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Skeleton plays an important role in representing the essential feature of garment in image. General skeleton extraction methods often yield many short skeletal branches. Though short branches reflect the geometric details of the garment, they are obstacles in extracting the essential features. The purpose of this paper is to provide an approach to hierarchically remove them to reveal the level of details (LOD) of the skeleton, thus both the essential skeleton and the geometric skeletal branches can be definitely extracted and separated.

First, the initial garment image skeleton is extracted and smoothed. Then, the hierarchically removing mechanism is established on scoring the importance of each skeletal branch by an altered PageRank method and computing the symmetry among skeletal branches.

Experimental examples show that this method can extract and separate garment essential skeleton as well as geometric skeletal branches hierarchically. Garments in same class have a similar essential skeleton with detailed differences, so this approach can be potentially applied in garment recognition and style specification.

Traditionally, there is almost no work attempts to build LOD in skeleton of planar shapes. This paper provide an automatic device for building LOD skeleton for garment image. In another word, hierarchic skeletons with details in different prominence level are gradually established. And pairs of symmetric skeletal parts are found by taking advantage of symmetry characteristic of garment. This method is efficient in garment image skeleton extraction.

Clothing retrieval is very useful to help the clients to efficiently search out the apparel they want. Currently, the mainstream clothing retrieval methods are attribute semantics based, which however are inconvenient for common clients. The purpose of this paper is to provide an easy‐to‐operate apparels retrieval mode with the authors' novel approach of clothing image similarity measurement.

The authors measure the similarity between two clothing images by computing the weighted similarities between their bundled features. Each bundled feature consists of the point features (SIFT) which are further quantified into local visual words in a maximally stable extremal region (MSER). The authors weight the importance of bundled features by the precision of SIFT quantification and local word frequency that reflects the frequency of the common visual words appeared in two bundled features. The bundled features similarity is computed from two aspects: local word frequency; and SIFTs distance matrix that records the distances between every two SIFTs in a bundled feature.

Local word frequencies improves the recognition between two bundled features with the same common visual words but different local word frequency. SIFTs distance matrix has the merits of scale invariance and rotation invariance. Experimental results show that this approach works well in the situations with large clothing deformation, background exchange and part hidden, etc. And the similarity measurement of Weight+Bundled+LWF+SDM is the best.

This paper presents an apparel retrieval mode based on local visual features, and presents a new algorithm for bundled feature matching and apparel similarity measurement.

Detailed body sizes are prerequisite for made to measure or customized manufacture. Nowadays, detailed body sizes can be precisely obtained by using 3D scanners, however, the high prices of the scanners block the population for such approaches. The purpose of this paper is to provide an economical and accurate data-driven method which can predict detailed body sizes with a small number of feature sizes.

First, the representative body sizes are extracted from dozens of detail body sizes by using factor analysis and garment knowledge. Among the representative body sizes, those that are easy to be measured are selected as the feature parameters (FPs). Second, by mining the database of the body sizes, mapping from the FPs to the detailed body sizes is expressed by a combination of radial basis function and multiply linear regression. Thus, for an individual human body, his/her detailed body sizes can be predicted by a small number of FPs.

First, FPs which are easily measured and represent the main shape information of a human body are extracted. Second, detailed body sizes can be functionally predicted by the FPs.

Traditionally, measuring dozens of body sizes for each human body is tiresome and the accuracy of the sizes depends on the experience of the gaugers. In this paper, a small number of body sizes are selected as the FPs which are easy to be measured and can functionally express the other body sizes. Thus, by only measuring the FPs, the detailed body sizes can be intelligently and automatically predicted. This approach is meaningful to improve the intelligence and accuracy of the measurement, so that even an inexperienced gauger is competent to obtain accurate detailed body sizes.

The 3D dynamic clothing simulation is widely used in computer-added garment design. Collision detection and response are the essential component and also the efficiency bottleneck in the simulation. The purpose of this paper is to propose a high efficient collision detection algorithm for 3D clothing-human dynamic simulation to achieve both real-time and virtually real simulation effects.

The authors approach utilizes the offline data learning results to simplify the online collision detection complexity. The approach includes two stages. In the off-line stage, model triangles with most similar deformations are first, partitioned into several near-rigid-clusters. Clusters from the clothing model and the human model are matched as pairs according to the fact that they hold the potential to intersect. For each cluster, a hierarchical bounding box tree is then constructed. In the on-line stage, collision detection is checked and treated parallelly inside each cluster pairs. A multiple task allocation strategy is proposed in parallel computation to ensure efficiency.

Reasonably partitioning the 3D clothing and human model surfaces into several clusters and implementing collision detection on these cluster pairs can efficiently reduce the model primitive amounts that need be detected, consequently both improving the detection efficiency and remaining the simulation virtual effect.

