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Research Highlights
 JST-ERATO Intelligent Optical Synthesizer (IOS) Project Adaptive learning system using big data based machine learning Drag reduction and relaminarization of wall-turbulence by traveling wave control

Researcher Video Profiles
 Kaoru Minoshima, Professor, Department of Engineering Science, Graduate School of Informatics andEngineering Maomi Ueno, Professor at the Graduate School of Informatics and Engineering Hiroya Mamori, Associate Professor, Department of Mechanical and Intelligent Systems Engineering

Topics Improved catalytic processes for the synthesis of phenolNews and Events
 Delegation of NHTD visits UEC
 UEC signs Agreement of Double Degree Program with IPN

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JST-ERATO Intelligent Optical Synthesizer (IOS) Project
Optical technology has not been utilized to its full potential, although there is no doubt about its importancefor applications in daily life and science and technology. By appropriate control of the unique properties of
optical waves with high precision and wide dynamic range opens of the possibilities for innovative
applications over a broad range of fields.
The main goal of the JST-ERATO Intelligent Optical Synthesizer (IOS) Project [1,2] is to develop opticaltechnology for controlling properties of light waves based on advanced laser technology, referred to as"Optical Frequency Combs" (OFCs). Simply started, OFCs is composed of large numbers of equally spaced,
optical frequency modes. This discovery was awarded the Nobel prize in physics in 2005 and has been known
as the most precise "optical ruler" ever made by human-beings.
However, in spite of the attractive features of OFCs, there is a huge gap between technology available for
producing light sources and real-world applications. To bridge the gap, there is high expectation of researchon the development of technology to utilize the full properties of OFCs and for basic application technologies
utilizing its unique features.
Kaoru Minoshima, the Research Director of the ERATO-IOS project and professor of the UEC, and her
project team are developing a diverse range of fundamental technologies for controlling broad aspects of
optical waves based on OFCs, such as in time, frequency, space, phase, polarization, coherence, as an "opticalsynthesizer", which is an analogy of musical instruments known as synthesizers. Moreover, the ERATO-IOS
project team are utilizing the controllability of light for broad innovative applications, which are based on
original and innovative applications of the full range of properties of OFCs, not only as previously reported
precision frequency rulers, but as extremely accurate tools for multi-dimensional information conversion andcontrol over a wide dynamic range.
The researchers have developed a wide variety of OFC sources exhibiting world-class performances forapplications, such as the highest repetition rate low-noise Yb:fiber OFC that was stabilized without additional
amplification [3], a fiber laser which emits highly coherent two OFCs ("dual-comb") simultaneously enablinghigh-precision, broadband and rapid spectroscopy with practical light source [4], and mode-filtering techniquefor high sensitive spectroscopy that can be applied for real-time environmental and medical sensing [5,6]. The
team have also developed new spectroscopy techniques for direct and rapid characterization of the complex
properties of a broad range of materials and devices with a wide dynamic range in time, frequency, and spacedomains [7,8]. An ultrafast three-dimensional imaging method was also developed, which is expected to be
applied to rapid sensing of microscopic to large objects without losing its high accuracy [9,10]. The team has
also developed a high-accuracy long distance measurement technique with automatic compensation for air
turbulence by propagated light itself that is expected to play a major role in industry and big science. Notably,

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

one of the OFCs developed in this project was installed in an astronomical observatory for future application
for searching extrasolar planets.
The technologies being developed have opened up new and important applications of light, and there is thepotential for many other applications covering a broad range of fields.

