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IEEE International Conference on Automation science and engineering (CASE)

作者： Mario Di Ferdinando Alessandro Modesti Nicola Epicoco Stefano Di Gennaro Pierdomenico Pepe Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L’Aquila L’Aquila Italy Department of Engineering LUM University Casamassima BA Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the last decade, control systems for Unmanned Aerial Vehicles (UAVs) have been an important topic in research due to their increasing usage in many practical fields, and have been extensively studied by resorting to classical continuous-time nonlinear design. Although this approach can simultaneously incorporate different control requirements in the design, the computed control laws are approximated and applied within digital environments in absence of formal performance guarantees. In this work, we present a methodology for the design of dynamic quantized sampled-data event-based stabilizers for a class of time-varying nonlinear systems at the aims of applying the proposed strategy to the autonomous navigation problem of UAVs. Starting from classical nonlinear design achieving tracking of prescribed trajectories in continuous time (emulation approach), we prove the existence of a suitably fast sampling and of an accurate quantization of the input/output channels ensuring the practical stability of the error dynamics by means of quantized sampled-data event-based controllers. Simulations performed in an non-ideal setting confirm the potential of the approach.

关键词： Asymptotic stability Quantization (signal) Accuracy Design methodology Autonomous aerial vehicles Stability analysis Trajectory Nonlinear dynamical systems Vehicle dynamics Time-varying systems

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Characterization of Systematic Bias in ALOS-2 Multilooked Interferograms

Characterization of Systematic Bias in ALOS-2 Multilooked In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryu Sugimoto Yu Morishita Masanobu Shimada Ryo Natsuaki Chiaki Tsutsumi Ryosuke Nakamura Toru Kouyama Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Japan Department of Electrical Engineering and Information Systems The University of Tokyo Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

关键词： Fading channels Systematics Soil measurements Time series analysis Soil moisture Sentinel-1 Geoscience and remote sensing

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Brain organoids in motor cortex involved in motor output 24

Brain organoids in motor cortex involved in motor output

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9th International Conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Fan, Xiaoman Xu, Yuchen Xu, Shibo Yang, Yuqi Shi, Lufeng Liu, Yan Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies School of Engineering Westlake University China State Key Laboratory of Reproductive Medicine Institute for Stem Cell and Neural Regeneration Nanjing Medical University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China Department of Neurosurgery Sir Run Run Shaw Hospital Medical College Zhejiang University China

ISBN: (纸本)9798400717970

Motor function is essential in daily life while brain damage in motor system such as stroke would lead to long term motor function deficits. Brain organoids, a novel in vitro stem cell culture technology, offer a new idea for motor function recovery. In this study, we pumped out the motor cortex of forelimbs to mimic brain injury in mice and implanted human brain organoids into the cavity combined with microwire electrodes to simultaneously record neural signals from both the organoids and the host animals. Our findings indicate that the brain organoids can be involved in motor output and the involvement can be improved through behavioral training. © 2024 Copyright held by the owner/author(s).

关键词： Stem cells

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Origin of the high propensity for nanoscale deformation twins in CrCoNi medium-entropy alloy

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Journal of Materials science & Technology 2024年第16期183卷 63-71页

作者： Nan-Jun Liu Zhang-Jie Wang Jun Ding Mark Asta Robert O.Ritchie Bin Gan Evan Ma Zhi-Wei Shan Center for Advancing Materials Performance from the Nanoscale(CAMP-Nano) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Center for Alloy Innovation and Design(CAID) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720United States Department of Materials Science&Engineering University of CaliforniaBerkeleyCA 94720United States Beijing Key Laboratory of Advanced High Temperature Materials Central Iron and Steel Research InstituteBeijing 100081China

Single-phase face-centered cubic(fcc)high/medium-entropy alloys(H/MEAs)exhibit a much higher tendency to form nanoscale deformation twins than conventional fcc metals with similar low stacking fault energies(SFEs).This extraordinary propensity for nanotwin formation in H/MEAs cannot therefore be ex-plained by their low SFEs ***,using in situ compression tests of CrCoNi in comparison with Ag nanopillars inside a transmission electron microscope,we found that in the CrCoNi MEA,a high density of nanoscale twins continuously formed with an average thickness of 4.6 *** contrast,for similar experiments on Ag with almost identical SFE,following the nucleation of a few twins,they could further thicken to above one hundred nanometers by twin boundary *** dynamics calculations indicated that in the highly-concentrated CrCoNi solid solution,the magnitude of the energy barriers for nucleating a stacking fault as a twin precursor in the pristine lattice and for the thickening of an existing twin both span a wide range and largely overlap with each ***,twin thickening through successive addition of atomic layers is prone to discontinuation,giving way to the nucleation of new twins at other sites where a lower energy barrier is encountered for partial-dislocation mediated fault formation.

关键词： High-entropy alloys CrCoNi Nanotwins Twin nucleation

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Characterization of 940 nm PCSEL Under Nanosecond Pulsed Operation with Peak Power of Up to 3.12 W 16

Characterization of 940 nm PCSEL Under Nanosecond Pulsed Ope...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Lim, Jun-Yuan Liu, Te-Hua Hong, Yu-Heng Kuo, Hao-Chung Wu, Chao-Hsin Graduate School of Advanced Technology National Taiwan University Taipei106319 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei106319 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei106319 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei106319 Taiwan

This paper examines 940 nm PCSELs for pulsed operation, demonstrating watt-level output at nanosecond pulse widths. The findings underscore the potential of PCSELs for high-performance pulse operations. oels codes: (1... 详细信息

ISBN: (纸本)9798350372076

关键词： Semiconductor lasers

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in Thailand 9

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Range-Agnostic Multi-View Depth Estimation with Keyframe Selection

Range-Agnostic Multi-View Depth Estimation with Keyframe Sel...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Andrea Conti Matteo Poggi Valerio Cambareri Stefano Mattoccia Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic System (ARCES) Brussels Belgium Sony Depthsensing Solutions University of Bologna Italy

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Methods for 3D reconstruction from posed frames require prior knowledge about the scene metric range, usually to recover matching cues along the epipolar lines and narrow the search range. However, such prior might not be directly available or estimated inaccurately in real scenarios – e.g., outdoor 3D reconstruction from video sequences – therefore heavily hampering performance. In this paper, we focus on multi-view depth estimation without requiring prior knowledge about the metric range of the scene by proposing RAMDepth, an efficient and purely 2D framework that reverses the depth estimation and matching steps order. Moreover, we demonstrate the capability of our framework to provide rich insights about the quality of the views used for prediction. Additional material can be found on our project page.

关键词： Measurement Three-dimensional displays Correlation Video sequences Memory management Estimation Cameras

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