Real-time Interface Research Articles

A Generative Framework for Designing Linear-Modular Housing Integrated with a Real-Time User Interface

A Generative Framework for Designing Linear-Modular Housing Integrated with a Real-Time User Interface

Research on fatigue life of carbon fiber reinforced polymer strengthened single-sided butt welded joints utilizing resin pre-coating for strong adhesive bonding

Single-sided butt welding is a common connection method used in box beam structures such as excavator booms. Single-sided butt weld joints are prone to fatigue failure since the structures are subjected to cyclic loads over extended periods. The purpose of this study is to improve the fatigue performance of single-sided butt welded joints. Carbon fiber reinforced polymer (CFRP) strengthened single-sided butt welded joint specimens with permanent backing plates were prepared by pre-coating method, and constant-amplitude tension–tension fatigue tests were carried out on the specimens. The effects of welding groove parameters and CFRP reinforcement on fatigue life were studied. The test results show that the CFRP-strengthened specimens without blunt edges and with a groove angle of 40° show the highest average fatigue life. The single-sided bonding of 10 layers of CFRP sheets effectively inhibits the propagation of fatigue cracks and significantly improves the fatigue life of welded joints. In the CFRP/welded joint composite structure, the strain monitoring results show that the peak strain of the interface is the largest in the middle position, and the peak strain rises sharply in the rapid crack propagation stage, which indicates that the real-time interface strain peak can reflect the fatigue crack propagation. It provides an early warning method for real-time monitoring of fatigue life failure of welded structures in practical engineering.

Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis

This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

Human-in-the-loop control of dynamics and robotics using augmented reality

Human-machine interaction (HMI) and human-robot interaction (HRI) offer innovative approaches for the analysis of structural dynamics both in laboratory and real-world scenarios. In vibratory experimentation, an external force is generated to evaluate dynamic responses of structures. One mode of generation is to use electrodynamic shakers. Sensors are attached to the structure to measure dynamic responses and can be deployed by robots. In post-disaster inspections robotic deployment ensures the safety of inspectors and requires control. If the interface between operators and the controls was augmented, then operators can visualize experiments, exciter levels, and define robot input while maintaining awareness of the area of interest. Robots can provide better aid to humans if control of the robot is: (1) quantified and presented to the human; and (2) conducted in real-time for human feedback. This research proposes using Augmented Reality (AR) to provide operators with sensor feedback and control for analysis of dynamic structures with robots. This method improves cognition by allowing the operator to maintain awareness of structures while adjusting conditions accordingly with the assistance of the new real-time interface. One interface application is developed to plot sensor data in addition to voltage, frequency, and duration controls for vibration simulations. Two more applications are developed following the same framework, one to control the position of a mediating robot and one to control the frequency of the robot's movement. The applications serve as a first-step approach investigating potential robotic sensor deployment in disaster assessment of structures. This paper presents the proposed model for the new vibratory control loop and then compares the new approach with a traditional method by measuring time delay in control input. The study also demonstrates that AR enhances time efficiency and accuracy in controlling robotic arm positions, matching actuator frequencies, and deploying sensors with minimal time delay.

Design and Application Research of a UAV-Based Road Illuminance Measurement System

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Automatic dressing change and monitoring infusion speed control system

Intravenous drip is a common form of treatment, but the wrong rate of injection or dose of medication can have serious consequences for patients. Monitoring devices can detect the speed of drug infusion and drug response in real time, helping medical care personnels adjust treatment regimens in a timely manner and reduce the risk of human error. At present, some rooms in the Intensive Care Unit (ICU) require a sterile environment. In view of the above situation, this paper proposes a new device, which adds the function of automatic dressing change to the intravenous injection monitoring device, and synchronizes the obtained data to the user interface in real time by using the Internet of Things (IoT), so as to reduce the frequency of medical staff entering and leaving the ICU, realize remote monitoring of the intravenous injection process and data related to automatic dressing change, and improve the treatment effect of patients and the work efficiency of medical staff.

Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations.

Scalable-produced micro-elastic triboelectric sensing ground for all-weather large-scale applications

Large-scale smart floor will be one potential form for a human–machine interface, but is also facing many difficulties and challenges in the materials science and information engineering. Here, we present a straightforward method for fabricating large-scale micro-elastic triboelectric nanogenerator (ME-TENG) arrays, which can be assembled as triboelectric sensing ground for real-time interface interaction and all-weather monitoring. ME-TENG unit of 6 by 6 arrays offers a maximum power density of 5.84 mW·m−2 under a load resistance of 70 MΩ. It displays a sensitivity of 132.48 mV/kPa in the pressure range of 0 to 180 kPa. The electrical output signals have remained stable in the durability tests over 10 days with various weather conditions. Furthermore, successfully demonstrated a regional comprehensive security monitoring (RCSM) system for enabling all-weather monitoring of the features, position, and movement trajectories of an object. Even more gratifying is that the ME-TENG unit of 30 cm by 30 cm prepared by a simple and fast “all-in-one” method, takes costs of 14.84 dollars/m2 and the production period of 2 h, compared with the most cost-effective piezoelectric sensors, the cost and duration were reduced by 98.67 % and 42.86 %, respectively. This work will greatly contribute to the development and implementation of large-scale sensing and the multidimensional security system.

An integrated development environment based situational awareness for operational reliability evaluation in wind energy systems incorporating uncertainties

Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively.

