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:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
In this work, modeling and determination of the stationary eccentricity and attitude angle for a short journal bearing with single external pressurization at an arbitrary point injection port are treated. As for hybrid journal bearing, both hydrodynamic and injection forces are simultaneously considered in the Reynolds Equation. Modeling of this point injection port is performed using a Dirac spatial delta function, which allows obtain pressure fields in closed analytical form. The pressure field given by analytical expression allows to determine, also in analytical form, the pressure force components, equilibrium eccentricity and attitude angle. A special case, when the applied load is collinear with the pressurized injection direction is analyzed, which corresponds to an injection port located at the upper part of the journal bearing. Tables relating eccentricity, attitude angle, external pressurization force and Sommerfeld number for the attained stationary position are given.
Today’s popularity of the Internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the Internet medium. These are achieved via various means one of which is the Distributed denial of service attacks – which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the Deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic (9pt).