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Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research.

Today's global business environment, characterized by unprecedented competitive pressures and sophisticated customers that demand speedy solution creates a bigger set of potential suppliers to evaluate and to choose from. To deal with the complexities of the supplier selection process, an integration of Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP) and Preemptive Goal Programming (PGP) techniques is proposed. A QFD matrix is used to display the degree of relationship between each pair of requirement for suppliers and supplier evaluating criterion. This paper employs the AHP first to measure the relative importance weighting for each of the requirements in the QFD process. Secondly, it is used to assess the evaluating score for each of the candidate suppliers for each particular supplier-evaluating criterion. PGP is built to deal with some suppliers' constraints such that the total value of purchase (TVP) becomes maximum and the total cost of purchase (TCP) minimum.

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