References
1. http://www.jst.go.jp/erato/minoshima/en/index_e.html
2. http://www.ru.uec.ac.jp/e-bulletin/feature/2014/research-an-optical-comb-for-medicine-environmentastronomy-and-other-applications.html
3. Y. Ma, B. Xu, H. Ishii, F. Meng, Y. Nakajima, I. Matsushima, T.R. Schibli, Z. Zhang, K. Minoshima,
Low-noise 750MHz spaced ytterbium fiber frequency combs, Optics Letters 43 (2018) 4136-4139.
4. Y. Nakajima, Y. Hata, K. Minoshima, High-coherence ultra-broadband bidirectional dual-comb fiber laser,
Opt. Express 27 (2019) 5931-5944.
5. A. Nishiyama, S. Yoshida, T. Hariki, Y. Nakajima, K. Minoshima, Sensitivity improvement of dual-comb
spectroscopy using mode-filtering technique, Opt. Express 25 (2017) 31730-31738.
6. Y. Nakajima, A. Nishiyama, K. Minoshima, Mode-filtering technique based on all-fiber-based external
cavity for fiber-based optical frequency comb, Opt. Express 26 (2018) 4656-4664.
7. A. Asahara, A. Nishiyama, S. Yoshida, K.-i. Kondo, Y. Nakajima, K. Minoshima, Dual-comb spectroscopy
for rapid characterization of complex optical properties of solids, Optics Letters 41 (2016) 4971-4974.
The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

8. A. Asahara, K. Minoshima, Development of ultrafast time-resolved dual-comb spectroscopy, APL
Photonics 2 (2017) 041301.
9. T. Kato, M. Uchida, K. Minoshima, No-scanning 3D measurement method using ultrafast dimensional
conversion with a chirped optical frequency comb, Scientific Reports 7 (2017) 3670-3671-3678.
10. E. Hase, T. Minamikawa, T. Mizuno, S. Miyamoto, R. Ichikawa, Y.-D. Hsieh, K. Shibuya, K. Sato, Y.
Nakajima, A. Asahara, K. Minoshima, Y. Mizutani, T. Iwata, H. Yamamoto, T. Yasui, Scan-less confocal
phase imaging based on dual-comb microscopy, Optica 5 (2018) 634-643.

・Author: Kaoru Minoshima, Professor, Research Director, 'JST-ERATOMINOSHIMA Intelligent Optical Synthesizer Project'
・Affiliations: Department of Engineering Science, Graduate School of Informatics
and Engineering, UEC, Tokyo.
・Website: http://www.femto-comb.es.uec.ac.jp/
・Researcher Video Profiles:
http://www.ru.uec.ac.jp/e-bulletin/researcher-video-profiles/2019/
kaoru-minoshima.html

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Adaptive learning system using big data based machine learning
Over the past few decades, many studies conducted in the field of learning science have reported that
scaffolding plays an important role in human learning. To scaffold a learner efficiently, a teacher should
predict how much support a learner must have to complete tasks and then decide the optimal degree ofassistance to support the learner's development. Nevertheless, it is difficult to ascertain the optimal degree ofassistance for learner development.
In this study, we assumed that optimal scaffolding is based on a probabilistic decision rule: given a teacher's
assistance to facilitate the learner development, an optimal probability exists for a learner to solve a task. To
ascertain the optimal probability, we developed a scaffolding system that provides adaptive hints to adjust thepredictive probability of the learner's successful performance to the previously determined certain value, using
a statistical machine learning technology.
Furthermore, using the scaffolding system, we compared learning performances by changing the predictive
probability. Our results showed that scaffolding to achieve 0.5 learner success probability provides the best
performance. Also experiments demonstrated that a scaffolding system providing 0.5 probability decreases thenumber of hints (amount of support) automatically as a fading function according to the learner's growthcapability.

Intelligent scaffolding system to provide adaptive hints.

