Real-time Situation Research Articles

A building information modeling-based fire emergency evacuation simulation system for large infrastructures

During fire emergencies, people in large infrastructures are difficult to comprehend real-time fire dynamics and identify available evacuation routes, leading to lengthy evacuation time and unnecessary loss of life and property. This study aims to develop a fire emergency evacuation simulation system for large infrastructures. This system integrates the data from building information modeling platform, fire dynamics simulator, and evacuation simulation platform through customized developments. Real-time fire situation is transmitted to the evacuation simulation platform to assess the impact of dynamic fire spread on the evacuation of people. The behavioral and psychological characteristics of evacuees of different genders and age groups are considered in simulated evacuations. An algorithm for optimizing evacuation route planning is designed to improve the utilization of each evacuation exit and provide a visualization of evacuation routes. This proposed system was validated at a busy subway station in China. The evacuation efficiency increased by 12.87%. It is concluded that this system can provide real-time dynamic optimal evacuation route planning for enhanced emergency management.

Physiological workload assessment with the help of heart rate and perceived exertion rating on handcart pullers of wholesale trade markets of Delhi.

Background . Handling a heavy load on a handcart is common in the wholesale and retail trade markets in India and many parts of the globe. These carts transport goods between major markets, warehouses and transporters. We assessed the physiological workload among handcart pullers in terms of cardiovascular load (CVL), energy expenditure (EE), heart rate (HR), physiological cost of work and subjective rating of perceived exertion. Methods . Physiological workload was assessed with the help of HR during resting, working (15 minutes) and recovery state with a smart wearable device to extrapolate percentage of CVL, EE and physiological cost of work among handcart pullers. Subjective assessment of exertion was measured with the Borg 5-point rating scale among 35 cart pullers. Data were analysed using Microsoft Excel version 2010. Results . It was found that in handcart-pulling activity, EE was 11.706 kJ/minute, average working HR was 128 beats/minute and physiological cost of work was 89.09 beats/minute with no loads on handcart. This signifies that the activity falls under heavy physiological workload. The average percentage of CVL was found to be 50.5%, which falls under acceptable category for persons with a healthy cardiovascular system. Thirty-one (88.6%) of the participants reported the activity to be moderately heavy, whereas 4 (11.4%) participants mentioned it to be light. Conclusions . Handcart-pulling operation in this study without load on cart was found to be a heavy physiological workload. In real-time situations, the load, road conditions, long duration and traffic congestion is likely to result in a higher range of physiological workload on cart pullers.

Speech Emotion Recognition using Deep Learning

It is frequently simple to identify emotions in human-to-human encounters since they may be seen in speech, body language, and facial expressions. Nevertheless, recognising human emotion in human-computer interactions (HCI) can be difficult. Speech emotion recognition (SER), with the aim of just identifying emotions through vocal intonation, has arisen as a way to enhance this connection. In this paper, a SER system based on deep learning methodologies is proposed.Here, RAVDESS dataset was utilised to assess the suggested system. To select the most appropriate vocal features that represent speech emotions for this MFCC is used. LSTM, CNN, and a hybrid model that combines CNN and LSTM are three different deep learning models we used to construct our SER system. By examining these various strategies, We were able to establish the most effective model for properly recognising emotional states from speech data in real-time situations. Overall, this work indicates the usefulness of the suggested deep learning approach.

ICT and social media digital initiatives for effective dissemination of Soybean technologies

In India, the use of social media and ICTs has gained popularity in the last decade consequent to the digital revolution. In line with these developments, the ICAR-Indian Institute of Soybean Research has started use of social media platforms like WhatsApp, YouTube and Facebook etc. for dissemination of information related to the improved soybean production technologies developed by the Soybean R&D system and facilitating feedback mechanisms among the clients and their interaction with the research system using different ICT tools. Institute has also developed many software to use the potential of Information Communication Technologies (ICT) in best manner. These ICT tools help the farming as well as scientific community in solving different problems and improve and issues as well as for decision making in real time situations.

Anomaly Detection Based on a 3D Convolutional Neural Network Combining Convolutional Block Attention Module Using Merged Frames.

