Error Identification Research Articles

Semantic-based neural network repair for AI models in dynamic environments

This paper addresses a critical challenge in the field of artificial intelligence (AI) and deep learning (DL) - the debugging and repair of AI models within dynamic environments. The rapid development of frameworks like TensorFlow or PyTorch can introduce version incompatibilities, potentially breaking existing code. To tackle this issue, it can be seen that a semantic-based neural network repair method that leverages an existing semantics called ExAIS is proposed, defined in the Prolog logic programming language. This method identifies architectural and parameter errors in AI models and generates debug messages to facilitate the repair process. Key contributions of this paper include introducing a novel semantic-based AI model repair method capable of suggesting multiple changes to rectify inefficiencies in AI models. We evaluate the effectiveness of our approach in a real-world context by collecting a set of real bugs from AI developers. The background section provides an overview of AI framework testing, machine learning, neural architecture search (NAS), and deep learning. The methodology section details the repair method based on ExAIS semantics, covering error identification and repair suggestions as the two main steps. The evaluation section showcases the repair effectiveness on automatically generated and manually written models, emphasizing the applicability and efficiency of the approach.

A Survey on the Usefulness of ChatGPT as a Modern Tool for Research in China

ChatGPT is famous for assisting in research, among other AI-generated research assistance modules. It is being updated and its users are getting reliable assistance in research work. However, the current study focused on measuring the impact of ChatGPT on academic research, finding theory, data analysis, proofreading, and error identification. The motivation for this research was to contribute knowledge of how ChatGPT is helpful for researchers for university-level scholars in China. Data was collected from scholars working in Chinese universities. Considering the convenience sampling method, a survey-based approach was used to collect data. The study's empirical findings confirmed the significant impact of ChatGPT on academic research, finding theory, data analysis, proofreading, and error identification. This research addressed the gaps in the literature as previously scholarly research has not focused on the phenomena investigated by this research. Hence, this study has some practical implications for advancing the use of ChatGPT for Chinese university-level scholars to improve their research contributions. Chinese universities are recommended to use ChatGPT in their scholarly research work, but careful use is required. Furthermore, the scholars are supposed to acknowledge the use of artificial intelligence (AI) generated tools in research publications.

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

A high constancy and noise suppression voltage shift generator in SEIR-based BIST circuit for ADC linearity test

—The built-in self-test (BIST) circuit is designed to be highly integrated with ADC under test and tests the static linearity based on the stimulus error identification and removal (SEIR) method. A novel level shift generator is proposed for high-precision ADC testing to break the resolution limitation caused by thermal noise and non-linearity. The double sampling operation realized by the chopper circuits cancels the sampling kT/C noise, and the circuit further reduces the amplifier's thermal noise by reducing the noise bandwidth. Besides, this paper presents a new method to achieve a highly linearity-constant voltage shift by applying the negative-C technique. Implemented in 180 nm CMOS process, the simulation results show that the noise power of the voltage shift is reduced by 10 dB, and the constancy of voltage shift is only 3.7 ppm over the entire ADC input range. Benefitting from the noise and linearity performance enhancement, this BIST circuit can test 18-bit ADC to 18-bit accuracy level with one-tenth sampling points.

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error

AbstractSensorless control of permanent magnet synchronous motor (PMSM) can increase the reliability of electric actuators of more electrical aircraft. Numerical online parameter estimation method will enhance the performance for sensorless control methods based on sliding mode observer (SMO). However, numerical online parameter identification methods typically assume zero rotor position estimation error to ensure full rank of the identification equation set. This assumption actually leads to errors in parameter identification, resulting in steady‐state errors in rotor position estimation. This paper focuses on a compensation method for PMSM online parameter identification that takes into account rotor position estimation error. This method is used for sensorless control based on SMO, using model reference adaptive systems for online parameter identification, and no additional parameter adjustment required. Validation is conducted on a PMSM experimental platform with maximum load of 15kW. The experimental results of 1000 r/min steady state operation proved the effectiveness of the method, and the dynamic experiments with 5 and 15kW loads proved that the method has no significant adverse effect on the system dynamics.

