Real-time Solution Research Articles

Predefined-time robust adaptive gradient neural network for solving linear time-varying equations and its applications

Adaptive gradient neural networks (AGNN) have been extensively employed as effective models for addressing time-varying problems. However, the existing AGNN are convergent in infinite time and only considered in noiseless environment. Consequently, the AGNN with a certain convergence time and noise tolerance is urgently required. Motivated by the advantages of predefined-time convergence, a novel predefined-time robust adaptive gradient neural network (PRAGNN) is developed for solving linear time-varying equations. It can be concluded that PRAGNN can efficiently obtain real-time solutions of linear time-varying equations within predefined time, independent of initial conditions, by properly selecting parameters. In the process of solving linear time-varying equations, PRAGNN can avoid matrix inversion, which reduces the computing cost compared with zeroing neural networks (ZNN). Furthermore, PRAGNN can completely resist both bounded vanishing noise and bounded non-vanishing noise. Numerical experiments demonstrate the effectiveness of PRAGNN. Finally, PRAGNN has been successfully utilized in 2-D dynamic positioning based on the angle of arrival (AOA) positioning algorithm, as well as in alternating currents (AC) computing.

Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications

In recent years, the increasing complexity of quality control in aircraft assembly has necessitated the development of innovative and integrated solutions. This paper introduces a novel framework based on cloud-edge-end architecture to enhance quality monitoring systems in aircraft assembly production. The framework consists of five parts: cloud, edge, end, network, and the trained model. By leveraging the advantages of cloud computing, edge computing, and terminal devices, this framework aims to provide a comprehensive, real-time, and efficient quality monitoring solution. Key technologies within the cloud-edge-end framework are proposed, including data collection, storage, and transmission, deep learning methods based on Channel Attention Convolutional Neural Network (CACNN), and feedback mechanisms from the cloud layer to the lower layers. These innovations enable effective information sharing and utilization at various stages of the aircraft assembly process, providing operational managers with timely and accurate insights into assembly quality. A prototype system application has been implemented at an aircraft assembly site, facilitating process data monitoring and assembly status analysis. Experimental validation of the proposed CACNN algorithm has demonstrated its capability to monitor hole diameters online. Various case studies confirm the framework's effectiveness, showcasing substantial improvements in monitoring accuracy and operational efficiency.

Hybrid Learning Integrated Remote Laboratory: A Pedagogical Strategy for Future Practicum Learning

The hybrid learning integrated remote laboratory, an innovation in educational technology, is a practical and real-time solution that allows students to access and interact with laboratory equipment remotely via the internet network. This research aims to analyze the implementation and effectiveness of this model in an embedded systems practicum course. The study, conducted using a quasi-experimental method, involved 35 students in the experimental group and 36 in the control group. The learning model in the experimental group was implemented with a differentiated approach, allowing students to participate in face-to-face learning in the laboratory or attend online. The technology in this research was built with an interactive user interface in the form of an e-learning integrated remote laboratory application, providing online students with access to learning materials, discussion forums, assignments, chat, video conference, and an online microcontroller coding editor. The use of the online microcontroller coding editor empowers students to create programs and control physical equipment in the laboratory, such as Arduino modules, several sensors, and output devices, remotely and in real-time. Descriptive analysis and t-tests were used to analyze students’ comprehension of the embedded systems course. The test results showed a difference in the average academic achievement of students, with the learning outcomes of the experimental group students being higher than those of the control group. This model, therefore, demonstrates its impact on optimal and practical learning in the embedded system course.

Banking FinTech and stock market volatility? The BIZUM case

This paper investigates whether and how the adoption of FinTech by incumbent banks affects their stock price volatility. BIZUM, a Spanish FinTech real-time digital payment solution was adopted by incumbent banks in 2016 and therefore provides new evidence of real-world ex-post implementation. The results indicate that the adoption of BIZUM by incumbent banks had a significant effect, reducing their stock price volatility after it was launched. This finding suggests that investors were informed of and acknowledged the advantages of BIZUM, thus, use their adoption of FinTech Start-up strategy to offset adverse market circumstances. This paper provides insights for investors and international institutions regarding the role of the pricing of banking related assets, implications for incumbent banks whose portfolios are exposed to investments in disruptive technology and for banking regulators and authorities vis-à-vis risk related considerations of the adoption by banks of FinTech strategies.