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Reference
・Maomi Ueno and Yoshimitsu Miyazawa, IRT-Based Adaptive Hints to Scaffold
Learning in Programming, IEEE Transactions on Learning Technologies, IEEE
computer Society, 11, No.4, 415-428, (2018).
・Researcher Video Profiles:
http://www.ru.uec.ac.jp/e-bulletin/researcher-video-profiles/2019/maomi-ueno.ht
ml

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Drag reduction and relaminarization of wall-turbulence by traveling wave
control
Energy saving is important to reduce transportation costs of vehicles as well as their impact on theenvironment. In this context, because skin-friction drag increases significantly in turbulent flow, it isimportant to study flow control techniques for turbulent flow.
Turbulent flow is a strongly nonlinear phenomenon having a very complex structure consisting of i.e., vorticaland velocity streak structures. Therefore, it is difficult to decrease the skin-friction drag in turbulent flow.
In a recent report, Hiroya Mamori and colleagues showed traveling wave control of turbulent flow. The
traveling wave control is the blowing and suction from the wall in the form of the traveling waves.
They performed direct numerical simulations of the fully developed turbulent channel flow controlled bytraveling wave-like blowing and suction. Furthermore, the effect of control parameters on the drag reduction
effect was also extensively investigated.
They found that the turbulent flow becomes the laminar flow with positive energy saving when waves travelin the same direction as the base flow, and they reveal the parameter range of relaminarization phenomenon.
Traveling wave control is very simple, and attractive for control effects. Notably, the control is expected to beextended in wider situations, including separation flow control.
Note
This research was conducted at the Tokyo University of Agriculture and Technology. Hiroya Mamori iscurrently at the University of Electro-Communications, Tokyo.

AL-1 is running to calculate the square root of 3

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Reference
・Authors: Hiroya Mamori, Kaoru Iwamoto, and Akira Murata
・Title of original paper: Effect of the parameters of traveling waves created byblowing and suction on the relaminarization phenomena in fully developed
turbulent channel flow
・Journal, volume, pages and year: Physics of Fluids 26, 015101 (2014).
・Digital Object Identifier (DOI): 10.1063/1.4851256
・Affiliations: (Current) Department of Mechanical and Intelligent Systems
Engineering, The University of Electro-Communications
・Department Website: http://www.mamorilab.mi.uec.ac.jp
・Researcher Video Profiles:
http://www.ru.uec.ac.jp/e-bulletin/researcher-video-profiles/2019/hiroya-mamori.
html

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Kaoru Minoshima, Professor, Department of Engineering Science, Graduate
School of Informatics and Engineering
Optical frequency comb technology
Kaoru Minoshima is focused on expanding the applications of
optical technology that is limited at the moment. For example,
one reason is that at several 100 terahertz (THz) the
frequency of light is too high to directly control andmanipulate signals using conventional technology.
Minoshima and colleagues are developing advanced optical source known as "optical frequency comb
technology". "Based on this technology, my group is developing "optical synthesizers", which are tools thatenable versatile control and manipulation of light analogous to musical instruments," says Minoshima".
An optical frequency comb consists of an advanced laser that emits a spectrum of light consisting of manyhighly precise, equally spaced optical frequency modes. It is also an extremely accurately controlled ultrafast
pulsed laser that emits optical pulses with a very short time duration. Therefore, optical frequency comb
technology provides very accurate controllability of multi-dimensional information, for example, in time,
space, and frequency domains. Minoshima's group is conducting research to realize "optical synthesizer
technology" for applications in a wide range of fields in science and technology.
Specific examples of technology being studied include high-sensitivity spectroscopic sensing forenvironmental, medical, and astronomical spectroscopy applications; new spectroscopy technique for direct,
rapid, high-precision, and high-sensitive characterization of multiple properties of materials; high-accuracy
long distance measurement techniques with compensation for air turbulence; and ultrafast three-dimensional
shaped measurement techniques that simultaneously realize both broad range and high accuracy. It is
important to note that these applications cannot be realized by conventional technology.
"We will continue this research to fully utilize the properties of light with the aim of cultivating innovative
applications in over a broad range of science and technology fields, such as environment, biomedical,
astronomy and space technology."

Further information
Kaoru Minoshima
Professor, Department of Engineering Science, Graduate School of Informatics and Engineering, The
University of Electro-Communications.

