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A plant growth chamber system equipped with aerosol generators for studying aerosol-vegetation interactions

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Particuology 2024年第2期85卷 122-132页

作者： Masao Gen Seiji Ikawa Masahiro Yamaguchi Fong Zyin Lim Takeshi Izuta I.Wuled Lenggoro Graduate School of Bio-Applications and Systems Engineering Tokyo University of Agriculture and TechnologyKoganeiTokyo184-8588Japan institute of Multidisciplinary Research for Advanced Materials Tohoku UniversitySendai980-8577Japan Faculty of Agriculture Tokyo University of Agriculture and TechnologyFuchuTokyo183-8509Japan Graduate School of Fisheries and Environmental Sciences Nagasaki UniversityNagasaki852-8521Japan institute of Agriculture Tokyo University of Agriculture and TechnologyFuchuTokyo183-8509 Japan Department of Applied Physics and Chemical Engineering Tokyo University of Agriculture and TechnologyKoganeiTokyo184-8588Japan institute of Global Innovation Research Tokyo University of Agriculture and TechnologyFuchuTokyo183-8509Japan

Understanding aerosol-vegetation interactions is vital in ***,the interactions remain elusive partly due to the lack of suitable plant growth chamber ***,deposition of submicron particles on leaf surfaces is challenging due to its low deposition velocities compared to larger *** this work,we present a plant-growth chamber that was used to study the effect of sub-micron black carbon(BC)particles on the growth and photosynthesis of *** chamber system simultaneously enables the growth of multiple plants in pots and the deposition of submicron particles onto *** spraying methods assisted by ultrasonic and electrostatic forces were employed as aerosol generators to realize the particle *** flow regime inside the chamber was numeri-cally calculated to predict the transportation of aerosol particles,suggesting the optimal operating conditions of the *** gas-phase particle size distribution measurements showed that gener-ated BC particles were suspended in submicron diameter *** aerosol generators were examined in the chamber using three conductor and insulator substrates as a model of plant *** observations and spectroscopic analysis ascertained that submicron BC particles generated from our generators were deposited on all substrate *** the developed chamber system,systematic studies can be performed to advance the fundamental understanding of aerosol-vegetation interactions.

关键词： Aerosol Exposure chamber Spray drying Black carbon

Model-free method for LQ mean-field social control problems with one-dimensional state space

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Control Theory and technology 2024年第3期22卷 479-486页

作者： Zhenhui Xu Tielong Shen School of Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Department of Engineering and Applied Sciences Sophia UniversityTokyo 102-8554Japan

This paper presents a novel model-free method to solve linear quadratic(LQ)mean-field control problems with one-dimensional state space and multiplicative *** focus is on the infinite horizon LQ setting,where the conditions for solution either stabilization or optimization can be formulated as two algebraic Riccati equations(AREs).The proposed approach leverages the integral reinforcement learning technique to iteratively solve the drift-coefficient-dependent stochastic ARE(SARE)and other indefinite ARE,without requiring knowledge of the system dynamics.A numerical example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： Mean-field control Social optima Infinite horizon Reinforcement learning

Computer-controlled ultra high voltage amplifier for dielectric elastomer actuators

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Biomimetic Intelligence & Robotics 2024年第1期4卷 14-23页

作者： Ardi Wiranata Zebing Mao Yu Kuwajima Yuya Yamaguchi Muhammad Akhsin Muflikhun Hiroki Shigemune Naoki Hosoya Shingo Maeda Department of Mechanical and Industrial Engineering University of Gadjah MadaYogyakarta 55281Indonesia Department of Mechanical Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Department of Engineering and Science Shibaura Institute of TechnologyTokyo 135-8548Japan Department of Electrical Engineering Shibaura Institute of TechnologyTokyo 135-8548Japan Department of Engineering Science and Mechanics Shibaura Institute of TechnologyTokyo 135-8548Japan Living Systems Materialogy(LiSM)Research Group International Research Frontiers Initiative(IRFI)Tokyo Institute of TechnologyYokohama 226-8501Japan

Soft robotics is a breakthrough technology to support human-robot *** soft structure of a soft robot can increase safety during human and robot *** of the promising soft actuators for soft robotics is dielectric elastomer actuators(DEAs).DEAs can operate silently and have an excellent energy *** simple structure of DEAs leads to the easy fabrication of soft *** simplicity combined with silent operation and high energy density make DEAs interesting for soft robotics *** actuation follows the Maxwell-pressure *** pressure produced in the DEAs actuation depends much on the voltage *** DEAs requires high voltage to gain an *** the power consumption of DEAs is in the milli-Watt range,the current needed to operate the DEAs can be *** commercially available DC-DC converters can convert the volt range to the kV *** order to get a voltage in the 2-3 kV range,the reliable DC-DC converter can be pricy for each *** problem hinders the education of soft actuators,especially for a newcomer laboratory that works in soft electric *** paper introduces an entirely do-it-yourself(DIY)Ultrahigh voltage amplifier(UHV-Amp)for education in soft ***-Amp can amplify 12 V to at a maximum of 4 kV *** a demonstration,we used this UHV-Amp to test a single layer of powdered-based *** strategy to build this educational type UHV-Amp was utilizing a Cockcroft-Walton circuit structure to amplify the voltage range to the kV *** its current state,the UHV-Amp has the potential to achieve approximately 4 *** created a simple platform to control the UHV-Amp from a personal *** near future,we expect this easy control of the UHV-Amp can contribute to the education of soft electric actuators.