An Accelerated Dual-Integral Structure Zeroing Neural Network Resistant to Linear Noise for Dynamic Complex Matrix Inversion

The problem of inverting dynamic complex matrices remains a central and intricate challenge that has garnered significant attention in scientific and mathematical research. The zeroing neural network (ZNN) has been a notable approach, utilizing time derivatives for real-time solutions in noiseless settings. However, real-world disturbances pose a significant challenge to a ZNN’s convergence. We design an accelerated dual-integral structure zeroing neural network (ADISZNN), which can enhance convergence and restrict linear noise, particularly in complex domains. Based on the Lyapunov principle, theoretical analysis proves the convergence and robustness of ADISZNN. We have selectively integrated the SBPAF activation function, and through theoretical dissection and comparative experimental validation we have affirmed the efficacy and accuracy of our activation function selection strategy. After conducting numerous experiments, we discovered oscillations and improved the model accordingly, resulting in the ADISZNN-Stable model. This advanced model surpasses current models in both linear noisy and noise-free environments, delivering a more rapid and stable convergence, marking a significant leap forward in the field.

Clinical application of novel mobile AI solution for real-time detection and differential diagnosis in breast ultrasound: The first prospective feasibility study.

574 Background: Existing artificial intelligence (AI) solutions for breast ultrasound faced limitations due to their non-real-time detection and server dependency. However, novel mobile real-time AI solution now enable on-device detection and differential diagnosis, aiding in immediate decision-making. This study aims to evaluate the feasibility and effectiveness of such a mobile AI solution in clinical settings, by comparing it against expert breast imaging diagnoses. Methods: From August to December 2023, a feasibility study was conducted in tertiary medical center of Taiwan using mobile AI solution (CadAI-B for breast, BeamWorks Inc., Korea). CadAI-B is connected via HDMI or DVI to an ultrasound equipment using tablet PC. It supports healthcare providers to detect suspicious breast lesions by highlighting suspicious area during breast ultrasound scanning, and to make a differential diagnosis by providing BI-RADS categories and malignancy score (0-100%). The rate of real-time detection in malignancies and area under the receiver operating characteristic curve (AUC) was calculated. Distribution of BI-RADS categorized by CadAI-B were compared with those by breast imaging experts on independent review. Results: The analysis included 33 patients with an average age of 47 years (IQR, 44 – 62). There were 14 malignancies, 17 benign lesions, and 2 normal cases, and 30 patients (90.9%) underwent biopsy. For patients with malignancies, CadAI-B successfully identified all malignancies. Overall diagnostic performance, as AUCs calculated by the malignancy score and BI-RADS, was 0.835 and 0.850, respectively. The per-patient sensitivity of CadAI-B was 100.0%, while the specificity was 52.6%. The BI-RADS distribution between CadAI-B and expert was the same in malignant cases. In benign cases, CadAI-B categorized 9 cases (50.0%) as C4A or C4B, while experts classified 13 cases (72.2%), indicating a potential reduction in the need for biopsy. Conclusions: This study demonstrates that real-time processing capabilities of CadAI-B enable the identification of critical lesions during breast ultrasound, helping to reduce unnecessary biopsy. These findings may be helpful in diagnostic decisions and training of non-experts in clinical settings and offer the potential to improve workflow efficiency. [Table: see text]

Surveying Emerging Trends in DDoS Defense

This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.

