Modern passenger cars have a comprehensive embedded distributed system with a huge number of bus devices interlinked in several communication networks. The number of (distributed) features and hence the risk of undesi...
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ISBN:
(纸本)9781538646496
Modern passenger cars have a comprehensive embedded distributed system with a huge number of bus devices interlinked in several communication networks. The number of (distributed) features and hence the risk of undesired feature-interaction within this distributed system rises significantly. Such distributed automotive features pose a huge challenge in terms of efficient testing. Bringing together Combinatorial Testing with Automated feature-Interaction Testing reduces the testing effort for such features significantly.
Bicycles become popular again in the transportation system because they could serve as useful tools for convenient, economical, and environmentally friendly short-trips. In the traditional public bicycle system, custo...
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Bicycles become popular again in the transportation system because they could serve as useful tools for convenient, economical, and environmentally friendly short-trips. In the traditional public bicycle system, customers need to reach a fixed public bicycle station before they can rent a public bicycle. However, the current station-free bicycle-sharing systems can allow customers to borrow or return shared bicycles, which would be distributed almost anywhere. Such new systems bring new optimisation problems for bicycle management. The authors study a placement optimisation problem that highlights the distribution characteristics of shared bicycles, aiming to minimise the total walking distance of customers. The proposed model is a 0-1 mix-integer non-linear programme. To solve the model, they propose a bi-level solving framework. The upper-level model optimises the locations of supply stations. The lower-level model optimises the number of bikes assigned to each demand site. A test based on the campus of Tsinghua University is employed to validate the proposed model. The optimal location suggested by their model is significantly different from the location suggested by models that ignore the distributed feature. Their model performs better in terms of reducing the total walking distance.
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification ...
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ISBN:
(纸本)9781538635247
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based features deeply represented by Word2vec model. First, TF-IDF is utilized to calculate the weight of characteristic words in text so that the distributed text-based feature representation can be effectively enhanced. Then on the base of these, support vector machine (SVM) algorithm is applied to establish the classification model. The experimental results demonstrate that the proposed approach can not only better represent the text vector, but also significantly improve the recognition accuracy of Chinese spam.
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