The current methods only utilize the dynamic clothing-human status; the authors algorithm furthermore combines the intrinsic correspondence relationship between clothing and human clusters to efficiently shrink the detection query scope to accelerate the detection speed. Moreover, partitioning the model into several independent clusters as detection units is much more profitable for parallel computation than current methods those treat the model entirety as the unit.

To enhance the pleasure experience of clothes shopping online, finding satisfactory clothing and similar clothing recommendations to customers should be available and accurate. The purpose of this paper is to present a method for automatically computing the similarity between two apparels and giving an effective recommendation.

Based on a tabular layout of article characteristics the authors built a clothing information model to describe clothing. The clothing attributes are classified according to excavating features of the model. After the proposal of the computation algorithm for various attributes, an efficient similarity computation method is developed to obtain similar clothes with the given cloth. To prevent error and information omission during the computation, the analytic hierarchy process method and entropy method are adopted by the integrated weights as a control.

Clothing is a non‐rigid product which has a lot of crossover and complicated attributes and features. This paper found a tabular layout of article characteristics can explain the clothing clearly. Through experiments the authors found the weight of attributes to have a great influence on similar results during the similarity computation.

This paper presents a new way to describe clothing information, and present the algorithm for attributes computation.

This study aims at developing a better understanding of the different mechanisms that affect technology collaboration portfolio management. How do firms manage their technology collaboration portfolio? Despite some thoughtful scholars have advanced the understanding of the phenomenon of technology collaboration portfolio, there is not much research that has been done in terms of understanding the endeavors of firms when they collectively use a range of actors for the best interests of the firms. Additionally, little attention has been paid to the trade-offs and managing mechanisms for the collaborations between different partners from a portfolio-level perspective, especially in emerging markets.

A multiple-case study of two Chinese high-tech firms, an inductive approach.

The authors identified three primary mechanisms that underlie successful knowledge creation and application in technology collaboration portfolio context: informally mobilizing boundary-spanning brokers for domestic academic collaborations, formally institutionalizing learning activities for industry collaborations and integrating formal and informal mechanisms for technology collaborations between focused firms and foreign organizations.

The authors extend the line of organizational ambidexterity literature with a focus on strategic alliance, proposing that firms need to balance academic and industry collaborations from a portfolio level. Moreover, the authors intend to extend the literature of alliance portfolio by suggesting three different learning mechanisms of managing different technology collaborations for the purpose of balancing successful knowledge creation and application.

The purpose of this study is to solve the issues of time-consuming and complicated computation of traditional measures, as well as the underutilization of two-dimensional (2D) phase-trajectory projection matrix, so a new set of features were proposed based on the projection of attractors trajectory to characterize the friction-induced attractors and to reveal the tribological behavior during the running-in process.

The frictional running-in experiments were conducted by sliding a ball against a static disk, and the friction coefficient was collected to reconstruct the friction-induced attractors. The projection of the attractors in 2D subspace was then mapped and the distribution of phase points was adapted to conduct the feature extraction.

The evolution of the proposed moment measures could be described as “initial rapid decrease/increase- midterm gradual decrease/increase- finally stable,” which could effectively reveal the convergence degree of the friction-induced attractors. Moreover, the measures could also describe the relative position of the attractors in phase–space domain, which reveal the amplitude evolution of signals to some extent.

The proposed measures could reveal the evolution of tribological behaviors during the running-in process and meet the more precise real-time running-in status identification.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

This study employs situated learning theory to elucidate the mechanisms of interorganizational collaboration by analyzing the relationships among absorptive capacity, institutional compensation, task cognitive integration and interorganizational collaboration in BIM-enabled construction projects.

An online questionnaire survey was conducted with managers and professionals involved in building information modeling (BIM-) enabled construction projects, and 220 valid responses were received. Data were analyzed by means of the linear regression models and bootstrap method.

The results show that (1) absorptive capacity, institutional compensation and task cognitive integration have a positive impact on interorganizational collaboration; (2) institutional compensation partially mediates the effect of absorptive capacity on interorganizational collaboration; (3) task cognitive integration fully mediates the effect of absorptive capacity on interorganizational collaboration; (4) institutional compensation and task cognitive integration serially and fully mediate the relationship between absorptive capacity and interorganizational collaboration and (5) the serial mediating model has a greater indirect effect than the other two models considered in this study.

This study contributes to the body of knowledge by demonstrating the way to break through the three types of organizational boundaries (i.e. syntactic, semantic and pragmatic organizational boundaries) and provide an internal collaborative mechanism from the perspective of situated learning theory. This study presents the critical effects of absorptive capacity, institutional compensation and task cognitive integration on interorganizational collaboration, selects the enhanced mediating model for explaining the effects of absorptive capacity on interorganizational collaboration and enables managers to update the traditional collaborative model in BIM-enabled construction projects.