Error Detection Efficiency Research Articles

Establishment and evaluation of Voting algorithm-based internal quality control (ViQC), a patient-based real-time quality control

BackgroundPatient-Based Real-Time Quality Control (PBRTQC) has emerged as a supplementary programme to traditional internal quality control (iQC) mechanisms. Despite its growing popularity, practical applications in clinical settings reveal several challenges. The primary objective of this research is to introduce and develop an Artificial Intelligence (AI)-based method, named Voting algorithm based iQC (ViQC), designed to enhance the precision and reliability of existing PBRTQC systems. MethodsIn this study, we conducted a retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital, Nanjing, China, to provide an unbiased data set. The Voting iQC (ViQC) algorithm, established by the principles of the Voting algorithm, was then developed. Its analytical performance was evaluated through the calculation of random errors (RE). Subsequently, its clinical efficacy was assessed by comparison with five statistical algorithms: Moving Average (MA), Exponentially Weighted Moving Average (EWMA), Moving Median (movMed, MM), Moving Quartile (MQ), and Moving Standard Deviation (MovSD). ResultsThe ViQC model incorporates a variety of machine learning models, including logistic regression, Bayesian methods, K-Nearest Neighbor, decision trees, random forests, and gradient boosting decision trees, to establish a robust predictive framework. This model consistently maintains a false positive rate below 0.002 across all six evaluated error factors, showcasing exceptional precision. Notably, its performance further excels with an error factor of 3.0, where the false positive rate drops below 0.001, and achieves an accuracy rate as high as 0.965 at an error factor of 2.0. The classification effectiveness of ViQC model is evaluated by an area under the curve (AUC) exceeding 0.97 for all error factors. In comparison to five conventional PBRTQC statistical methods, ViQC significantly enhances error detection efficiency, maximum reducing the trimmed average number of patient samples required for detecting errors from 724 to 168, thereby affirming its superior error detection capability. ConclusionThe new established PBRTQC using artificial intelligence yielded satisfactory performance compared to the traditional PBBTQC in real world setting.

Errors, error detection and correction efficiency in the container number code

The container number is an 11-digit code that uses a single parity check of modulo 11 to check the authenticity of the container numbers. The world relies on shipment containers to transport goods. Various types of cargos have been erroneously delivered in different places. The modulo 11 container number is divided into four parts. The owner code that consists of three capital letters of the Latin alphabet to indicate the owner or principal operator of the container. Such code needs to be registered at the Bureau International des Conteneurs in Paris to ensure uniqueness worldwide (per ISO 6346). The equipment category identifier consists of one of the following three capital letters of the Latin alphabet U for all freight containers, J for detachable freight container-related equipment, Z for trailers and chassis. The serial number consists of six numeric digits, assigned by the owner or operator, uniquely identifying the container within that owner/operator’s fleet. The check digit consists of one numeric digit providing a means of validating the recording and transmission accuracies of the owner code and serial number. The objective of this study is to determine the efficiency of the current modulo 11 container number in the error detection and correction. While the modulo 11 checksum calculation method is an effective way to detect errors in container numbers, it is not foolproof and errors can still occur. Modulo 11 container number code does not detect some transposition and substitution errors. The container number code and its check digit do not possess inherent error correction capabilities. This study therefore recommends a new modulo 13 container number code with higher error correction and detection capability.

Deep learning for predicting refractive error from multiple photorefraction images

BackgroundRefractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN). It not only extracts the features of each image, but also fully utilizes the sequential relationship between images. In this article, we develop a system to predict the spherical power, cylindrical power, and spherical equivalent in multiple eccentric photorefraction images.ApproachFirst, images of the pupil area are extracted from multiple eccentric photorefraction images; then, the features of each pupil image are extracted using the REDNet convolution layers. Finally, the features are fused by the recurrent layers in REDNet to predict the spherical power, cylindrical power, and spherical equivalent.ResultsThe results show that the mean absolute error (MAE) values of the spherical power, cylindrical power, and spherical equivalent can reach 0.1740 D (diopters), 0.0702 D, and 0.1835 D, respectively.SignificanceThis method demonstrates a much higher accuracy than those of current state-of-the-art deep-learning methods. Moreover, it is effective and practical.

Analyzing the Application of Multimedia Technology Assisted English Grammar Teaching in Colleges

To address the problem that the traditional English composition is manually reviewed, which leads to low efficiency of grammar error checking and heavy teaching burden for teachers and seriously affects the quality of English teaching, a verb grammar error checking algorithm based on the combination of a multilayer rule model and a deep learning model is proposed with current multimedia technology. First, the basic principles and network structure of the long short-term memory network LSTM are specifically analyzed; then, a multilayer rule model is constructed; after that, an attention model-based LSTM verb grammar checker is designed, and a word embedding model is used to encode the text and map it to the status space to retain the textual information. Finally, the proposed algorithm is experimentally validated using the corpus dataset. The experimental results revealed that the accuracy, recall, and F1 values of the proposed algorithm in verb error detection were 45.51%, 28.77%, and 35.5%, respectively, which were higher than those of the traditional automatic review algorithm, indicating that the proposed algorithm has superior performance and can improve the accuracy and efficiency of grammar error detection.

FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects.

Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional error control in the design of CM systems with high-performance requirements remains urgent. As an additional control of CM systems, we propose to use indivisible error detection codes based on a positional number system. In this work, we evaluated the indivisible code using the average probability method (APM) for the binary symmetric channel (BSC), which has the simplicity, versatility and reliability of the estimate, which is close to reality. The APM allows for evaluation and compares indivisible codes according to parameters of correct transmission, and detectable and undetectable errors. Indivisible codes allow for the end-to-end (E2E) control of the transmission and processing of information in digital systems and design devices with a regular structure and high speed. This study researched a fractal decoder device for additional error control, implemented in field-programmable gate array (FPGA) software with FEC for short-reach optical interconnects with multilevel pulse amplitude (PAM-M) modulated with Gray code mapping. Indivisible codes with natural redundancy require far fewer hardware costs to develop and implement encoding and decoding devices with a sufficiently high error detection efficiency. We achieved a reduction in hardware costs for a fractal decoder by using the fractal property of the indivisible code from 10% to 30% for different n while receiving the reciprocal of the golden ratio.

Directed Dynamic Symbolic Execution for Static Analysis Warnings Confirmation

Currently, there is no doubt among experts in the field of program certification and quality assurance that automated program analysis methods should be used to find bugs that lead to program security vulnerabilities. The national standard for the secure software development requires the use of source code static analysis tools as one of the measures of software quality assurance at the development stage and the application of dynamic analysis and fuzz-testing of the source code at the qualification testing stage. Fundamental limitations of automated program analysis and testing methods make it impossible to carry out simultaneously exhaustive and precise analysis of programs for errors. Thereof, researches are nowadays carried out aimed at reducing the effect of fundamental limitations on the quality and productivity of automated software error detection methods. This paper discusses an approach that combines methods of source code static analysis and dynamic symbolic execution in order to increase the program error detection efficiency.

Real-time fault tolerant full adder design for critical applications

In the complex computing system, processing units are dealing with devices of smaller size, which are sensitive to the transient faults. A transient fault occurs in a circuit caused by the electromagnetic noises, cosmic rays, crosstalk and power supply noise. It is very difficult to detect these faults during offline testing. Hence an area efficient fault tolerant full adder for testing and repairing of transient and permanent faults occurred in single and multi-net is proposed. Additionally, the proposed architecture can also detect and repair permanent faults. This design incurs much lower hardware overheads relative to the traditional hardware architecture. In addition to this, proposed design also provides higher error detection and correction efficiency when compared to the existing designs.

Judging the judges' performance in rhythmic gymnastics.

Rhythmic gymnastics (RG) is an aesthetic event balancing between art and sport that also has a performance rating system (Code of Points) given by the International Gymnastics Federation. It is one of the sports in which competition results greatly depend on the judges' evaluation. In the current study, we explored the judges' performance in a five-gymnast ensemble routine. An expert-novice paradigm (10 international-level, 10 national-level, and 10 novice-level judges) was implemented under a fully simulated procedure of judgment in a five-gymnast ensemble routine of RG using two videos of routines performed by the Greek national team of RG. Simultaneous recordings of two-dimensional eye movements were taken during the judgment procedure to assess the percentage of time spent by each judge viewing the videos and fixation performance of each judge when an error in gymnast performance had occurred. All judge level groups had very modest performance of error recognition on gymnasts' routines, and the best international judges reported approximately 40% of true errors. Novice judges spent significantly more time viewing the videos compared with national and international judges and spent significantly more time fixating detected errors than the other two groups. National judges were the only group that made efficient use of fixation to detect errors. The fact that international-level judges outperformed both other groups, while not relying on visual fixation to detect errors, suggests that these experienced judges probably make use of other cognitive strategies, increasing their overall error detection efficiency, which was, however, still far below optimum.

An Investigation into Soft Error Detection Efficiency at Operating System Level

Electronic equipment operating in harsh environments such as space is subjected to a range of threats. The most important of these is radiation that gives rise to permanent and transient errors on microelectronic components. The occurrence rate of transient errors is significantly more than permanent errors. The transient errors, or soft errors, emerge in two formats: control flow errors (CFEs) and data errors. Valuable research results have already appeared in literature at hardware and software levels for their alleviation. However, there is the basic assumption behind these works that the operating system is reliable and the focus is on other system levels. In this paper, we investigate the effects of soft errors on the operating system components and compare their vulnerability with that of application level components. Results show that soft errors in operating system components affect both operating system and application level components. Therefore, by providing endurance to operating system level components against soft errors, both operating system and application level components gain tolerance.