Smartbands and Behavioural Interventions in the Classroom: Multimodal Learning Analytics Stress-Level Visualisations for Primary Education Teachers

ABSTRACT Students’ stress levels may affect their well-being, attentiveness and learning outcomes in primary education classrooms. Positive behavioural interventions and support actions conducted by teachers may alleviate students’ stress levels, especially when addressing special educational needs. In this multimodal learning analytics study, students in a classroom were all given a smartband for their wrist during regular curriculum activities. Data comprised the semester of a single subject as a part of a research project conducted in Sweden. Biobehavioural stress-related arousal of students’ autonomic nervous system was visualised and analysed through distinguished behavioural modes. Additional data include naturalistic observational notes and two short teacher interviews. Research methodology and strategies for innovative implementation were presented and discussed alongside contextual details. For example, stress-level visualisations can aid actionable adjustments of behavioural intervention intensity and provide students’ attentiveness overview for teachers that sequence curricular activities during planning. Findings show an interdisciplinary basis for cost-effective real-time dynamic solutions that involve visual dashboards with advantages to understanding student learning, both at a school-wide system level and for the classroom, if viewed optimistically. However, research on the topic is still in its infancy, notably with ethical risks as a growing pain.

An optimisation-based approach to reduce fuel consumption and emissions from shipping navigation

This study presents an optimisation-based approach to reduce fuel consumption and emissions from shipping navigation. The main objective is to improve energy efficiency and simultaneously turn a case-study vessel compliant with Carbon Intensity Indicator (CII) proposed by IMO. This optimisation module has been devised as part of a new robust integrated real-time digital solution that will involve a significant number of both technical and operational measures in practice aiming to optimise operational efficiency (duringnavigation and port calls). Namely, the tool will be capable of situational awareness and decision support to reduce fuel consumption and Green House Gas (GHG) emissions from shipping and must be combined with intrinsic vessel systems to improve vessel hydrodynamic performance, resulting also in improved vessel safety and widening of the operational weather window

Simulation-Driven Universal Surrogates of Coupled Mechanical Systems: Real-Time Simulation of a Forestry Crane

Abstract Preparing simulation-driven surrogates for a coupled mechanical system can be challenging because the associated mechanical and actuator dynamics demand high-fidelity numerical solutions. Proposed here is a universal hydraulic surrogate (UHS), which can provide solutions to high-fidelity mechanical systems with a universal actuator in a surrogate-assisted monolithic approach. The UHS acts as an alternative to the standard lumped fluid theory by eliminating the hydraulic pressures differential equations. A surrogate-assisted universal actuator uses an approximated model to define hydraulic force in high-fidelity mechanical systems. The approximated force model was developed through training against the dynamics of a one-dimensional (1D) hydraulic cylinder and spring-damper. A covariance matrix adaption evolutionary strategy (CMA-ES) was used as an optimization algorithm to minimize differences between the standard dynamics and UHS approaches at the position and velocity levels. The robustness of resulting UHS was validated to predict the behaviors of the simple four-bar mechanism and the forestry crane. The focus was on numerical accuracy and computational efficiency. The maximum percent normalized root mean square error (PN-RMSE) between the states of the approximated force model and lumped fluid theory were approximately 2.04% and 6.95%, respectively. The proposed method was approximately 52 times faster than the standard lumped fluid theory method. By providing accurate predictions outside the training data, the simulation-driven UHS promises better computational performance leading to real-time simulation solutions for the coupled mechanical systems. The UHS can be applied in simulation, optimization, control, state and parameter estimation, and Artificial Intelligence (AI) implementations for coupled mechanical systems.

Remotely controlled smart monitoring system of hermetic paddy storage to reduce postharvest losses in Bangladesh