关键词： Dielectric elastomer actuators Electric amplifier Soft actuators Soft robotics Soft actuator education

Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning

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Photonics Research 2024年第8期12卷 1681-1688页

作者： RUI TANG SHUHEI OHNO KEN TANIZAWA KAZUHIRO IKEDA MAKOTO OKANO KASIDIT TOPRASERTPONG SHINICHI TAKAGI MITSURU TAKENAKA Department of Electrical Engineering and Information Systems The University of TokyoTokyo 113-8656Japan Quantum ICT Research institute Tamagawa UniversityTokyo 194-8610Japan National institute of Advanced Industrial Science and technology Ibaraki 305-8568Japan

Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning,leveraging the inherent parallel nature of *** various schemes have been proposed and demonstrated to realize such photonic matrix accelerators,the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic *** this work,we propose a silicon microring resonator(MRR)optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation,potentially enabling the acceleration of both the inference and training phases of deep *** demonstrate a 4×4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers,achieving a classification accuracy of 93.3%.Subsequently,we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1%in the same inference task after ***,we simulate a convolutional neural network for handwritten digit recognition,using a 9×9 MRR crossbar array to perform the convolution *** work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

关键词： resonator neural matrix

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Optimization of body configuration and joint-driven attitude stabilization for transformable spacecraft under solar radiation pressure

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Astrodynamics 2024年第1期8卷 47-60页

作者： Yuki Kubo Toshihiro Chujo institute of Space and Astronautical Science Japan Aerospace Exploration AgencyKanagawa252-5210Japan Department of Mechanical Engineering Tokyo Institute of TechnologyTokyo152-8550Japan

The solar sail is one of the most promising space exploration systems due to its theoretically infinite specific impulse achieved through solar radiation pressure(SRP).Recently,researchers have proposed“transformable spacecraft”capable of actively reconfiguring their body configurations using actuatable *** spacecraft,if used similarly to solar sails,are expected to significantly enhance orbit and attitude control capabilities owing to their high redundancy in control degrees of ***,controlling them becomes challenging due to their large number of inputs,leading previous researchers to impose strong constraints to limit their potential control *** study focuses on novel attitude control techniques for transformable spacecraft under *** developed two methods,namely,joint angle optimization to obtain arbitrary SRP force and torque,and momentum damping control driven by joint angle *** proposed methods are formulated in a general manner and can be applied to any transformable spacecraft comprising front faces that can predominantly receive the SRP on each *** validity of our proposed method is confirmed through numerical *** study contributes to making most of the high control redundancy of transformable spacecraft without the need for expendable propellants,thus significantly enhancing the orbit and attitude control capabilities.

关键词： solar sail transformable spacecraft attitude-joint coupled motion attitudestabilization

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer Science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary Science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

Low-temperature graphitization of lignin via Co-assisted electrolysis in molten salt

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Green Energy & Environment 2024年第9期9卷 1449-1458页

作者： Shijie Li Wei-Li Song Xue Han Qingqing Cui Yan-li Zhu Shuqiang Jiao institute of Advanced Structure technology Beijing Institute of Technology State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing State Key Laboratory of Explosion Science and technology Beijing Institute of Technology

The ever-growing energy demand and environmental issues have stimulated the development of sustainable energy ***,an efficient and environmentally friendly electrochemical transformation technology was proposed to prepare highly graphitized carbon materials from an abundant natural resource-lignin （LG）.The preparation process mainly includes pyrolytic carbonization of raw LG material and electrochemical conversion of amorphous carbon ***,with the assistance of Co catalyst,the graphitization degree of the products was significantly improved,in which the mechanism was the removal of heteroatoms in LG and the rearrangement of carbon atoms into graphite ***,tunable microstructures （nanoflakes） under catalytic effects could also be observed by controlling the electrolytic *** with the products CN1 （without catalyst） and CN5 （with 10%catalyst）,the specific surface area are 158.957 and202.246 m2g-1,*** used as the electrode material for lithium-ion batteries,CN5 delivered a competitive specific capacity of～350 m Ah g-1（0.5 C） compared with commercial *** strategy proposed in this work provides an effective way to extract value-added graphite materials from lignin and can be extended to the graphitization conversion of any other amorphous carbon precursor materials.

关键词： Lignin Graphitic carbon Electrochemical conversion Lithium-ion batteries

Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN

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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU School of Computer Science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

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