Real-time Performance Data Research Articles

Dynamic Lexicon-Based Sentiment Analysis Architecture Using Nonlinear Feature Optimization

Dynamic Lexicon-Based Sentiment Analysis (DLSA) improves sentiment detection by using adaptive, nonlinear feature optimization techniques. The model targets gaps in traditional methods, which often miss complex and subtle sentiment patterns. DLSA integrates modules for text preprocessing, lexicon and phrase extraction, and feature optimization with genetic algorithms. The system refines feature sets by selecting, combining, and adjusting features based on real-time performance data. Key findings show DLSA outperforms traditional models like SLPCF in accuracy, precision, and recall, especially in analyzing nuanced text data. This approach provides a flexible and efficient solution, enhancing the accuracy of sentiment classification in varied applications.

Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Enhancing industrial decision-making through Multi-Criteria Decision-Making approaches and ML-Integrated Frameworks

Decision-making in current industrial contexts has shifted from intuition to a data-driven approach, requiring prompt processing of huge datasets. However, conventional Multi-Criteria Decision Making (MCDM) methodologies fall short of navigating the intricacy of large datasets. This paper introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques such as artificial neural networks. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and improved recommendations compared to traditional MCDM methods. While results are promising, future work should focus on robustness testing particularly in terms of its dependence on real-time data, to ensure sustained efficacy and mitigate potential biases in recommendations over time.

Visual Design and Evaluation of Public Space Scene Based on Virtual Reality Technology

Public space scenes are being transformed by virtual reality (VR) technology, offering immersive and interactive experiences that redefine how people engage with their surroundings. Through VR simulations, users can explore and interact with virtual public spaces in ways that were previously impossible. From virtual parks and plazas to digital replicas of iconic landmarks, VR technology allows users to experience the ambiance and atmosphere of public spaces from the comfort of their own homes. Moreover, VR enables designers and planners to experiment with different layouts, amenities, and features, facilitating participatory design processes and community engagement. This paper presents a visual design and evaluation framework for public space scenes using virtual reality (VR) technology, enhanced by Virtual Parallel Edge Hashing Computing (VPE-HC). The framework aims to optimize the design and user experience of public spaces by leveraging VR technology to create immersive and interactive simulations. Through simulated experiments and empirical validations, the effectiveness of the VPE-HC-enhanced VR-based design and evaluation process is evaluated. Results demonstrate significant improvements in user satisfaction, engagement, and usability compared to traditional design methods. For instance, users interacting with public space scenes designed using VPE-HC reported a 40% increase in perceived safety and a 25% improvement in overall satisfaction ratings. Additionally, the framework enabled designers to optimize spatial layouts and amenities based on real-time user feedback and performance data, leading to more effective and user-centric design solutions. These findings underscore the potential of VPE-HC in enhancing the visual design and evaluation of public space scenes based on VR technology, facilitating more inclusive and user-friendly urban environments.

Design of Visual Communication Experience Based on Virtual Reality Technology

Visual communication experiences have been revolutionized by virtual reality (VR) technology, offering users immersive and captivating interactions with digital content. Through VR headsets and immersive environments, users can engage with visual content in unprecedented ways, feeling as though they are physically present within the digital space. Whether exploring virtual art galleries, immersive storytelling experiences, or interactive visualizations, VR technology provides users with a sense of presence and agency, enhancing their connection to the content. Furthermore, VR enables creators to design highly engaging and interactive visual experiences that transcend traditional media formats, allowing for innovative storytelling techniques and immersive narratives. This paper presents the design of a novel visual communication experience leveraging virtual reality (VR) technology enhanced by Multi-Spectral Multi-Point Co-Ordination (MSMPC). The proposed design aims to create immersive and engaging visual experiences that transcend traditional media formats. Through simulated experiments and empirical validations, the effectiveness of the MSMPC-enhanced VR-based visual communication experience is evaluated. Results demonstrate significant improvements in user engagement, immersion, and satisfaction compared to conventional approaches. The users interacting with the MSMPC-enhanced VR experience reported a 40% increase in immersion levels and a 25% improvement in overall satisfaction ratings. Additionally, the design enabled creators to optimize visual storytelling techniques based on real-time user feedback and performance data, leading to more impactful and effective communication. These findings highlight the potential of MSMPC in enhancing visual communication experiences based on VR technology, paving the way for innovative and immersive storytelling in various domains.

