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Flexoelectric Catalysts Based on Hierarchical Wrinkling Surface of Centrosymmetric High-Entropy Oxide

ACS Nano 2023年第17期17卷 17417-17426页

作者： Wu, Ping Yun Le, Kim Tuyen Lin, Hsun-Yen Chen, Yu-Ching Wu, Po-Han Wu, Jyh Ming Department of Materials Science and Engineering National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan High Entropy Materials Center National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan Ph.D. Program in Prospective Functional Materials Industry National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan

A high-entropy oxide nanocomposite with Ag(CuZn)(AlCr)2O4 and CuO phases is fabricated to form an abundantly hierarchical wrinkled surface. Application of a mechanical force to the nanocomposite resulted in a nonhomogeneous strain gradient at the interface between the Ag(CuZn)(AlCr)2O4 and CuO phases, changing the local charge distribution and creating flexoelectric polarization that delayed electron/hole recombination. Transmission electron microscopy energy-dispersive X-ray spectroscopy mapping revealed that the Ag, Cu, Zn, Al, Cr, and O elements were highly distributed throughout the nanocomposite. The nanocomposite produced 2116 μmol·g-1 h-1 of H2 without external light irradiation, which is 980% higher than the H2 produced by the same nanocomposite under the photocatalytic process. A strong electrical field is observed at the interface between the Ag(CuZn)(AlCr)2O4 and CuO phases, demonstrating that a flexoelectric potential (flexopotential) is established at the structural boundaries because the strain gradient is localized at these interfaces. The nanocomposite is a promising approach for environmentally friendly energy production. © 2023 American Chemical Society.

关键词： Nanocomposites

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Intelligent Adjustment of Temperature Control Parameters Based on deep Reinforcement Learning for Stretch Blow Molding Machine 25

Intelligent Adjustment of Temperature Control Parameters Bas...

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25th International Conference on Mechatronics Technology, ICMT 2022

作者： Hsieh, Ping-Cheng Su, Guan-Lin Lin, Yi-Te Lin, Wei-Bo National Changhua University of Education Department of Mechatronics Engineering Changhua Taiwan Feng Chia University Department of Mechanical and Computer-Aided Engineering Taichung Taiwan Feng Chia University Ph.D. Program of Mechanical and Aeronautical Engineering Taichung Taiwan

ISBN: (数字)9781665461955

ISBN: (纸本)9781665461955

Stretch blow molding is the main technology used in the production of PET bottles. The stretch blow molding machine is usually composed of preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Among them, the temperature control of the heating system is one of the key factors affecting the quality of PET bottle, especially in the face of the environmental temperature changes greatly between morning and evening in some seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process is highly dependent on experience of the personnel. It has become a production challenge for the bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent adjustment technique of temperature control parameters. The proposed method can improve the problems such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. The experimental results show that the proposed method can adjust the temperature control parameters automatically in the heating process to eliminate the effect of the environmental temperature change and to control the surface temperature of the preforms stably within ±3 °C of the target temperature (the requirement is within±5 °C). © 2022 IEEE.

关键词： deep learning

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Cytometry in the Short-Wave Infrared

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ACS Nano 2024年第28期18卷 18534-18547页

作者： Liu, Te-I Wang, Jhih-Shan Nguyen, Ai-phuong Raabe, Marco Quiroz Reyes, Carlos Jose Lin, Chih-Hsin Lin, Ching-Wei Institute of Atomic and Molecular Sciences Academia Sinica Taipei City106319 Taiwan Department of Materials Science and Engineering National Taiwan University Taipei City106319 Taiwan Department of Physics University of Stuttgart Stuttgart70174 Germany Department of Chemistry National Tsing Hua University Hsinchu300044 Taiwan International Ph.D. Program in Biomedical Engineering Taipei Medical University New Taipei City235603 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University New Taipei City235603 Taiwan

