Search results

The purpose of this paper is to validate the accuracy of point cloud data generated from a 3D body scanner.

A female dress form was scanned with an X‐ray computed tomography (CT) system and a 3D body scanning system. The point cloud data from four axial slices of the body scan (BS) data were compared with the corresponding axial slices from the CT data. Length and cross‐sectional area measurements of each slice were computed for each scanning technique.

The point cloud data from the body scanner were accurate to at least 2.0 percent when compared with the CT data. In many cases, the length and area measurements from the two types of scans varied by less than 1.0 percent.

Only two length measurements and a cross‐sectional area measurement were compared for each axial slice, resulting in a good first attempt of validation of the BS data. Additional methods of comparison should be employed for complete validation of the data. The dress form was scanned only once with each scanning device, so little can be said about the repeatability of the results.

Accuracy of the point cloud data from the 3D body scanner indicates that the main issues for the use of body scanners as anthropometric measurement tools are those of standardization, feature locations, and positioning of the subject.

Comparisons of point cloud data from a 3D body scanner with CT data had not previously been performed, and these results indicate that the point cloud data are accurate to at least 2.0 percent.

The purpose of this study on architecture, design, ergonomics and anthropometry was to ensure compliance with the human-machine-work environment, minimize human error and obtain anthropometric measurements accurately, safely and rapidly.

The developed system efficiently extracted anthropometric data for 15,243 individuals with an accuracy rate of 98.8 per cent, focusing on the values for “shoulder breath” and “body depth.” In this study, a new anthropometric measurement system was developed and subsequently applied to obtain anthropometric measurements easily and quickly. The effect of the newly collected anthropometric data on the design discipline was evaluated.

The findings highlighted the need to update the anthropometric data used in other design studies. In addition to contributing to designing discipline, the updated anthropometric data are considered suitable for use in many different fields.

The design discipline and related disciplines are expected to take advantage of these measurements. Updating the aforementioned data will also be easier and faster because of the simplicity and affordability of the system.

This is the first and only such study in Turkey with regard to the up-to-date anthropometric measurements obtained and the size of the database created.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.

First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.

The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.

The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate.

Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system.

The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.

This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis.

In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

Current literature recognised big data as a digital revolution affecting all organisational processes. To obtain a competitive advantage from the use of big data, an efficient integration in a performance measurement system (PMS) is needed, but it is still a “great challenge” in performance measurement research. This paper aims to review the big data and performance measurement studies to identify the publications’ trends and future research opportunities.

The authors reviewed 873 documents on big data and performance carrying out an extensive bibliometric analysis using two main techniques, i.e. performance analysis and science mapping.

Results point to a significant increase in the number of publications on big data and performance, highlighting a shortage of studies on business, management and accounting areas, and on how big data can improve performance measurement. Future research opportunities are identified. They regard the development of further research to explain how performance measurement field can effectively integrate big data into a PMS and describe the main themes related to big data in performance measurement literature.

This paper gives a holistic view of big data and performance measurement research through the inclusion of numerous contributions on different research streams. It also encourages further study for developing concrete tools.

The purpose of this paper is to present a model of researching clothing anthropometrics at the Manchester Metropolitan University in the UK (MMU model), to demonstrate steps in devising size charts by analysing raw data, to relate key aspects of size charts to raw data, and to generate debate on such methods that impinge on the disseminated knowledge in this specialised area. Although sizing is important to consumers, retailers and manufacturers, this area has received scarce attention in the literature.

The MMU model presents step‐by‐step processes in generating size charts. Data from 150 women generated descriptive statistics (mean, standard deviation, percentiles); these were utilised to devise seven sizes of a body measurements table. Correlations were used to determine relationships, resulting in size charts with a defined size range and grading increments that are relatable to utilisation by consumers, retailers and manufacturers.

A step‐by ‐step model of analysing raw data is presented. A verifiable size chart, codes, grading increments and size limits relatable to data are generated. The usefulness of size charts is therefore contextualised.

This paper discusses only one model of researching clothing anthropometrics and provides a related conceptual framework; this could be the basis for future research and debate in this area.

For competitiveness, efficient sizing is useful for marketing, especially in creating niches, targeting customers and facilitating consumer satisfaction.

The MMU model provides an initial conceptual framework at one institution, a benchmark for similar practice in academia and industry and subsequent debate in literature.

The purpose of this paper is to propose a method for hand measurement based on image and marker watershed algorithm, and combine the data to analyze the shape and characteristics of the hand.

A portable hand image capturing instrument was designed and manufactured, and the hand images and dimensions of 328 young men in Zhejiang area were obtained. The outer contour curve of the hand and the key points of finger root, fingertip, wrist and knuckle position were extracted. Then, the size of each hand part was calculated. The hand data obtained from the two-dimensional image was compared with the manual measurement data. Finally, the hands were classified according to the measurement data, and the relationship between hand control size and hand length, hand width and the relationship between hand length and height were explored.

The data comparison results show that the two measurement methods have high data consistency and are replaceable. In addition, analyzing the data obtained four major characteristic factors that affect the shape of the hand, divided the hands of young men in Zhejiang into five categories, and obtained the regression equations of basic hand size, hand length and hand width, and obtained the regression equation of hand length and height.

The method proposed in this study to obtain hand size based on the image and mark watershed algorithm has lower requirements on the external environment and testers, conforms to the development trend of applying artificial intelligence to anthropometric engineering and provides a useful reference value for data collection of gloves specification design. In addition, the results of data analysis can provide a valuable reference basis for consumer hand shape predictions, which can be used to guide the research and production of hand instruments, the design of specifications series and the purchase of hand products.

Progress monitoring and data-based intervention are unique special education developments stemming from efforts to find an effective alternative to diagnostic/prescriptive instruction. Springing from research on Curriculum-based Measurement (CBM) in the late 1970s and early 1980s at the Minnesota Institute for Research on Learning Disabilities, the approach has generated a large body of empirical research and development. While the original work demonstrated that teachers could be more effective using progress monitoring in data-based intervention, most research and development activity has focused on development and extensions of the CBM model with less attention to data-based intervention. While research on progress monitoring has occurred at a high rate, widespread implementation of progress monitoring has been spurred by both federal funding and commercial development. As might be expected, all of this activity has resulted in a large set of successes and disappointments that are described here. For better or worse, as progress monitoring and data-based intervention have been incorporated into Response to Intervention (RTI) models it seems likely that the future of progress monitoring and data-based intervention is tied to the future of RTI. The question is whether this linking will result in adding to the set of successes or to that of disappointments for this unique special education innovation.