Real-time System Research Articles

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Cascade Latent Heat Storage for Solar Cooking Applications

The present investigation explores the potential of multiple phase change material (PCM) based storage for real-time solar thermal applications. There are two types of storage configurations tested in this work for the simultaneous charging and discharging operation, i.e., single PCM-based NaNO3 storage and 2-stage cascade latent heat storage having NaNO3 and NaNO2 as the storage mediums in the consecutive stages. The storage is designed to achieve the end-use temperature condition of 513 K (the highest temperature required for cooking) with the latent thermal energy accumulation capacity of 1 MJ. The study envisages that instead of using a single PCM in the storage unit, 2-stage cascading of the PCMs can improve energy accumulation and retrieval in storage. After 8 hours of operation, none of the storage achieves the complete charging state. The energy accumulated in the NaNO3/NaNO2 cascade storage is found to be 2.6 times higher than the energy accumulated in the NaNO3 storage. NaNO3/NaNO2 storage achieves the desired constant outlet temperature of cold heat transfer fluid for chapati making (531 K) after 165 minutes of operation. At this moment, the latent energy accumulated in the NaNO3 and NaNO3/NaNO2 storage is observed as 35% and 14%, respectively

Advanced Techniques for Real-time Facial Expression Recognition Using Deep Learning

Abstract—Developing systems that can automatically recognize and interpret human emotions from facial expressions is the aim of the quickly expanding field of facial emotion identification and detection research. This technology finds applications in a wide range of areas, including as healthcare, marketing, security, and human-computer interface. Using computer vision and machine learning algorithms, facial emotion recognition systems analyze a face’s features and classify it into numerous emotional categories, including joyful, sorrowful, angry, fearful, and surprised.The three steps in the multi-step process that goes into identifying facial emotions are face detection, facial feature extraction, and emotion categorization. Thanks to recent advances in deep learn- ing, facial emotion detection systems can now identify emotions with high resilience and precision. Further, the development of real-time face expression recognition systems has opened up new avenues for applications such as sentiment analysis, emotional intelligence, and affective computing. This technology could fundamentally alter human-machine interactions and open the way to more compassionate and personalized relationships.A multitude of applications, including virtual assistants, mental health aids, and human-centered technology, will be greatly impacted by the development of systems for identifying and de- tecting facial expressions. Artificial intelligence (AI) technologies that recognize emotions allow people to interact with the digital world in more intelligent and flexible ways. But the complexity of emotion identification lies in the fact that it requires context and geometric elements in addition to facial expressions.

Multiscale Seismic Monitoring in the Bedretto Underground Laboratory for Geosciences and Geoenergies (BULGG)

Abstract The Bedretto Underground Laboratory for Geoenergies and Geosciences (BULGG) is located in south-central Switzerland in the middle of a 5.2-km-long tunnel, which connects the Bedretto valley to the Furka railway tunnel. BULGG is a multidisciplinary laboratory that facilitates experiments and research across various fields. From a seismological perspective, a dense seismic network is deployed that allows real-time monitoring of both natural and induced seismicity occurring in the tunnel and the surroundings. In addition, a multilevel monitoring approach during experiments leads to the generation of real-time high-resolution earthquake catalogs issuing event-based alerts and is the input for a simple traffic light system (magnitude and/or ground-motion based), which provides essential information for the advanced traffic-light system (probabilistic approach). We have set up two separate real-time monitoring systems that monitor the background seismicity, as well as injection experiments, with both systems built on the SeisComP framework. The background monitoring, serving as the backbone network, includes broadband sensors at the surface and along the tunnel, as well as strong-motion sensors and high-frequency geophones along the tunnel and in boreholes. The sampling rate is divergent and depends on sensor type and proximity to faults (200–2000 Hz). Acoustic emission sensors and high-frequency accelerometers sampled at 200 kHz constitute the experimental setup that locates in multiple experimental volumes, which include fluid injections, extractions, and tunneling activities. All sensors transmit real-time data to a common server (SeedLink), which serves multiple clients for processing, real-time visualization, archiving via SeisComP, and risk control via dedicated software. A standardized workflow is applied to both background and experimental monitoring, encompassing automatic picking, automatic phase association and location, and magnitude estimation. Advanced methods are implemented in real time that include double-difference relocation and earthquake detection based on waveform cross correlation. BULGG provides a unique environment to implement novel methods in observational and network seismology across scales.

