Search results

The size prediction of garment is an important part in the process of the garment design and production, and it is also one of the most important features in warp-knitted computer-aided design system. The purpose of this paper is to realize the auto-generation of the garment templates using JavaScript and WebGL technologies, based on the prediction of the size of warp-knitted seamless sportswear.

The warp-knitted jacquard technology is used to produce the warp-knitted seamless sportswear, which is divided into suits and tights. In order to achieve the purpose of this study, the dimensions of four kinds of jacquard patterns knitted under different knitting conditions are measured and the crosswise and longitudinal size shrinkage percentages are also calculated. Then, the relationship between the yarn count and the drawing density as well as the size shrinkage percentage is studied and a size prediction model for warp-knitted jacquard fabric is established. Next, according to the results of the size calculation, the point positions of the garment boundary in the mathematical coordinate system is determined. The color formula is built by the two-dimensional mathematical matrix. Finally, combined with the coordinate position and color information, the template can be drawn automatically.

Based on the size prediction model of warp-knitted garment, the template generation of warp-knitted full-form sportswear on WebGL-enabled web browser is realized, which is proven to be an effective computer-aided design method for warp-knitted garments.

Because of limited researches, only two groups of yarns and four kinds of jacquard patterns were studied. A vaster database should be built and smooth curve, accurate coordinate needs to be optimized in the further research.

The size prediction model for warp-knitted jacquard garment and garment template auto-generation of warp-knitted computer-aided design system will simplify the fabric technical design process, shorten design time and improve the efficiency of new product development.

The size prediction model for warp-knitted jacquard garment and garment template auto-generation of warp-knitted computer-aided design system will provide the industries a guidance for new sample development and it also can shorten the development time and lower cost.

This author analyzes the relationship between the size characteristics and knitting technology of warp-knitted jacquard patterns, proposes a model of size prediction and realizes the auto-drawing of the garment template in the warp-knitted CAD system, which provides a reference for the new product design and development of warp-knitted seamless sportswear.

The purpose of this paper is to obtain circumference sizes from 2D feature sizes in the parts of three vital measurements of young female, the dimensions of chest width, chest depth, waist width, waist depth, hip width, hip depth, chest girth, waist girth and hip girth of 400 young female samples are collected.

Inside which, 300 samples are used as learning samples, and the remaining 100 samples are used as training samples, the sample data are entered to the network constructed by support vector machine regression (SVR) and the predictive value of circumference sizes are gained.

Finally, the regression model is established between 2D feature size and the corresponding circumference size. Through the trained prediction model based on SVR, the circumference sizes in three vital measurement parts of a new sample are predicted for convenient mass measurement.

The research of measurement regression relationship in parts of three vital measurements of young female is the basis for conveniently obtaining dimensions in garment mass measurement. It can provide the accurate data to feminine dress industry, and has high precision.

The application of objective measurement of the mechanical properties of fabrics in the apparel industry began around 1975 in the Hirakata area, which is one of the centres of men's suit production in Japan. At that time the KESF system had been developed and thereafter spread rapidly. The measurement of mechanical data under low‐load level by the KESF provided useful information for the apparel engineers who needed some means of fabric measurement by which the tailoring process might be controlled. The fabric dimensional stability testing using steam press was also standardised at that time (HESC 103A method). At present, the KESF data and the stability data are essential for apparel engineers and are used widely in the Japanese apparel industry. In addition to the use of objective measurements in each factory, a centre for objective fabric inspection has been recently initiated in the Hirakata area, for the inspection and control of fabric by the objective system for tailoring process control. In addition, a co‐operative work between the apparel engineers and the university has been carried out to develop a new equation for predicting the good appearance of a suit on the basis of fabric mechanical data. Automatic tailoring such as automatic overfeed action on the basis of fabric mechanical property is also carried out under the co‐operation of the university, the apparel industry, and a sewing machine manufacturer (Juki) in Hirakata. The progress of these projects is presented.

