Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
The research analyses how vulnerability and urban inequality influence the slums of San Francisco de Asis, Pichcana, Hualashuata, located in the La Esperanza sector, Chilca district, Huancayo province, Peru. The ecosystemic approach is proposed to analyze and unravel the transversal vulnerability of the neighborhood, incorporating the learning network and experiences COVID-19, its application is based on ideas of interventions in the neighborhood as, clean solutions; in the sensitive networks of water, health, technology; being gestated this way the conceptual model proposal, that gives us the opportunity to construct new paradigms for the city, in times of pandemic and post pandemic
This study proposes a new diagnosis method for identifying the vibration data of the multi-level fault of gear. The diagnosis method is based on the feature representation of the vibration signal and optimized machine learning model. Firstly, a deep Auto-Encoder (AE) bottleneck network is constructed with the two hidden layers to extract the fault features of gear vibration data, named AE-BtF. In which, the AE-BtF use the basic unsupervised learning algorithm to reveal the significant characteristics in the complex data with the nonlinear, non-station properties. The obtained features can provide good discriminability for fault diagnosis task. Secondly, an optimal classifier model is formed to perform supervised fine-tuning and classification. This model is based on the least square support vector machine (LSSVM) classifier and chemical reaction optimization algorithm (CRO), named CRO-LSSVM. The meta-heuristics CRO algorithm is used to exploit the appropriate parameters for the LSSVM. Based on the vibration data of gear fault status, the proposed AE-BtF-CRO-LSSVM technique shows a good ability for identifying the gear fault accuracy. The diagnosis results have demonstrated that the AE-BtF based feature extraction in conjunction with the CRO-LSSVM classifier model can achieve higher accuracies than the other popular classifier models to 60%
