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:
This paper attempts to focus on inverse steady state thermoelastic problem of thin rectangular plate by means of internal moving heat source. For a given problem known second kind boundary condition and initial condition is applied. This is analyzed and solved by applying Fourier cosine transform and Marchi-Fasulo transform to obtain temperature distribution and its results are is an infinite series. The various different transformation and boundary conditions have been used for solving this kind of determination. Temperature distribution, thermal deflection and different kind of stresses have been focused and elaborated by considering the condition of temperature. This work puts lights on evaluation of unknown function. The integral transform technique yields the solution of inverse problem.
High-dimensional data is interpreted with a considerable number of features, and new problems are presented in groups. The so-called "high dimension" is initially created to explain the common increase in time complexity of many computational problems, and therefore the performance of general aggregation algorithms is unsuccessful. Accordingly, many works focused on introducing new technologies and aggregation algorithms to process data with higher dimensions. Standard algorithms for all aggregate algorithms are the fact that they need a different essential evaluation of the similarity between data objects. However, current aggregation algorithms still have some open research problems. In this review work, they provide a summary of the results of the high-dimensional data space and its effects on different aggregation algorithms. It also provides a detailed overview of several grouping algorithms with several types: subspace methods, model-based grouping, density-based grouping methods, partition-based grouping methods, etc., including a more detailed description of the recent work of its advantages and disadvantages in Solve the problem of higher-dimensional data. The scope of future work is also discussed at the end of the work to expand existing compilation methods and algorithms