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

Condition‐based maintenance management for two‐stage continuous deterioration with two‐dimensional inspection errors

AbstractInspections often perform imperfect outcomes during maintenance processes owing to human errors, management issues and other limitations. In particular, such imperfection affects the maintenance management of multistage deterioration significantly due to both false state identification and measurement errors, whose quantitative analysis, however, is seldom reported in the literature. To fill these gaps, this paper devises a condition‐based maintenance management strategy oriented to two‐stage continuous degradation under two‐dimensional inspection imperfection. Specifically, a threshold‐based replacement is executed under the normal‐working state if the detected degradation value exceeds the preset limit; additionally, preventive replacement is immediately performed once the defective state is identified. Notably, the detection outcome rather than the actual working condition decides how preventive maintenance operates. The long‐run cost rate is minimized via the optimization of the inspection cycle and replacement limit. Besides, numerical experiments conducted on train bogie bearing are provided, showing substantial superiorities over cost‐effectiveness promotion and performance improvement.

Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method

Human error constitutes one of the primary causes of accidents, particularly in the context of loading and unloading operations involving road trucks, especially those carrying petroleum products. The process of identifying and evaluating human errors within these operations involves several key steps. Initially, all sub-tasks associated with loading and unloading are meticulously identified and analyzed utilizing Hierarchical Task Analysis (HTA), achieved through direct observation, document examination, and interviews. Subsequently, potential human error modes within each task are delineated using the Systematic Human Error Reduction and Prediction Approach (SHERPA). Finally, essential data for determining the criticality, probability, and severity of each error are gathered through expert elicitation and the application of Fuzzy Inference Systems (FIS).Through the analysis of SHERPA worksheets, a total of 37 errors during loading operations and 14 errors during unloading operations of petroleum products were identified. Among these errors, the predominant category during loading operations was action errors, comprising 31 instances, while communication errors were the least frequent, occurring only twice. Similarly, action errors were most prevalent during unloading operations, constituting 13 instances. These errors were further categorized and ranked based on their risk levels, resulting in 27 levels for loading operations and 12 levels for unloading operations.The consistent occurrence of action errors underscores the need for implementing control measures to mitigate their frequency and severity. Such strategies may include periodic training sessions to reinforce proper work procedures and the development of monitoring checklists, among other interventions.

Identification and Compensation Method of Unbalanced Error in Driving Chain for Rate-Integrating Hemispherical Resonator Gyro.

The accuracy of the signal within a driving chain for the rate-integrating hemispherical resonator gyro (RI-HRG) plays a crucial role in the overall performance of the gyro. In this paper, a notable and effective method is proposed to realize the identification and compensation of the unbalanced error in the driving chain for the RI-HRG that improved the performance of the multi-loop control applied in the RI-HRG. Firstly, the assembly inclination and eccentricity error of the hemispherical resonator, the inconsistent metal conductive film layer resistance error of the resonator, the coupling error of the driving chain, and the parameter inconsistency error of the circuit components were considered, and the impact of these errors on the multi-loop control applied in the RI-HRG were analyzed. On this basis, the impact was further summarized as the unbalanced error in the driving chain, which included the unbalanced gain error, equivalent misalignment angle, and unbalanced equivalent misalignment angle error. Then, a model between the unbalanced error in the driving chain and a non-ideal precession angular rate was established, which was applicable to both single channel asynchronous control and dual channel synchronous control of the RI-HRG. Further, an unbalanced error identification and compensation method is proposed by utilizing the RI-HRG output with the virtual precession control. Finally, the effectiveness of the proposed method was verified through simulation and experiments in kind. After error compensation, the zero-bias instability of the RI-HRG was improved from 3.0950°/h to 0.0511°/h. The results of experiments in kind demonstrated that the proposed method can effectively suppress the non-ideal angular rate output caused by the unbalanced error in the driving chain for the RI-HRG, thereby further improving the overall performance of the RI-HRG.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

"Covering provider": an effort to streamline clinical communication chaos.

This report describes a root cause analysis of incorrect provider assignments and a standardized workflow developed to improve the clarity and accuracy of provider assignments. A multidisciplinary working group involving housestaff was assembled. Key drivers were identified using value stream mapping and fishbone analysis. A report was developed to allow for the analysis of correct provider assignments. A standardized workflow was created and piloted with a single service line. Pre- and post-pilot surveys were administered to nursing staff and participating housestaff on the unit. Four key drivers were identified. A standardized workflow was created with an exclusive treatment team role in Epic held by a single provider at any given time, with a corresponding patient list column displaying provider information for each patient. Pre- and post-survey responses report decreased confusion, decreased provider identification errors, and increased user satisfaction among RNs and residents with sustained uptake over time. This work demonstrates structured root cause analysis, notably engaging housestaff, to develop a standardized workflow for an understudied and growing problem. The development of tools and strategies to address the widespread burdens resulting from clinical communication failures is needed.

