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Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.

First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.

In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.

This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

Thirty years of rapid development and economic change have created organizations and work relations in China that would have been unthinkable at the start of transition. In December of 1978, the Chinese Communist Party agreed with Deng Xiaoping to allow agricultural privatization, a stark contrast to the communes of Mao Zedong's era. This change established the financial foundation that would lead to development in eastern, coastal cities and that would ultimately fuel an extraordinary transformation of China's economy and its global position. As a result, organizational structures have changed, and new organizational forms have emerged. There have also been dramatic changes in the way work organizations behave and in the nature and implications of work. This volume provides a glimpse into the state of organizations and work at the 30-year mark. The contributors are top scholars in the field, including many who have observed and studied China's transition for decades, who are drawing on some of the most up-to-date and innovative data sources available. The chapters are samples of the current work of these researchers that, taken together, provide a snapshot of the state of research on China's organizations and work behaviors as transition enters its fourth decade.

The aim of this paper is to demonstrate that synergy between firms and local institutions may lead to success even in an economic environment which cannot offer competitive advantages to high tech industrial clustering. However, the condition for such a complex result is convergence between cluster‐based regional development policy, the related industry national strategy and the central government preferential policy for less developed areas.

The experiences selected as case studies are the Xi'an National Civil Aerospace Base in Shaanxi Province (China) and CampaniAerospace in Campania region (Italy). As information was gathered both through policy documents (China and Italy) and direct interviews to cluster's stakeholders (Italy), the outcome is, according to the criteria suggested by Eisenhardt and Yin, a qualitative research. Comparing economic data provided by the respective countries' Statistical Offices, the authors assessed that Shaanxi Province and the Campania region share a “peripheral” position within their respective national context. Starting from this, the authors analyzed the formal documents which reported the experience of the two clusters.

The finding is that the outcome (the status of cluster which is assumed as “a value in itself”) of the convergence between firms' will to gain agglomeration advantages and the policymakers' plan to redress interregional economic disparities is definitely not an efficiency‐driven process. In the high tech sectors which are very sensitive to the increasing competition for technology on the international market, the pursuit of efficiency may, on the contrary, be dissipated.

As attested by the comparative literature, aims and performance of industrial clusters differ in accordance with the stage of economic development as well as the institutional and regulatory framework. The paper demonstrates, however, that in the take‐off stage, it is the position occupied by the host region within the national context which determines the behaviour of the actors concerned as well as the outcome of their commitment.

The purpose of this paper is to identify the determinants for currency internationalization and forecast the potential of RMB as an international reserve currency.

This paper performs linear or non-linear regressions of the shares of eight major international reserve currencies as the reserve assets in global central banks on the macro economic and financial variables of their corresponding countries to identify the determinants for their international positions, and conducts an “counter-factual simulation” for the potential of RMB as an international reserve currency.

This paper finds that the economic size and the “network externalities” are the most important determinants for the international status of a reserve currency; that exchange rate volatility has negative impacts; the conditions for the RMB internationalization are basically available. The simulation for the potential of RMB as an international reserve currency reveals that the international role of RMB could surpass that of the Japanese Yen and the British Pound, and get close to Euro in the coming 15 years. Based on the empirical evidence, this paper suggests a promoting strategy for RMB internationalization.

This paper has not taken the influence of economic systemic and political factors on the process of RMB internationalization into account.

RMB internationalization promotion should follow the strategy of “stably create RMB international demand in the initial period and dramatically release the RMB overseas supply in the latter period” in the coming 15 years.

The conclusions and policy implications are from the results of the empirical analysis on the 45-year historical experience on the eight main international currencies.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

This paper analyzes and discusses the fundamental role of new urbanization in the construction of a new development pattern based on the specific practice of China’s new urbanization construction, combining the classical Marxist ideas in urbanization and the theory of the industrial capital circulation, and proposes to remove blockages in the domestic and international circulations through developing new urbanization and construct a new development pattern with the domestic circulation as the mainstay and the domestic and international circulations promoting each other, thereby achieving high-quality national economic operation.

Based on the theory of industrial capital circulation, this paper elucidates the general process of domestic and international economic circulations, critically examines the bottlenecks and challenges in economic circulation, and emphasizes the mechanisms through which new urbanization contributes to the formation of a new development pattern.

The people-centered new urbanization can effectively release domestic demand, optimize investment supply, and stabilize market participants’ expectations, linking the national economic circulation from production to consumption and alleviating supply and demand mismatches, which has a fundamental role in building the new development pattern.

We should focus on creating a comprehensive domestic demand system based on counties while fostering a unified domestic market led by urban clusters and metropolitan areas to stabilize and enhance the expectations of both domestic and international market participants.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.