Real-time Algorithm Research Articles

Real-time analysis of liquid jet sample delivery stability for an X-ray free-electron laser using machine vision

Automated evaluation of optical microscopy images of liquid jets, commonly used for sample delivery at X-ray free-electron lasers (XFELs), enables real-time tracking of the jet position and liquid jet hit rates, defined here as the proportion of XFEL pulses intersecting with the liquid jet. This method utilizes machine vision for preprocessing, feature extraction, segmentation and jet detection as well as tracking to extract key physical characteristics (such as the jet angle) from optical microscopy images captured during experiments. To determine the effectiveness of these tools in monitoring jet stability and enhancing sample delivery efficiency, we conducted XFEL experiments with various sample compositions (pure water, buffer and buffer with crystals), nozzle designs and jetting conditions. We integrated our real-time analysis algorithm into the Karabo control system at the European XFEL. The results indicate that the algorithm performs well in monitoring the jet angle and provides a quantitative characterization of liquid jet stability through optical image analysis conducted during experiments.

Movement Recognition and Injury Risk Assessment of Wushu Athletes Based on Computer Vision

This paper aims to study a method of movement recognition and injury risk assessment for Wushu athletes based on computer vision. Studying the optimization of Wushu athletes’ movement trajectory can effectively improve the athletes’ movement quality. Based on a hybrid real-time synchronization algorithm, the arm motion trajectory model of martial arts athletes is studied. A dynamic and static arm recognition algorithm is proposed, and the motion feature extraction method in Wushu movement is studied. Dynamic arm recognition typically relies on the collection of video or image sequences. These sequences contain the position, shape, and motion trajectory of the arm at different time points. Static arm recognition mainly relies on the acquisition of a single image or image frame. The dynamic arm recognition algorithm captures the motion trajectory and changes of the arm by processing video or image sequences, while the static arm recognition algorithm only processes individual images or image frames. On this basis, the design of the computer software architecture of the digital site system is completed. The real-time collection, analysis, display and storage of motion information and assembly configuration are realized. The simulation results show that the method has high accuracy and provides a powerful scientific basis for improving athletes’ movement injuries. To predict the risk of injury an athlete may suffer during training or competition.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

Brief communication: Real-time estimation of the optimal tip-speed ratio for controlling wind turbines with degraded blades

Abstract. Rotor performance is adversely affected by the wear and tear of blade surfaces caused, for example, by rain, snow, icing, dirt, bugs and aging. Blade surface degradation changes the aerodynamic properties of the rotor, which in turn changes the optimal tip-speed ratio (TSR) and the corresponding maximum power coefficient. Below the rated wind speed, if a turbine continues to operate at the manufacturer-designed optimal TSR, the rotor power could decrease more than necessary unless the optimal TSR is corrected to compensate for blade degradation or blade surfaces are restored. Re-tuning the tip-speed ratio can lead to an improvement in energy capture without blade repairs. In this work, we describe a real-time algorithm to re-tune the tip-speed ratio to its optimal but unknown value under blade degradation. The algorithm uses power measurements only and the Log-Power Proportional-Integral Extremum Seeking Control (LP-PIESC) strategy to re-tune the TSR. The algorithm is demonstrated in simulations to command the set-point TSR required by a generator speed control loop that maximizes power at below-rated wind speeds. Comparison of this solution with a baseline controller that uses the optimal TSR for a rotor with clean blades demonstrates improvements in energy capture between 0.5 % and 3.4 %, depending on the severity of blade degradation and the wind conditions. These results are obtained using the OpenFAST simulation tool, the National Renewable Energy Laboratory (NREL) 5 MW reference turbine and the NREL-developed Reference Open-Source Controller (ROSCO).

Design studies on electronics and data acquisition of a real time diamond spectrometer for the SPARC neutron camera.

The design of a compact 2 × 2 diamond matrix with independent and redundant pixels optimized for the spectrometric neutron camera of the SPARC tokamak is presented in this article. Such a matrix overcomes the constraints in dynamic range posed by the size of a single diamond sensor while keeping the ability to perform energy spectral analysis, marking a significant advancement in tokamak neutron diagnostics. A charge pre-amplifier based on radio frequency amplifiers based on InGaP technology transistors, offering up to 2GHz bandwidth with high robustness against radiation, has been developed. A first single-channel device has been tested and proven to provide a fast signal development time of 20-25ns, necessary to mitigate pileup effects while offering precise energy measurements. As the diamond sensors may suffer from polarization effects due to the trapping of charges at the diamond/metal interface, a periodical bias inversion can guarantee optimal performance. To facilitate that, a reversible high voltage power supply has been developed. The ongoing development of data acquisition equipment and real-time processing algorithms based on programmable gate arrays further enhances the neutron camera's capabilities.