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Website: http://www.femto-comb.es.uec.ac.jp/
Research Highlight:
http://www.ru.uec.ac.jp/e-bulletin/research-highlights/2019/jst-erato-intelligent-optical-synthesizer-ios-project.html

The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585© 2019 The University of Electro-Communications.

Maomi Ueno, Professor at the Graduate School of Informatics and Engineering
Adaptive learning system using big data based machine learning
Maomi Ueno is conducting research on machine learning,
artificial intelligence, big data analysis, centering on
Bayesian statistics. I this video he describes his group's
research on education system with artificial intelligence using
machine learning. The system adaptively supports the system
according to the student's abilities and is called adaptive learning.
Recently, tablet PCs have been introduced in schools in vast quantities and are spreading explosively.
Conventional adaptive learning of artificial intelligence estimates where students stall and teaches themcarefully from there. However, many educational experts say, "students should not be taught too much or too
little about the difficult parts of education. Let students think for themselves and give adaptive and minimum
support as possible for education to be effective". So, what is the best support? That is the main question inthis study.
Ueno thought that when students were solving problems, whether it would be actually possible to give theminimum level of support. And, excellent teachers were predicting the probability that students can achieve
their task with their support. In addition, he hypothesized that there might be the probability of giving acorrect answer that is optimal for enhancing the ability of students. That is, excellent teachers thought thatbeing able to predict the performance of learners before supporting them was an important requirement.
This is a very difficult task, but in fact, machine learning in artificial intelligence is good at predicting theperformance from students based on their previous performance at learning new tasks. Therefore, Ueno
proposed a framework that selects and presents support/hints so that the probability for a correct answer
becomes 0.5 using machine learning to analyze the past learning history of students, before supporting
students.
Specifically, Ueno and his colleagues have developed an adaptive learning system that evaluates the ability ofstudents based on tasks to write computer programs. When students make a mistake, the system gives hints toso that the probability of students giving the correct answer to a problem is 0.5.
First, after a lesson on programming with a simple example of a program, we conducted a post test on students
by implementing the conventional system and the new proposed method with various prediction probabilities
and compared the learning effects. As a result, they found that it is effective for learning to assign a hint sothat the probability of correct answer is 0.5 just by setting a slightly difficult task it at first. Surprisingly, itturned out that teaching methods that teach everything from scratch as in the past had the least learning effect
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and the students were only able to do the tasks that were taught. They cannot solve questions that were slightly
different from the initial task.
In the system that gives hints so that the correct answer probability becomes 0.5, as the student's ability
increases in the learning process, Ueno found that the amount of support necessary gradually decreases, that is,
the number of hints decreases gradually, and even if assistance was necessary initially, the students gradually
become independent.
Ueno found that this process is the most effective learning process. Currently, we are developing new systems
for mathematics and other subjects such as English. "In addition, although I am using a mathematical modelthat can be applied only to closed problems, I would like to develop a mathematical model that can be applied
to open problems such as writing essays and other needs of evaluators."
Further information
Maomi Ueno
Professor at the Graduate School of Informatics and Engineering, The University of Electro-Communications.
Research Highlight:
http://www.ru.uec.ac.jp/e-bulletin/research-highlights/2019/adaptive-learning-system-using-big-data-based-m
achine-learning.html

