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.

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Submission Deadline

Volume - 66 , Issue 01
20 Jan 2024

Upcoming Publication

Volume - 66 , Issue 01
31 Jan 2024

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. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi

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-11-07-2020-10878
Total View : 310

Title : Comparative Outlier Detection Design Based on K-Means Clustering for Data Grouping Absorption High School National Exam

Abstract :

Outliers appear as extreme values but often contain information that is very important so it needs to be examined first whether the data is still used or issued. Outlier detection is a hot topic for research. Improved new technologies and a variety of applications cause an increase in the need for outlier detection. The outlier method was successfully applied in various fields, such as education, economics, business, health, space, geology, and credit cards. This study aims to discuss the design of outlier detection methods using the KMeans Clustering method. This study uses four different types of identification tests, there are Extreme Standard Deviation (ESD), Shapiro-Wilk W Test, Dixon-Type Test, and Boxplot-Rule. The results showed that the design of outlier detection methods can be used to compare and find which is more effective in solving problems in the grouping of national exam absorption data in Mathematics. Grouping is done by using 40 indicators of competency achievement on 5 materials tested: Algebra, Geometry and Trigonometry, calculus, and Statistics. Further research can be carried out at the implementation and evaluation stages of the design of this outlier detection method

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Journal ID : TRKU-10-07-2020-10877
Total View : 326

Title : Probabilistic Rainfall Threshold for Flood Early Warning in the Upper Watersheds, Java Island, Indonesia

Abstract :

The use of rainfall data for flood early warning prediction have been highlighted by several researchers, in the last couple of decades. This study investigates the involvement of antecedent daily rainfall, for the determination of rainfall thresholds, to be used for flood early warning purposes at the upper watershed, Java island, Indonesia. An inventory of 70 flood occurrences for the period of 1992–2017 was compiled, and rainfall data were retrieved from 37 stations. First, calculate the critical discharge to determine the flood status in each watershed based on the results of statistical analysis of the frequency of the data series of discharge. Second, a procedure for the calculation of rainfall thresholds for flood occurrence was followed consisting of four steps: i) determining the rainfall associated with each inventory of flood occurrence and nonoccurrence; ii) the antecedent daily rainfall was calculated for 1 to 7 days for the selected dates and watersheds; iii) the optimum number of antecedent rainfall days was evaluated; and (iv) empirical rainfall thresholds for flood occurrence were determined. The results showed flood occurrences are best predicted using a combination of daily rainfall and 7 days of antecedent rainfall for all alert zones (A, B, C and D) including regional model (RRTM) with a negative relationship between antecedent rainfall and daily rainfall. Rainfall threshold models have an overall accuracy of more than 95%. It has provided evidence that the flood event in the study area is preceded by soil conditions that is saturated due to rain a few days before the flood

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