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6th International Conference on Biomedical Signal and Image Processing, ICBIP 2021

作者： Kim, Heejeong Sadakata, Kazuya Kudo, Hiroyuki Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan

ISBN: (纸本)9781450390507

Recently, to realize low-dose scan in CT imaging, a number of iterative reconstruction methods with Total Variation (TV) regularization have been investigated. In particular, reconstruction methods based on proximal splitting framework have been actively researched thanks to the following two reasons. First, they allow to use the TV regularization term, which is known to be a non-differentiable function that cannot be handled with classical optimization methods. Second, using the proximal splitting leads to a new class of iterative reconstruction methods which cannot be found in the literature. The major drawback of existing research in this direction is that most of them use the proximal splitting which divides the cost function into a sum of only two terms like famous Chambolle-Pock algorithm and FISTA. In this paper, we propose a unified framework to construct a class of row-action-type iterative methods which converge very fast using frameworks of multi proximal splitting which divides the cost function into a sum of arbitrary number of sub-cost functions (more than two). The use of multi proximal splitting naturally allows us to construct row-action-type iterative methods converging to an exact minimizer of the cost function very quickly. In mathematical literature, there exist only three different frameworks of multi proximal splitting, which are the Passty splitting, Dykstra-like splitting, and modified Dykstra-like splitting. We develop three new iterative methods, i.e. the Passty iterative method, Dykstra-like iterative method, and modified Dykstra-like iterative method, by using these frameworks, for the case where the cost function is a sum of the standard least-squares data fidelity and the TV regularization term. We have compared the proposed three iterative methods with an empirical standard method using ordered-subset technique called OS-SIRT-TV method. The results demonstrate that the performances of proposed methods significantly outperform OS-SIRT

关键词： Image reconstruction

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Path Planning Considering Energy Consumption of Crawler Robots in Mountain Environments

Path Planning Considering Energy Consumption of Crawler Robo...

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IEEE/SICE International Symposium on System Integration

作者： N. Adachi H. Date Graduate School of Science and Technology Degree Programs in Systems and Infomation Engineering University of Tsukuba Tsukuba Ibaraki Japan

In the forestry industry, the demand for task automation is increasing to solve the labor shortage problem. We examine the path planning problem for tasks such as afforestation, harvesting, and surveying, in which a robot travels a predetermined set of locations. We specifically address the problem of path planning for minimization of energy consumption for the operation of a crawler robot in an environment with slopes. This problem requires consideration of the energy loss caused by ascending and descending in the mountainous environment, besides the energy loss due to friction during on-the-spot rotation specific to the crawler robot, and is impossible to treat as the standard Traveling Salesman Problem(TSP). We apply the Generalized Traveling Salesman Problem (GTSP) known as the TSP extension to this problem. This allows the proposed method to account for energy consumption during movement. We modeled the energy consumption of the robot during straight movements and rotations through a preliminary experiment. A graph reflecting the energy consumption model can be generated for evaluation of the path planning by setting the points to be traversed on the point cloud map after 3D scanning of an outdoor unstructured terrain field with flat ground and sloped ground. It is confirmed that the proposed method can plan a path that reduces energy consumption by 22% for a standard TSP solution that quasi-minimizes Euclidean distance.

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, information and Communication Engineers.

关键词： Deep learning

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

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

In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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CNN Model Compression by Merit-Based Distillation

CNN Model Compression by Merit-Based Distillation

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International Colloquium on Signal Processing & Its Applications, CSPA

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

Deep learning has advanced dramatically in recent years, and especially large convolutional neural networks (CNNs) have shown outstanding performance in a wide variety of tasks. However, such large-scale CNNs may not be able to be utilized in practice because they require a lot of computational resources. Therefore, there is a need to realize a CNN in a small model that has high performance similar to the large CNN. One method to solve these problems is Distillation, which uses a model of large scale and high performance as the teacher and a small model as the student model, and performs model compression by transferring the knowledge of the teacher model to the student model. However, in distillation, even when the teacher model incorrectly guesses the training input, the student model learns to mimic the teacher. This can prevent the student model from learning better. In this paper, we propose Meritbased Distillation, in which the accuracy of the teacher model’s output determines the degree to which the student model mimics the teacher model’s output, thereby preventing the student to learn undesired mappings. In the experiment, the effect of the Merit-based Distillation is evaluated. Consequently, the proposed method outperforms the improvements in the generalization by the student models trained by other known methods.

关键词： Training Deep learning Computational modeling Convolutional neural networks Task analysis

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Machine Learning Curriculums Generated by Classifier Ensembles

Machine Learning Curriculums Generated by Classifier Ensembl...

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International Colloquium on Signal Processing & Its Applications, CSPA

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

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.

关键词： Training Face recognition Stability criteria Machine learning Dogs Classification algorithms Task analysis

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

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

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International Colloquium on Signal Processing & Its Applications, CSPA

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

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.

关键词： COVID-19 Adaptation models Pandemics Infectious diseases Stochastic processes Predictive models Data models

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On a New Rough Clustering Based on Optimization

On a New Rough Clustering Based on Optimization

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Sakuya FURUTA Yasunori ENDO Master's Program in Risk and Resilience Engineering Degree Programs in Systems and Information Engineering Graduate School of Science annd Technology University of Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The purpose of this study is to develop and investigate a new rough clustering based on optimization. We propose a restructured algorithm using only one variable, instead of the algorithm described using two variables in existing methods. We also perform numerical examples using artificial data and the Iris and Wine datasets. This research has the potential to be used as a method for determining false negatives and false positives in cancer diagnosis.

关键词： Iris Accuracy Clustering algorithms Computational complexity Optimization Cancer

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Affinity-Weighted RandAugment for Problem-Oriented Augmentation

Affinity-Weighted RandAugment for Problem-Oriented Augmentat...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuna Park Tomoumi Takase Keisuke Kameyama Masaki Onishi Degree Programs in Systems and Information Engineering and Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Japan Institute of Systems and Information Engineering University of Tsukuba

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Data augmentation is a technique with the potential to improve model generalization performance. Automatic Augmentation, which autonomously designs suitable data augmentation strategies, has significantly improved performance. One of the effective Automatic Augmentation is RandAugment (RA), which randomly selects methods to apply from the set of available augmentations. However, its selection with equal probabilities for all available augmentations is not ideal. In this study, we propose Affinity-Weighted RandAugment which assigns weights to the selection probabilities for the available augmentations in RA. The weights are calculated based on the value of Affinity, which represents the distributional similarity between original and augmented data. Affinity provides the prediction of the effect of each augmentation according to both the model and the dataset in advance. This enables the assignment of weights to the selection probabilities with low computational cost. As an additional effort, whether Affinity could be obtained at a much lower computational cost was considered. Based on these insights we implemented Single-Pass Affinity-Weighted RA, which can achieve effective weighting for RA without additional or separate learning. Experiments in image classification using CIFAR-10/100 and Describable Textures Dataset (DTD) demonstrated that Affinity-Weighted RandAugment achieves better test accuracy in comparison with the other known RA methods.

关键词： Accuracy Computational modeling Neural networks Probability Predictive models Data augmentation Data models

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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