Modulation Error Research Articles

An intelligent operational supervision system for operability and reliability analysis of operators manual actions in task implementation

In the paper, we present a framework for comprehensive operational feasibility analysis from both functional and structural safety perspectives using the measures of hazard and reliability. Within the framework, a pattern-matching algorithm is proposed for operation supervision and underlying human error mode identification using procedure-based action library. An intelligent operational supervision system is further developed in integration with operational mission reliability analysis. The capabilities of procedure-guided operation navigation and supervision as well as operability and reliability of operators manual actions are validated by a case study of reactor boron and water makeup system for manual makeup task implementation in QT virtual simulation environment. The validation results show that the intelligent operational supervision system is capable of procedural operation navigation and supervision. The trend impact of proactive operations can be effectively profiled using the measures of reliability in synchronous prediction and supervision.

A collaborative design platform for new alloy material development

To overcome the shortcomings of the conventional trial and error mode for new material development, a full-process collaborative design platform for steel rolling is developed based on an industrial internet of things (IIoT) system in this study. Equipment, process and product entities are modeled in both the physical domain and the cyber domain. A systematic data-driven Mamdani-type fuzzy modeling methodology is proposed to map the relationship between material chemical compositions, organizational structures, process parameters and mechanical performances. The proposed methodology employs a random forest (RF) algorithm to select important parameters from mechanism models, simulation models and production process variables, utilizes a K-means algorithm to merge diverse steel grades into sub-clusters, and implements a multi-objective particle swarm optimization (MOPSO) algorithm to further improve the fuzzy model in terms of both the structure and the membership function parameters. A dataset of 3500 steel coils collected by the prototype platform built in a large hot rolling mill is used to evaluate the performance of the proposed approach. Experiment results show that the proposed methodology performs well in predicting the yield strength, tensile strength and elongation, with the coverage probability over 90% under 10% deviation and about 70% under 5% deviation on average.

Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning

Purpose. We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors. Methods. From the modeling perspective, the DL model’s output is the fluence maps of IMRT plans. However, from the clinical planning perspective, the plan quality evaluation should be based on the clinical dosimetric criteria such as dose-volume histograms. To account for the complex and non-intuitive relationships between fluence map prediction errors and the corresponding dose distribution changes, we propose a novel error analysis approach that systematically examines plan dosimetric changes that are induced by varying amounts of fluence prediction errors. We investigated four decomposition modes of model prediction errors. The two spatial domain decompositions are based on fluence intensity and fluence gradient. The two frequency domain decompositions are based on Fourier-space banded frequency rings and Fourier-space truncated low-frequency disks. The decomposed error was analyzed for its impact on the resulting plans’ dosimetric metrics. The analysis was conducted on 15 test cases spared from the 200 training and 16 validation cases used to train the model. Results. Most planning target volume metrics were significantly correlated with most error decompositions. The Fourier space disk radii had the largest Spearman’s coefficients. The low-frequency region within a disk of ∼20% Fourier space contained most of errors that impact overall plan quality. Conclusions. This study demonstrates the feasibility of using fluence map prediction error analysis to understand the AI agent’s performance. Such insights will help fine-tune the DL models in architecture design and loss function selection.

MRI-based thalamic volumetry in multiple sclerosis using FSL-FIRST: Systematic assessment of common error modes.

FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL-FIRST's algorithm is based on shape models derived from non-MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL-FIRST on MRIs from people with multiple sclerosis (PwMS). FSL-FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated. In the entire quantitative volumetric group, the mean volumetric error of FSL-FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 =18.14, p<.01 and χ2 =64.89, p<.001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = -.28, p<.001). In PwMS, FSL-FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.

