Mechanical Engineering

This course provides research training for students through the experience of belonging to a specific laboratory at the University of Tokyo. Students carry out an original research project under the guidance of assigned faculty members. Through a full-time commitment, students will be able to improve their research skills by applying the basic principles and knowledge from the literature related to the research questions, and by developing the skills to collect, interpret, and critique data in order to resolve a research question or evaluate a design for a research project. At the conclusion of the program, students submit their final work (paper, presentation, report etc.) as instructed by their lab supervisors

In this course students acquire a broad knowledge base and develop analytical and critical thinking skills. Students actively participate in seminars, read assigned texts and research papers, and analyze research data. Students also discuss results obtained in their own experiments with peers and senior laboratory members.

This course emphasizes hands-on laboratory experience and teaches students research background, relevant theories, and basic laboratory techniques relevant to their field of study. Students formulate a research plan, implement it by conducting experiment-based research, and convey the results in scholarly presentations. Students submit a written research report at the end of the course.

This course provides a physical understanding of phenomena and concepts in advanced water flows and introduces calculation methods to analyze a number of important hydraulic problems. The course deals mainly with free-surface flows with an emphasis on open-channel hydraulics.

The Individual Research Training Senior (IRT Senior) Course is an advanced course of the Individual Research Training B (IRT B) course in the Tohoku University Junior Year Program in English (JYPE) in the spring semester. Though short-term international exchange students are not degree candidates at Tohoku University, a similar experience is offered by special arrangement. Students are required to submit: an abstract concerning the results of their IRT Senior project, a paper (A4, 20-30 pages) on their research at the end of the exchange term, and an oral presentation on the results of their IRT Senior project near the end of the　term.

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by permission of the instructor. This course offers a study of electric drives. The course discusses topics including fundamentals of electromechanical conversion systems and fundamentals of electrical machines; DC machines; brushless machines with trapezoidal back emf; brushless machines with sinusoidal back emf; and principle of static conversion. The course discusses: the fundamentals of static and electromechanical conversion systems; the configuration of basic power electronic conversion systems, of main electrical machines, either direct current (DC) or alternate current (AC), and of electric drives used in automotive sector; the topology, control principles, input, and output characteristics of main DC and AC electric drives; modeling power electronic converters, control system, electrical machines, and full drive systems with reference to application for torque and speed control; and how to represent an electric drive in terms of energetic conversion system, for the integration in a multiphasic model of a vehicle.

The core spirit of machine learning is to learn the relevance of the data hidden in the data from the existing data through the mathematical model of the fusion hypothesis, so as to achieve the purposes of quantitative analysis, inference exploration, prediction, decision-making, etc. Machine learning can be roughly divided into two categories: machine learning hand-crafted features and deep learning. The main difference between the two is that the former is artificially designed and selected to describe the characteristics of the data, while the latter relies on deep learning theory to extract features.

This course mainly focuses on the introduction and exploration of the first type of machine learning (machine learning with hand-crafted features). The course uses actual medical imaging data to introduce typical methods of hand-crafting various features. And through actual clinical problems, implement hand-crafted features and understand their advantages and disadvantages. At the same time, in the part of machine learning algorithm, it will cover a variety of supervised, unsupervised and hybrid learning methods, such as: Linear Discrimination, decision Tree, Neural Network, Support Vector Machine, Bayesian Learning, Clustering, Reinforcement Learning and other methods.

This course teaches students the physics of heat transfer. Topics include steady conduction, transient conduction, convection, radiation, and heat exchangers. For all of these topics, practical implementation through solving small design-like problem is studied.

Design is often regarded as the central creative activity of engineering. This course enhances the skills of analysis and synthesis required to develop solutions to open-ended problems. This course teaches techniques for the effective evaluation and communication of design ideas. To support this the students also acquire a knowledge of materials and manufacturing processes selection along with the most common component/system failure modes.