In Bangladesh, where agriculture forms the backbone of the economy, efficient grain storage is crucial, particularly for paddy storage. Traditional methods like gunny bags and plastic containers are common, but the recent introduction of hermetic storage technologies, such as GrainPro bags and hermetic cocoons, have significantly reduced postharvest losses paddy. However, most research focuses on oxygen (O2) and carbon dioxide (CO2) levels in those open storage systems. This study introduces an innovative, cost-effective remote monitoring system for hermetically sealed grain storage, designed using locally sourced components, including a Digital Hygrometer, WiFi Mini IP Camera, and a Remote Controller unit. Tested over a 125-day period at Advanced Storage Lab, Bangladesh Agricultural University, the system effectively monitored the internal environment of the storage units. Key findings show that the internal temperature of the storage bags varied between 16.7°C and 29.2°C, demonstrating lesser fluctuation compared to the ambient temperature range of 16.1°C to 33.5°C. The relative humidity inside the storage units remained stable between 45 % and 54 %, in contrast to the external humidity range of 45 % to 81.2 %. Additionally, using the Steffe and Singh equation, an increase of 0.512 % in Equilibrium Moisture Content (EMC) of paddy was recorded. Notably, there was no evidence of insect or mold growth after 125 days of storage. This study's remote monitoring system not only marks a significant advancement in hermetic grain storage technology but also contributes to sustainable agricultural practices. It provides a practical, real-time solution to monitor and manage key environmental parameters, ensuring the preservation of grain quality over time.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

Real-Time Ideation Analyzer and Information Recommender

The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.

Implementation and Evaluation of a National Multidisciplinary Kidney Genetics Clinic Network Over 10 Years

IntroductionDiagnostic genomic sequencing is the emerging standard of care in nephrology. There is a growing need to scale up the implementation of genomic diagnostics nationally to improve patient outcomes. MethodsThis pragmatic study provided genomic or genetic testing to patients with suspected monogenic kidney disease through a national network of kidney genetics clinics. We sought to evaluate the experiences of implementing genomic diagnostics across Australia and associated diagnostic outcomes between 2013 and 2022. ResultsWe successfully established and expanded a nationwide network of 20 clinics as of 2022, concurrently developing laboratory, research, and education programs to scale the clinical application of genomics in nephrology. We report on an Australian cohort of 1,506 kidney patients, of whom 1,322 received their test results. We assessed barriers to implementation in the nephrology context and, where possible, applied real-time solutions to improve clinical processes over ten years. ConclusionDeveloping a multidisciplinary kidney genetics model across multiple health services nationally was highly successful. This model supported optimal care of individuals with monogenic kidney disease in an economically responsible way. It has continued to evolve with technological and service developments and is now set to scale further as genomic testing for kidney patients transitions to healthcare system funding.

"FLOODGUARD: An IoT-Powered Real-Time Flash Flood Monitoring and Forecasting System"

Abstract: In recent times, the surge in flooding incidents has become a pressing global issue, inflicting extensive damage on both lives and livelihoods. While floods remain an inevitable force of nature, their devastating impact can be alleviated. With the aid of cutting-edge technologies like the Internet of Things (IoT), proactive flood forecasting has become increasingly feasible. Through the seamless integration of IoT data, populations can receive early warnings and evacuate to safety, safeguarding both lives and valuables. A real-time solution is imperative, leveraging IoT data streams to deliver timely flood alerts. Our study presents an IoT-driven prototype designed to collect hydrological data from rivers, encompassing metrics such as water flow, level, and discharge, alongside meteorological parameters like temperature, humidity, wind speed, and direction. Employing a Long Short-Term Memory (LSTM) model, we analyze and classify collected data based on water discharge, level, rainfall, and temperature, predicting flood events as "no alert," "yellow alert," "orange alert," or "red alert." Given the dynamic nature of rivers, accurately measuring total water discharge poses challenges. To address this, we propose a novel methodology utilizing water flow, sectional area width, and average depth. Furthermore, we tackle the complexities associated with accurately measuring rainfall due to erratic weather conditions. Our system demonstrates robust performance in predicting flood event states, achieving high F1-scores of 97% for "no alert," 97% for "yellow alert," 96% for "orange alert," and 98% for "red alert.

