevent the grid from becoming more vulnerable to cyber-attacks. This study proposes a threat modeling and risk assessment approach tailored to smart grids incorporating solar PV systems. The approach involves identifying, assessing, and mitigating risks through threat modeling and risk assessment. A threat model is designed by adapting and applying general threat modeling steps to the context of smart grids with solar PV. The process involves the identification of device assets and access points within the smart grid infrastructure. Subsequently, the threats to these devices were classified utilizing the STRIDE model. To further prioritize the identified threat, the DREAD threat-risk ranking model is employed. The threat modeling stage reveals several high-risk threats to the smart grid infrastructure, including Information Disclosure, Elevation of Privilege, and Tampering. Targeted recommendations in the form of mitigation controls are formulated to secure the smart grid’s posture against these identified threats. The risk ratings provided in this study offer valuable insights into the cybersecurity risks associated with smart grids incorporating solar PV systems, while also providing practical guidance for risk mitigation. Tailored mitigation strategies are proposed to address these vulnerabilities. By taking proactive measures, energy sector stakeholders may strengthen the security of their smart grid infrastructure and protect critical operations from potential cyber threats.

Real-time photovoltaic energy assessment using a GSM-based smart monitoring system: Addressing the impact of climate change on solar energy estimation software

Climate change is altering weather patterns, leading to increased variability in cloud cover, precipitation, and temperature. These changing patterns make it more challenging to accurately forecast solar radiation levels, which directly impact solar energy generation. This study, evaluates the solar energy potential using a real-time GSM-based communication system combined with an off-grid PV power plant in Tehran and Ahvaz, two regions in Iran with contrasting climates. To measure the output voltage, C++ language programming was used in Arduino_IDE programming language was utilized along with an Arduino Nano board, ATmega328P microcontroller, and sensors for measuring current, voltage, temperature, humidity, and time. The recorded data was sent through short message service. The results were compared with simulations from PVsyst, PVGIS, and the Global Solar Atlas. The findings revealed that the PV power production potential in Tehran, which is characterized by a cold and wet climate, was 4.101 kwh.kwp −1 with a capacity factor of 17.09%. In contrast, Ahvas, with its warm and dry climate, had a potential of 3.271 kwh.kwp −1 and a capacity factor of 13.63%. These values differed significantly from the estimates provided by PVsyst and the Global Solar Atlas. The Global Solar Atlas overestimated the PV potential by 15% and 18.2% in Tehran and Ahvaz, respectively, while PVsyst underestimated it by more than 15% in both locations. The real-time energy production was measured at 246.1 Wh and 196.27 Wh, compared to the estimated values of 283 Wh and 232 Wh from the Global Solar Atlas, 178.36 Wh and 142.63 Wh from PVsyst, and 221.4 Wh and 196.9 Wh from PVGIS in Tehran and Ahvaz, respectively. These results underscore the importance of accounting for recent climate changes when predicting near real-time solar generation, particularly in remote areas and regions that deviate from global trends.

Insights into SARS-CoV-2 in Angola during the COVID-19 peak: Molecular epidemiology and genome surveillance.

In Angola, COVID-19 cases have been reported in all provinces, resulting in >105,000 cases and >1900 deaths. However, no detailed genomic surveillance into the introduction and spread of the SARS-CoV-2 virus has been conducted in Angola. We aimed to investigate the emergence and epidemic progression during the peak of the COVID-19 pandemic in Angola. We generated 1210 whole-genome SARS-CoV-2 sequences, contributing West African data to the global context, that were phylogenetically compared against global strains. Virus movement events were inferred using ancestral state reconstruction. The epidemic in Angola was marked by four distinct waves of infection, dominated by 12 virus lineages, including VOCs, VOIs, and the VUM C.16, which was unique to South-Western Africa and circulated for an extended period within the region. Virus exchanges occurred between Angola and its neighboring countries, and strong links with Brazil and Portugal reflected the historical and cultural ties shared between these countries. The first case likely originated from southern Africa. A lack of a robust genome surveillance network and strong dependence on out-of-country sequencing limit real-time data generation to achieve timely disease outbreak responses, which remains of the utmost importance to mitigate future disease outbreaks in Angola.