The purpose of this paper is to develop a method for defining and categorizing upper lateral somatotypes for clothing size systems used for clothing pattern creation based on directional angles of 3D space vectors.

3D data for 317 men in their twenties obtained from the fifth Size Korea survey were used in this study. Standard landmarks and surfaces were set on the 3D shapes, and six space vector angles of the lateral form were defined and measured. Relationships among the measurement results were clarified, and the results were compared with those obtained using existing classifying methods.

The measurement of the defined directional angles indicated that the swayback type and bend-forward type had the two extreme values, and the straight type was between the two values. The analysis of the correlation between six directional angles indicated that some points in the lower area of the upper body had a high correlation with other points in the lower area.

The subjects of this study were limited to lateral somatotypes, and there is a need for future studies that focus on frontal somatotypes. This research is confined to the upper lateral somatotypes of men in their twenties. Further study is needed to extend the results of this study to other body types such as those of elderly and overweight persons.

Major angle measurements quantified by the somatotypes can be specifically reflect in developing and revised to the right patterns which is spread body shell replica or individual pattern for MTM.

This objective somatotype analysis method can be involved in determining individual body somatotype of ordermade clothes or can provided the accurate information interactively to MTM automatic customized pattern making system.

Accurate measurements of size, shape, and posture were applied and characterized to realize the process. Accuracy was improved compared to existing 2D analysis methods through three-dimensional analysis using directional space vector angles based on 3D forms.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.

The purpose of this paper is to understand how users behave and evaluate how systems with users are essential for interactive information retrieval (IIR) research. User study methodology serves as a primary approach to answering IIR research questions. In addition to designing user study procedures, understanding the limitations of varying study designs and discussing solutions to the limitations is also critical for improving the methods and advancing the knowledge in IIR.

Given this unresolved gap, we apply the faceted framework developed by Liu and Shah (2019) in systematically reviewing 131 IIR user studies recently published (2016–2021) on multiple IR and information science venues.

Our study achieve three goals: (1) extracting and synthesizing the reported limitations on multiple aspects of user study (e.g. recruitment, tasks, study procedures, system interfaces, data analysis methods) under associated facets; (2) summarizing the reported solutions to the limitations; (3) clarifying the connections between types of limitations and types of solutions.

The bibliography of user studies can be used by students and junior researchers who are new to user-centered IR studies as references for study design. Our results can facilitate the reflection and improvement on IR research methodology and serve as a checklist for evaluating customized IIR user studies in varying problem spaces.

To our knowledge, this work is the first study that systematically reviews the study limitations and solutions reported by IIR researchers in articles and empirically examines their connections to different study components.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

The purpose of this paper is the proposal of fatigue-life-prediction curve for cast aluminum alloy A356-T6 with different casting defect sizes.

Four kinds of A356-T6 fatigue specimens were sampled from the actual large-scale cast component, where the cooling rates were different. In addition, three kinds of A356 were casted under different casting conditions to simulate different defect sizes in the actual component. Subsequently, rotating bending fatigue tests were conducted using those samples. The maximum sizes of casting defects were quantitatively evaluated through microstructural observation and extreme value statistics. The fatigue limits of all samples were predicted using hardness and defect sizes based on modified Murakami’s equation.

The modified equation for fatigue limit prediction in A356-T6 was proposed. Fatigue limits were successfully predicted using the proposed equation.

Fatigue limit prediction method using hardness and maximum defect size was limited to steels. This paper proposed the modified method for A356-T6 aluminum alloy with lower elastic modulus. The method was valid for A356-T6 with different defect sizes.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

In this chapter we ask a simple question: how can we tell if strategic management research is making progress? While other limitations are noted, we argue that it is the absence of metrics for gauging research progress that is most limiting. We propose that research should focus on measures of effect size and that “precision” and “generalizability” in our predictions of important phenomena represent the core metrics that should be used to judge whether progress is occurring. We then discuss how to employ these metrics and examine why existing research practices are likely to hinder efforts to develop cumulative knowledge.