With the recent rise in violent crime, the real-time situation analysis capabilities of the prevalent closed-circuit television have been employed for the deterrence and resolution of criminal activities. Anomaly detection can identify abnormal instances such as violence within the patterns of a specified dataset; however, it faces challenges in that the dataset for abnormal situations is smaller than that for normal situations. Herein, using datasets such as UBI-Fights, RWF-2000, and UCSD Ped1 and Ped2, anomaly detection was approached as a binary classification problem. Frames extracted from each video with annotation were reconstructed into a limited number of images of 3×3, 4×3, 4×4, 5×3 sizes using the method proposed in this paper, forming an input data structure similar to a light field and patch of vision transformer. The model was constructed by applying a convolutional block attention module that included channel and spatial attention modules to a residual neural network with depths of 10, 18, 34, and 50 in the form of a three-dimensional convolution. The proposed model performed better than existing models in detecting abnormal behavior such as violent acts in videos. For instance, with the undersampled UBI-Fights dataset, our network achieved an accuracy of 0.9933, a loss value of 0.0010, an area under the curve of 0.9973, and an equal error rate of 0.0027. These results may contribute significantly to solve real-world issues such as the detection of violent behavior in artificial intelligence systems using computer vision and real-time video monitoring.

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

Secure Storage of Computer Network Data based on Cloud Computing

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.

Study of the Thermal-Hydraulic Characteristics of a Printed Circuit Heat Exchanger Using a Novel Iterative Inversion Method Combined with Genetic Algorithm

Printed circuit heat exchangers are getting more and more applications in various energy systems. Detailed data monitoring is essential to understand their operation situations in real time. Nevertheless, the commonly monitored parameters are inadequate to evaluate the local flow and heat transfer characteristics when the fluid undergoes an obvious property variation in the channel. In this work, a measurement system for printed circuit heat exchangers similar to those in industrial applications was set up to study the thermal-hydraulic characteristics of supercritical CO2 with non-linear physical properties. The experimental results show that the heat transfer coefficient can be increased by 82.9–109.6% when the mass flow rate increases from 0.15 to 0.42 kg/s. By comparison, the heat transfer coefficient can be increased by 70.7% when the fluid state approaches the critical point. A novel iterative inversion method was proposed to deduce the coefficients of the heat transfer and friction factor correlations based on the collected fundamental parameters. The deviations of the heat transfer rate and friction coefficient between the measured data and the predicted results by the derived correlations were both within 10%, indicating the feasibility and reliability of the inversion method.

SMART ACCESSIBILITY SYSTEM FOR VISUALLY IMPAIRED PEOPLES USING ARTIFICIAL INTELLIGENCE

Disability is the state of a person in which one has to depend on others for their own needs. Visual impairment is one of the disabilities of a human being. To date numerous methods had been proposed to enhance the life style of visually impaired and blind people. Still purchasing products in the e-shopping and accessibility application without others support is tricky one for them. The paper describes a system that provides the guidance for them to identify and purchase their products in the supermarket application. The audio instructions will assist them inside the supermarket application based on the real time situations. To make the supermarket in a smarter way the billing system is automated. Hence it eliminates the existing queuing system in the supermarket. The ultimate aim of this system is to eliminate others support for visually impaired people in shopping and provide them a convenient and sophisticated environment. On implementing this system, it facilitates the blind people shopping and accessibility, save the customer's time and promotes business sales. Keywords: Disability, Visual, Impairment, E-shopping, Accessibility.

DESIGN REAL-TIME DATA PROCESSING SYSTEMS FOR AI APPLICATIONS.

Online analytics systems are vital for ensuring the high efficiency of AI in response to real-time situations requiring agile decision-making. The present paper explores real-time data processing and topology, featuring the application of edge computing and cloud-based services and systems. Through simulation reports, the study shows how these systems handle significant data traffic and minimal delays in healthcare monitoring, automated transport systems, and smart homes. Possible data consistency, system growth, and redundancy issues are recognized, and recommendations are made to improve navigation system dependability and effectiveness. It is possible to improve AI in various industries with the support of progressive apt processing solutions.

Deformation Monitoring of Monopole Communication Towers Based on Multi-Source Data Fusion

Monopole communication towers play an irreplaceable role in modern communication systems. Because of its high flexibility, lateral loads control the deformation of a monopole communication tower. Dynamic displacement can be used to describe structural vibration characteristics and real-time deformation situations. Therefore, this article proposes a monopole communication tower deformation monitoring method based on multi-source data fusion using dynamic displacement as the evaluation indicator. This method calculates the strain displacement using the strain-mode superposition methodology. Then, the strain displacement and acceleration obtained are processed using Kalman filtering technology to reconstruct the real-time displacements of the monopole communication tower. The effectiveness of this method was verified using numerical simulations and model experiments, respectively. In addition, parametric analysis shows that this method is suitable for processing multi-rate data, has good noise resistance in strong noise environments, and can effectively reconstruct the displacement near the tower bottom. The results indicate that the method has favorable robustness and can accurately reconstruct low-frequency and high-frequency displacements of the monopole communication tower.