A Novel Framework on Real Time Chat Interface

In this extensive research paper, we delve deeply into the intricate process of developing a real-time chat interface website utilizing cutting-edge web technologies such as ReactJS, Chat Engine, sockets, hooks, props, JavaScript, and APIs. The primary aim of this project is to cater to the evolving demands for efficient communication and collaboration platforms in the contemporary digital landscape. Through meticulous analysis, detailed experimentation, and critical evaluation, we explore the functionalities, design principles, social relevance, and future implications of the developed application. This research paper serves as a comprehensive guide, providing in-depth insights into the utilization of modern web technologies for constructing interactive and engaging web applications, while also shedding light on the broader societal impacts and implications for the future of digital communication. The advancement of web technologies has facilitated the development of real-time communication platforms that transcend geographical barriers. This research paper delves into the comprehensive development process and technical intricacies involved in creating a real-time chat interface website using modern web technologies, including ReactJS for the frontend, Chat Engine for the backend, and auxiliary technologies such as sockets, hooks, props, JavaScript, and APIs. The project aims to address the growing need for efficient communication and collaboration platforms in today's digital landscape. Through detailed analysis, experimentation, and critical evaluation, this paper explores the functionalities, design principles, social relevance, and future implications of the developed application

喷杆倾角控制系统的设计与实验

During field operations of the spray boom sprayer, the distance between the ends of the spray boom and the height of the crop canopy affects the uniformity of spraying, requiring operators to manually adjust the spray boom to be parallel to the crop canopy, which impacts operational efficiency. This study presents the design of a boom tilt control system, consisting of a main control node, distance measurement node, vehicle tilt detection node, and spray boom tilt control node. The bus communication protocol for the spray boom tilt control system is defined according to the ISO11783 standard, and a serial communication network is designed, along with the development of a real-time dynamic monitoring interface for the spray boom. The system automatically monitors the height of the boom and the tilt of the vehicle, makes decisions based on the detection information, controls the electric actuator, and adjusts the tilt of the boom. Leveraging the advantages of fast computing speed and user-friendly human-machine interface of the PC, as well as the high cost-effectiveness and small size of the microcontroller, and the multi-master-slave structure of the CAN bus, this system can complete data acquisition, processing, and other functions required for spray boom tilt control, achieving automatic adjustment of spray boom tilt. This enhances spray uniformity and operational efficiency of the sprayer, while reducing the workload of operators.

Development of real-time brain-computer interface control system for robot

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.

Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

Abstract This paper validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.

Applications of artificial intelligence in prostate cancer histopathology.

The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology.

Automatic control of reactive brain computer interfaces

This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.

High power voltage multiplier (VM) PFC Converter with inherent ZCS Characteristics and high step-ip gain for enhanced low-line efficiency

In this paper, a Voltage Multiplier (VM) converter configuration has been designed for high Power PFC application, ensuring reduced losses and high system efficiency.The conventional PFC system is improved with adding VM circuit configuration that enhances the static gain features with moderate duty ratio (D), and hence, the reverse recovery (RR) issue is resolved. The VM section inherently adds two main specific features of clamping behavior and ZCS phenomenon to the PFC system. An experimental prototype of 2 kW, 600 V VM PFC converter configuration is designed and its effectiveness is validated under low line input of 100 V , operating at a high duty ratio, D = 0:67. The Real-Time Interface (RTI) feature of dSPACE1104 and MATLAB/Simulink are used to give the control signal for the verification of the system. Always, less than half of the output voltage (300 V ) appears across the switches of the converter, hence, voltage stress of the switch is minimized. Also, ZCS phenomenon is evident during switching commutations due to the snubber circuit behavior of the resonant tank, included in the proposed PFC converter. Hence, the overall system efficiency is improved. Furthermore, higher system efficiency is maintained for different rating which has been tested in this research work.

Cepstral Analysis-Based Artifact Detection, Recognition, and Removal for Prefrontal EEG

This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the artifact detection task and a 82.79% accuracy on the 6-category eye movement classification task. A statistical value-based artifact removal method is proposed and evaluated on a public EEG database, where an accuracy improvement of 3.46% is obtained on the 3-category emotion classification task. In order to make a confident decision of each 5s EEG segment, the algorithm requires only 0.66M multiplication operations. Compared to the state-of-the-art approaches in artifact detection and removal, the proposed method features higher detection accuracy and lower computational cost, which makes it a more suitable solution to be integrated into a real-time and artifact robust Brain-Machine Interface (BMI).

Soft Sensor Development for Real-Time Interface Tracking in Multiple Product Pipelines Based on Knowledge and Data

Summary Petroleum products are usually consecutively transported in the same multiple-product pipeline, and the occurrence of mixed oil happens during the process. Accurately tracking the mixed oil interface is essential for the optimal scheduling of oil transportation. However, complicated operating conditions and unavoidable measurement noise pose challenges to methods for locating the interface. The data-driven modeling method is a potential solution, but it may face limitations due to issues of overfitting, especially when the data set is contaminated with noise. To tackle such problems, in this paper, we propose a knowledge-informed Bayesian-Gaussian mixture regression (KIBGMR) model to enable the real-time tracking of the interface. The KIBGMR employs finite Gaussian distribution to learn the multimode characteristics of input data, including the hydrothermal data, measured density of tail oil, and the velocity of interface transportation and output data including the measured density of tail oil and interface transportation velocity. Subsequently, it utilizes the prior knowledge related to the regression coefficient through the Bayesian treatment. Evaluations demonstrate that the R2 index achieved by the proposed model in predicting the interface arrival time is greater than 0.98, even with the contaminated data set. This research can help operators accurately grasp the location of the mixed oil interface, formulate reasonable valve switch operations for better management of the mixed oil section, and provide a reference for the method of knowledge-data hybrid modeling.

A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert’s feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework’s evaluation and validation show the proposed framework’s feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device.