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

SCR + ASC systems control by backward induction with adaptive grid and different disturbance scenarios

The purpose of this study is to enhance control strategies for selective catalytic reduction (SCR) and ammonia slip catalyst (ASC) systems, aiming to effectively reduce NOx emissions from automotive engines during realistic driving cycles. Despite the effectiveness of these after-treatment systems (ATS), their dynamic and non-linear characteristics present significant challenges in achieving precise control. Therefore, this research proposes a hybrid approach that combines backward induction (BI) as the primary optimization technique with model predictive control (MPC) framework for real-time application. The article introduces a reduced-state control-oriented model of the SCR + ASC system, which is embedded into the BI algorithm to calculate optimal control actions within a finite horizon. Additionally, it is proposed an alternative approach for adapting the grid of model states within the BI algorithm, effectively reducing the computational cost. This adjustment enables the algorithm to operate in real-time with near-optimal results, as confirmed by experimental validation. Lastly, the study explores how different degrees of knowledge regarding system disturbances impact the strategy’s performance, examining three distinct scenarios: constant prediction horizon, probabilistic description, and full knowledge of the prediction horizon.

System for monitoring building heat consumption

Relevance. Energy consumption and efficient use of district heating networks affect the environment, society and the economy. Solutions are needed that can significantly reduce specific thermal energy losses. Using a technically and economically feasible way to assess energy efficiency allows these decisions to be made. Currently, assessing the energy efficiency of buildings raises significant questions among specialists. There is not much modern specialized literature in this area. The problem of energy efficiency requires more rigorous attention to urban planning issues. Thus, refined methods for assessing the energy efficiency of buildings make it possible to simultaneously save capital investments and ensure efficient consumption of thermal energy. Aim. To develop a system for assessing the real heat consumption of buildings to ensure optimization of dispatching of centralized heat supply systems in real time. Methods. Computer modeling of the state of heat consumption of buildings with various characteristics and schemes for their connection to centralized heat supply networks; methods of geographic information analysis. Results. The authors have proposed the system for monitoring the heat consumption of buildings. This system allows assessing the energy efficiency of buildings through the maximum use of computer and software technologies. It is shown that the calculated heat flows of buildings used to predict savings when modernizing buildings do not always correspond to the actual heat loads, which can lead to an incorrect assessment of investment projects. The authors obtained graphical dependences of the quantities influencing specific heat losses on various primary independent factors. Conclusions. The constructed system for monitoring the heat consumption of buildings allows, together with the use of geoinformation analysis methods, obtaining a fairly complete picture of the state of heat consumption for city areas and an assessment of the energy characteristics of buildings.

Design of a Real-Time Monitoring System for Electroencephalogram and Electromyography Signals in Cerebral Palsy Rehabilitation via Wearable Devices

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process.

Model for converting data into NoSQL data warehouse for developing a real-time financial data warehouse system

This research introduces a novel model, the Financial Data Warehouses API (FDW-API), developed using PHP, Node.js, and Express.js. The model is designed to transform banking credit dataset information into a data warehouse format using a Non-Only SQL (NoSQL) database, stored in JSON format. Three types of databases were employed: MongoDB Node, MongoDB Serverless, and Cassandra. The study includes a comparative analysis of the data retrieval speed from all three databases. The model's applicability was tested in a real-time credit approval web application, demonstrating its effectiveness in transforming and storing data. Testing involved loading datasets ranging from 200, 300, 400, 500, 600, 800, and 1000 entries. Results indicate that the MongoDB serverless database outperformed others in terms of efficiency. Additionally, the FDW-API model streamlines data transformation and storage, facilitating real-time analysis and decision-making for financial institutions and data-driven businesses. Its flexibility integrates seamlessly with existing systems, reducing development time and costs, while its scalability accommodates growing data volumes and evolving business needs, providing a valuable tool for strategic insights and competitive advantage.