Cytometry plays a crucial role in characterizing cell properties, but its restricted optical window (400-850 nm) limits the number of stained fluorophores that can be detected simultaneously and hampers the study and utilization of short-wave infrared (SWIR;900-1700 nm) fluorophores in cells. Here we introduce two SWIR-based methods to address these limitations: SWIR flow cytometry and SWIR image cytometry. We develop a quantification protocol for deducing cellular fluorophore mass. Both systems achieve a limit of detection of ∼0.1 fg cell-1 within a 30 min experimental time frame, using individualized, high-purity (6,5) single-wall carbon nanotubes as a model fluorophore and macrophage-like RAW264.7 as a model cell line. This high-sensitivity feature reveals that low-dose (6,5) serves as an antioxidant, and cell morphology and oxidative stress dose-dependently correlate with (6,5) uptake. Our SWIR cytometry holds immediate applicability for existing SWIR fluorophores and offers a solution to the issue of spectral overlapping in conventional cytometry. © 2024 The Authors. Published by American Chemical Society.

关键词： Oxygen

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POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF dEEP LEARNING LANd COVER CLASSIFICATION USING VERY-HIGH RESOLUTION SATELLITE IMAGERY 44

POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNI...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Hakim, Yofri Furqani Tsai, Fuan International Ph.D. Program in Environmental Science and Technology National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Land cover is one of the fundamental data utilized in spatial analysis for a range of applications, including climate, environment, natural resources, agriculture, forestry, planning, health, and even social issues. The demand for land cover data is diverse, ranging from global scale, regional scale, and detailed scale. Furthermore, data updates also become a necessity for users. The current trend is the requirement for more accurate and up-to-date land cover data. The growing demand for accurate and up-to-date land cover data is in sync with improvements in satellite imagery data acquisition technology, which now offers improved spatial resolution and more effective satellite imagery data processing. Satellite imagery data processing technology is also growing rapidly with the advance of artificial intelligence for semantic classification or segmentation. This research uses a deep learning approach to classify land cover on Pleiades very high-resolution satellite imagery. A post-classification enhancement is also carried out to improve the consistency and accuracy of the deep learning classification results. The preliminary research results show that post-classification enhancement with the algorithms proposed in this study can increase the accuracy of classification results using deep learning-based approaches by approximately 2%. The original Overall Accuracy and Kappa of the deep learning classification results were 0.84 and 0.79. After the post-classification enhancement process, the Overall Accuracy and Kappa values increased to 0.86 and 0.81. © 2023 ACRS. All Rights Reserved.

关键词： deep Learning Image Morphology Land Cover Classification Post-Classification Enhancement Semantic Segmentation U-Net

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A dual-channel Converter with a Positive Output and a Negative Voltage Output

A Dual-channel Converter with a Positive Output and a Negati...

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International Symposium on computer, Consumer and Control (IS3C)

作者： Yau Yeu-Torng Luu Thanh-phu Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

In this paper, a dual-channel converter with a positive output and negative voltage output is proposed. It integrates a positive voltage output converter and a negative voltage output converter, and shares the same switches. The number of active components can be reduced. In addition, the circuit can achieve dual output voltage control with a single controller and PWM drive signal by appropriately designing the ratio of the number of windings of the coupling inductor. A regulated positive voltage output and negative voltage output can be achieved.

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Small-Molecule Analysis Based on dNA Strand displacement Using a Bacteriorhodopsin photoelectric Transducer: Taking ATP as an Example

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Sensors 2023年第17期23卷 7453-7453页

作者： Chen, Hsiu-Mei Wang, Wen-Chang Chen, Hong-Ren Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan

A uniformly oriented purple membrane (PM) monolayer containing photoactive bacteriorhodopsin has recently been applied as a sensitive photoelectric transducer to assay color proteins and microbes quantitatively. This study extends its application to detecting small molecules, using adenosine triphosphate (ATP) as an example. A reverse detection method is used, which employs AuNPs labeling and specific dNA strand displacement. A PM monolayer-coated electrode is first covalently conjugated with an ATP-specific nucleic acid aptamer and then hybridized with another gold nanoparticle-labeled nucleic acid strand with a sequence that is partially complementary to the ATP aptamer, in order to significantly minimize the photocurrent that is generated by the PM. The resulting ATP-sensing chip restores its photocurrent production in the presence of ATP, and the photocurrent recovers more effectively as the ATP concentration increases. direct and single-step ATP detection is achieved in 15 min, with detection limits of 5 nM and a dynamic range of 5 nM–0.1 mM. The sensing chip exhibits high selectivity against other ATP analogs and is satisfactorily stable in storage. The ATP-sensing chip is used to assay bacterial populations and achieves a detection limit for Bacillus subtilis and Escherichia coli of 102 and 103 CFU/mL, respectively. The demonstration shows that a variety of small molecules can be simultaneously quantified using PM-based biosensors. © 2023 by the authors.

