Error Rate Measurements Research Articles

Prediction of non-magnetic metal sphere radius using real part slope of inductance

The existing eddy current measurement thickness technology is mainly for one or more layers of metal plate. However, in real life, the radius of spherical metal is also a critical parameter, so using an eddy current sensor to measure it is also an important research topic. In this paper, real part slope of inductance is used as the characteristic value to characterize the radius of the non-magnetic metal sphere. Compared with the traditional real part of inductance, the addition of slope improves the accuracy and stability of detection data. The experimental results show that the maximum error rate of radius measurement is less than 2%, and the overall data stability is good, which can meet the measurement requirements.

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.

Design of A Monitoring System for the Implementation of Energy Management in the Electrical Engineering Study Program Workshop of the Medan State Polytechnic

The researcher wants to make a KWH meter monitoring tool with estimated usage costs so that the tools he makes can help the community to minimize costs in the electricity consumption area, especially in the workshop room located on the 1st floor of building C at Medan State Polytechnic. The long-term goal of this research is to develop an Internet of Things (IoT)--based KWH meter monitoring tool that can read current, voltage, frequency, power factor, and power consumption. This utilization shows the use of costs that have been incurred in real-time by displaying the results on the LCD and can also be from the user's mobile phone. In this study, the strategy used in the energy management framework was determined using SWOT analysis. From the results of the measurement and monitoring of electrical energy consumption obtained the results that, energy management is carried out by applying Standard Operating Procedures for the use of electrical energy in the electrical engineering workshop room in order to save more minimal energy consumption with a large error (Error) PZEM in measuring Voltage is 0.0198% in measuring Current is 0.612%. in measuring Cosphi is 0.01%. in measuring Frequency is 0.003%. in measuring Power is 0.183%. And the average measurement error rate is below 0.5%, namely 0.23%.

Sofware-Defined Radio Testbed for I/Q Imbalanced Single-Carrier Communication Systems

An end-to-end testbed for In-phase and Quadrature (I/Q) Imbalance (IQI) communication systems based on Software-Defined Radio (SDR) is presented. The scenario under consideration is a Single-Input–Single-Output (SISO) single-carrier communication where the transmitter is heavily affected by IQI, whose effects are mitigated through digital signal processing at the receiver. The presented testbed is highly configurable, enabling the testing of different communication and IQI parameters. Crucial insights into the practical implementation of IQI mitigation techniques, specifically through the use of asymmetric signaling at the receiver, are provided. Initially, a detailed description of the mathematical framework is given. This framework serves as the foundation for the subsequent discussion on system implementation, effectively bridging the gap between research on IQI mitigation and its practical application in single-carrier architectures. Over-The-Air (OTA) Symbol Error Rate (SER) measurements for different constellations validate the receiver design and implementation. The source code of the presented testbed is publicly available.

Predicting the Optimal Treatment for Diseases Using the Genetic Method by Develop (PSO) Optimization Technique

Recently, most researchers have been interested in finding appropriate solutions to the spread of diseases, and the data on diseases considered big data, so dealing with big data is difficult in terms of obtaining highly accurate results because of the missing values. To enhance the deals with this type of data, this paper will use artificial intelligence techniques to design a system to predict the optimal treatment for diseases, regardless of disease type with normalization and processing missing data for dataset of diseases . The Particle Swarm Optimization (PSO) algorithm used to find the best solutions since genetic algorithms have characteristics that distinguish them from other algorithms such as mutation and crossover. The 1X method of crossover has been used to develop the PSO algorithm to give the best prediction for the appropriate treatment for these diseases by using the mathematical equations that are explained in 1.3.1. To prove the enhancement of the PSO algorithm after development, the results obtained from the PSO algorithm were compared before and after development by measuring the Fest Fitness Value (accuracy), Error rate, and MSE measurement, the results displayed improvement after development of the algorithm by increased values of accuracy reached to 94% and decreased of Error rate and MSE