Real-time adaptive entry trajectory generation with modular policy and deep reinforcement learning

Recently, data-driven entry trajectory generation algorithms of hypersonic vehicles were highlighted for their high accuracy with low computational costs. Learning a unique black box model mapping the state to control variables is expected. However, insufficient adaptability remains a challenging problem when applying the algorithms in practice, especially for multi-missions. In this article, a real-time entry trajectory generation algorithm with modular policy is proposed to achieve adaptability to various missions, such as changing targets and emergency entry. Based on the modular idea, the entry trajectory problem is decomposed into two decision problems, i.e., adjusting the profile of the angle of attack (AOA) to determine the flight capability and planning the bank angle to ensure the satisfaction of task constraints, which correspond to AOA module and bank module respectively. Then, algorithms are developed by utilizing deep reinforcement learning (DRL) to train the two modules with which an intelligent entry trajectory generation algorithm is proposed to achieve real-time trajectory design in a wide range of reachable areas. Moreover, a non-uniform discretization approach with a state-related independent variable is proposed to deal with state misalignment and contradiction in stepsize settings. The simulation results demonstrate that the proposed algorithm can provide a reliable trajectory in a very short time and adapt to various tasks.

Adaptive real-time similar repetitive manual procedure prediction and robotic procedure generation for human-robot collaboration

Manual procedure recognition and prediction are essential for practical human-robot collaboration in industrial tasks, such as collaborative assembly. However, current research mostly focuses on diverse human motions, while the similar repetitive manual procedures that are prevalent in real production tasks are often overlooked. Furthermore, the dynamic uncertainty caused by human-robot interferences and the generalisation of individuals, scenarios, and multiple sensor deployments pose challenges for implementing manual procedure prediction and robotic procedure generation. To address these issues, this paper proposes a real-time, similar repetitive procedure-oriented human skeleton processing system that employs the human skeleton as a robust modality. It utilises an improved deep spatial-temporal graph convolutional network and a FIFO queue-based discriminator for real-time data processing, procedure prediction, and generation. The proposed method is validated on multiple datasets with tens of individuals engaged in a real dynamic and uncertain human-robot collaborative assembly cell and able to run on entry-level hardware. The results demonstrate competitive performance of handcraft feature-free, early prediction and generalisation on individual variance, environment background, camera position, lighting conditions, and stochastic interference in human-robot collaboration.

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

Sonification of a juggling performance using spatial audio

We developed a new musical instrument by equipping juggling balls with accelerometers, gyroscopes, and WiFi sensors. The system measures acceleration and rotation, allowing us to distinguish between ball movement and static states. We can determine the direction of each throw based on the acceleration data. A movement-driven instrument animates the virtual audio source position based on the throw direction. A different synthesizer is assigned to each ball, resulting in a synesthetic experience. The system integrates the juggling patterns simulator with OSC and MIDI API, allowing real-time rhythm generation and composition.

Position-attitude coupling guidance and control for asteroid landing with a flexible lander

Landing on an asteroid is a highly valuable endeavor accompanied by significant challenges, making it a promising area for further research. This paper aims to present a flexible lander specifically designed for asteroid landing missions while also studying the guidance and control techniques involved in its descent phase. Firstly, the paper briefly introduces the mission simulation model and asteroid environment from previous work. It then provides a detailed exposition on the guidance model which facilitates trajectory planning and the attitude control model used for managing the lander's attitude. The parametric trajectory is subsequently applied to obtain the position-attitude coupling trajectory that allows for the flexible lander's trajectory planning. The generating set search method is used to optimize the parametric trajectory under the appropriate constraints, ensuring high efficiency. In tracking the proposed trajectory, an open-loop control is applied for position tracking, while a closed-loop attitude controller is proposed for attitude control. An attitude control technique involving a combination of the nonsingular sliding mode control method with RBF to capture perturbations and unmodeled dynamics is adopted, and its global stability is proven using a Lyapunov-based approach. Monte Carlo simulation is conducted to test the performance of the proposed trajectory planning technique with results showing that real-time trajectory generation and refreshing are achieved. The feasibility and accuracy of the proposed guidance and control technique are further verified with another set of Monte Carlo simulation. Finally, the paper highlights one successful scenario including the descent and touching phases of asteroid landing with the flexible lander, illustrating its feasibility further.

FPGA-driven random walk noise generation for tunable laser linewidth control

We propose a novel approach for real-time generation of random walk noise. Our approach aims to generate natural laser phase noise and construct a linewidth tunable laser. By utilizing field- programmable gate array circuits and digital-to-analog converters, we generate noise that is subsequently combined with low-frequency analog noise. This combined noise is then fed into a lithium niobate phase modulator to emulate phase fluctuations. Our method facilitates accurate control over the laser linewidth spectrum, closely approximating actual noise characteristics.