A two-layer energy management system for a hybrid electrical passenger ship with multi-PEM fuel cell stack

The hybrid combination of hydrogen fuel cells (FCs) and batteries has emerged as a promising solution for efficient and eco-friendly power supply in maritime applications. Yet, ensuring high-quality and cost-effective energy supply presents challenges. Addressing these goals requires effective coordination among multiple FC stacks, batteries, and cold-ironing. Although there has been previous work focusing it, the unique maritime load characteristics, variable cruise plans, and diverse fuel cell system architectures introduce additional complexities and therefore worth to be further studied. Motivated by it, a two-layer energy management system (EMS) is presented in this paper to enhance shipping fuel efficiency. The first layer of the EMS, executed offline, optimizes day-ahead power generation plans based on the vessel's next-day cruises. To further enhance the EMS's effectiveness in dynamic real-time situations, the second layer, conducted online, dynamically adjusts power splitting decisions based on the output from the first layer and instantaneous load information. This dual-layer approach optimally exploits the maritime environment and the fuel cell features. The presented method provides valuable utility in the development of control strategies for hybrid powertrains, thereby enabling the optimization of power generation plans and dynamic adjustment of power splitting decisions in response to load variations. Through comprehensive case studies, the effectiveness of the proposed EMS is evaluated, thereby showcasing its ability to improve system performance, enhance fuel efficiency (potential fuel savings of up to 28%), and support sustainable maritime operations.

An underwater sound event detection algorithm based on Temporal Graph Convolutional Network

Accurate underwater sound event detection (SED) and real-time underwater situation awareness plays an important role in intelligent sonar system. However, little research on underwater SED has been done because underwater acoustics have very complex and dynamic spectral and temporal characteristics which make underwater SED difficult to solve. The goal of SED is to recognize type and time of the input audio signal and most of SED research have been focused on airborne sound so far. To address underwater SED problem, we adopt temporal graph convolutional network (T-GCN), which is originally designed for traffic prediction and is composed of GCN and gated recurrent unit (GRU) To capture spectral and temporal information simultaneously, the GCN is used to learn topological structures to capture spectral correlation and the GRU is used to learn changes of input sound to capture temporal correlation. The proposed model utilizes sound spectrogram and its annotation with temporal information as input and then the GCN and GRU of the model is employed to solve underwater SED problem. Experiments using urban SED dataset and underwater sound dataset, demonstrate that our proposed model expresses connection of spectral and temporal information effectively and shows reliable underwater SED performance accordingly.

Dielectrically modulated ferroelectric-TFET (Ferro-TFET) based biosensors

The study discovers the biosensing analysis of Ferro-TFET. The biosensor developed is based on the dielectric modulation technique. Besides the incorporation of nano-cavities close to both the source and drain region results in the increased capture area. Ferro-TFET biosensor is analyzed for performance parameters such as threshold voltage (SVTH), ON-OFF current ratio (SION/IOFF), and drain current (SId) sensitivity. The characterization is done for biomolecules considering charge to be neutral, positive, and negative for different values of dielectric constants between the immobilization layer and insulator interface. Also, an exhaustive evaluation is done for the case when nano-cavities are partially filled which initiates due to the steric hindrance and is found to be conferred with the real-time situation. The work investigated nano-cavities considering partially filled for the arrangement of biomolecules in nature of distribution similar to convex, concave, increasing, and decreasing configuration in addition to the situation of biomolecules placed in nanocavities an asymmetric distribution. In the work, different sensitivity parameters of Ferro-TFET are compared with the state-of-the-art biosensors presented in the literature to establish the efficiency of the proposed biosensor. The Ferro-TFET biosensor improves the performance in comparison to conventional TFET-based biosensors as ferroelectric material when used as oxide material improves the subthreshold swing, improves the gate control over the channel, and establishes its applicability in low-power biosensor design.

3D Visual Reconstruction as Prior Information for First Responder Localization and Visualization.

In professional use cases like police or fire brigade missions, coordinated and systematic force management is crucial for achieving operational success during intervention by the emergency personnel. A real-time situation picture enhances the coordination of the team. This situation picture includes not only an overview of the environment but also the positions, i.e., localization, of the emergency forces. The overview of the environment can be obtained either from known situation pictures like floorplans or by scanning the environment with the aid of visual sensors. The self-localization problem can be solved outdoors using the Global Navigation Satellite System (GNSS), but it is not fully solved indoors, where the GNSS signal might not be received or might be degraded. In this paper, we propose a novel combination of an inertial localization technique based on simultaneous localization and mapping (SLAM) with 3D building scans, which are used as prior information, for geo-referencing the positions, obtaining a situation picture, and finally visualizing the results with an appropriate visualization tool. We developed a new method for converting point clouds into a hexagonal prism map specifically designed for our SLAM algorithm. With this combination, we could keep the equipment for first responders as lightweight as required. We showed that the positioning led to an average accuracy of less than 1m indoors, and the final visualization including the building layout obtained by the 3D building reconstruction will be advantageous for coordinating first responder operations.