Application of 3D Image Technology in Rural Planning

The well-being of villages and villagers is directly related to the development of urban-rural relations. Rural development is an important part of poverty alleviation, as well as the main goal and means of rural rejuvenation, because rural planning affects rural economic development and rural revitalization. Electronic imaging can improve the speed of rural area planning, and can also model the planned scheme. However, the current rural planning still lacks top-level design, which cannot improve the overall structural design of rural planning, and the data resources of rural planning are not perfect. Therefore, this article studied the direction of rural planning and design by analyzing the focus, problems, and external environment characteristics of rural planning, and then analyzed the application characteristics in rural planning according to the process of 3D image technology. By reducing the complex design links in rural planning, the office efficiency of planning and the 3D visualization of planning model can be promoted, so as to improve the effect of rural planning and the construction service of rural planning. The simulated annealing algorithm showed that the planning efficiency and average planning speed of 3D image application in rural planning were gradually increasing; the average planning efficiency was about 1.80, and the average planning speed was about 1.05. The planning efficiency increased by 1.20 in the whole process, while the average planning speed increased by 0.33 in the whole process. Through comparison, it can be seen that the planning rationality of rural planning under 3D image was 12.16% higher than the original one, while the measurement error rate of some teachers was 47.28% lower. In a word, 3D imaging and electronic imaging can improve the architectural design and layout planning of rural planning.

A mean flow velocity estimation scheme based on flow field characteristics and distribution point optimization for carbon monitoring in coal-fired power plants: A numerical study

CO2 online monitoring provides a method to obtain carbon emissions by directly quantifying the flue gas flow rate and CO2 concentration. However, significant flow rate measurement errors arise due to the complexity of turbulence in large-diameter stacks. To address the issue, this study utilizes turbulence numerical simulation combined with mean velocity estimation error analysis to investigate the flow field of typical stacks in coal-fired power plants. The simulation results demonstrated that the evolution of turbulence exaggerates velocities. The flow field exhibits stable turbulence characteristics in a single-introduced stack, while in the double-introduced stack, the flow field dynamically evolves due to asymmetric collisions and abrupt expansions. Furthermore, the effects of load and temperature on the estimation error of flow velocity are established. The optimum monitoring height, the ideal chord angle and the stepped number of deployment points based on the log-linear method are determined. Within the single-introduced stack, a correction factor is introduced and a corresponding formula is established that links estimation errors with monitoring heights. This resulted in a dramatic reduction of the maximum estimation error from 14% to 0.3%. Finally, the systematic and simplified optimization scheme for estimating mean flow velocity is summarised, which provides a powerful reference for flow rate measurement for online CO2 monitoring in coal-fired power plants.

Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback.

In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences.

Sub-6 GHz beamforming with low-cost software-defined radio: Design, testing, and performance evaluation

The rapid evolution of mobile communication systems has led to the introduction of novel technologies and techniques that address existing challenges, such as interference caused by multiple users and transmission through the millimetre-wave band. In this paper, we present a novel approach that utilises a low-cost software defined radio (SDR) unit to enable transmitting and receiving beamforming in the sub-6 GHz band. We provide a detailed account of the underlying concepts and background of beamforming and SDR, outline the design of multiple components, and report on the comprehensive testing and verification of a real-life prototype using over-the-air (OTA) methods. Our results demonstrate that the proposed approach achieves outstanding performance, with measurements of bit error rate (BER) that outperform existing solutions. In summary, this work contributes to the ongoing efforts to enhance mobile communication systems by providing an innovative, high-performance solution that is cost-effective and reliable using SDR technology in the sub-6 GHz band.

