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SummaryIn this paper, we propose a recommendation model of application customer to reduce the time and cost of the salesperson by recommending the application customer to salesperson. The model is built based on customer information, premise information, and two new features which are extracted from salesperson's feedback. Estimation precision is evaluated by three algorithms: SVM, Decision Tree and Random Forest. We applied three algorithm against five data sets. As a result, the highest estimation precision was 49

Action recognition using skeleton data (3D coordinates of human joints) is an attractive topic due to its robustness to the actor's appearance, camera's viewpoint, illumination, and other environmental conditions. However, skeleton data must be measured by a depth sensor or extracted from video data using an estimation algorithm, and doing so risks extraction errors and noise. In this work, for robust skeleton-based action recognition, we propose a deep state-space model (DSSM). The DSSM is a deep generative model of the underlying dynamics of an observable sequence. We applied the proposed DSSM to skeleton data, and the results demonstrate that it improves the classification performance of a baseline method. Moreover, we confirm that feature extraction with the proposed DSSM renders subsequent classifications robust to noise and missing values. In such experimental settings, the proposed DSSM outperforms a state-of-the-art method.

Cutting fluids (CFs) are chemical liquids or aqueous emulsions of mineral (or synthetic) oil widely used in metal-machining processes. They contain toxic organic compounds and petroleum products, and spent CFs contain numerous small metal particles derived from the processing of metal workpieces. The iron fine particles (IFPs) in CFs can diminish the quality and precision of machine products. Machining industries purchase large amounts of CFs, which they must treat appropriately and from which they must remove the IFPs; therefore, cost-effective ways to treat spent CFs are needed. In this study, we evaluated the effectiveness of collecting and separating the IFPs and treating organic matter in spent CFs using microbubbles (MiBs). We found that numerous IFPs with sizes of ~1 μm were suspended in spent CFs and that they could be very effectively removed by bubbling with MiBs and skimming the surface of the CFs. The lifetime of the CFs could be doubled via this treatment. The cost for treating spent CFs using MiBs was 12% lower than the cost of traditional treatment. These results strongly suggest that bubbling with MiBs is a cost-effective and eco-friendly way to treat spent CFs.