Hybrid Teaching Model of College English Based on Collaborative Filtering Recommendation Algorithm

A recommendation system is a powerful tool that analyzes user preferences and behaviors to provide personalized suggestions or recommendations. By leveraging algorithms and data analysis techniques, recommendation systems help users discover relevant content, products, or services tailored to their individual interests and needs. Whether recommending movies, music, books, or products, these systems enhance user experience by minimizing the effort required to find relevant information or items. Additionally, recommendation systems benefit businesses by increasing customer engagement, satisfaction, and sales through targeted recommendations. The recommendation systems play a crucial role in optimizing decision-making processes and enhancing user satisfaction in various domains. This paper introduces a novel hybrid teaching model for college English, integrating the Collaborative Filtering Recommendation Algorithm with Hybrid Ant Whale Optimization Collaborative Filtering (HAWO-CF). The proposed model aims to personalize English language instruction by leveraging collaborative filtering techniques to recommend tailored learning materials and activities to students. Through simulated experiments and empirical validations, the efficacy of the HAWO-CF-enhanced hybrid teaching model is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional methods. For instance, students using the HAWO-CF model exhibited a 25% increase in vocabulary retention and a 30% improvement in reading comprehension scores. Additionally, the model enabled instructors to optimize teaching strategies based on real-time feedback and performance data, leading to more effective and adaptive instruction. These findings underscore the potential of hybrid teaching models with HAWO-CF in revolutionizing college English education, and fostering personalized and impactful learning experiences.

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.

QoE Sustainability on 5G and Beyond 5G Networks

The 5G network not only causes many internet real-time broadband services to become wireless, such as virtual reality and cloud gaming, but also promotes mobile-inherent brand-new emerging services, like in-vehicle real-time video communications. One of the main visions of 5G and beyond 5G is to provide human-centric communications, not only for diverse services, but also better quality of experience (QoE) for users. In the meantime, as with the time variance challenges faced by all wireless networks, the problem of controllability for QoE is still noticeable in 5G. This article presents an AI-enabled QoE predicting and sustainability architecture, cooperating with 5G network data analytic function, network slicing, and MEC technologies to collect cross-layer performance data in real time, as well as adjust network resources accordingly. Thereby, it can support QoE-demanding services, create business innovation, and improve energy efficiency. In addition to illustrating the operation of the proposed architecture with one video streaming scenario, a machine learning QoE predicting model, its performance of the field trial, and research directions are discussed.

Sensing technologies for construction productivity monitoring

Construction is a complex and dynamic industry that requires constant monitoring and evaluation to ensure project success. In recent years, the need for accurate and real-time performance data has become more pressing, making traditional evaluation methods based on manual data collection and direct observation less effective. To address this challenge, sensor technologies have emerged as a promising solution for construction performance monitoring. The objective of this article is to present a comprehensive overview of the current state of sensor applications for construction performance monitoring, including recent advancements in this field. Additionally, the article will examine the advantages and disadvantages of sensor technologies in the context of construction performance monitoring.

A telemedical interface for at-home cognitive testing.

The COVID-19 pandemic has limited in-lab cognitive testing. While at-home alternatives exist (testing over the phone), differences in test design and delivery complicate direct comparison of most in-lab and at-home tests. Here we describe the design, infrastructure, and implementation of the California Cognitive Assessment Battery (CCAB), a cognitive test battery and administration system. Using automated remote administration over cellular networks, identical computerized cognitive tests can be administered at-home or in the lab. The CCAB comprises 30+ computerized cognitive tests. Test delivery is automated through text, text-to-speech, and graphical instructions; test scoring is also automated, using automatic speech recognition for verbal responses. The test delivery infrastructure consists of two components: the test kit (MS Surface Pro tablet computer with mouse, headset, and cellular dongle) , and a web-browser application "CCAB Examiner," used by the test administrator to control test delivery, view real-time progress and scores, and monitor/chat with the participant via AV feeds. The system is coordinated by a cloud-based server which also delivers the CCAB Examiner application to any browser. Once connected, the "CCAB Test Station" application and CCAB Examiner communicate over a commercial Communication as a Service (CAAS) provider, which transmits AV streams and a text messaging protocol. CCAB Test Station is designed to isolate control, communications, and testing functions for maximal fault tolerance. For example, test administration continues automatically even if Examiner and Test Station lose connection. Test data is uploaded to the server during sessions and re-synced when the test kit returns. 442 participants underwent three 90-minute CCAB test sessions on successive days (72 total tests per participant). More than 98% of participants finished all three sessions, and >99% of tests were completed. Cellular connection failures occurred occasionally, but >98% resulted only in the loss of A/V feeds for the examiner, while tests continued automatically and examiners were able to monitor real-time performance data. The CCAB permits identical tests to be administered at home or in the laboratory, while cellular-network based remote administration allows for nationwide access for all socioeconomic groups, even for people in remote areas without Wi-Fi, or those with limited technical expertise.

