Corrado Grassi, Achim Schröter, Yves Simon Gloy and Thomas Gries
The purpose of this paper is to deal with the energy efficiency of textile weaving machines. Increasing energy costs and environmental impact are a challenge for textile…
Abstract
Purpose
The purpose of this paper is to deal with the energy efficiency of textile weaving machines. Increasing energy costs and environmental impact are a challenge for textile manufacturers as well as for the developers of textile production machines. As example, air jet weaving is the most productive but also most energy consuming weaving method.
Design/methodology/approach
A method based on energy efficiency considered as the main requirements in the design phase has been developed at the Institut für Textiltechnik der RWTH Aachen University (ITA), Aachen, Germany, in order to improve energy efficiency of air-jet weaving machines. Technological developments are always concerned about low energy costs, low environmental impact, high productivity and constant product quality. The high degree of energy consumption of the method can be ascribed to the high need of compressed air required by the relay nozzles during the weft insertion process.
Findings
The relay nozzles of the air-jet weaving technology consume up to 80 percent of the air required by the weft insertion process. At ITA a new nozzle concept was developed. The developed geometry is a so called high-volume-low-pressure nozzle, based on convergent nozzle aerodynamic theory.
Originality/value
By employing such new concept of relay nozzles within the weft insertion process, energy savings are possible up to 30 percent.
Details
Keywords
Yi-Chun Chang, Kuan-Ting Lai, Seng-Cho T. Chou, Wei-Chuan Chiang and Yuan-Chen Lin
Telecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be…
Abstract
Purpose
Telecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.
Design/methodology/approach
The authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.
Findings
The authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.
Research limitations/implications
The study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.
Originality/value
This paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.