A Review of Geometric Error Modeling and Error Detection for CNC Machine Tool

Error compensation can improve the accuracy of machine tools effectively. Among the error sources affecting the accuracy of CNC machine tool, geometric error is always set as a key performance criterion. This paper summarizes several methods of geometric error modeling and reviews the characteristics of different methods. Furthermore, available methods for measuring geometric errors have been reviewed also based on the advanced instruments. This work aims at enhancing the efficiency of error detection and give a perspective for the application of error compensation in the future.

The Development of Matlab Toolbox in Measurement Error Analysis

When analyzing various phenomena, it is necessary to use actual numerical values acquired from measurement in order to make analyses and judgments that are precise. However, all measurements will encounter system errors, thus affecting the accuracy of the results. In order to improve measurement precision, such measurement errors must be detected early. In the past, the detection of measurement error mainly depended on manual computations, and the measurement data were substituted in existing equations in order to reach the results. This paper aims at reducing the complexity of measurement error calculation and improving the efficiency of analysis, by developing a computer toolbox with eleven kinds of evaluation criteria based on Matlab GUI. An actual case is presented in order to verify the accuracy of the toolbox. The resulting toolbox can not only quickly detect measurement errors in the measurement data, but it also greatly decreases the required extent of manual computation, improving both the accuracy and the efficiency of measurement error detection.

SU‐E‐T‐439: Implementation of an Iterative Deconvolution Method in a Daily In‐Vivo Verification Procedure with the DAVID System

Purpose: The DAVID system (PTW‐Freiburg, Germany) is a multiwire ionization chamber placed in the accessory holder, capable of continuously monitoring treatment deliveries such as IMRT. As shown in previous work, an iterative deconvolution algorithm can correct the blurring of the signal profile by the lateral transport of secondary electrons within the chamber. First results of the implementation of the procedure into clinical routine and of the resulting enhanced error detection efficiency are presented. Methods: The measured signal profile of the wires can be regarded as resulting from a convolution of the true photon fluence profile with a lateral response function. After measuring the latter for each wire by simple slit measurements, a true signal can be approximated by an iterative deconvolution algorithm described in previous work of our group. Prior to the patient treatment, data for each field of an IMRT plan are measured during the pre‐treatment verification. The data are deconvolved on‐line and stored as reference values. During the daily treatment of the patient the measured and deconvolved readings are compared on‐line with the reference values. Results: The application of the procedure is important for DAVID chambers designed for MLCs with 5 mm leaf width. In these the profile blurring by laterally scattered secondary electrons in the chamber can be of the order of the primary signal. By applying the deconvolution procedure the error detection efficiency of the DAVID system can be enhanced by a factor of approximately 2. The deconvolution of the profile is performed on‐line with no remarkable loss of time. Conclusions: The implemented deconvolution algorithm corrects for the signal profile blurring of the DAVID chamber by laterally scattered electrons. The study has been implemented into the daily routine.

Gross Error Detection and Data Reconciliation using Historical Data

With the information technology applied widely to process industry, a large amount of historical data which could be used for obtaining the prior probabilities of gross error occurrence is stored in database. To use the historical data to enhance the efficiency of gross error detection and data reconciliation, a new strategy which includes two steps is proposed. The first step is that mixed integer program technique is incorporated to use the prior information to detect gross errors. The second step is to estimate all detected gross errors and adjust process data with material, energy, and other balance constrains. In this step an improved method is proposed to achieve the same effect with traditional method through adjusting the covariance matrix. Novel prior information criteria are described and performance of this new strategy is compared and discussed by applying the strategy for a challenging test problem.

Router group monitoring: making traffic trajectory error detection more efficient

Detecting errors in traffic trajectories (i.e., packet forwarding paths) is important to operational networks. Several different traffic monitoring algorithms such as Trajectory Sampling, PSAMP, and Fatih can be used for traffic trajectory error detection. However, a straight-forward application of these algorithms will incur the overhead of simultaneously monitoring all network interfaces in a network for the packets of interest. In this paper, we propose a novel technique called router group monitoring to improve the efficiency of trajectory error detection by only monitoring the periphery interfaces of a set of selected router groups. We analyze a large number of real network topologies and show that effective router groups with high trajectory error detection rates exist in all cases. However, for router group monitoring to be practical, those effective router groups must be identified efficiently. To this end, we develop an analytical model for quickly and accurately estimating the detection rates of different router groups. Based on this model, we propose an algorithm to select a set of router groups that can achieve complete error detection and low monitoring overhead. Finally, we show that the router group monitoring technique can significantly improve the efficiency of trajectory error detection based on Trajectory Sampling or Fatih.

