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共 151 条记录，以下是11-20 订阅

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Machine Learning Curriculums Generated by Classifier Ensembles 19

Machine Learning Curriculums Generated by Classifier Ensembl...

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19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023

作者： Huang, Tzu-Jui Kameyama, Keisuke University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781665476928

Image classification is a fundamental task that attempts to classify the images into the classes they belong to. CNN is commonly used for the classification due to its flexibility and stability. This paper treats Curriculum Learning as an assistant method that decides the procedure the CNN ought to be trained. Curriculum Learning is generally defined as a method that divides and orders the training datasets into several different levels of learning difficulties. This paper utilizes the idea of ensemble classification that trains according to the order based on the consensus of different machine learning models. As a result, the proposed Ensemble-Generated Curriculum Learning model achieves higher accuracy compared to that of a single-stage CNN learning with dog face recognition and CIFAR-10 datasets. © 2023 IEEE.

关键词： Curricula

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LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19 19

LSTM-based Forecasting using Policy Stringency and Time-vary...

引用

19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023

作者： Maniamfu, Pavodi Kameyama, Keisuke University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781665476928

Accurate forecasting of the number of infections is an important task that can allow health care decision makers to allocate medical resources efficiently during a pandemic. Two approaches have been combined, a stochastic model by Vega et al. for modelling infectious disease and Long Short-Term Memory using COVID-19 data and government's policies. In the proposed model, LSTM functions as a nonlinear adaptive filter to modify the outputs of the SIR model for more accurate forecasts one to four weeks in the future. Our model outperforms most models among the CDC models using the United States data. We also applied the model on the Canadian data from two provinces, Saskatchewan and Ontario where it performs with a low mean absolute percentage error. © 2023 IEEE.

关键词： COVID-19

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NUMERICAL ANALYSIS FOR CLARIFYING DYNAMIC BUCKLING BEHAVIOR OF PWR-CV SUBJECT TO A SEISMIC EXCITATION 30

NUMERICAL ANALYSIS FOR CLARIFYING DYNAMIC BUCKLING BEHAVIOR ...

引用

30th International Conference on Nuclear engineering, ICONE 2023

作者： Hidaka, Shinjiro Shoji, Gaku Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Ibaraki Japan Division of Engineering Mechanics and Energy Institute of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (纸本)9784888982566

Clarifying the dynamic buckling behavior of PWR steel reactor containment vessels (PWR-CVs) subject to seismic excitation is critical for improving a numerical seismic design scheme for PWR-CVs. In evaluating the static elastic-plastic buckling strength for PWR-CVs by a finite element (FE) analysis, first we compute the moments and axial forces acting onto the multiple lumped mass beam model of a targeted PWR-CV from the linear dynamic analysis, second using the buckling evaluation formula on JEAG 4601 addenda-1984 we calculate the equivalent static load distributions in terms of shear and axial forces as input loads to the FE model. This static FE buckling analysis for a PWR-CV idealizes dynamic buckling phenomena as be static phenomena, so we cannot basically consider the effects of dynamic behavior on the buckling strength of a PWR-CV. To tackle more realistic buckling assessment of a PWR-CV, we carried out the linear dynamic analysis for a PWR-CV FE numerical model and discussed the features of the response considering the oval vibration modes. It was revealed that the response accompanying the oval vibration modes showed the lower value compared with that from the static FE buckling analysis. In contrast, in a previous study by Hidaka et al., 2022, it was discussed that the buckling load of static FE buckling analysis for a PWR-CV is higher than the buckling load of buckling evaluation formula on JEAG4601. As a result, the ratio of buckling load to response in the linear dynamic FE analysis was within 3% of that in the static FE buckling analysis. © 2023 by JSME.

关键词： Elastoplasticity

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Implementation and Performance Evaluation of Memory System Using Addressable Cache for HPC Applications on HBM2 Equipped FPGAs 28th

Implementation and Performance Evaluation of Memory System...

引用

28th International European Conference on Parallel and Distributed Computing , Euro-Par 2022

作者： Fujita, Norihisa Kobayashi, Ryohei Yamaguchi, Yoshiki Boku, Taisuke Center for Computational Sciences University of Tsukuba Tsukuba Japan Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031312083

When we apply field programmable gate arrays (FPGAs) as HPC accelerators, their memory bandwidth presents a significant challenge because it is not comparable to those of other HPC accelerators. In this paper, we propose a memory system for HBM2-equipped FPGAs and HPC applications that uses block RAMs as an addressable cache implemented between HBM2 and an application. This architecture enables data transfer between HBM2 and the cache bulk and allows an application to utilize fast random access on BRAMs. This study demonstrates the implementation and performance evaluation of our new memory system for HPC and HBM2 on an FPGA. Furthermore, we describe the API that can be used to control this system from the host. We implement RISC-V cores in an FPGA as controllers to realize fine-grain data transfer control and to prevent overheads derived from the PCI Express bus. The proposed system is implemented on eight memory channels and achieves 102.7 GB/s of the bandwidth. It overcomes the memory bandwidth of conventional FPGA boards with four channels of DDR4 memory despite using only 8 of 32 channels of the HBM2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data transfer

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Evaluating a How-to Tip Machine Comprehension Model with QA Examples collected from a Community QA Site 35

Evaluating a How-to Tip Machine Comprehension Model with QA ...

