- 概要
This research utilizes the characteristics of terahertz waves, used in next-generation communications and autonomous driving, to identify the materials of plastic waste. It improves existing recycling technologies and can be applied to evaluate the quality of recycled plastics, ensuring stable production of high-quality recycled plastics. It enables easy development of devices to solve various issues in containers and packaging and automobile recycling, contributing to the realization of decarbonization and a circular economy.
- 従来技術との比較
Conventional plastic waste identification and sorting technologies use specific gravity sorting or near-infrared devices. Particularly, near-infrared sorting technology has accumulated an enormous amount of data and serves as the primary sorting technology in plastic recycling plants. However, near-infrared devices struggle with identifying black plastics, additives, and degradation. This technology uses terahertz waves to measure and evaluate transmission and absorption characteristics, allowing for identification of black plastics, additives, and degradation.
- 特徴・独自性
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- In recent years, there has been increasing global attention on plastic waste issues, such as marine pollution from drifting garbage and microplastics, the overseas export of plastic waste resources, and the increase in disposable containers like plastic bags and straws, especially due to the impact of COVID-19. There is growing demand for advanced identification and recycling of plastic waste materials, especially in the context of achieving the Sustainable Development Goals (SDGs) and realizing a circular economy.
- The research group from Tohoku University, Shibaura Institute of Technology, and Shizuoka University has conducted research on the commercialization of advanced sorting devices for plastic packaging waste. We have successfully identified mixed plastics containing black plastics, additives, and flame retardants, which were difficult to identify with existing devices, by utilizing the characteristics of terahertz waves. We have also confirmed the ability of terahertz waves in assessing degradation caused by UV or long-term use. Furthermore, the method has been shown to be effective for distinguishing bioplastics, which is expected to see increasing demand in the future, in addition to plastic waste from container packaging, automobiles, and home appliances.
- These identification technologies can be applied to properly sort plastic waste generated by the “The Plastic Resource Circulation Act,” enacted in 2022, contributing greatly to securing high-quality recycled resources through plastic waste resource recycling.
- Our research group conducts interdisciplinary research with experts in various fields: social engineering, resource circulation (Professor Jeongsoo YU), optical engineering (Professor Tadao TANABE of Shibaura Institute of Technology and Professor Tetsuo SASAKI of Shizuoka University), information science and big data analysis (Associate Professor Kazuaki OKUBO), data collection and analysis, international cooperation (Specially Appointed Lecturer Gaku MANAGO), social experiments, and behavioral economics (Assistant Professor Xiaoyue LIU). We address the needs from social, economic, and environmental issues both domestically and internationally, working from diverse perspectives to solve challenges and contribute to the creation of a sustainable society. Collaboration and networking with private companies, government agencies, research institutions, and civic organizations are also expected.
- 実用化イメージ
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This technology can be applied to the development of plastic waste identification and sorting devices from processes such as containers and packaging recycling, automobile recycling, and home appliance recycling, as well as the production and quality evaluation of recycled plastics.
Researchers
Graduate School of International Cultural Studies
Jeongsoo Yu
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- 特徴・独自性
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- Aiming at developing practical quantum optimization technology known as quantum annealing, we are working on exploring basic technologies that can overcome the limitations and applications in collaboration with multiple companies. The advantage of the method is that it can be used simply by formulating a cost function that draws the goal to be optimized once, but we are not limited to the original method. We extend it by considering a much easier problem, sequential optimization by learning, black box optimization, etc.. In particular, it is being applied to automated driving, logistics in factories, and evacuation guidance during disasters.
- 実用化イメージ
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Applications to route search problems such as automatic driving of various vehicles, evacuation route guidance at the time of disaster, process scheduling and a large number of combination problems. We can provide a solution to combinatorial optimization in each industry. (Transportation / distribution, manufacturing, materials, drug discovery, etc.)