Identification and assessment of nursing task errors in emergency department using SHERPA technique and offering remedial strategies

BackgroundImproving health care quality is a global concern, and providing patients with safe healthcare is a WHO Patient Safety priority. The technological revolution in health services has highlighted the role of human factors in committing such errors, and since the number of medical errors, including nursing errors, increases on a daily basis. ObjectivesThis research aimed to identify the causes of Nursing Task Errors in Emergency Department Using SHERPA Technique and adopt strategies to prevent and reduce them.Participants were 47 nurses (24 women and 23 men), who worked morning, evening and night shifts. The nurses were taught the methodology and talked about the objective of the study; they were then interviewed and observed for six months while working. This way, their tasks and subtasks were demarcated and the Hierarchical Task Analysis (HTA) was performed. SHERPA Error Mode Checklist was used to identify errors and their various types and then risk assessment was conducted. ResultsA total of 45 tasks, 234 subtasks, and 326 errors in regard to nursing activities were identified in the emergency department, with the highest frequency for action errors (61.34%) and the lowest for communication errors (1.84%). Also, there were 7.24 errors per task. The highest and lowest number of errors were at the ‘unfavorable’ (19.07%) and,‘acceptable (safe)’ (6.15%) risk levels, respectively. Based on the finding staff training, workload elimination,work process modification and updating guidelines are recommended. ConclusionThe results of present study were also submitted to the University Medical Executive management Committee to make individual, educational and organizational reforms (guidelines and instructions) in order to optimize the situation.

한국인 학습자의 중국어 성조 습득 발전 연구 - 3성 변조를 중심으로

Unlike Invariable tonality, which has a fixed pitch, Variable tonality exhibits dynamic changes during the pronouncing process. Among them, Tone 3 sandhi belongs to intangible modulation that leaves its appearance intact, but only makes temporary changes when pronounced, so Chinese learners have great difficulty in acquiring it. This study discussed the acquiring pattern of Tone 3 sandhi in Korean learners, and examined whether Chinese level and learning period affect Tone 3 sandhi through an experimental study. The results showed that, It was difficult for Korean learners to acquire 3+2 and 3+3, while 3+1 and 3+4 are relatively easy to acquire. Although the number of modulation errors of higher learner decreased noticeably as the learning period increased, the lower learner did not show much difference, and rather showed a reverse development from 3+3. Based on experimental results, this study makes several suggestions on Chinese tonal training.

Selection of optimal points for monitoring a single frequency network in digital terrestrial television of the Irkutsk region

The article considers selecting points methodology for the main parameters monitoring of digital terrestrial television in densely populated areas of the Irkutsk region. Requirements for the choice of the measurement site have been developed. The measurements were carried out using a powerful broadcasting area of individual transceiver objects operating in a single frequency network. To carry out a research a control and measuring complex was created and a number of measurements were carried out in different areas of the Irkutsk region. During the experiment, the terrain relief, the zone of reliable reception, and the overlapping zones of signals from various stations of the single-frequency network were taken into account. The results of measuring the electromagnetic field strength, modulation error rates, bit error rates are presented in the paper. The methodology presented in the work allows you to identify errors timely and interference in a single-frequency network improving the reliability of the network and the quality of services provided.

The most sensitive initial error modes modulating intensities of CP- and EP- El Niño events

The two types of El Niño events simulated by the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) model and its “spring predictability barrier” (SPB) associations are examined. By conducting the ensemble hindcast experiments related to the sea temperature on the whole Pacific, both of the predictions for CP- and EP-El Niño show a significant SPB phenomenon, but the CP-El Niño features a much weaker SPB compared to the EP-El Niño. Further analyses revealed that, for CP-El Niño events, the initial sea temperature errors of the North Pacific with triple-like shape, referred to as negative Victoria Mode (VM) induces the largest prediction errors in Niño4 areas and modulates the intensities of CP-El Niño events. While for EP-El Niño events, the initial sea temperature errors in the subsurface layer of the western equatorial Pacific and the upper layer of the Southeast Pacific (15–30ºS) with the meridional mode induce the largest prediction errors in Niño3 areas and modulates the intensities of EP-El Niño events. Obviously, results stress that, in order to reduce final prediction errors and obtain better predictions in terms of intensity on the two types of El Niño events, we should mainly focus on initial sea temperature accuracy in not only the subsurface layer of the west equatorial Pacific but also the surface layer of southeast Pacific and the region covered by the VM-like mode in the North Pacific.