Time Series Modelling and Forecasting Foreign Direct Investment using Linear and Nonlinear Models: The Case of Nigeria

This study addresses the crucial need for accurate forecasting of Foreign Direct Investment (FDIT) trends in Nigeria. FDIT plays a pivotal role in the country's economic growth and development efforts, driving industrialization, infrastructure enhancement, and job creation. However, predicting FDIT accurately is essential for policymakers, investors, and researchers to formulate effective strategies and decisions. This study conducts a comparative analysis of four FDIT forecasting models: Simple Exponential Smoothing (SES), Holt Exponential Smoothing (HES), ARIMA, and NNAR in Nigeria, utilizing R version 4.3.2 and forecast and nnetar packages for implementation. The FDIT dataset is partitioned into 80:20 training-test samples, with models applied to each subset for performance evaluation. Leveraging historical FDIT data, the study evaluates the predictive performance of each model over a specified period, employing error measures such as RMSE, MAE, MASE and MAPE to assess accuracy and reliability. Additionally, residual normality tests and visual inspections validate model assumptions. Results indicate varying levels of accuracy and predictive capability among the models, with HES and NNAR models displaying superior performance compared to SES and ARIMA, evidenced by measure error rates. Residual normality tests affirm the suitability of the models for FDIT forecasting. The study contributes empirical evidence to the existing literature, providing valuable insights into forecasting techniques for FDIT in Nigeria. These insights can guide policymakers, investors, and researchers in selecting appropriate models for forecasting FDIT trends and making informed decisions. Future research avenues could explore advanced modelling techniques and additional variables to further enhance the accuracy and reliability of FDIT forecasts within the Nigerian context.

Intelligent decision-making in healthcare telemonitoring via forward-backward chaining and IoT

Healthcare telemonitoring has emerged as a promising approach to remotely monitor patients remotely, enabling timely intervention and personalized care. Internet of things (IoT) device-generated patient data necessitates innovative solutions for intelligent healthcare decision-making, as current methods struggle to provide timely, context-aware, and data-driven recommendations, resulting in suboptimal patient care. This study aims to develop an intelligent decision-making framework for healthcare telemonitoring by leveraging forward-backward chaining and IoT technology. The research focuses on a system using forward-backward chaining algorithms to analyze real-time patient data from IoT devices. It utilizes machine learning models to adapt to changing conditions and refine decision-making, demonstrating its ability to provide real-time context-aware recommendations. Temperature, blood pressure, oxygen level, and heart rate measurement errors are 2.01%, 1.74 to 2.13%, 0.61%, and 1.45%, respectively. The success rate of early disease diagnosis using an expert system is 81%, with an average application interface responsiveness time of 4.978 s. The integration of IoT data with intelligent decision-making algorithms in healthcare telemonitoring has the potential to revolutionize patient care. However, future work should focus on scalability and interoperability for diverse healthcare settings.

A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

In this study, we proposed a system to reduce the speaker’s suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker’s silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer’s body area on the screen. To closely evaluate the system’s effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.

Utilizing deep learning in chipless RFID tag detection: an investigation on high-precision mm-wave spatial tag estimation from 2D virtual imaging

ABSTRACT Due to their high price, RFID tags are not yet widely used in applications for monitoring, tracing, and information inscribing, such as manufacturing lines, supply chains, and inventory management. The currently utilized barcode-like tracing techniques lack security, experience data limitation, are semi-automated, and are prone to external damage. Chipless RFID technology is a potential replacement, able to overcome shortcomings. However, due to the uncertainty created by the absence of a communication antenna and microchip, the detection of chipless RFID tags has a 2–5% detection error rate. The recent advancement in spatial chipless RFID technology has opened up a plethora of opportunities to use deep learning, which is becoming increasingly popular, to detect chipless RFID tags from a 2D virtual image created from the backscattered signal. In this study, we presented a comprehensive and exhaustive investigation of several deep learning techniques to improve the detection capability of deep learning-based chipless RFID tag detection. Our proposed fine-tuning methods yielded better mathematical metrics on the state-of-the-art on its original setup. A confusion matrix used to measure error rates on unseen data shows that the improved deep learning architecture we introduced works at roughly 99.99% accuracy.

Miniature Ultrasonic Spatial Localization Module in the Lightweight Interactive.

