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

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-15-03-2020-10524
Total View : 401

Title : Weighted Decision Tree Model for Breast Cancer Detection

Abstract :

Breast cancer detection is one of imbalanced classification problem in machine learning. The breast cancer dataset consists of significantly more class of non-cancerous observations than the cancerous observations. The classification of imbalanced dataset is a problem to machine learning algorithm due to the fact that the standard machine learning algorithms assume the class in the dataset are balanced or equal. The imbalance of the classes in breast cancer dataset makes the detection of breast cancer more difficult with the existing standard machine learning algorithms. This is because the algorithms are biased prediction due to the class imbalance in the dataset. In this research, a solution to imbalanced classification problem is proposed by proposing a weighted decision tree model for breast cancer detection. Finally, the performance of the proposed model is tested and result reveals an accuracy of 94.03% is achieved. Moreover, experimental test on the breast cancer dataset shows that better performance is achieved by the proposed model as compared to the standard decision tree model

Full article
Journal ID : TRKU-14-03-2020-10523
Total View : 170

Title : Effect of Multi Pulses on Glass Drilling Using CO2 Laser

Abstract :

Laser drilling is one of the earliest applications of lasers in materials processing. Less than 0.25mm in diameter are difficult to drilled mechanically. Laser drilling offers good choices for small hole drilling, especially for hard and brittle materials such as ceramics, but cracks appearance is one of the most difficulties that appears in this drilling. Therefore, this paper aims to study the effect of number of pulses on the drilling of soda lime glass (SLG) using under water laser drilling technique. A 1.15 mm thickness SLG sheets were immersed 1mm below the de-ionized water surface, then irradiated with CW CO2 laser. The laser parameters used were (19, 20.5 and 22) W power, (5, 7.5 and 10) sec exposure time and (2, 3 and 4) pulses. The drilled points were investigated under optical transmission microscope. Then the upper diameter, lower diameter, crack length and taper angle for these drilled holes were measured by analyzing the OM images using ImageJ software. Clearly appeared that hole diameter and the crack lengths could be controlled by the laser power and no. of pulses. When power or pulses were increased, the hole diameter increased. While the length of cracks is decreased with increasing no. of pulses and increased when the power increased. The good results found at laser power 24 W, five sec. and one pulse for hole diameter, while the minimum crack length was found at four pulses, five sec. and 19 W power

Full article