引用

35th Pacific Asia Conference on Language, information and Computation, PACLIC 2021

作者： Li, Tingxuan Bai, Shuting Zhu, Fuzhu Utsuro, Takehito Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan

In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to human. However, in the area of non-factoid QA, there are only limited numbers of datasets for training QA models. So within the field of the non-factoid QA, Chen et al. (2020) developed a dataset for training Japanese tip QA models. Although it can be shown that the trained Japanese tip QA model outperforms the factoid QA model, this paper further aims at answering tip questions more closely related to daily lives. Specifically, we collect community QA examples from a community QA site and then apply the trained Japanese tip QA model to those community QA examples. Evaluation results show that the trained tip QA model outperforms the factoid QA model when testing against those community QA examples. © Proceedings of the 35th Pacific Asia Conference on Language, information and Computation, PACLIC 2021.

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Image reconstruction framework for helical cone-beam CT by combining compressed sensing and deep learning

Image reconstruction framework for helical cone-beam CT by c...

引用

International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Kawamata, Kentaro Kazuo, Subaru Takizawa, Hotaka Kudo, Hiroyuki Degree Programs in Systems and Information Engineering Graduate School of Systems and Information Engineering University of Tsukuba Tennoudai 1-1-1 Tsukuba305-8573 Japan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Compressed sensing (CS) image reconstruction in CT suffers from the drawbacks such as 1) appearance of staircase artifacts and 2) loss in image textures and smooth intensity changes. These drawbacks stem from the fact that CS is based on approximating the image by a piecewise-constant function. To overcome this drawback, we have already proposed a framework to improve image quality in CS using deep learning. In this framework, FBP reconstructed image and CS (TV or Nonlocal TV) reconstructed image are inputted to CNN with two input channels and single output channel, and a final reconstructed image is obtained by the output of CNN. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework is extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework was extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. The extension was done in the following way. First, we prepare N different 2-D denoising CNN (CNN1, CNN2, ?, CNNN) dependent on the slice position n. Each slice of the short-scan FDK reconstruction without denoising yi and with 3-D TV (or Nonlocal TV) denoising zi are inputted to CNNn with the closest slice index n, which yields a corresponding output image for each slice xi. The final reconstructed image is obtained by stacking every slice xi (i = 1, 2, ?, I). © 2021 SPIE.

关键词： Image reconstruction

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Source Data-less Efficient Transfer Learning Based on Layer Importance Index Using Activated Feature Map

Source Data-less Efficient Transfer Learning Based on Layer ...

引用

2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Kanda, Daigo Nobuhara, Hajime University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Department of Intelligent Interaction Technologies Tsukuba Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Transfer learning performance in convolutional neural networks (CNNs) depends on the selection of retraining and fixation layers. The conventional method requires source data, which increases the cost proportionally to the data's size. To solve this problem, this study proposed a source-data-less layer importance index that indicates the layers that should be retrained. Consequently, using the maximum activated feature maps generated by the model itself instead of the activated feature maps, the proposed method eliminates the need for any source data. Further, the experimental results on the five image classification datasets demonstrated a strong positive correlation of the proposed layer importance index compared with the conventional method. In addition, the transfer learning method using the layer importance index significantly improved the average classification accuracy compared with fine-tuning. CNNs have become indispensable owing to various applications, and thus, this study aimed at further enhancing its capabilities is relevant to the present and coming times. © 2022 IEEE.

关键词： Convolutional neural networks

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A Note on Even-Sized Spectral Clustering 13

A Note on Even-Sized Spectral Clustering

引用

Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Miyakawa, Hiroki Endo, Yasunori Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Ibaraki Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan

ISBN: (纸本)9798350373332

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples. © 2024 IEEE.

关键词： Unsupervised learning

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3D point cloud reconstruction based on stereo camera and rtk-gps with high accuracy under sequential correction 9

3D point cloud reconstruction based on stereo camera and rtk...

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9th IEEE Global Conference on Consumer Electronics, GCCE 2020

作者： Liu, Shuyi Zhang, Heming Kawai, Shin Nobuhara, Hajime University of Tsukuba Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Tsukuba 1-1-1 Tennoudai Ibaraki305-8573 Japan

ISBN: (纸本)9781728198026

In the field of three-dimensional (3D) point cloud reconstruction, a commonly used algorithm is called Real-Time-Appearance-Based-Mapping. Although this algorithm performs well in outputting a point cloud with high accuracy in real time, its processing efficiency is expected to decline when Jetson Nano is installed on drones, as additional memory is required to calculate loop closures. To increase the processing speed and accuracy of 3D point cloud reconstruction based on Jetson Nano, we propose a new algorithm for drone flight, that does not rely on overlap. The proposed method combines the point cloud and real-time kinematic Global Positioning System, and its effectiveness is demonstrated by experiments in a real environment. © 2020 IEEE.

关键词： Drones

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Implementation and Performance Evaluation of Collective Communications Using CIRCUS on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field Programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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