The advancement of spatial interaction technology has greatly enriched the domain of consumer electronics. Traditional solutions based on optical technologies suffers high power consumption and significant costs, making them less ideal in lightweight implementations. In contrast, ultrasonic solutions stand out due to their lower power consumption and cost-effectiveness, capturing widespread attention and interest. This paper addresses the challenges associated with the application of ultrasound sensors in spatial localization. Traditional ultrasound systems are hindered by blind spots, large physical dimensions, and constrained measurement ranges, limiting their practical applicability. To overcome these limitations, this paper proposes a miniature ultrasonic spatial localization module employing piezoelectric micromechanical ultrasonic transducers (PMUTs). The module is comprised of three devices each with dimension of 1.2 mm × 1.2 mm × 0.5 mm, operating at a frequency of around 180 kHz. This configuration facilitates a comprehensive distance detection range of 0-800 mm within 80° directivity, devoid of blind spot. The error rate and failure range of measurement as well as their relationship with the SNR (signal-to-noise ratio) are also thoroughly investigated. This work heralds a significant enhancement in hand spatial localization capabilities, propelling advancements in acoustic sensor applications of the meta-universe.

Implementasi Metode Fuzzy Logic dan IoT untuk Klasifikasi Kondisi Kesehatan Denyut Jantung berbasis Android

Heart rate is one of the health indicators that needs to be paid attention to. With several limitations that occur among the community, such as the availability of equipment and limited knowledge, it means that many people have heart rate abnormalities that are difficult to detect early. This research aims to build a system that can be used anytime and anywhere to monitor and determine heart conditions on an ongoing basis. The system was built using the waterfall method, starting from the analysis stage, data collection, and implementation to the testing stage. This research utilizes Internet of Things technology and the fuzzy logic method to carry out health classification by defining linguistic variables, namely BPM (beats per minute), age, and user activity, and the defuzzification process to obtain heart rate condition classification results. The results of the research show that this monitoring system has been successfully built according to the design stage, namely in the form of an Android application. Testing was carried out on 14 application functionalities, and everything was as expected. This system is also able to measure and transmit heart rate data quickly to the Android application, with a heart rate measurement error of 6.6%. Apart from that, by testing using 10 data samples, the fuzzy logic algorithm was successfully implemented by producing 9 correct classifications and 1 incorrect classification.

Targeting zero medication administration errors in the pediatric intensive care unit: A Quality Improvement project

BackgroundMedication errors and adverse drug events have a significant impact on mortality and morbidity among hospitalized children, and are more likely to occur in critical care settings due to the fast-paced environment and patient vulnerability. There is no exception to this rule in our pediatric intensive care unit, a 28-bed unit at a tertiary care children's hospital in Riyadh, Saudi Arabia. Problem assessmentA medication administration error rate of 6.25–8.05/1000 patient days was reported in our unit (48 errors), taking into account only errors that reached patients. Toward improving patient safety, a project was launched to reduce medication errors. DesignMultidisciplinary quality improvement team reviewed baseline data and analyzed medication errors that occurred in 2019. Five Plan-Do-Study-Act cycles were implemented. As an outcome measure, the medication error rate was monitored. ResultsThe outcome measure of medication administration error rates was monitored quarterly. An improvement of 75% during the first quarter of 2021 to a rate of zero medication errors/1000 patient days during the first quarter of 2022. A decrease in medication errors was attributed to improved situational awareness and increased compliance with assisted technology. ConclusionMedication errors can be decreased by deploying various interventions utilizing human- and technology-based approaches. When it comes to reducing medication errors in the pediatric intensive care unit, a multidisciplinary approach is paramount. Implications for clinical practiceThis study suggests several ways to reduce medication errors. Implementing information technology systems and involving pharmacists in medication management can help prevent errors. Enhancing teamwork, communication, and collaboration among healthcare professionals is also important. Clinical risk management strategies, nursing interventions, and adherence to medication safety guidelines are essential, especially for pediatric and neonatal populations. Considering these clinical implications can guide healthcare professionals and organizations in addressing medication errors and enhancing patient safety.