Researchers
Graduate School of Information Sciences
Masayuki Ohzeki
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- 概要
Our group studies a range of unsteady flow phenomena leveraging data science, nonlinear machine learning, complex network theory, information theory, and computational fluid dynamics. Our ultimate goal is to build a data-oriented foundation for real-time analysis, modeling, and control of unsteady flows ubiquitously appearing in various situations around small air vehicles, airplanes, motor vehicles, and fluid-based industrial machines.
- 従来技術との比較
Equipped with nonlinear machine learning-based sparse sensor reconstruction and data compression supported through traditional numerical and experimental analysis, our approach enables high-resolution reconstruction, real-time prediction, and control of flow fields with limited availability of data. These techniques are aimed at analyzing and controlling large-scale, complex nonlinear flow phenomena that have been challenging to tackle with conventional linear methods.
- 特徴・独自性
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- ・Real-time spatiotemporal flow field reconstruction from sparse sensors is enabled by turbulence super-resolution analysis with machine learning.
- ・Understanding and modeling of unsteady fluid flows at low cost is made possible through low-dimensional manifold identification and compression.
- ・Development of explainable machine-learning approaches for analyzing causal vortex interactions based on complex network theory and information theory.
- ・Multi-modal data analysis through the fusion of numerical, experimental, and theoretical data.
- 実用化イメージ
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Our group aims to develop technologies that accurately sense, predict, model, and control fluid flows —such as air and water— around objects including airplanes, automobiles, and wind turbines, even with sparse sensor information.
These technologies can contribute to society in various ways, including: ・Improving fuel efficiency and safety of aircraft ・Enhancing the aerodynamic performance of vehicles for energy savings ・Supporting disaster prevention through wind flow prediction during emergencies
We actively seek to co-create innovations through joint research with industrial companies interested in the following areas:
・Predicting and controlling fluid flows using AI and machine learning ・Understanding flow structures through information theory and network science ・Building highly accurate and reproducible models by integrating traditional fluid dynamics with modern data-driven methods
Equipped with physics-based nonlinear machine learning, we are working to develop groundbreaking fluid analysis technologies that benefit a wide range of industrial, environmental, and societal applications.
Researchers
Department of Aerospace Engineering, Graduate School of Engineering
Kai Fukami
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- 概要
Computational social science seeks to understand social phenomena and human behavior using advanced computational methods, including machine learning, simulations, and digital experiments. The field covers a wide range of topics—from opinion formation on social media to spatial social segregation, where different social groups are geographically separated—and actively explores new possibilities offered by innovative generative AI technologies, such as large language models. In this term, it aims to contribute both to establishing a new research framework and to solving practical social issues.
- 従来技術との比較
A key advantage of computational social science methods lies in their ability to dynamically and accurately capture complex social phenomena and human behavior, overcoming limitations of traditional social sciences that primarily rely on small-scale surveys and static data.
First, by directly analyzing the vast amounts of behavioral data generated daily, it becomes possible to understand large-scale, high-granularity social processes that were previously difficult to grasp. Second, the use of computational methods allows for the discovery of complex patterns and the structure of new social phenomena that might be overlooked by human intuition or conventional theories alone. Third, by combining simulations and digital experiments, the field has moved beyond mere description and correlation analysis to verifying causal mechanisms and future scenarios.
In this way, computational social science complements traditional methodologies and possesses the advantage of developing social science into a discipline that contributes to solving social problems.
- 特徴・独自性
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- This field is positioned as a new research framework that integrates social science and information science. It establishes the subject and direction of analysis based on theoretical considerations from social science, then verifies and develops those theories through empirical analysis and simulations that utilize computational techniques and big data.
- 実用化イメージ
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By leveraging information technology and big data to examine diverse social phenomena and human behavior, the field offers valuable knowledge and expertise for understanding current societal conditions and for designing and evaluating effective intervention strategies.
Researchers
Graduate School of Arts and Letters
Lyu Zeyu
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