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 06
18 Jul 2020
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Upcoming Publication

Volume - 62 , Issue 06
31 Jul 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-12-04-2020-10679
Total View : 241

Title : Design and Implementation of Electronic Human Resource Management System for Duhok Polytechnic University

Abstract :

Nowadays, Information and Communication Technology (ICT) is used to build professional electronic systems as a big step towards the E-government system. One of the most important sectors that belong to the E-government is E-university. The universities of the Kurdistan region suffer from the classical approach, so building the E-university system will push all other sectors towards the E-government system. Hence, improving human resource management is an important direction within E-university. An efficient proposed human resource management system for Duhok Polytechnic University (DPU) called DPU Electronic Human Resource Management System (DPU-EHRMS) is proposed in this paper. The services of this system cover each of DPU's presidency, four colleges and eight institutes that belong to DPU. The proposed system consists of eleven modules that provide four groups of services. The first group is related to applicant services: online job application. The second group is related to staff services: registration, appreciation and punishment, promotion and bonus, leaves, archive, payroll, and summary of service. The third group is related to institutions and presidency services: authentication, post, and statistics. And the fourth group is for university services: statistics. Three campuses are selected (Duhok, Zakho, and Shekhan) to implement and test the proposed DPU-EHRMS. These campuses include six institutions. The System Usability Scale (SUS) is used as an evaluation tool to get the results via special questionnaire forms that are checked by the academic and administrative staff of the same institutions. An acceptable evaluation score of the questionnaire is obtained which is about (77.80). According to the practical implementation of DPU-EHRMS, there is an opportunity of copying other Kurdistan universities which can be considered as a great step towards the E-government

Full article
Journal ID : TRKU-12-04-2020-10678
Total View : 199

Title : Analysis and Implementation of the K-Nearest Neighbor Algorithm to Recognize the Batak Toba Character

Abstract :

The Batak Toba character is one of the characters in the Batak Letter family (Batak character) which is used by the Batak Toba ethnic community to write the Batak Toba Language. Similar to the character that are related to it, the characters and spelling are not too different from other character such as Karo, Dairi, Mandailing or Simalungun. The use of the Batak Toba character is still very limited, also the introduction to the character. This study aims to recognize the Batak Toba character and to determine the accuracy of the k-nearest neighbor algorithm by implementing it to recognize the Batak Toba character. In the k-nearest neighbor algorithm there are two stages of implementation namely the training process and testing process. These two processes have different stages that need to be executed to obtain the right classification results and in accordance with the character of the Batak Toba character. By using KNN, it was found that the accuracy for each Batak Toba character that was successfully identified by using a collective method was around 72-90%

Full article

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