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Although pretesting is an essential part of the questionnaire design process, the range of methodological work on pretesting issues is limited. The present paper concentrates on the effect of the pretest survey method on error detection by contrasting respondents who are interviewed personally with those who receive an impersonal survey method. The interaction between survey method and respondent knowledge of the questionnaire topic is also considered. The findings show that the pretest method does have an effect on the error detection rate of respondents; however, the hypothesised interaction between method and knowledge was not unequivocally supported. The detailed results illustrate which error types are affected by the method used during pretesting. Implications for future research are considered.

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.

The purpose of this paper is to present a novel algorithm to handle space environment induced errors in the space‐robot software.

The radiations in outer space may induce transient errors in micro‐processors, this phenomena will make software behavior unpredictable, and the existing software fault tolerance methods have been restricted in non‐multi‐threaded operation systems, non‐component‐based frameworks, non‐cacheable micro‐processors, non‐distributed environments, etc. A software model for space‐robot software, based on adaptive redundancy, is developed and a corresponding run‐time error detection algorithm is presented in this paper. Software was monitored and run‐time transient error would be detected and processed.

Experiments indicate that this method introduces about 30‐35 percent time overhead and about 200‐230 percent memory overhead. It also increases the fault detection rate to 84‐92.5 percent. Moreover, the model and algorithm is effective in a realistic space robot environment.

A redundancy model is developed and an error detection algorithm is introduced in this paper. Experiments demonstrate it can provide space‐robot software with good protection against the radiation induced transient errors.

Human reliability in computer programming can be improved by reducing human errors. The traditional approaches for error reduction in industry are not applicable to minimizing errors in computer programming. Proposes a model for error reduction in software prior to its final release. The model consists of two modules, an error detection module, and an error correction module. A computational procedure is outlined for determining the optimal number of detection and correction stages prior to the final release of the software.

Elements of national and organizational cultures can contribute much to the success of error management in organizations. Accordingly, this study aims to consider how errors were approached in two state university departments in Turkey in relation to their specific organizational and national cultures.

The study follows a qualitative case study design, and the data were collected through five focus groups. The cases under consideration were two state university departments of different organizational sizes.

The results showed that organizational and national culture elements (collectivism, high power distance and relatively low future orientation) significantly interacted with error management practices. In both of the organizations studied, there were found to be limited attempts to prevent the errors unless there was an emergent situation. Error detection was shown to be slow and hindered because of indirect communication among staff. Ultimately, effective error management in these organizations was identified as being unattainable because of negative emotional reactions to errors, lower reporting, restricted communication, potential face loss considerations and lack of feedback.

The findings of the current work extend earlier error management research with empirical data drawn from two cases in the higher education domain. Thus, the study offers preliminary research into the error process in education, and contributes to future research relating organizational culture to error processes.

Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.

In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.

To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.

This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

The automated manufacturing systems of the future can only be feasible if they have capabilities to recover automatically from faults and errors effectively and efficiently. Reports on the work carried out looking at error recovery problems in manufacturing cell controllers. Cell control systems also invariably manage and schedule work in an automated cell as well as carrying out the general tasks of communications, sequencing and recording. Presents a model for error recovery capability which uses system information, data and prior knowledge of errors to recover from system errors. Elucidates the structure and operation of the cell controller developed. Work so far has shown promise in achieving automatic recovery capability in cell control systems. Finally identifies further developments for future work.

The purpose of this paper is to provide much needed data to staff working with archival digitization on cost and benefit of visual checks during quality control workflows, and to encourage those in the field of digitization to take a data-driven approach to planning and workflow development as they transition into large-scale digitization.

This is a case study of a cost benefit analysis at the Triangle Research Libraries Network. Data were tracked on time spent performing visual checks compared to scanning production and error type/discovery rates for the consortial grant “Content, context, and capacity: a collaborative large-scale digitization project on the long civil rights movement in North Carolina”.

Findings show that 85 percent of time was spent scanning and 15 percent was spent on quality control with visual checks of every scan. Only one error was discovered for every 223 scans reviewed (0.4 percent of scans). Of the six types of error identified, only half cause critical user experience issues. Of all errors detected, only 32 percent fell into the critical category. One critical error was found for every 700 scans (0.1 percent of scans). If all the time spent performing visual checks were instead spent on scanning, production would have increased by 18 percent. Folders with 100 or more scans comprised only 11.5 percent of all folders and 37 percent of folders in this group contained errors (for comparison, only 8 percent of folders with 50 or more scans contained errors). Additionally, 52 percent of all critical errors occurred in these folders. The errors in larger folders represented 30 percent of total errors, and performing visual checks on the large folders required 32 percent of all visual check time.

The data gathered during this research can be repurposed by others wishing to consider or conduct cost benefit analysis of visual check workflows for large-scale digitization.

To the authors' knowledge, this is the only available dataset on rate of error detection and error type compared to time spent on quality control visual checks in digitization.

In the past several decades, considerable research has been dedicated to the development of mobile systems that can traverse vertical surfaces. For the control of the climbing robot, high-precision sensing of the climbing robot’s heading angle during movement is very important. This paper aims to propose a vision-based scheme for the 2D direction angle detection of wall-climbing robots.

First, the authors proposed a method based on image geometric transformation to transform a camera image into a front view image of the wall, as the position and direction angle of the robot can be detected from the transformed image to eliminate the need for calibration of the camera’s internal and external parameters. Second, the AngleNet model is proposed to detect the 2D direction angle of the wall-climbing robot. Third, a training sample expansion strategy is proposed, which greatly decreased the workload of annotating training samples for AngleNet.

The single image processing time of AngleNet on the GPU is only 1.7 ms, which satisfies the demands of real-time processing. The mean and maximum error of predicted direction angle on the 556 samples of the test set are 1.1° and 3.8°, respectively.

This research offers an effective method for measuring the climbing robot’s direction angle in a complex outdoor environment. Combined with position detection, it can provide high-precision position and direction angle measurement information for the motion control of the climbing robot.