Real-time data-based performance analysis of a large-scale building applied PV system

Solar photovoltaic (PV) is one of the fastest-growing renewable technologies. Building applied photovoltaic systems can significantly help reduce the energy and environmental loads of buildings. This study analyzes the performance of a 425 kW building applied photovoltaic system by investigating important technical, economic, and environmental parameters. These analyses have been carried out with the help of year-long real-time performance data. The technical performance indicators examined in this study include direct current and alternating current power output, array yield, specific (final) yield, references yield, array losses, module and overall PV conversion efficiency, capacity factor, and performance ratio. The main strengths of the study include the large size of the BAPV system, and a comprehensive techno-economic and environmental analysis based upon real-time monitored data. The PV system has been found to annually produce 835.60 MWh and 814.88 MWh of DC and AC power output, respectively. The performance ratio varies from 74.03% in August to 84.62% in January with an annual figure of 78.09%. The annual capacity factor is determined to be 21.85%. For a project life of 25 years, the simple payback period and Net Present Value have been calculated to be 6.92 years and USD 866,565.24, respectively. Over the project lifetime, the savings of CO2, CH4 and N2O are calculated to be 15,666 tons, 483.75 kg, and 83.25 kg respectively.

Rateworkspace: BIM integrated post-occupancy evaluation system for office buildings

The feedback obtained from occupants regarding their comfort needs and performance of buildings is critical for assessing occupant satisfaction, identifying the operation and maintenance (O&M) issues in time and for improving resource efficiency in buildings. Current facility management (FM) systems and occupant feedback collection practices, however, have limitations in supporting effective decision-making in FM, as they lack the necessary contextual data related to the occupant feedback (e.g., building geometry, systems, elements). Building Information Modeling (BIM)-enabled FM systems are used for combining different types of FM information with building models; however, occupant feedback is still not effectively utilized in FM since it is not integrated with BIM. In this study, a BIM integrated post-occupancy evaluation system prototype is developed for: (1) collecting occupant feedback along with the contextual information related to the feedback items in a structured way, and (2) presenting this information as integrated with BIM to the facility managers. This enables conducting spatio-temporal queries and supports effective decision-making by visualizing the collected feedback. The prototype was designed by using qualitative shadowing with FM teams to identify information needs and use case analysis to determine how contextual data integrated with BIM could be collected from office occupants who are non-technical persons with limited information on building models. This paper identifies the FM query categories that are required to process the occupant feedback and describes the RateWorkSpace prototype developed for office buildings. The deployment of the prototype in a real-world office demonstrates that the proposed system is applicable, practical, usable, and that real-time building performance data can be both collected and analysed with the developed system. This has the potential to increase the effectiveness of the FM and O&M processes, and help to create office spaces with optimized energy use and occupant comfort that also supports occupant well-being and productivity.

An improved compact propulsion system model based on batch normalize deep neural network

Abstract Aero-engine on-board steady state model is an important part of many advanced engine control algorithms. In order to build a high accuracy and real-time steady-state onboard model in a large envelope, an ICPSM (improved compact propulsion system model) based on batch normalize neural network is proposed in this paper. Compared with piecewise linearization model and support vector machine model, conventional CPSM which is mainly composed of baseline model and nonlinear sub model has the advantages of high real-time performance and small data storage. However, as the similarity conversion error increases with the distance from the design point, the cumulative error of the conventional baseline model also increases, which makes the model unable to maintain high accuracy in the full envelope. Thus, a high accuracy baseline model in full envelope based on batch normalize neural network is proposed in this paper. The simulation result shows that compared with the conventional compact propulsion system model, the percentage error of parameters of the improved compact propulsion system model based on the batch neural network is reduced by two times, the single step operation time is reduced by 18%, and the data storage of the onboard model is reduced as well.

An improved compact propulsion system model based on batch normalize deep neural network

Abstract Aero-engine on-board steady state model is an important part of many advanced engine control algorithms. In order to build a high accuracy and real-time steady-state onboard model in a large envelope, an ICPSM (improved compact propulsion system model) based on batch normalize neural network is proposed in this paper. Compared with piecewise linearization model and support vector machine model, conventional CPSM which is mainly composed of baseline model and nonlinear sub model has the advantages of high real-time performance and small data storage. However, as the similarity conversion error increases with the distance from the design point, the cumulative error of the conventional baseline model also increases, which makes the model unable to maintain high accuracy in the full envelope. Thus, a high accuracy baseline model in full envelope based on batch normalize neural network is proposed in this paper. The simulation result shows that compared with the conventional compact propulsion system model, the percentage error of parameters of the improved compact propulsion system model based on the batch neural network is reduced by two times, the single step operation time is reduced by 18%, and the data storage of the onboard model is reduced as well.