SU‐GG‐T‐183: Enhanced Accuracy of IMRT Photon Fluence Profile Surveillance: Iterative Resolution Correction of the DAVID Chamber

Purpose: The DAVID system (PTW‐Freiburg, Germany) is a multiwire ionization chamber placed in the accessory holder, capable of continuously monitoring treatment delivery. The system supplements the pre‐treatment dosimetric plan verification for advanced radiotherapy techniques such as IMRT and rotational therapy. Each detection wire signal is associated to the opening width of the MLC leaf pair it is surveying. The blurring of the lateral transport of secondary electrons within the chamber is corrected by an iterative deconvolution algorithm hence achieving enhanced error detection efficiency. Method and Materials: The measured signal profile S(x) is regarded as the result of a convolution of the true photon fluence profile P(x) with the lateral response function fξ(x) (ξ = sequential number of a wire). The fξ(x) was measured for each detection wire by opening only the MLC leaf pair it is associated with. The iterative algorithm consists in a sequence of approximations for Pn(x) which quickly converges towards the desired true P(x). Each Pn(x) is numerically convolved with fξ(x) and from the comparison of the result with S(x), the next approximation Pn+1(x) is derived. Results: The fine structure of the fluence profile is restored through the deconvolution. The lateral resolution of the DAVID system has been improved and error detection efficiency of the DAVID system can be enhanced by a factor of 2. The deconvolution of each IMRT segment can be performed in less than 1 sec. Conclusion: The developed iterative deconvolution method is capable of correcting for the blurring of the DAVID chamber signal profiles due to the lateral transport of scattered electrons within the chamber. The short computational time needed makes the algorithm feasible to be implemented in the daily routine to gain an increased specificity in IMRT photon fluence profile surveillance.

Industrial Processes: Data Reconciliation and Gross Error Detection

Process data plays a vital role in industrial processes, which are the basis for process control, monitoring, optimization and business decision making. However, it is inevitable that process data measurements will be corrupted by random errors. Therefore, data reconciliation has been developed to improve accuracy of process data by reducing the effect of random errors. Unfortunately, reconciled values would be deteriorated by gross errors, which may be present during measurement. Therefore, gross error detection is necessary to guarantee the efficiency of data reconciliation, which has been developed to identify and eliminate gross errors in process data. In this paper, a review of data reconciliation and gross error detection and relevant industrial applications are presented. As the efficiency of data reconciliation and gross error detection largely depends upon the locations of sensors, sensor networks design is also included in the review. Meanwhile, some achievements of the authors are also included.

Error detection and correction data using weighted checksum

The problem of increasing the efficiency of error detection and correction data using weighted checksum. The procedures correcting single and multiple errors with re-shipment of the controlled data block. For the model of a symmetric binary channel obtained theoretical estimates of the probabilities of error correction that allow reasonable approach to the choice of technology fixes. The theoretical analysis of the effectiveness of the proposed approach, based on which the estimates are obtained for reducing the amount of transmitted data for correcting errors in comparison with known technologies

Comparing quality assurance methods and the resulting design strategies: Experiences from complex designs

Design quality assurance methods are compared in terms of cost and error detection efficiency using statistical data from several complex designs (Complexity>100.00 gates). A suitable strategy for error detection as a function of system complexity is identified, and the resulting design flow is described. The statistical control of the design process as a feedback loop to achieve error prevention is demonstrated. Using data from complex projects practical advice for managers is given.

Gross error detection and stage efficiency estimation in a separation process

Accurate process models are required for optimization and control in chemical plants and petroleum refineries. These models involve various equipment parameters, such as stage efficiencies in distillation columns, the values of which must be determined by fitting the models to process data. Since the data contain random and systematic measurement errors, some of which may be large (gross errors), they must be reconciled to obtain reliable estimates of equipment parameters. The problem thus involves parameter estimation coupled with gross error detection and data reconciliation. MacDonald and Howat (1988) studied the above problem for a single-stage flash distillation process. Their analysis was based on the definition of stage efficiency due to Hausen, which has some significant disadvantages in this context, as discussed below. In addition, they considered only data sets which contained no gross errors. The purpose of this article is to extend the above work by considering alternative definitions of state efficiency and efficiency estimation in the presence of gross errors.

Evaluating the effectiveness of reliability-assurance techniques

In the software-development life cycle, various reliability assurance techniques are routinely performed in an attempt to reduce the number of errors in a software product. These reliability-assurance techniques commonly consist of a variety of technical reviews and several levels of testing. Although the utilization of these techniques is growing, there is a lack of quantitative data on their effectiveness. This paper describes two metrics, error-detection efficiency and cost effectiveness, which were used to evaluate the reliability-assurance techniques employed in a large software project. The results of the study are also presented.