Ionic liquid-reinforced transparent, stretchable, conductive organic ionic gel with ultra-high sensory capability and ultra-robust impact-resistance

Traditional conductive hydrogels face challenges in environmental stability and mechanical limitations, constraining their applications in wearable devices. Here, we present a novel organic ionic gel by the construction of PVA/EG double polymer network and ionic liquid, named POIG. It showcases outstanding tensile strength (3.01 MPa) and extensibility (820 %). POIG excels in both low and high-speed impacts, dissipating over 90 % of energy in low-speed impacts and outperforming commercial foam five times in high-speed impacts (1.4 kJ/m). The addition of an ionic liquid in POIG creates a stable, uniform, and highly ordered internal conductive structure, leading to ultra-low strain responsiveness (0.2 %), ultra-fast response times (≈10 ms), high linearity (R2 = 0.999), and excellent sensing performance. High stability is also confirmed through 4000 cycles of both tensile and compression testing. Additionally, we developed a real-time sign language recognition system using deep learning, accurately identifying ten gestures with 98.5 % accuracy. This research offers a durable and innovative organic material for future wearable technology and smart devices.

4D Single-particle tracking with asynchronous read-out single-photon avalanche diode array detector

Single-particle tracking techniques enable investigation of the complex functions and interactions of individual particles in biological environments. Many such techniques exist, each demonstrating trade-offs between spatiotemporal resolution, spatial and temporal range, technical complexity, and information content. To mitigate these trade-offs, we enhanced a confocal laser scanning microscope with an asynchronous read-out single-photon avalanche diode array detector. This detector provides an image of the particle’s emission, precisely reflecting its position within the excitation volume. This localization is utilized in a real-time feedback system to drive the microscope scanning mechanism and ensure the particle remains centered inside the excitation volume. As each pixel is an independent single-photon detector, single-particle tracking is combined with fluorescence lifetime measurement. Our system achieves 40 nm lateral and 60 nm axial localization precision with 100 photons and sub-millisecond temporal sampling for real-time tracking. Offline tracking can refine this precision to the microsecond scale. We validated the system’s spatiotemporal resolution by tracking fluorescent beads with diffusion coefficients up to 10 μm2/s. Additionally, we investigated the movement of lysosomes in living SK-N-BE cells and measured the fluorescence lifetime of the marker expressed on a membrane protein. We expect that this implementation will open other correlative imaging and tracking studies.

A Method for Extracting Joints on Mountain Tunnel Faces Based on Mask R-CNN Image Segmentation Algorithm

The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification and extraction of joints on tunnel face images. First, digital images of tunnel faces were captured and stitched, resulting in 286 complete images suitable for analysis. Then, the joints on the tunnel face were extracted using traditional image processing algorithms, the commonly used U-net image segmentation model, and the Mask R-CNN image segmentation model introduced in this paper to address the lack of recognition accuracy. Finally, the extraction results obtained by the three methods were compared. The comparison results show that the joint extraction method based on the Mask R-CNN image segmentation deep learning model introduced in this paper achieved the best joint extraction effect with a Dice similarity coefficient of 87.48%, outperforming traditional methods and the U-net model, which scored 60.59% and 75.36%, respectively, realizing accurate and efficient acquisition of tunnel face rock joints. These findings suggest that the Mask R-CNN model can be effectively implemented in real-time monitoring systems for tunnel construction projects.

Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation

Background The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.

Novel three-dimensional architectured ZnMgAl ternary layered double hydroxide@reduced graphene oxide nanocomposites as electrode material for high-performance supercapacitor