Development of arduino assisted data acquisition system for solar photovoltaic array characterization under partial shading conditions

This research proposed the data acquisition system (DAS), which has a capability to collect real-time voltage and current at variable load resistance during an experimental characterization analysis of 3 × 3 size, photo voltaic (PV) system, under partial shading conditions (PSCs). In addition, the system is economical and minimizes the testing period for PV system characterization relative to traditional approaches. Analogue voltage and current sensors are integrated with the open-source Arduino platform to quantify and store real-time performance data in the SD card assembly. Performance parameters such as voltage and power at global maximum power point (GMPP), with minimized power loss (PL) and improved fill factor (FF) shows the effectiveness of the proposed system under the PSCs. Real-time hardware is developed and its performance is compared with the MATLAB/Simulink performance, with percentage errors as low as 0.48%, 1.95% and 1.37% under different shading case.

Real-time novelty detection of an industrial gas turbine using performance deviation model and extreme function theory

Novelty detection is crucial to ensure the availability and reliability of an industrial gas turbine. With the application of modern health monitoring systems, there is an ample amount of data gathered from gas turbines, however they are usually from normal events with limited knowledge of any novelty. In current practice, the unknown event is detected by comparing with a model of normality through pointwise approaches, which is inefficient in terms of false alarms or missing alarms. This paper proposes an accurate novelty detection approach using performance deviation model and extreme function theory. The model is established from the multi-sensor real-time performance data. Outputs of the model, that is, the deviation curves, are considered as functions instead of individual data points to test the status of the system as ‘normal’ or ‘abnormal’ by the extreme value theory. The effectiveness of the proposed approach is demonstrated by the monitoring data from a single shaft gas turbine on site. Compared with other traditional methods, the proposed approach is superior in terms of high detection accuracy and high sensitivity with a good balance between the false alarm rate and missing alarm rate. This paper provides a reliable approach for the real-time health monitoring of the industrial gas turbines.

A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process.

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has led to delays in delivery, which raises ship production costs. Therefore, this paper proposes a methodology for shipyard ship block assembly plants that enables objective process progress measurement based on real-time work performance data, rather than the empirical judgment of a site manager. In particular, an IoT-based physical progress measurement method that can automatically measure work performance without human intervention is presented for the mounting and welding activities of ship block assembly work. Both an augmented reality (AR) marker-based image analysis system and a welding machine time-series data-based machine learning model are presented for measuring the performances of the mounting and welding activities. In addition, the physical progress measurement method proposed in this study was applied to the ship block assembly plant of shipyard H to verify its validity.

The Effect of Athletic Chest Protectors on the Performance of Manual and Mechanical CPR: A Simulation Study.

ObjectivesSudden cardiac arrest is a leading cause of death in athletes. Although athletes wear various athletic chest protectors (ACPs) to prevent commotio cordis (CC), cardiac arrest cases still occur. Although it is established that cardiopulmonary resuscitation (CPR) quality affects outcome, little research has evaluated the effect of ACPs on CPR compressions quality. This study aimed to observe whether: (1) ACPs impact depth, rate, and hand positioning of both bystander and LUCAS CPR. (2) LUCAS devices affect CPR performance compared with traditional compressions.MethodsAn observational, prospective, convenient sample of 26 emergency medicine residents performed CPR on a high-fidelity Laerdal mannequin, which recorded real-time performance data. Baseline CPR for 1- and 2-minute cycles, CPR on a mannequin wearing the ACP, and ACP removal time was recorded. LUCAS CPR performance was measured at baseline and over the ACP.ResultsBystander CPR had a statistically significant difference in both hand placement and compression rate for baseline versus ACP compressions (85% vs 57%, P < 0.05; 138 vs 142, P < 0.05, respectively), but not in compression depth (51.08 vs 50.05 mm, P = 0.39). LUCAS CPR had no significant difference in CPR performance. Bystander versus LUCAS CPR had a significant difference in compression rate (138 vs 101, P < 0.01), but not in depth or hand placement.ConclusionsAthletic chest protectors significantly impacted hand placement during bystander CPR, which may diminish CPR quality. Considering ACP removal required only 5.4 seconds, removing the ACP before standard CPR may improve quality.

Design and implementation of distributed asynchronous data aided computer information interaction system

Under the influence of novel corona virus pneumonia epidemic prevention and control, it has put forward higher requirements for data storage and processing for personnel management system. The distributed asynchronous data aided computer information interaction system can solve the problem of multi node concurrent data processing. The traditional computer information interaction system has poor real-time performance, low precision and asynchronous data processing ability. The invocation features of message queuing asynchronous caching mode are combined with the standardization of Web services and cross language with cross platform access features in this paper. Through the combination of the two technologies, a flexible and universal asynchronous interaction architecture of distributed system is established. Based on Web service technology and system to system access, the call and response of tasks between modules are carried out in the system, which makes the interaction between the whole system have the characteristics of message driven. The test result shows that the system proposed in this paper has good real-time performance and strong data processing ability. It is suitable for the data interaction of distributed personal management system under the influence of novel corona virus pneumonia epidemic prevention and control.

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.