Dynamic Optimization Fabrics for Motion Generation

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show that convergence to desired trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. In addition, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a nonholonomic mobile manipulator with 10 degrees of freedom and avoidance of a moving human, supporting the theoretical findings.

A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate.

Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55-62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness.Approach. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method, offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification.Main results. The online BCI system achieved an average accuracy of 88.87% ± 3.05% and an information transfer rate of 51.83 ± 2.77 bits min-1under the low flickering perception condition.Significance. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology.

Retracted: Real-Time Generation Method of Oil Painting Style Brushstrokes Based on Inverse Reinforcement Learning

Retracted: Real-Time Generation Method of Oil Painting Style Brushstrokes Based on Inverse Reinforcement Learning

Stabilized Performance Maximization for GAN-based Real-Time Authentication Image Generation over Internet

Providing the completely automated public test to tell computers and humans apart (CAPTCHA) services that are not vulnerable to learning is an important issue. Image-based CAPTCHA services have strengthened authentication by taking advantage of the fact that it is more difficult for computers to understand images, but the rapid growth of deep learning has made it possible to break and attack authentication. Image-based CAPTCHA uses pre-stored data, which enables learning and results in vulnerability. This paper presents an adaptive generative adversarial network (GAN) selection scheme using time-average image quality maximization subject to system/buffer stability. By using GAN, it can generate and provide new images for CAPTCHA authentication every time, preventing deep learning from learning images and enhancing security. In the image generation process, the trade-off exists between image quality and generation time, and in consideration of this trade-off, delay aware image-based authentication Lyapunov-based algorithm is proposed for stable and maximized performance. Moreover, through the performance evaluation, we investigate and show the existence of trade-off between generation time and generated image quality in the image generation process in both quantitative and qualitative manner.

Parallel CV-QRNG with Strict Entropy Evaluation

Continuous-variable quantum random number generators (CV-QRNGs) have promising application prospects thanks to their advantages such as high detection bandwidth, robustness of system, and integratability. In major CV-QRNGs, the generation of random numbers is based on homodyne detection and discretization of the quadrature fluctuations of the EM fields. Any defectiveness in physical realization may leak information correlated with the generated numbers and the maximal amount of randomness that can be extracted in presence of such side-information is evaluated by the so-called quantum conditional min-entropy. The parallel CV-QRNG overcomes the rate bottleneck of the previous serial type scheme. As a type of device-trusted QRNG, its security needs to be better guaranteed based on self-testing or monitoring that can be rigorously enforced. In this work, four sideband modes of vacuum state within 1.6 GHz detection bandwidth were extracted parallelly as the entropy source, and 16-bit analog-to-digital conversion in each channel was realized. Without making any ideal assumptions, the transfer function of the homodyne and quantization system was measured based on beat method to calibrate the evaluation of the min-entropy. Based on the rigorous entropy evaluation with a hash security parameter of εhash = 2−110, a real-time generation rate of 7.25 Gbps was finally achieved.

A dynamic holographic modelling method of digital twin scenes for bridge construction

ABSTRACT Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.

A real-time collision-free maneuver generation algorithm for autonomous driving

In this paper we propose a real-time maneuver generation algorithm for Autonomous Vehicles (AVs). Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the AV’s dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable maneuver regulation optimal control problem. Maneuver regulation has intrinsic robustness with respect to standard trajectory tracking that stems from the requirement of following a desired path with a desired velocity profile assigned on it. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on two different scenarios. Finally, experimental results are presented to demonstrate the efficiency of the proposed algorithm both in terms of computational effort and dynamic features captured.