Prediction of disease burden of esophageal cancer in China based on ARIMA and NNAR models

Objective To explore the time series characteristics of the disease burden of esophageal cancer in China and predict the disease burden of esophageal cancer. Methods The incidence, mortality, and disability adjusted life year (DALY) of esophageal cancer in China from 1990 to 2019 were collected. autoregressive integrated moving average (ARIMA) model and neural network autoregression (NNAR) model were established based on the data from 1990 to 2016, and model prediction performance was verified by comparing 2017-2019 forecast data with actual data through mean absolute percentage error (MAPE), modulation error ratio (MER), mean absolute error (MAE) and root mean squared error (RMSE). The better model was applied to predict the disease burden of esophageal cancer from 2020 to 2024. Results From 1990 to 2019, the overall disease burden of esophageal cancer in China showed a fluctuating upward trend, with the incidence rate rising by 33.26%, the mortality rate rising by 21.26%, and the DALY rate rising by 6.66%. The predicted values of disease burden by ARIMA model and NNAR model were basically consistent with the actual dynamic trend. The incidence rate of esophageal cancer in China from 2020 to 2024 would be 20.375/100 000, 21.057/100 000, 21.380/100 000, 21.341/100 000, 21.080/100 000, and mortality rate would be 18.834/100 000, 19.647/100 000, 20.407/100 000, 20.889/100 000, 20.988/100 000, and the DALY rate would be 418.192/100 000, 431.123/100 000, 442.780/100 000, 452.376/100 000, and 459.358/100 000. Conclusions The disease burden of esophageal cancer in China will increase slightly from 2020 to 2024. The NNAR model demonstrates good prediction performance and accuracy in simulating the disease burden of esophageal cancer in China, and provides a reference method for short-term prediction of the disease burden.

Research on Error Concealment for Video Communication

The video sequence restoration described in this article has been extended to many areas of our lives today. People use it to perform various restoration tasks, making it possible for some pictures that were impossible to restore in the past. This paper uses experimental methods to study the error concealment and traditional image restoration in video compression, and mainly studies the CDD model algorithm, TV model algorithm and error mode. This article also has a very important meaning for the related research of video communication error control, that is, the research of digital image restoration technology, in the post-processing of film and television works, the fuzzification of network video, and the restoration of old documentaries.

O documentário como forma de divulgar ciência

Documentário constitui um gênero audiovisual com potencial para Divulgação Científica, que pode ser utilizado como recurso didático em sala de aula. O presente estudo teve como objetivo analisar o documentário “Quando éramos macacos” a partir de três categorias: modo de representação, características flutuantes e erros conceituais. A análise dos dados possibilitou identificar que o modo de representação participativo foi o predominante. Além disso, foram encontradas algumas características flutuantes, como por exemplo o uso de reconstituições, as quais podem auxiliar em um melhor entendimento do tema, assim contribuindo para a aproximação com o público. Destaca-se que aspectos da Natureza da Ciência foram abordados no documentário, entretanto, percebe-se erros conceituais, tanto relacionados ao conteúdo, quanto a dublagem. Por fim, entende-se que uma das contribuições deste estudo é de sinalizar para a importância de uma leitura crítica de recursos audiovisuais antes da sua utilização como potencial recurso educativo.

Characterization of RF Power Amplifier for Narrow and Wide Band Memory Polynomial Implementations

Linearly efficient RF power amplifiers have a tremendous role in wireless communication and radar systems as they lie at the front end of most RF systems. In today’s world of wireless communication, it is not an easy task to design a RF power amplifier that is linearly efficient. There are two main key challenges that one face for making RF power amplifier’s behavior linearly efficient. First is to characterize RF power amplifier’s coefficients smartly. Second is to propose an approach that works on input signal and make its behavior inverse to that of the designed amplifier behavior so that overall response of the system becomes linear. For countering first challenge, most advanced universally accepted algorithms like Memory Polynomial, Generalized Hammerstein, Cross-term Memory Polynomial and Cross-term Hammerstein are implemented to design RF power amplifier models. For countering second challenge, latest DPD algorithms are implemented which make net response of a system linear. The memory models for modelling RF power amplifier are categorized for narrowband and wideband applications. The narrowband power amplifier models include Memory Polynomial and Cross-term Memory Polynomial models whereas wideband power amplifier models include Generalized Hammerstein and Cross-term Hammerstein models. In this paper, various performance indicators like Standard Deviation (SD), Third Order Intercept (TOI), Intermodulation Distortion Products (IMD3), Modulation Error Ratio (MER), Spurious Free Dynamic Range (SFDR) and Error Vector Magnitude (EVM) are used to characterize RF power amplifier for both narrow and wide band applications. The simulation results show that under narrowband applications, Cross-term Memory Polynomial model works best as it has least standard deviation and is also satisfying other performance parameters up to appreciable level with and without DPD algorithm implementation. While for wideband applications, Cross-term Hammerstein model satisfies the performance measuring parameters excellently.

An Approach to Analyze Diagnosis Errors in Advanced Main Control Room Operations Using the Cause-Based Decision Tree Method

Advancements in the nuclear industry have led to the development of fully digitized main control rooms (MCRs)—often termed advanced MCRs—for newly built nuclear power plants (NPPs). Diagnosis is a major part of the cognitive activity in NPP MCRs. Advanced MCRs are expected to improve the working environment and reduce human error, especially during the diagnosis of unexpected scenarios. However, with the introduction of new types of tasks and errors by digital MCRs, a new method to analyze the diagnosis errors in these new types of MCRs is required. Task analysis for operator diagnosis in an advanced MCR based on emergency operation was performed to determine the error modes. The cause-based decision tree (CBDT) method—originally developed for analog control rooms—was then revised to a modified CBDT (MCBDT) based on the error mode categorizations. This work examines the possible adoption of the MCBDT method for the evaluation of diagnosis errors in advanced MCRs. We have also provided examples of the application of the proposed method to some common human failure events in emergency operations. The results show that with some modifications of the CBDT method, the human reliability in advanced MCRs can be reasonably estimated.

Study on the alignment error of complex modulation for focusing flattop beam shaping

The complex modulation is applied to indirectly generate a focusing flattop beam, which can modulate the amplitude and phase in the optical pupil combining with the phase retrieval theory. The simulation results show that this algorithm has good uniformity. However, the alignment errors of various components in the actual optical system have an impact on the beam uniformity, especially after the implementation of SLM (Spatial Light Modulator) error calibration, the beam uniformity still hardly meets the requirements. In this paper, the error source of the complex modulation beam shaping system is theoretically analyzed, while the Mixed Region Amplitude Freedom (MRAF) method is selected as a comparative reference in this research. It is found that the defocus aberration in the SLM illumination is the main aberration which has a special grid-facula, and the error source is proved through the actual experiment. Finally, then the correction is carried out, the complex modulation has better uniformity than MRAF.

Accurate annotation of protein coding sequences with IDTAXA.

The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA’s utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

Temperature Experiment and Compensation Algorithm Design for Fiber Gyros in Rapid Startup Inertial Navigation System

This article describes a practical thermal experiment and compensation algorithm for fiber gyros in an inertial navigation system with a rapid startup and short time operation characteristic. Multi-position cold startup experiments at discrete temperature points is designed to fully stimulate the thermal error mode. And due to the nonlinear characteristic of the error, a piecewise spline algorithm is applied to precisely identify and build the error model. After the compensation, the gyro instability in the full temperature range is reduced from the level of 5 °/h to better than 1 °/h.

A human reliability analysis methodology based on an extended Phoenix method for severe accidents in nuclear power plants: Qualitative analysis framework

The mitigation of the Fukushima accident exposed the detrimental action of human errors once again. However, deficiencies in existing human reliability analysis (HRA) methods prevent the effective prediction and analysis of potential human errors during the mitigation of severe accidents following core damage. To address this problem, this study presents a qualitative analysis framework for emergency response personnel (ERP) reliability analysis under severe accident conditions. This framework is developed using the Phoenix HRA method in conjunction with analyzing the ERP response characteristics and human-related issues from previous accidents, literature surveys, drill observations and interviews, as well as an assessment of the applicability of existing HRA methods. The analysis framework includes the establishment of an ERP response tree and a human failure fault tree (FT), as well as the identification and compilation of ERP cognitive functions, human error modes (HEMs), performance-influencing factors (PIFs), and the construction of Bayesian belief networks, to provide methodological guidance for the prevention of human errors under severe accident conditions.

