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 - 63 , Issue 08
10 Aug 2021
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Upcoming Publication

Volume - 63 , Issue 07
31 Jul 2021

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. Bulletin of National Institute of Health Sciences

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-26-03-2020-10601
Total View : 196

Title : The Lichen Type Identification as a Bioindicator of Air Quality of Sukolilo District In Surabaya, Indonesia

Abstract :

Bioindicators are organisms or biological responses that indicate the entry of certain substances in the environment. Lichen (Moss Crust) is an indicator plant that is sensitive to air pollution. One of methods to determine the condition of pollution in an area is to look at the macroscopic appearance of Lichen (moss crust) attached to trees or rocks in an area. The aim of this research is to investigate the level of air pollution in Sukolilo District, Surabaya, Indonesia using a bioindicator (lichen). A total of 7 villages (Klampis Ngasem, Menur Pumpungan, Nginden Jangkungan, Gebang Putih, Semolowaru, Medokan Semampir, Keputih) in Sukolilo District were selected as sampling points. Two methods are used to determine air quality in Sukolilo District, namely by biomonitoring the presence of Lichen as well as by measuring the size of Lichen found. Data analysis was performed by identifying the results of both methods with the The Hawksworth and Rose Index indicator table to determine air quality. The results showed that there were two types of Lichen identified in Sukolilo District, namely Lichen Crustose and Folilose with an average size of Lichen 4-6 cm. So it can be concluded that air quality based on the presence of Lichen is classified as Poor and air quality based on Lichen's size is classified as moderate. Therefore, it is concluded that the level of air pollution can be measured by using a Lichen bioindicator

Full article
Journal ID : TRKU-25-03-2020-10599
Total View : 199

Title : Recurrent Neural Networks to Identify Fault in Transmission Line

Abstract :

The transmission system is the connecting part of the power station and, distribution is capable of being forwarded to the load center. If there is a fault in the transmission line by interrupting the electricity supply to the load, then this will cause a loss for consumers. Therefore, another technique is needed to identify the fault in the electrical power distribution system accurately and quickly by reducing search time and speeding up the repair process. This study will present a method to identify fault by classifying and estimating the location of a fault in the 115 kV transmission system. This technique is performed by combining Discrete Wavelet Transformation (DWT) and Recurrent Neural Networks (RNNs) of Elman. DWT aimed at extracting information of transient signals for each phase current and zero sequence current during one cycle when the fault starts. Elman RNNs are classified to detect a fault in each phase and ground, while Elman RNNs are used to measure the location of the fault in the transmission line. Training and testing data be carried out for the simulation of short circuit fault under different fault resistance and varying starting angle. Short circuit fault applied in the transmission line to 115 kV bus LK to BK on 63km line lengths. The fault classification results obtained are the accuracy of 100%, and the estimated location of fault received the most significant average error value is 1.4%

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