In the Software Development Arena, students take the initiative in solving social problems.

AY2022-2023 Project List
  No.    Project Instructor
22-01 Smart Museum YOSHIOKA Rentaro
22-02 High-speed autonomous driving on low-friction road surfaces OKUYAMA Yuichi
22-03 Smart Learning WATANOBE Yutaka
22-04 Smart Information Visualization TAKAHASHI Shigeo
22-05 TBD
22-06 TBD

01. Smart Museum

  • YOSHIOKA Rentaro
  • Role : Improve visitor experience at the Fukushima Museum
  • Target : Visitor support and exhibition design
  • Value : Improve visitor learning and knowledge acquisition
  • Currently, it is difficult to obtain objective data on visitor's behavior in viewing the exhibits that is necessary for evaluation/improvement
  • Also, it is currently difficult to understand the visitor's learning experience so as to provide necessary support
  • To improve the situation, it is desired to be able to objectively grasp visitor's behavior within the exhibit hall
  • Furthermore, it is required to collect visitor's learning experience without hindering their experiences
  • Therefore, a system that objectively measures visitor behavior and supports curators for analysis and interpretation will be developed
  • Visitor's satisfaction in terms of learning improves.
  • Realize objective thorough measurement of visitor behavior
  • Develop devices/applications for visitors that encourages/induces appreciation of exhibits
  • Develop advanced computational methods to analyze and visualize visitor behavior
  • Develop a system for curators that encourages analyzing and interpreting the measured/computed visitor behavior
  • Develop a system to assist curators in designing exhibitions based on the analysis and interpretation
  • Knowledge experience, activity sensing devices, image recognition, data visualization, data analysis, human-computer-interaction, human-computer-collaboration
Research Database

02. High-speed autonomous driving on low-friction road surfaces

  • OKUYAMA Yuichi
  • Role : Improve availability of autonomous driving on low-friction surfaces
  • Target : Modeling methods for automotive driving environments and automated learning mechanisms for driving
  • Value : Acquisition of automated driving algorithms on low-friction surfaces
  • Currently, automated driving on low-friction surfaces is difficult
  • Driving on low-friction surfaces is different from normal driving
  • Development on real vehicles is difficult due to cost and environmental reproducibility
  • Using radio-controlled drifting cars that simulate driving on low-friction surfaces
  • Obtaining driving data and development of automated driving in small-size cars or simulator are desired
  • Development of a simulator for racetracks that mimic low-friction surfaces and radio-controlled drifting cars
  • Realization of automated driving of drifting radio-controlled cars on real-tracks
  • Development of a simple 3D modeling method for radio-controlled circuits
  • Development of a simulator that simulates the physical behavior of a drift RC car
  • Development of a system to automatically learn to drive on the simulator
  • Development of a system to absorb differences between simulator and real world observations and driving
  • 3D modeling, artificial intelligence, reinforcement learning, sim2real, autonomous driving, drifting/sliding
Research Database

03. Smart Learning

  • WATANOBE Yutaka
  • Role : Reduce educational disparities in accordance with SDGs 4 (Quality Education for All)
  • Target : Learning support and educational support in the educational field
  • Value : Visualization to motivate learning, explanation methods to facilitate understanding, user interfaces and machine learning models for autonomous learning environments
  • There are educational disparities in education, especially ICT education, due to regional, school, and economic conditions
  • Currently, business and educational methods related to enriching content, training systems, and competitive/gaming elements are the mainstream
  • However, for skills exercises such as programming, for example, easy-to-understand explanations and feedback are necessary, but a lack of instructors is becoming a problem
  • Therefore, we will develop a smart learning environment and its subsystems to support learners' self-directed learning
  • Improving learner motivation or self-directed learning efficiency
  • Development of visualization methods to view and manage the state of the learner
  • Development of representation techniques to explain and execute algorithms and procedures
  • Development of user interfaces that can adapt to the learner's situation
  • Development of machine learning models to support autonomous learning
  • Development of data mining and data analysis methods for learning data
  • Educational technologies, visualization, educational data mining, adaptive learning, autonomous learning, programming, user interface/experience, artificial intelligence

Research Database

04. Smart Information Visualization

  • TAKAHASHI Shigeo
  • Role : Enhanced readability in abstract data visualization
  • Target : Visual metaphor design for interactive visual data analysis
  • Value :Egocentric visual data analysis with Human-in-the-Loop
  • The amount of available data continues to grow regardless of its type
  • The demand for extracting important features from such large-scale complicated data also increases
  • This leads to the need of developing egocentric visualization of such extracted data for individual analysts
  • We tackle this challenge by implementing an interactive system for information visualization
  • We also design new visual metaphors to improve the readability of the data
  • Help analysts understand the data according to individual requirements interactively through visualization
  • Prepare the data structure of networks having vertices with multivariate vectors and edges connecting them
  • Find 2D optimal layouts of the networks by referring to inherent relationships and similarities among vertices
  • Transform the networks into visual metaphors by successively deleting minor edges according to their importance
  • Transform the network into visual metaphors by extracting the major connectivity among vertices
  • Visualize the networks from their global structures to local features via visual metaphors by continuously controlling the connectivity among vertices
  • Network visualization, network drawing, visual metaphor design, interactive visualization
Research Database