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Hiroya Mamori, Associate Professor, Department of Mechanical and Intelligent
Systems Engineering
Simulating the control of turbulent flow
Hiroya Mamori is studying the control of turbulent flow byusing numerical simulation. Recently, the energy saving isvery important because it is needed to decrease the transport
cost and the environmental impact. In fluid mechanics, flow
control to reduce drag is important.
"We are studying flow control technique to decrease the skin-friction drag in turbulent flow by using
computer simulation," says Mamori. "Especially, we are focusing on the traveling wave-like control."The traveling wave control means that the wave is imposed on the wall in the turbulent flow. When the wave
travels in the streamwise direction, then not only the skin-friction drag reduction, but also relaminarization
phenomenon is obtained. In that case, 70% of the drag reduction is obtained with positive energy saving. This
control is very attractive, and Mamori and his colleagues are trying to extend this control in wider situations.
On the other hand, integration of fluid mechanics and informative engineering is also hot topic nowadays. In
Mamori's laboratory, they are studying new turbulent flow control techniques by using neural network, forexample. "We hope that it becomes new control techniques in near future."
Further information
Hiroya Mamori
Associate Professor, Department of Mechanical and Intelligent Systems Engineering, The University ofElectro-Communications.
Department WebSite: http://www.mamorilab.mi.uec.ac.jp/
Research Highlight:
http://www.ru.uec.ac.jp/e-bulletin/research-highlights/2019/drag-reduction-and-relaminarization-of-wall-turbu
lence-by-traveling-wave-control.html

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Improved catalytic processes for the synthesis of phenol
Researchers at the University of Electro-communications, Tokyo report a single-site catalytic platform withhigh selectivity for the single-step synthesis of phenol in a paper appeared in ACS catalysis.
The cumene process is an energy-intensive industrial three-step process (one of the steps is explosive) used to
produce phenol (C6H5OH), a chemical used as precursor for many industrially important materials, including
polymers, drugs and herbicides. It would be highly desirable to find an efficient and less environmentally
harmful way to produce phenol, and the best option would be to synthesize it directly starting from benzene,O2 and N2O in a single-step catalytic process. Ideally, this would be a gas-phase flow reaction on a solid
catalyst, which would make the reaction efficient and result in reduced resource consumption andeasy-to-separate products.
Yasuhiro Iwasawa and colleagues from the University of Electro-communications, Tokyo, reported theselective oxidation of benzene to phenol using large alkali metals as active sites incorporated in zeolite pores.
There results defy conventional wisdom on catalytic processes, whereby alkali and alkaline metal ions cannot
activate benzene, O2 and N2O when they absorb separately. The reactions, which were characterized using a
combination of synchrotron techniques, display very high conversion and selectivity, in particular for Rb andCs ions adsorbed on a type of zeolite called β-zeolite.
Two reaction paths were studied: in the first, benzene reacts with N2O, in the second, with O2 in the presenceof NH3. Density functional theory calculations were used to understand the mechanism underlying both
catalytic reactions. In the first case, the reaction starts with the adsorption of benzene and N2O; in the next
step, the O-N bond in N2O dissociates, a O-C bond forms on benzene and the H atom attached to the C atom
moves to the O, so that phenol is formed and N2 desorbs. In the second reaction, which has a performance lessstriking than the first, benzene, O2 and NH3 co-adsorb; the dissociation of O2 is activated by NH3 and, as in theprevious case, an O-C bond is formed on benzene, and the H atom on the C atom migrates to the O atom,
forming phenol. Because the reaction happens on a single ion site, a large reaction platform is needed, which
explains why Cs and Rb, which both have large diameters, work better than other alkali and alkaline metal
ions. The regulation of their confinement and local coordination structure by the β-zeolite pore structure also
plays an important role.
The authors optimized the catalyst fabrication and reaction conditions, modifying the metal precursors,
sources of zeolites and reaction temperature to try to achieve a performance good enough to make the process
appealing for industrial applications.
Importantly, the activation barriers are sufficiently small that the reactions can proceed at low temperature. As
the authors conclude, "the present findings present a new approach for designing efficient selective C−H
activation catalysis under mild conditions."
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Graphical abstract from the paper

