Technology Reports of Kansai University

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.

Submission Deadline

Volume - 62 , Issue 09
09 Oct 2020
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Upcoming Publication

Volume - 62 , Issue 08
30 Sep 2020

Aim and Scope

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:

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks.

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Food science, Food engineering, Food microbiology, Food packaging, Food preservation, Food technology, Aseptic processing, Food fortification, Food rheology, Dietary supplement, Food safety, Food chemistry.

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-17-07-2020-10898
Total View : 4

Title : Cost Optimization Study of Two-stage Bevel Helical Gearboxes

Abstract :

The mass, dimension, and size of a gearbox have strong effect on the gearbox cost. Consequently, in order to minimize the cost of a gearbox, the optimization of the partial gear ratios should be found. This study targets to find the optimum partial gear ration of two-stage bevel helical gearboxes base on the objective function of minimizing gearbox cost. Nine main process parameters are adopted to probe their influences on the output response. A plan of simulation experiment is conducted to serve for finding regression model. It is revealed that the total gearbox ratio parameter, ug, has strongest impact on the response u1 when compared to those of others. Besides, the predicted values given by regression model are significantly consisted to experiments

Full article
Journal ID : TRKU-16-07-2020-10897
Total View : 279

Title : Detection System of Solar Flare Occurrence in PROBA2 SWAP Images Using Seeded Region Growing and Machine Learning

Abstract :

Along with the development of technology, space weather activity becomes a very important thing in science. It’s caused by whether activity that occurs in space can affect the activities of life on the earth. Therefore, it’s important to be able to detect whether events in space, including solar flares. We believe that there has not been a single solar flare prediction study that did a prediction using PROBA-2 SWAP data, because flares are difficult to catch at that frequency. Nearly all previous researches have been focused on SOHO / MDI and SDO / HMI satellite. If the two satellites can’t capture the image for some reason, then PROBA2 SWAP satellite imagery can be an alternative. This research is aimed to implement image processing and machine learning methods on SWAP PROBA2 satellite imagery to predict event numbers of solar flare. The machine learning algorithm used is random forest, while the segmentation algorithm used is seeded region growing. This solar flare prediction research using SWAP PROBA2 satellite imagery produced the best f-measure value of 0.897

Full article

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