ATHENA I—An architecture for near real-time physiological signal monitoring and pattern detection

Smart healthcare has wide application possibilities, including physiological signals analysis. With the development and improvement of microelectronic devices and sensors, wearable sensors can acquire physiological signals with quality and support remote health monitoring. Due to the growing use of these devices, there is a need for easy and flexible ways to use them in health monitoring. To contribute in this context, we developed the ATHENA I, an architecture focused on using wearables to acquire physiological signals and applying artificial intelligence to classify health patterns in real-time. Because of the constant improvement of wearable devices, we focused on developing a flexible and efficient architecture. Since many health centers outside the central regions of the metropolis have reduced connectivity, we designed an architecture capable of operating entirely outside the data network. The architecture prototype features BITalino wearable sensor and a Raspberry Pi single-board computer. For a complete prototype evaluation, we performed stress-inducing experiments conducted by psychologists to acquire a new dataset of physiological signs. We used this dataset in pattern classification in offline operations and near real-time situations. Our purpose with these experiments is to predict stress, being able to differentiate the normal state from the stress state. The classification results for the stress patterns showed promising results, with accuracy above 98.72% for binary classification (stress, no-stress); and 92.72% for classification with three classes (pre-stress, stress, and post-stress). In the real-time classification, we obtained an accuracy of 69.00%. The architecture presented excellent communication and operation stability. During the experiments, the architecture performed short and long acquisitions efficiently.

Adaptive scalable spatio-temporal graph convolutional network for PM2.5 prediction

PM2.5 Prediction is a complex task of large-scale spatio-temporal analysis, which not only needs comprehension of static geospatial knowledge and relative features but also needs to analyze the real-time situation. This paper discusses the characteristics of the static graph and the dynamic graph in spatio-temporal series tasks. An Adaptive Scalable Spatio-temporal Graph Convolutional Network(ASGCN) model is proposed to predict PM2.5. To capture and analyze the characteristics of the time series period of PM2.5, a time convolution network based on the strategies of inception and gating is proposed and used as a temporal module. A dynamic graph idea is adopted to distinguish the spatio-temporal similarity of different periods. And an adaptive weighted multilayer graph convolution network is used to process static and dynamic graphs, aiming to analyze the spatial relationship of PM2.5 stations. The convolution network with the inception and gating improves the time-series feature capture ability, and adaptive static and dynamic graphs enhance the spatial relationship analysis ability. The temporal and spatial modules of the model are relatively independent, which benefits obtaining the potential information of datasets to improve the prediction accuracy. At the same time, these modules cooperate to make the model adaptable to various data. We choose a great number of comparative models and design a thorough experimental scheme including single-step prediction, multi-step prediction, hyperparameter experiments, and ablation experiments on two real PM2.5 datasets collected in China. Finally, the model achieves performance close to or better than the current state-of-the-art models selected for comparison in prediction tasks.

Real-Time Detection Algorithm for Kiwifruit Canker Based on a Lightweight and Efficient Generative Adversarial Network.

Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.

Riverside: A design study on visualization for situation awareness in cybersecurity

Real-time situation awareness is a key challenge of cybersecurity defense. Visual analytics has been utilized for this purpose, but existing tools tend to require detailed knowledge about the network, which can be challenging in large-scale, production networks. We conducted an interview study involving 24 security professionals to gather requirements for the design, development, and evaluation of visualization to aid situation awareness in cybersecurity. Using these findings, we designed a visualization tool – called RIVERSIDE – for providing a real-time view of the dynamically changing computer network to support situation awareness. We evaluated Riverside in a user study involving 10 participants. Participants were placed in an incident response scenario that tasked them to identify malicious activity on a network. 20% of the users identified all attack component, while an additional 40% only missed one component.

Handoff Control and Resource Allocation for RAN Slicing in IoT Based on DTN: An Improved Algorithm Based on Actor–Critic Framework

As a three-layer association of Internet of things equipment (IoTE) -network slicing (NS) -base station (BS) in radio access network (RAN) slicing, handoff control and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing are investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then an improved algorithm based on actor-critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for Handoff Control and an optimizer for Resource Allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.