Analyzing Translation Errors of Trainee Translators: A Case Study on Tourism Promotional Material

The present study aims to scrutinize the trainee translators’ errors in their translations of tourism promotional material, a brochure, from Turkish into English. Thus, this study is designed as a descriptive qualitative research and corroborated with quantitative analysis, presenting a thorough analysis of the translated texts based on the American Translation Association (ATA) translation error taxonomy. The framework of this study employs Corder’s taxonomy of phases for error analysis, namely, selection of a text, identification of errors, classification of errors, and explanation of possible reasons for those errors. The data discussed in this paper derive from the translated texts of fifteen trainee translators i.e., third- and fourth-year T&I students at a private university enrolled in the Cultural Aspects of Translation and Interpretation course. The results revealed a statistical difference between the main error categories. According to the findings, trainee translators made most errors in the form of the language, i.e. target language mechanics, followed by errors in meaning and errors in writing quality. In acting as a “cultural mediator” in tourism text translations, trainees have also been found to be not so effective as they are supposed to be. With the results unfolded, it is hoped to benefit to both translation professors and trainees themselves.

Identification and Compensation of the Flexure-Induced Motion Error with a Voice Coil Motor

Identification and Compensation of the Flexure-Induced Motion Error with a Voice Coil Motor

Piston Error Automatic Correction for Segmented Mirrors via Deep Reinforcement Learning.

The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements compared with other methods. However, it is often difficult to obtain a high accuracy in practical application situations with this method because of the difference between the training model and the actual model. The reinforcement learning algorithm does not need to model the real system when operating the system. However, it still retains the advantages of supervised learning. Thus, in this paper, we placed a mask on the pupil plane of the segmented telescope optical system. Moreover, based on the wide spectrum, point spread function, and modulation transfer function of the optical system and deep reinforcement learning-without modeling the optical system-a large-range and high-precision piston error automatic co-phase method with multiple-submirror parallelization was proposed. Finally, we carried out relevant simulation experiments, and the results indicate that the method is effective.

Cognitive Factors Leading to Human Error: A Major Contributing Factor for Quality Deviations in Pharmaceutical Industry

The pharmaceutical sector is one of the top research-based sectors in the world which continuously produces new medications that improve and save human lives. Patients, as well as healthcare professionals depend on pharmaceutical companies for the treatment of ailments, so it is very important to develop a good quality product following Good manufacturing practices guidelines that ensure the quality, safety, and efficacy of the product. Human cognitive errors are a major cause of quality deviations. These quality deviations result in product recalls, a ban on products by regulators where the company’s reputation will be tarnished which can result in loss of sales and revenue. Human errors can be identified and mitigated by various techniques. In this paper, we discuss the factors contributing to cognitive errors, the impact of cognitive errors on the pharmaceutical industry, the identification of the cognitive errors causing current good manufacturing practices (cGMP) deviations, and challenges in the mitigation of cognitive errors. A survey questionnaire and the form human error assessment and reduction technique (HEART) tool were also developed and discussed in the manuscript.

Differentiation of petro-sourced plastic microfilaments from organic microfilaments by SEM-EDX in environmental samples

Quantifying microplastics (MPs) in marine environments is challenging due to the complexities of differentiation from other materials. This study aims to distinguish petro-sourced plastic microfilaments from organic ones in environmental samples using scanning electron microscope coupled with energy dispersive X-ray (SEM-EDX) analysis. Therefore, 38 particles resembling MPs (PRMPs) from sediments and organisms in Madagascar were analyzed. MPs identification thresholds were determined from SEM-EDX analysis of macroplastics and seaweed, the primary sources of microfilaments. SEM-EDX results of the 38 PRMPs were compared with identification/misidentification made by five MPs observation experts. Results indicated that a carbon/oxygen (C/O) ratio >1 distinguished macroplastics and MPs from other sources (seaweed and organic microfilaments). SEM-EDX analysis identified 81.58% of PRMPs as MPs. The average error made by one observer was 36.32%. These findings highlight the need for a method to assess identification error in MPs quantification and recommend SEM-EDX analysis of microfilaments to gauge observer error.

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness.

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder's ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.