The Contributions to Registration Efficiency of The Fast Neutron Reactions on The Nuclei of The Heavy Oxide Scintillators

The results of the study of the contributions of the interaction reactions of fast neutron sources of 239Pu-Be and 252Cf to the counting efficiency of registration by oxide scintillators CdWO4, ZnWO4, Bi4Ge3O12 and Gd2SiO5, presented. The amount of gamma quanta per input neutron emitted from final nuclei excited in the reactions of inelastic scattering (n, nʹγ)in, resonant scattering (n, n)res and capture (n, γ)res and radiation capture (n, γ)cap was measured. PMT R1307 operating in single-electron mode was used as a photodetector, the background rate was ~ 5*103 s-1. The measured efficiency ε for scintillators ø40x40 mm was 752 for ZWO, 532 for CWO, 37 for GSO, and 23 for BGO in "counts/neutron" units, measurement error rate ~ 3-5%. The formation of the detector response is influenced by the parameters of the scintillator nuclei, such as the values of the interaction cross sections in the resonance region, the density of nuclear levels of the final nuclei, the lifetime of excited nuclear states, the upper limit of the resonance region of the cross section, as well as the scintillation time and geometric parameters of the scintillators. A phenomenological model of the response of an oxide scintillator to fast neutrons is proposed.

Local false discovery rate estimation with competition-based procedures for variable selection.

Multiple hypothesis testing has been widely applied to problems dealing with high-dimensional data, for example, the selection of important variables or features from a large number of candidates while controlling the error rate. The most prevailing measure of error rate used in multiple hypothesis testing is the false discovery rate (FDR). In recent years, the local false discovery rate (fdr) has drawn much attention, due to its advantage of accessing the confidence of individual hypotheses. However, most methods estimate fdr through -values or statistics with known null distributions, which are sometimes unavailable or unreliable. Adopting the innovative methodology of competition-based procedures, for example, the knockoff filter, this paper proposes a new approach, named TDfdr, to fdr estimation, which is free of -values or known null distributions. Extensive simulation studies demonstrate that TDfdr can accurately estimate the fdr with two competition-based procedures. We applied the TDfdr method to two real biomedical tasks. One is to identify significantly differentially expressed proteins related to the COVID-19 disease, and the other is to detect mutations in the genotypes of HIV-1 that are associated with drug resistance. Higher discovery power was observed compared to existing popular methods.

Bidirectional single-multicore-fiber spatial channel network based on a spatial cross-connect and multicore EDFA that efficiently accommodates asymmetric data traffic

In current wavelength division multiplexed (WDM) networks, capacity is deployed in a symmetrical manner, in which wavelength channels provide the same capacity in both directions. However, real data traffic in metro and backbone networks is observed to be considerably asymmetric resulting in wasted capacity in an under-loaded direction. This data traffic asymmetry is expected to increase further in the forthcoming Beyond 5G era due to newly emerging bandwidth-hungry services based on cloud computing. In such a situation, the wasted link capacity in the under-loaded direction can no longer be overlooked. In this paper, we propose a single-multicore-fiber (MCF) bidirectional spatial channel network (SCN) architecture that efficiently accommodates up and down asymmetric traffic. We present a proof-of-concept demonstration of the asymmetric bandwidth allocation in a single-MCF SCN by constructing a single-MCF bidirectional ring network testbed that comprises two spatial cross-connects based on 19-core fiber core selective switches with an M×N wavelength-selective switch and a bidirectional 7-core erbium-doped fiber amplifier with reversible optical isolators. Experimental results for bit error rate measurements of WDM optical signals traveling through different cores in different directions show there is almost no optical signal-to-noise ratio penalty for transmission through the single-MCF bidirectional ring network testbed.

Fast 3D Object Measurement Based on Point Cloud Modeling

Automated object measurement is becoming increasingly important due to its ability to reduce manual costs, increase production efficiency, and minimize errors in various fields. In this paper, we present a novel approach to three-dimensional (3D) object measurement based on point cloud modeling. Our method introduces a fast point cloud modeling computation framework consisting of five stages: coordinate centralization, rotation and translation, noise filtering, plane projection, and geometric computation. Furthermore, we propose a fast convex hull optimization algorithm to reduce the high complexity problem of traditional convex hull calculation. Our extensive experiments demonstrate that our approach outperforms existing methods in terms of measurement error rate and time savings, with a maximum time saving of 31.03% under certain error conditions.