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The purpose of this paper is to develop a quantitative model to assess probability of errors and errors correction costs in parts feeding systems for assembly lines.

Event trees are adopted to model errors in the picking-handling-delivery-utilization of materials containers from the warehouse to assembly stations. Error probabilities and quality costs functions are developed to compare alternative feeding policies including kitting, line stocking and just-in-time delivery. A numerical case study is included.

This paper confirms with quantitative evidence the economic relevance of logistic errors (LEs) in parts feeding processes, a problem neglected in the existing literature. It also points out the most frequent or relevant error types and identifies specific corrective measures.

While the model is general purpose, conclusions are specific to each applicative case and are not generalizable, and some modifications may be required to adapt it to specific industrial cases. When no experimental data are available, human error analysis should be used to estimate event probabilities based on underlying modes and causes of human error.

Production managers are given a quantitative decision tool to assess errors probability and errors correction costs in assembly lines parts feeding systems. This allows better comparing of alternative parts feeding policies and identifying corrective measures.

This is the first paper to develop quantitative models for estimating LEs and related quality cost, allowing a comparison between alternative parts feeding policies.

Not only does the problem of correcting spelling errors by computer have a long history, it is evidently of considerable current interest as papers and letters on the topic continue to appear rapidly. This is not surprising, since techniques useful in detecting and correcting mis‐spellings normally have other important applications. Moreover, both the power of small computers and the routine production of machine‐readable text have increased enormously over the last decade to the point where automatic spelling error detection/correction has become not only feasible but highly desirable.

Students’ assignments are often much better in style and organisation than the email messages they send to theirteachers. Some teachers, including myself, often ‘covertly’ correct students’ email messages for style, organisation,content, or correctness. While some students appreciate this extra effort from the teachers, others see it as an inhibitingintrusion. However, I have frequently noticed that students who are corrected repeatedly improve in writing emails. Myresearch concerns both the use of academic email writing and the correction of errors in student emails, and concludesthe following: students usually write only formal emails to their teachers; those instructors who correct email errors do notoffer explicit error correction; and if email writing were taught to the students, it would offer variety in the writing genresstudents currently compose

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using optical character recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. The purpose of this paper is to investigate how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives.

A series of experiments with different micro-task’s structures and text lengths were conducted with 753 workers on the Amazon’s Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures were devised.

The analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image.

The study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction.

This is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.

The aim of this paper is to propose a new coning correction algorithm, based on the singular perturbation technique, for the attitude update computation with non‐ideal angular rate information.

Unlike conventional coning correction algorithms, the new method uses angular rate two‐time scale model to construct the coning correction term of attitude update. In order to achieve balanced real/pseudo coning correction performance, the selection guidelines of the new algorithm parameters are established.

Performance of the new algorithm is evaluated by comparison with the conventional algorithm in no ideal sensors undergoing stochastic coning environments. The accuracy of attitude update can be improved effectively with reduced computational workload by using this new coning algorithm as compared with conventional ones.

The proposed coning correction algorithm can be implemented with angular rate sensors in UAV (unmanned aerial vehicle) and other aircrafts attitude estimation for navigation and control applications.

Singular perturbation is an effective method for structuring coning correction algorithm with filtered angular rate outputs in stochastic coning environments. The improved coning correction algorithm based on singular perturbations reduces the real and pseudo coning effects effectively as compared with conventional ones. It is proved to be valid for improvement of accuracy with reduced computations of the attitude update.

The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors.

The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database.

Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building.

The main limitations of the study is the assumption that accuracy floor altitude information is available.

Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

Attitude and heading are very important measurements on board aircraft. In modern solutions they are measured by the attitude and heading reference system (AHRS). In some small unmanned systems, the GPS track angle is used for heading corrections instead of the magnetometer; then, the system measures the track angle instead of heading. With a temporary lack of correction signals, the measurement error increases very quickly. Similarly, a quick increase in the measurement error is observed when a magnetic heading sensor used for correction stops working properly. This study aims to propose measurement of the roll angle for yaw angle correction.

AHRS algorithms were designed; typical maneuvers were analyzed. The method was verified by simulation and in flight testing analysis. For quantitative analyses, a performance index was proposed.

The method enables reduction of the yaw angle error caused by the gyros bias error. This study presents the idea, results of simulations and flight testing data analysis and discusses advantages and limitations of the presented method.

The presented methodology can be implemented in AHRS systems for manned and unmanned aircraft.

This study enables more accurate measurement of the yaw angle in the case of missing correction signals.

This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

Part V analyzes the details of how to assess materiality. It first tackles qualitative versus quantitative criteria and the role of professional judgment. It then analyzes the selection of quantitative threshold, to expand to the choice of benchmarks. It contrasts the whole financial statements with subaggregates, line items, and components.

Specific sections contrast IASB, FASB, SEC, and other guidance on materiality applied to comparative information, interim reporting, and segment reporting.

The section on estimates mingles complex guidance coming from accounting, auditing, and internal control over financial reporting to explain how the management can improve its assessment of materiality concerning estimates.

After explaining the techniques to move from individual to cumulative misstatements, the part tackles verification ex post, and finally summarizes the intricacies of whether immaterial misstatements are permissible and their consequences.