关键词： photocurrents

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Surface defect identification using Bayesian filtering on a 3d mesh

Surface defect identification using Bayesian filtering on a ...

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作者： dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAd-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAd model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAd model into a high-density polygonal mesh, where each vertex represents a state variable in 3d space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAd domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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Robust Feature Learning Against Noisy Labels

Robust Feature Learning Against Noisy Labels

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IEEE International Conference on Image Processing

作者： Tsung-Ming Tai Yun-Jie Jhang Wen-Jyi Hwang Free University of Bozen-Bolzano NVIDIA AI Technology Center International Intercollegiate Ph.D. Program National Tsing Hua University Department of Computer Science and Information Engineering National Taiwan Normal University

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an efficient approach to tackle noisy labels by learning robust feature representation based on unsupervised augmentation restoration and cluster regularization. In addition, progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Our proposed design is generic and flexible in applying to existing classification architectures with minimal overheads. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.

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ROBUST PARAMETER ESTIMATION FOR RATIONAL ORdINARY dIFFERENTIAL EQUATIONS

arXiv

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arXiv 2023年

作者： Bassik, Oren Berman, Yosef Go, Soo Hong, Hoon Ilmer, Ilia Ovchinnikov, Alexey Rackauckas, Chris Soto, Pedro Yap, Chee Ph.D. Program in Mathematics CUNY Graduate Center New YorkNY United States Ph.D. Program in Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics North Carolina State University RaleighNC United States Department of Mathematics CUNY Queens College United States Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Mathematical Institute University of Oxford Oxford United Kingdom Department of Computer Science Courant Institute of Mathematical Science New York University New YorkNY United States

We present a new approach for estimating parameters in rational OdE models from given (measured) time series data. In typical existing approaches, an initial guess for the parameter values is made from a given search interval. Then, in a loop, the corresponding outputs are computed by solving the OdE numerically, followed by computing the error from the given time series data. If the error is small, the loop terminates and the parameter values are returned. Otherwise, heuristics/theories are used to possibly improve the guess and continue the loop. These approaches tend to be non-robust in the sense that their accuracy depend on the search interval and the true parameter values;furthermore, they cannot handle the case where the parameters are locally identifiable. In this paper, we propose a new approach, which does not suffer from the above non-robustness. In particular, it does not require making good initial guesses for the parameter values or specifying search intervals. Instead, it uses differential algebra, interpolation of the data using rational functions, and multivariate polynomial system solving. We also compare the performance of the resulting software with several other estimation software packages. © 2023, CC BY-NC-SA.

关键词： Parameter estimation

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Reusable Hydrogen Storage Tank with Flange design 10

Reusable Hydrogen Storage Tank with Flange Design

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Li, Yun-Qi Chen, Hsuan-Jung Yang, Hsi-Wen Chen, Chih-Yuan Chang, Shao-Fu Chen, Ssu-Ying Chen, Chien-Chon College of Engineering and Science Ph.D. Program in Materials and Chemical Engineering National United University Miaoli Taiwan Michigan State University Department of Chemistry MI48824 United States National United University Department of Materials Engineering Miaoli Taiwan Graduate Institute of Intellectual Property National Taipei University of Technology Taiwan National United University Department of Energy Engineering Miaoli Taiwan

ISBN: (纸本)9798350394924

For how to store and transport hydrogen, the first thought is to store hydrogen directly in the cylinder. However, this method is not desirable, because generally at least 700 atm high pressure, the hydrogen contained in the cylinder to be useful. Therefore it is very unsafe at such high pressures. In This paper, we design and production of a new generation of solid-state hydrogen storage and discharge container. The goal of 'development of solid-state hydrogen storage technology' is to break through the bottlenecks of hydrogen storage technology safety, hydrogen storage density, high-efficiency hydrogen absorption and release, transportation safety, and engineering practice. © 2024 IEEE.

关键词： Aluminum

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