Real-Time Policy Optimization for UAV Swarms Based on Evolution Strategies

Multi-agent decision-making faces many challenges such as non-stationarity and sparse rewards, while the complexity and randomness of the real environment further complicate policy development. This paper addresses the high-dimensional policy optimization problems of unmanned aerial vehicle (UAV) swarms. By modeling the problem scenario as a Markov decision process, a real-time policy optimization algorithm based on evolution strategy (ES) pre-training is proposed. This approach combines decision-time planning with background planning to evaluate and integrate different sets of policy parameters in a temporal context. In the experimental phase, the policy network is trained using both ES and REINFORCE algorithms on a constructed simulation platform. Comparative experiments demonstrate the effectiveness of using ES for policy pre-training. Finally, the proposed real-time policy optimization algorithm further improves the performance of the swarm by approximately 10% in simulations, offering a feasible solution for adversarial games between swarms and extending the research scope of evolutionary algorithms.

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

Rail Detection Research Status of Multi -Sensing Technology

Traditional rail inspection methods, primarily relying on manual inspection and basic equipment, face significant challenges including low efficiency, poor accuracy, and susceptibility to human error. This paper presents a comprehensive review of the current research status and future trends in multi-sensor rail transit inspection vehicles. The focus is on the integrated application of various sensing technologies, particularly inertial systems and visual sensing, to achieve high-precision and efficient measurement of rail geometrical parameters. An in-depth analysis of key technologies and challenges in railway track inspection is provided, exploring how advanced sensing methods can overcome the limitations of traditional approaches. The paper also examines the market prospects for rail transit inspection, highlighting the growing demand for more sophisticated and reliable inspection systems. Furthermore, potential future research directions in this field are outlined, including the development of AI-powered data analysis, real-time defect detection algorithms, and the integration of emerging technologies such as LiDAR and thermal imaging. This review aims to provide valuable insights for researchers, engineers, and industry professionals working on advancing rail inspection technologies.

Pre-and-post impact fall detection based on support vector machines using inertial and barometric pressure data

Objective: This paper presents a novel real-time algorithm for fall detection, which contextualizes falls by identifying activities occurring both pre- and post-impact utilizing machine learning techniques and wearable sensors. Methodology: The activities selected to contextualize fall events included standing, lying, walking, running, climbing stairs, and using the elevator. Data were collected using an inertial measurement unit and a barometric altimeter positioned on the participants’ lower backs. Thirteen healthy subjects were observed performing the activities and fall events were recorded from five healthy subjects. The proposed algorithm combines thresholding and cascade support vector machines (SVMs), whose robustness is enhanced by a verification process of the subject’s posture aimed at determining the occurrence of the fall more accurately. Results: The performance of the algorithm was evaluated in terms of the hit rate (HT) both offline and in real-time. From the activities studied, stairs climbing proved to be the most challenging to detect, with an offline HT of 85% and an online HT of 76 %. The overall offline performance was superior, with an HT of 96 %, compared to the performance achieved online, an HT of 91 %; in both cases the fall detection HT was 100 %. Conclusions: The algorithm can be used to recognize fall events occurring to any user, as it has the advantage of not needing prior adaptation due to the nonlinear nature of the SVMs. The cascade SVMs allow for using small sets of variables, leading to low computational cost and a suitable real-time implementation. These features, in addition to the posture verification process, make our algorithm suitable for activity recognition in non-laboratory environments.

Improved Real-Time Detection Transformer-Based Rail Fastener Defect Detection Algorithm

To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were made. Firstly, a Super-Resolution Convolutional Module (SRConv) was designed as a separate component and integrated into the Backbone network, which enhances the image details and clarity while preserving the original image structure and semantic content. This integration improves the model’s ability to extract defect features. Secondly, a channel attention mechanism was integrated into the self-attention module of RT-DETR to enhance the focus on feature map channels, addressing the problem of sparse attention maps caused by the lack of channel attention while saving computational resources. Finally, the experimental results show that compared to the original model, the improved RT-DETR-based rail fastener defect detection algorithm, with an additional 0.4 MB of parameters, achieved a higher accuracy, with a 2.8 percentage point increase in the Mean Average Precision (mAP) across IoU thresholds from 0.5 to 0.9 and a 1.7 percentage point increase in the Average Recall (AR) across the same thresholds.

Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features

This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

Dynamically variable intensity patterns projected using an optofluidic phase-shifter array

We present a concept for the dynamically variable definition of arbitrary intensity distributions using an array of optofluidic phase-shifters. These components consist of cylindrical fluidic surfaces whose interface may be controllably shaped using electrowetting-on-dielectrics actuation. Key to the generation of defined intensity distributions is the ability to calculate the required shape for this phase-shifting interface and to this end we present a novel procedure, to our knowledge, that combines a real-time optimization algorithm with an influence matrix approach to generate the required surface. We illustrate the effectiveness of this approach by reconstructing arbitrary surface profiles with one optofluidic phase-shifting component that is then used to project a desired two-dimensional intensity distribution. Using this approach, we further demonstrate the capability of a two-dimensional phase-shifter array to generate larger dynamically programmable intensity patterns by combining individual intensity distributions.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

A Real-Time Key-Finding Algorithm Based on the Signature of Fifths

The signature of fifths is a special kind of music representation technique enabling acquisition of musical knowledge. The technique is based on geometrical relationships existing between twelve polar vectors inscribed in the circle of fifths, which represent individual pitch-classes detected in a given composition. In this paper we introduce a real-time key-detection algorithm which utilizes the concept of the signature of fifths. We explain how to create the signature of fifths and how to derive its descriptors required by the algorithm, i.e., the main directed axis of the signature of fifths, the major/minor mode axis, the characteristic vector of the signature of fifths, the characteristic angle of the signature of fifths, and the angle of the major/minor mode. We performed a series of experiments to test the algorithm’s effectiveness. The results were compared with those obtained using key-detection approaches based on key-profiles. All experiments were conducted using works composed by J.S. Bach, F. Chopin, and D. Shostakovich. The distinctive features of the presented algorithm, with respect to the considered key-detection approaches, are computational simplicity and stability of the decision-making process.

실시간 항공영상 기반 UAV-USV 간 협응 유도·제어 알고리즘 개발

실시간 항공영상 기반 UAV-USV 간 협응 유도·제어 알고리즘 개발

Towards Advancing Real-Time Railroad Inspection Using a Directional Eddy Current Probe

In the field of railroad safety, the effective detection of surface cracks is critical, necessitating reliable, high-speed, non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of “lift-off noise” due to varying distances between the probe and the test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver (Tx-dRx) coils, aiming to enhance detection sensitivity and minimise the lift-off impact. The study optimises ECT probes employing different transmitter coils, emphasising three main objectives: (a) quantitatively evaluating each probe using signal-to-noise ratio (SNR) and outlining a real-time data-processing algorithm based on SNR methodology; (b) exploring the frequency range proximal to the electrical resonance of the receiver coil; and (c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with a figure-8 shaped transmitter coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. The simulation results closely align with experimental findings, validating the investigation of the optimal operational frequency and lift-off distance for selected probe performance, which are determined to be 0.3 MHz and 1 mm, respectively. The realisation of this project would lead to notable advancements in enhancing railroad safety by improving the efficiency of crack detection.

Development of the Real-time Algorithm for Intra-string Photovoltaic Modules Faults Detection and Classification

Development of the Real-time Algorithm for Intra-string Photovoltaic Modules Faults Detection and Classification

Research on Real-Time Roundup and Dynamic Allocation Methods for Multi-Dynamic Target Unmanned Aerial Vehicles.

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation.

Adaptive nonlinear filtering algorithms for removal of non-stationary noise in electronystagmographic signals

Non-stationary physiological noise poses significant difficulties due to its time-varying and previously unknown characteristics. When processing electronystagmographic signals, linear filtering can distort diagnostically significant rapid changes caused by saccadic eye movements. In such cases, nonlinear filters based on robust estimators are more appropriate, whereas linear filtering effectively reduces white noise in parts of a signal with linear behaviour. Therefore, an adaptive signal processing approach that varies in nonlinearity from highly nonlinear robust estimation to linear averaging and combines their benefits appears promising. We have proposed a low-complexity adaptive method for switching filter sets and relevant filter parameter settings based on the estimated noise level and local signal behaviour. This method does not require time for filter parameter modification and does not need prior information on the signal model and noise variance. Based on this method, we have designed adaptive nonlinear filtering algorithms to exploit the advantages of both the nonlinear robust and linear averaging estimators. We have evaluated the filtering quality statistically using the minimum mean square error criteria and the maximum signal-to-noise ratio for a model signal with different levels of additive Gaussian noise. The proposed real-time adaptive filtering algorithms demonstrate a significant efficiency improvement compared to the commonly used filters. The proposed adaptive myriad algorithm achieves the highest efficiency by adjusting a sample myriad linearity parameter depending on the local estimate of a signal scale and changing the sliding window length and the coefficient affecting the linearity parameter.