In this study, we assumed that optimal scaffolding is based on a probabilistic decision rule: given a teacher's
assistance to facilitate the learner development, an optimal probability exists for a learner to solve a task. To
ascertain the optimal probability, we developed a scaffolding system that provides adaptive hints to adjust thepredictive probability of the learner's successful performance to the previously determined certain value, using
a statistical machine learning technology.
Furthermore, using the scaffolding system, we compared learning performances by changing the predictive
probability. Our results showed that scaffolding to achieve 0.5 learner success probability provides the best
performance. Also experiments demonstrated that a scaffolding system providing 0.5 probability decreases thenumber of hints (amount of support) automatically as a fading function according to the learner's growthcapability.
・Authors: Shilpi Ghosh, Shankha S. Acharyya, Takuma Kaneko, Kotaro Higashi, Yusuke Yoshida,
Takehiko Sasaki, and Yasuhiro Iwasawa
・Title of original paper: Confined Single Alkali Metal Ion Platform in a Zeolite Pore for Concerted
Benzene C−H Activation to Phenol Catalysis
・Journal, volume, pages and year: ACS Catal. 8, 11979−11986 (2018)
・Digital Object Identifier (DOI): DOI: 10.1021/acscatal.8b03002
・Affiliations: Innovation Research Center for Fuel Cells, The University of Electro-Communications,
Chofu, Tokyo 182-8585, Japan, The University of Electro-Communications.

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Delegation of NHTD visits UEC
On January 11, 2019, Dr. Nguyen Van Kinh, the Director of National Hospital of Tropical Diseases (NHTD),
and Dr. Nguyen Vu Trung, the Vice Director of NHTD, visited the University of Electro-Communications(UEC).
President Fukuda, Dr. Nakano, the Members of the Board of Directors of UEC, and other professors of UEC
welcomed Dr. Nguyen Van Kinh and Dr. Nguyen Vu Trung, and they had active discussion for thecollaboration between the two institutions. After the meeting, they visited Laboratory of Prof. Kirimoto and
Asst. Prof. Sun in the Department of Mechanical and Intelligent Systems Engineering; and Laboratory of Prof.
Ishibashi in the Department of Computer and Network Engineering.
NHTD is one of the top hospitals in Vietnam in clinical education and research of infectious diseases. NHTD
and UEC signed cooperation agreement in March, 2018, and have been collaborating actively. Furthermore,
NHTD and 3 other institutions in Hanoi, and UEC have set up Global Alliance Laboratory, which can be
jointly used by faculties and students of the institutions, in December, 2018.

Group Photo

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UEC signs Agreement of Double Degree Program with IPN
On 11 December, 2018, the University of Electro-Communications (UEC) signed an Agreement of Double
Degree Program with the Instituto Politécnico Nacional (IPN), Mexico. The two institutes have been partner
institutions since 1998 and engaging in active collaboration, especially student exchanges.
The purpose of this agreement is to develop a Double Degree Program between PhD Program in
Communications and Electronics of IPN and the PhD Program of UEC, in order to train leaders and experts
with global perspective through the collaboration between the two institutes.

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The University of Electro-Communications (UEC) in Tokyo
is a small, luminous university at the forefront of pure and applied sciences, engineering, andtechnology research. Its roots go back to the Technical Institute for Wireless Commutations,which was established in 1918 by the Wireless Association to train so-called wireless engineers
in maritime communications in response to the Titanic disaster in 1912. In 1949, the UEC wasestablished as a national university by the Japanese Ministry of Education, and moved in 1957
from Meguro to its current Chofu campus Tokyo.
With approximately 4,000 students and 350 faculty, UEC is regarded as a small university, butwith particular expertise in wireless communications, laser science, robotics, informatics, andmaterial science, to name just a few areas of research.
The UEC was selected for the Ministry of Education, Culture, Sports, Science and Technology (MEXT)
Program for Promoting the Enhancement of Research Universities as a result of its strengths inthree main areas: optics and photonics research, where we are number one for the numberof joint publications with foreign researchers; wireless communications, which reflects ourroots; and materials-based research, particularly on fuel cells.
International Public Relations
The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585
E-mail : kokusai-k@office.uec.ac.jp
Website : http://www.uec.ac.jp/eng/

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182-8585 © 2019 The University of Electro-Communications.