Ternary layered double hydroxides (LDH) have recently attracted immense attention owing to their stupendous features, such as fine compositional tuning, multiple valence states, and superior electrochemical activity. Moreover, their intriguing structures and distinctive physiochemical properties delineate them as an exemplary material to serve as electrodes for high-performance supercapacitors (SCs). The present work reports the in-situ synthesis of Zinc Magnesium Aluminium LDH (ZMA) integrated with reduced graphene oxide (rGO) through a single-step hydrothermal approach. The resulting novel ternary ZMA@rGO nanocomposite (ZMAG) possesses a unique architecture incorporating hierarchical 3D nanoflowers of ZMA embedded on graphene nanosheets. The porous morphology of LDH improves interfacial charge transport, which results in more faradaic reactions at the electrochemically active sites. In addition, the conductive graphene nanosheets render a high specific surface area, endowing excellent capacitive behavior. The amalgamated electrochemical properties of ZMA and rGO develop a competent electrode material (ZMAG) for SCs. The ZMAG2 nanocomposite having ZMA and rGO in 1:1, evinces a specific capacitance (Cs) of 656.7 F/g at a current density (Id) of 1 A/g. Furthermore, the asymmetric supercapacitor (ASC) features admirable cycle stability (retains 89% of initial capacitance after 15,000 charge/discharge cycles). Herein, the real-time application of ZMA@rGO//AC ASC has also been demonstrated. Therefore, hierarchical porous nanostructure proclaims new prospects for fabricating proficient electrode materials for energy storage devices.

An Adaptive Learning Control for MIMO Nonlinear System with Nonuniform Trial Lengths and Invertible Control Gain Matrix

In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations.

Precise soil coverage in potato planting through plastic film using real-time image recognition with YOLOv4-tiny

Planting potatoes through plastic film with incomplete or excessive soil coverage over seed holes significantly impairs yield. Existing covering methods rely solely on mechanical transmissions, leading to bulky and inconsistent soil coverage of the seed holes. This paper reports an innovative method using a precise soil covering device based on the YOLOv4-tiny real-time object detection system to accurately identify potato plastic film holes and cover them with soil. The system adopts a lightweight and high-precision detection scheme, balancing increased network depth with reduced computation. It can identify holes in the plastic film in real-time and with high accuracy. To verify the effectiveness of YOLOv4-tiny real-time object detection system, a precise soil covering device based on this detection system has been designed and applied to a double crank multi-rod hill-drop planter. Field tests revealed that the system's average accuracy rate for detecting holes is approximately 98%, with an average processing time of 15.15 ms per frame. This fast and accurate performance, combined with the device's robust real-time operation and anti-interference capabilities during soil covering, effectively reduce the problems of soil cover omission and repeated covering caused by existing mechanical transmission methods. The findings reported in this paper are valuable for the development of autonomous potato plastic film precise soil covering devices for commercial use.

Lane and Object Detection using YOLO: Indian Roads Scenario

This research explores the feasibility of You Only Look Once (YOLO), a deep learning object detection algorithm, for lane and object detection in challenging Indian road environments. Traditional methods struggle with faded lane markings, dense and diverse traffic, and unpredictable scenarios. YOLO's speed and accuracy make it suitable for real-time ADAS applications. The methodology leverages Roboflow, a platform for computer vision tasks, to explore data acquisition, model selection, training, and evaluation. This research aims to contribute to developing safer and more reliable ADAS systems for Indian roads.

BCDA: A blockchain-based dynamic auditing scheme for intelligent IoT

Intelligent Internet of Things (IoT) provides services for machine learning applications by collecting real-time data, where data correctness crucially impacts the training accuracy of models. Data owners host incremental real-time data in the cloud and use blockchain as a public platform for data query, usage, and certification. Malicious attackers may manipulate data through methods like data poisoning, compromising model parameters. Blockchain-based data integrity verification (auditing) schemes can effectively detect and monitor such activities. However, managing the storage demands for massive on-chain data certificates poses significant challenges. Existing compressed certificate storage strategies, such as the Merkle-Hash tree (MHT), incur significant on-chain storage costs because the auxiliary paths and the entire tree structure must be uploaded during verification, resulting in an O(n) storage overhead. Polynomial commitments offer a promising alternative by reducing storage costs to hundreds of bytes. However, current polynomial commitment methods lack effective support for incremental data updates, which are essential for real-time applications. In this paper, we propose a novel polynomial commitment-based dynamic integrity verification scheme that implements cumulative commitment updates for the first time. Our scheme includes a new data block insertion and deletion strategy that maintains the original order of data blocks within polynomial commitments. Theoretical security analysis confirms the robustness of the proposed scheme, while simulation results demonstrate that it meets the efficiency requirements for blockchain-based distributed data integrity verification mechanisms.

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification.

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial-spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.

A Deep Reinforcement Learning Approach to DC-DC Power Electronic Converter Control with Practical Considerations

In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies.