Error Analysis Research Articles

State of health estimation embedded with hardware accelerator based on long short-term memory combined with Bayesian optimization considering extracted health indicator in charging conditions

Recently, the importance of renewable energy has increased as fossil fuel use is regulated worldwide to protect the environment. Accordingly, the use of electric vehicles (EVs) that use a battery as a power source is increasing and estimating the battery state for an efficient and safe operation of EV is a very significant task. This study proposes a method to estimate the state-of-health (SOH) in which batteries are a major state indicator. A health indicator (HI) capable of determining the state of the battery is derived from the charging part by considering the stopping situation in which there is a slight variation in the battery characteristics of the EV. As an integrity indicator, the partial capacity according to the voltage range set by the user can be considered, and the correlation between the SOH and HI is analyzed for validity verification. To learn the derived HI, a long short-term memory (LSTM) model, a type of deep-neural network, is designed, and model tuning was performed through Bayesian optimization. The absolute error of SOH estimation for the proposed LSTM model is 1.1 %, compared and validated against recurrent neural network (RNN) (1.7 %) and gated recurrent unit (GRU) (1.85 %). After validating the performance of the LSTM algorithm, an analysis of SOH estimation error (1 %) based on LSTM was conducted following pack-level experiments to assess scalability. Finally, for evaluation of operation within a hardware accelerator (HA) for integration into EVs, NVIDIA Jetson Nano was utilized. Upon mounting the algorithm on Jetson Nano, the mean absolute error (MAE) was 0.6581, mean absolute percentage error (MAPE) was 0.7209, and root mean square error (RMSE) was 0.7133, demonstrating excellent estimation performance.

Error Analysis of the Mixed Residual Method for Elliptic Equations

Error Analysis of the Mixed Residual Method for Elliptic Equations

An a Priori Error Analysis of a Problem Involving Mixtures of Continua with Gradient Enrichment

An a Priori Error Analysis of a Problem Involving Mixtures of Continua with Gradient Enrichment

Unconditional Error Analysis of VEMs for a Generalized Nonlinear Schrödinger Equation

Unconditional Error Analysis of VEMs for a Generalized Nonlinear Schrödinger Equation

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach

In recent years, numerous image encryption schemes have been developed that demonstrate different levels of effectiveness in terms of robust security and real-time applications. While a few of them outperform in terms of robust security, others perform well for real-time applications where less processing time is required. Balancing these two aspects poses a challenge, aiming to achieve efficient encryption without compromising security. To address this challenge, the proposed research presents a robust data security approach for encrypting grayscale images, comprising five key phases. The first and second phases of the proposed encryption framework are dedicated to the generation of secret keys and the confusion stage, respectively. While the level-1, level-2, and level-2 diffusions are performed in phases 3, 4, and 5, respectively, The proposed approach begins with secret key generation using chaotic maps for the initial pixel scrambling in the plaintext image, followed by employing the Fibonacci Transformation (FT) for an additional layer of pixel shuffling. To enhance security, Tribonacci Transformation (TT) creates level-1 diffusion in the permuted image. Level-2 diffusion is introduced to further strengthen the diffusion within the plaintext image, which is achieved by decomposing the diffused image into eight-bit planes and implementing XOR operations with corresponding bit planes that are extracted from the key image. After that, the discrete wavelet transform (DWT) is employed to develop secondary keys. The DWT frequency sub-band (high-frequency sub-band) is substituted using the substitution box process. This creates further diffusion (level 3 diffusion) to make it difficult for an attacker to recover the plaintext image from an encrypted image. Several statistical tests, including mean square error analysis, histogram variance analysis, entropy assessment, peak signal-to-noise ratio evaluation, correlation analysis, key space evaluation, and key sensitivity analysis, demonstrate the effectiveness of the proposed work. The proposed encryption framework achieves significant statistical values, with entropy, correlation, energy, and histogram variance values standing at 7.999, 0.0001, 0.0156, and 6458, respectively. These results contribute to its robustness against cyberattacks. Moreover, the processing time of the proposed encryption framework is less than one second, which makes it more suitable for real-world applications. A detailed comparative analysis with the existing methods based on chaos, DWT, Tribonacci transformation (TT), and Fibonacci transformation (FT) reveals that the proposed encryption scheme outperforms the existing ones.

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals’ performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Error analysis of dead reckoning navigation system by considering uncertainties in an underwater vehicle's sensors

Abstract In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

Mechanism analysis and accuracy prediction for kinematic errors of machine tool

Mechanism analysis and accuracy prediction for kinematic errors of machine tool

An error analysis on translation made by the sixth semester students of English Department of IKIP Gunungsitoli

The Objective off this study are to describe (1) the kinds of error made by the 6th semester students of English Department when translating Indonesia text into English, (2) the dominant error made by students, 3) how students made the error when translating Indonesia text into English text. This study used the descriptive qualitative research. The subject of this research is the 6th semester students and the object in this research is all the errors on translation made by students. The data collected to answer all the research problems are all the errors made by students. Based on data analysis, the writer finds that 1) students made error in Omission, Addition, Mis-formation, and Mis-ordering errors, 2) students made more dominant error in Mis-formation error, 3) students made error by: a) omitting an item that must appear in well-formed utterance, b) adding an item that must not appear in well-formed utterance, c) using the incorrect morpheme or group or structure, d) doing the incorrect placement of a morpheme or group of morpheme. The conclusion of this study is, the students are lack of vocabulary when doing translation. It is suggestion to the students to enrich their vocabulary and to the lecturer to enrich their students’ vocabularies by giving them daily new vocabularies.

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform.

GTS_CME: an open-source MATLAB-based software for the analysis of common mode errors in GNSS coordinate time series

GTS_CME: an open-source MATLAB-based software for the analysis of common mode errors in GNSS coordinate time series

Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

To understand the applicability of the temperature profile product of the FENGYUN-4A (FY-4A) geostationary interferometric infrared detector (GIIRS) in summer over the Qinghai–Tibet Plateau and to improve product quality, the error of GIIRS temperature products was analyzed based on radiosonde data. A long short-term memory network model (LSTM) was used to correct the GIIRS temperature profile product in summer over the plateau at a high altitude (above 500 hPa), and further evaluation of the corrected product was conducted. The results show that summertime GIIRS temperature retrievals over the Qinghai–Tibet Plateau had a positive bias above 150 hPa and a negative bias below 150 hPa, resulting in an overall negative bias. The root mean square error was between 2 and 2.9 K, and the root mean square error was relatively large at 100 hPa and above. The LSTM established in this study could effectively correct the GIIRS temperature over the plateau. The correlation and root mean square error of the corrected GIIRS temperature and the radiosonde observation temperature were significantly improved. Using a trained LSTM correction model to correct the hourly GIIRS temperature can improve the accuracy and usability of the product. After correction, the average bias of the GIIRS temperature compared to the ERA5 temperature product was reduced from −0.26 K to 0.06 K, and the root mean square error was reduced from 2.25 K to 1.25 K. The correction model can be applied to different seasons, and it can also correct the GIIRS temperature in larger areas based on other high-precision and high-resolution data, achieving good results, thus indicating that the correction model has universal applicability.

Moving to continuous classifications of bilingualism through machine learning trained on language production

Abstract Recent conceptualisations of bilingualism are moving away from strict categorisations, towards continuous approaches. This study supports this trend by combining empirical psycholinguistics data with machine learning classification modelling. Support vector classifiers were trained on two datasets of coded productions by Italian speakers to predict the class they belonged to (“monolingual”, “attriters” and “heritage”). All classes can be predicted above chance (>33%), even if the classifier's performance substantially varies, with monolinguals identified much better (f-score >70%) than attriters (f-score <50%), which are instead the most confusable class. Further analyses of the classification errors expressed in the confusion matrices qualify that attriters are identified as heritage speakers nearly as often as they are correctly classified. Cluster clitics are the most identifying features for the classification performance. Overall, this study supports a conceptualisation of bilingualism as a continuum of linguistic behaviours rather than sets of a priori established classes.

An Analysis of Grammatical Errors Committed by Students in Writing Argumentative Paragraph

Writing skills are very important for students to master to help them prepare for their future. However, grammar is one of the obstacles for students in writing English. This research aims to analyze grammatical errors made by students in writing argumentative paragraphs and to analyze the sources of these errors. Second semester English Language Education students were chosen as the subjects of this research. Furthermore, in analyzing the data, this research applies a combination of descriptive qualitative and descriptive quantitative methods. Apart from that, data was collected through collecting documents from the end-of-semester tests. The data analysis procedure consists of three specific steps, namely data reduction, data presentation, and drawing conclusions/verification. The results of the research found that students made 11 types of errors with a total of 165 errors. Most of them made omission of article errors with 39 (23.63%) errors, then simple addition 33 (20%) errors, alternating form 27 (16.36%) errors, omission of to be 21 (12.72%) errors, then omission of preposition 17 (10.30%) errors, omission of plural –s with 15 (9.09%) errors, misordering 6 (3.63%) errors, regularization 3 (1.81%) errors, and finally double marking and archi-form each 1 ( 0.60%) error. In addition, sources of errors were found in four types: interlingual transfer, intralingual transfer, context of learning, and communication strategy. The majority of students made mistakes due to intralingual transfer, 119 (72.12%) errors were found, followed by interlingual transfer 41 (24.84%) errors, communication strategy 3 (1.81%), and context of learning 2 (1.21%) errors.

Drift approximation by the modified Boris algorithm of charged-particle dynamics in toroidal geometry

AbstractIn this paper, we study the dynamics of charged particles under a strong magnetic field in toroidal axi-symmetric geometry. Using modulated Fourier expansions of the exact and numerical solutions, the long-term drift motion of the exact solution in toroidal geometry is derived, and the error analysis of the large-stepsize modified Boris algorithm over long time is provided. Numerical experiments are conducted to illustrate the theoretical results.

Medication-related incidents in acute care hospitals among different age groups: An analysis of national patient safety report data.

This study aimed to perform a nationwide analysis of medication errors (MEs) from hospitals using national reporting system data and to compare the ME patterns among different age groups. We analyzed medication-related incidents in acute care hospitals reported to the Korean Patient Safety Reporting and Learning System (KOPS), which is a patient safety reporting system, from July 2016 to December 2020. The stages of the medication use process, type of errors, medication class involved in MEs, and degree of harm were analyzed. Among a total of 5071 medication-related incidents, 37.7% (1911 cases) were incidents that caused patient harm and 1.2% caused long-term, permanent, and fatal harm. The proportion of medication-related incidents that resulted in harm was the highest among the <1-year-old age group (67 cases, 51.5%), followed by the elderly (≥ 65 years) (828 cases, 40.9%). The cases leading to patient death were most frequently reported in patients aged ≥65 years. Medication-related incidents occurred mainly in the administration stage (2954 cases, 58.3%), and wrong dose was the most frequently reported ME type. The most prevalent medication class occurring in the 20-64-year age group (256 cases, 11.7%) was 'antibacterials for systemic use', whereas 'contrast media' (236 cases, 11.6%) and 'blood substitutes and perfusion solutions' (98 cases, 19.3%) were the most prevalent drug classes in the ≥65- and <20-year-old age groups, respectively. It is necessary to establish guidelines for the prevention of medication-related incidents according to the medication use process and patient age group.

Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Abstract. Accurate estimation of global winds is crucial for various scientific and practical applications, such as global chemical transport modeling and numerical weather prediction. One valuable source of wind measurements is atmospheric motion vectors (AMVs), which play a vital role in the global observing system and numerical weather prediction models. However, errors in AMV retrievals need to be addressed before their assimilation into data assimilation systems, as they can affect the accuracy of outputs. An assessment of the bias and uncertainty in passive-sensor AMVs can be done by comparing them with information from independent sources such as active-sensor winds. In this paper, we examine the benefit and performance of a colocation scheme using independent and sparse lidar wind observations as a dependent variable in a supervised machine learning model. We demonstrate the feasibility and performance of this approach in an observing system simulation experiment (OSSE) framework, with reference geophysical state data obtained from high-resolution Weather Research and Forecasting (WRF) model simulations of three different weather events. Lidar wind data are typically available in only one direction, and our study demonstrates that this single component of wind in high-precision active-sensor data can be leveraged (via a machine learning algorithm to model the conditional mean) to reduce the bias in the passive-sensor winds. Further, this active-sensor wind information can be leveraged through an algorithm that models the conditional quantiles to produce stable estimates of the prediction intervals, which are helpful in the design and application of error analysis, such as quality filters.

Effective approach to assess higher heating value of biomass from ultimate and proximate analysis

AbstractFor proper design and operation of biomass‐based energy systems, it is important to determine the higher heating value (HHV) of biomass. In this paper, two machine learning (ML) approaches, namely extra trees (ET) and least squares support vector machine (LSSVM), are used to predict the value of HHV associated with biofuels. The data required for HHV calculation, including proximate and ultimate analyses datasets, were collected from the literature. The performances of these two ML approaches for predicting biomass HHV were then compared with other smart models available in the literature. Even though the available empirical models can predict the biomass HHV with acceptable precision, it was found that our proposed ML techniques have a superior performance based on the error analysis; the proposed approaches also consider all key biomass characteristics in the developed models. In addition, the ET model proved to be slightly more accurate compared to the LSSVM model. Additionally, the developed proximate‐based ET model showed better performance compared to the ultimate‐based ET model. The most influential parameters in the developed ET models for the proximate and ultimate approaches were determined to be ash fraction and carbon fraction, respectively. Finally, it was concluded that the smart modelling techniques can be utilized as a robust and reliable alternative predictive methodology to replace direct laboratory measurement of the biomass HHV.

Probabilistic Rounding Error Analysis of Modified Gram–Schmidt

Probabilistic Rounding Error Analysis of Modified Gram–Schmidt

An Augmented Mixed DG Scheme for the Electric Field

In this paper, a new augmented mixed DG formulation for the numerical approximation of the electrostatic field was introduced and studied. Its error analysis was carried out and an optimal error estimates as a function of the mesh size was obtained. Some numerical tests confirming the theoretical convergence were given.