Impact of the Crystallinity of Covalent Organic Frameworks on Photocatalytic Hydrogen Evolution

The design and synthesis of crystalline porous materials is a frontier research area, especially in photo(electro)catalysis, due to their fascinating semiconducting properties. In recent years, crystalline metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have received much research attention in heterogeneous catalysis, ranging from chemical conversion to solar energy production. In addition, COFs with well-ordered framework structures have been extensively studied in the field of heterogeneous photocatalysis for water splitting to produce hydrogen (H2) and oxygen (O2). Due to the synergistic effect of crystallinity, porosity, and conjugation in their frameworks, COFs have been widely explored. In this Review, we aim to discuss the effect of crystallinity of COFs on hydrogen generation via photocatalytic water splitting. We then briefly summarize the basic mechanisms of photocatalytic hydrogen generation, the advantages of crystalline semiconductors over amorphous materials, and the strategic designs of crystalline COFs. In addition, the state-of-the-art developments of crystalline COFs as organic semiconductors for photocatalytic hydrogen evolution have been systematically reviewed. We believe that understanding the structure–property relationship and photocatalytic performance for hydrogen evolution with respect to the long-range structural order of COFs is essential for the further development of innovative crystalline COF-based semiconductors in real-time hydrogen generation applications.

Low-Resource Generation Method for Few-Shot Dolphin Whistle Signal Based on Generative Adversarial Network

Dolphin signals are effective carriers for underwater covert detection and communication. However, the environmental and cost constraints terribly limit the amount of data available in dolphin signal datasets are often limited. Meanwhile, due to the low computational power and resource sensitivity of Unmanned Underwater Vehicles (UUVs), current methods for real-time generation of dolphin signals with favorable results are still subject to several challenges. To this end, a Masked AutoEncoder Generative Adversarial Network (MAE-GAN) model is hereby proposed. First, considering the few-shot condition, the dataset is extended by using data augmentation techniques. Then, to meet the low arithmetic constraint, a denoising autoencoder with a mask is used to obtain latent codes through self-supervised learning. These latent codes are then utilized in Conditional Wasserstein Generative Adversarial Network-Gradient Penalty (CWGAN-GP) to generate a whistle signal model for the target dataset, fully demonstrating the effectiveness of the proposed method for enhancing dolphin signal generation in data-limited scenarios. The whistle signals generated by the MAE-GAN and baseline models are compared with actual dolphin signals, and the findings indicate that the proposed approach achieves a discriminative score of 0.074, which is 28.8% higher than that of the current state-of-the-art techniques. Furthermore, it requires only 30.2% of the computational resources of the baseline model. Overall, this paper presents a novel approach to generating high-quality dolphin signals in data-limited situations, which can also be deployed on low-resource devices. The proposed MAE-GAN methods provide a promising solution to address the challenges of limited data and computational power in generating dolphin signals.

Measurement of Indicators of Industrial Food Processes Through Artificial Intelligence

Accurate measurement of food industrial process indicators is critical to ensuring product quality and safety. In this article, we propose the use of artificial intelligence (AI) as an efficient and accurate tool to measure and monitor these indicators. A methodology based on AI techniques is described, which includes real-time data processing and predictive model generation. The results obtained demonstrate that AI can significantly improve the measurement of indicators, reduce costs and increase the efficiency of industrial food processes. In conclusion, the implementation of artificial intelligence in the measurement of indicators of industrial food processes offers great advantages and benefits for the food industry.

An Overall Linearized Modeling Method and Associated Delay Time Model for the PV System

There are some significant nonlinearity and delay issues in photovoltaic (PV) system circuits. Therefore, it is very difficult for the existing classic linear control theories to be used in PV systems; this hinders the design of the optimal energy dispatch by considering real-time generation power forecasting methods. To solve this problem, an overall linearized model with variable weather parameters (OLM-VWP) of the PV system is proposed on the basis of small-signal modeling. Meanwhile, a corresponding simplified overall linearized model with variable weather parameters (SOLM-VWP) is presented. The SOLM-VWP avoids analyzing delay characteristics of the complex high-order PV system. Moreover, it can reduce hardware cost and computation time, which makes analysis of the transient performance index of the PV system more convenient. In addition, on the basis of the OLM-VWP and SOLM-VWP, a delay-time model with variable weather parameters (DTM-VWP) of the PV system is also proposed. The delay time of the system can be accurately calculated using the DTM-VWP, and it provides a preliminary theoretical basis for carrying out real-time energy scheduling of the PV system. Finally, simulations are implemented using the MATLAB tool, and experiments are conducted. The results verify that the proposed linearization model of the PV system is accurate and reasonable under varying irradiance and temperature conditions. Meanwhile, the results also verify that the proposed SOLM-VWP and DTM-VWP of the PV system are feasible. Additionally, the results show that some transient performance indexes (delay time, rise time, settling time, and peak time) can be solved by means of equations when the circuit parameters and real-time weather parameters are given.