A real-time solution method for three-dimensional steady temperature field of transformer windings based on mechanism-embedded cascade network

To enhance the computation efficiency and accuracy of three-dimensional steady temperature field of transformer windings, we propose a new non-invasive Reduced Order Model (ROM) based on a mechanism-embedded cascade network. Initially, a snapshot matrix is formed from the Full Order Model (FOM) and then combined with Proper Orthogonal Decomposition (POD) to extract key modal features that characterize the temperature field. Subsequently, a cascade network architecture, integrating Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN), is devised to swiftly map working condition parameters to modal coefficients. Additionally, the cascade network is embedded with condition sensitivity and modal contribution mechanisms to further enhance prediction accuracy. Finally, by linearly weighting the modes with predicted modal coefficients, a rapid reconstruction of the steady temperature field in transformer windings is achieved. Validation against Fluent software simulations and experimental measurements demonstrate a close agreement, with computational errors of less than 4K and an impressive single solution time of only 0.0087 s, which is 48760 times faster compared to Fluent software.

Fast parametric analysis of trimmed multi-patch isogeometric Kirchhoff-Love shells using a local reduced basis method

AbstractThis contribution presents a model order reduction framework for real-time efficient solution of trimmed, multi-patch isogeometric Kirchhoff-Love shells. In several scenarios, such as design and shape optimization, multiple simulations need to be performed for a given set of physical or geometrical parameters. This step can be computationally expensive in particular for real world, practical applications. We are interested in geometrical parameters and take advantage of the flexibility of splines in representing complex geometries. In this case, the operators are geometry-dependent and generally depend on the parameters in a non-affine way. Moreover, the solutions obtained from trimmed domains may vary highly with respect to different values of the parameters. Therefore, we employ a local reduced basis method based on clustering techniques and the Discrete Empirical Interpolation Method to construct affine approximations and efficient reduced order models. In addition, we discuss the application of the reduction strategy to parametric shape optimization. Finally, we demonstrate the performance of the proposed framework to parameterized Kirchhoff-Love shells through benchmark tests on trimmed, multi-patch meshes including a complex geometry. The proposed approach is accurate and achieves a significant reduction of the online computational cost in comparison to the standard reduced basis method.

An efficient parallel approach for quad-constellation GNSS real-time precise orbit determination enabling 5-second intervals updating

Real-time Global Navigation Satellite System (GNSS) precise orbits are essential prerequisites for real-time GNSS-based applications. However, the high computational demands of filter-based real-time multi-GNSS POD methods present a significant challenge for practical utilization. This study introduces an efficient parallel approach for quad-constellation GNSS real-time POD using the square root information filter (SRIF). In the proposed approach, parallel QR factorization algorithms are implemented to expedite parameter estimation based on SRIF, while parallel processing model is designed for real-time POD to enhance its computational efficiency. Validation experiments show that the computation time for measurement and time update in SRIF can be reduced by 78.5% and 80.5%, respectively, compared to the traditional serial approach. Additionally, parallel computing improves the computational efficiency of GNSS processing algorithms in real-time POD by approximately 70%. The proposed approach enables the completion of quad-system real-time POD solutions with 90 stations within 5 s, representing an impressive 73.0% reduction.

Dynamic optimization on quantum hardware: Feasibility for a process industry use case

The quest for real-time dynamic optimization solutions in the process industry represents a formidable computational challenge, particularly within the realm of applications like model-predictive control, where rapid and reliable computations are critical. Conventional methods can struggle to surmount the complexities of such tasks. Quantum computing and quantum annealing emerge as avant-garde contenders to transcend conventional computational constraints. We convert a dynamic optimization problem, characterized by an optimization problem with a system of differential–algebraic equations embedded, into a Quadratic Unconstrained Binary Optimization problem, enabling quantum computational approaches. The empirical findings synthesized from classical methods, simulated annealing, quantum annealing via D-Wave’s quantum annealer, and hybrid solver methodologies, illuminate the intricate landscape of computational prowess essential for tackling complex and high-dimensional dynamic optimization problems. Our findings suggest that while quantum annealing is a maturing technology that currently does not outperform state-of-the-art classical solvers, continuous improvements could eventually aid in increasing efficiency within the chemical process industry.