An integrated rough ELECTRE II approach for risk evaluation and effects analysis in automatic manufacturing process

Smart manufacturing is an essential part of fourth industrial revolution in which robotic machines can control and perceive automatically to provide effectiveness and convenience in production process. However, the existence of potential failures and defects not only influence the manufacturing process but also damages the resources and cause negative impacts on environment. Failure modes and effects analysis (FMEA) is a key approach to identify and eliminate possible failures and evaluate the risks from design, system and process. This research paper provides a novel FMEA approach for risk evaluation by integrating rough set theory and ELimination and Choice Translating REality (ELECTRE) II method to handle the subjectivity and uncertainty in experts’ judgements without much prior information, membership functions and additional adjustments. Rough numbers are used to study uncertainty in linguistic terms using intervals instead of single fixed values. The proposed approach is formulated by defining different types of concordance and discordance sets using optimization techniques based on statistical dispersion and maximum deviation method. The presented technique shows the strong, weak and neutral pairwise relations among failure modes by systemically comparing them from each risk component. The distance functions and averaging methods are applied to check the similarities and differences among error modes which improves the accuracy of the results. The developed rough FMEA approach is applied to identify the potential failures of robot working in optical cable industry and evaluate the risk components of manufacturing and production process. Rough ELECTRE II approach can be effectively applied to enhance the efficiency of working conditions and prevent the loss of crude materials and energy.

NMR configurations with novel majority voter circuits to mask multiple module faults

Mission, safety, and business-critical applications need uninterrupted operation with correct results. But due to the technology shrinking, the microelectronic circuits can be affected by many faults like permanent, transient, and intermittent. So, there is a considerable demand for designing fault-tolerant systems. Hardware redundancy configurations have been widely used to protect or mask faults. In this research article, to mask multiple faults, N-modular redundancy (NMR) configurations are deployed, namely 5-MR and 7-MR configurations, which are implemented to mask two and three function module errors. Two majority voter (MV) circuits with 5-MR and 7-MR have been proposed, respectively. Simulation results are obtained with Application-Specific Integrated Circuits (ASIC) and Field Programmable Gate Arrays (FPGA). With ASIC, 120 nm technology is used with BSIM4 Spice simulation parameters. Also, the majority voters are evaluated with a band-pass filter as the function module on Altera DE2-115 FPGA using the Quartus II synthesis tool. The very high-speed integrated circuits hardware description language is used with FPGA implementation. Simulation results reveal that the proposed MV circuits obtain better results than the existing methods.

Study of Optical Modulation based on Binary Masks with Finite Pixels

Binary holography has been applied with fast switching digital masks, e.g., digital micromirror device (DMD), to modulate light in recent years for a wide range of optical and scientific applications, such as trapping of cold atoms and 3D random-access two-photon fluorescence microscopy. However, the binarized modulation errors associated with the effect of discrete sampling, finite pixels, and intrinsic noises of algorithms have yet to be systematically investigated. In this paper, we present the development of an analytical model for a general binary mask with finite pixels. The model has established a deterministic link between the quality of the reconstructed wavefront to important parameters of the binary mask, including pixel size, pixel geometry, fill factor, and aperture. Based on the model, three case studies are presented, including (1) beam shaping, (2) 3D random-access scanning, and (3) multi-focus generation based on binary masks. The results show the model can precisely predict the wavefront distortion, power efficiency, position accuracy, and intensity uniformity for multi-focus generation as a function of binary mask parameters. As digital systems are highly repeatable, the predicted errors, e.g., position errors, can be compensated by adding appropriate phase to the designed holograms. The model and the simulation results may provide useful guidance to select appropriate binary devices and optimization algorithms for specific optical engineering applications, e.g., nanofabrication or nonlinear microscopy.