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检索条件"机构=Information Systems Architecture Course Advanced Institute of Industrial Technology"

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Generation of Independence for Classification in the Bio-inspired Networks 14

Generation of Independence for Classification in the Bio-ins...

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14th IIAI International Congress on advanced Applied Informatics, IIAI-AAI 2023

作者： Ishii, Naohiro Iwata, Kazunori Odagiri, Kazuya Matsuo, Tokuro Advanced Institute of Industrial Technology Information Systems Architecture Tokyo Japan Aichi University Department of Business Administration Nagoya Japan Sugiyama Jyogakuen University School of Culture-Information Studies Nagoya Japan Advanced Institute of Industrial Technology Information Systems Architecture Tokyo Japan

ISBN: (纸本)9798350324228

Machine learning, deep learning and neural networks are extensively developed in many fields, in which neural network architectures have shown a variety of applications. However, there is a need for explainable fundamentals in complex neural networks. In this paper, it is shown that bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network is created based on the bio-inspired retinal network. They have orthogonal bases which correspond to the Fourier bases. Second, the classification performance of the asymmetric network is compared to the conventional symmetric network, which is developed as a biological model for the sensory processing. Further, the higher dimensional orthogonal bases are generated and their replacement operations are shown to be useful for the classification. It is shown that the generation of higher dimensional independence is realized in the layered networks using Shur complement. © 2023 IEEE.

关键词： Deep learning

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Fabric Defect Detection System using YOLO 14

Fabric Defect Detection System using YOLO

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14th IIAI International Congress on advanced Applied Informatics, IIAI-AAI 2023

作者： Kawaguchi, Masashi Morimitsu, Yuta Watanabe, Ryusei Ikuta, Chihiro Ishii, Naohiro Umeno, Masayoshi National Institute of Technology Suzuka College Department of Electrical and Electronic Engineering Suzuka Japan Advanced Institute of Industrial Technology Information Systems Architecture Course Tokyo Japan Chubu University Department of Electronic Engineering Kasugai Japan

ISBN: (纸本)9798350324228

In textile processing companies, after purchasing fabric, dyeing, and other treatments, such as surface finishing are carried out before shipping. Defect detection technology is one of the most important issues in this field. Because if we ship a defective fabric without noticing the defect, it will cause damage to the delivery destination. Previously, defect detection of fabric has been performed using methods such as CNN. However, conventional methods such as general multilayer neural networks require two separate processing stages: object detection and identification. Therefore, there is a problem that sufficient real-time performance cannot be guaranteed in the inspection process on an actual manufacturing line. Therefore, in this study, we construct a fabric defect detection system using YOLO which is capable of real-time object detection. In the future, we aim to realize the automation of textile defect detection systems by the construction of an image recognition system using machine learning. It is expected that this will result from reduced detection errors by the human eye, improve detection accuracy, and reduce labor costs. © 2023 IEEE.

关键词： Machine learning

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One Solution to the Cost-Sharing Issue by the Sweden Game Experiment and Educational Effects 15

One Solution to the Cost-Sharing Issue by the Sweden Game Ex...

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15th International Congress on advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Kawaguchi, Masashi Kusaka, Takashi Ishii, Naohiro Umeno, Masayoshi National Institute of Technology Suzuka College Department of Electrical & Electronic Engineering Suzuka Japan National Institute of Technology Suzuka College Department of General Education Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Chubu University Department of Electronic Engineering Kasugai Japan

ISBN: (纸本)9798350383829

The issue of cost-sharing has been familiar since ancient times. Even today, it is used in various situations, such as cost-sharing for infrastructure development and contributions to neighborhood associations. Many methods have been proposed for the cost-sharing problem. One typical solution is the Shapley value. However, there are cases where the best solution cannot be obtained. In addition, if the solution is disadvantageous to the parties, there is a risk of dissatisfaction and failure to reach an agreement. On the other hand, gaming solutions are attracting attention. A mutually satisfactory solution can be obtained by gaming by solving the cost-sharing problem between the parties. Furthermore, better solutions than other methods have been derived. In this paper, we report on the optimal solution derived from the gaming results, its educational effects, and the results of a webinar on gaming in Finland and Japan. © 2023 IEEE.

关键词： Cost effectiveness

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Generation of Independence for Classification in the Bio-inspired Networks

Generation of Independence for Classification in the Bio-ins...

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Naohiro Ishii Kazunori Iwata Kazuya Odagiri Tokuro Matsuo Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japn Department of Business Administration Aichi University Nagoya Japan School of Culture-Information Studies Sugiyama Jyogakuen University Nagoya Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

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Meaning of the Clusters on Dimensionality Reduction by Word Clustering 12

Meaning of the Clusters on Dimensionality Reduction by Word ...

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12th International Congress on advanced Applied Informatics, IIAI-AAI 2022

作者： Deguchi, Toshinori Seo, Sinsuke Ishii, Naohiro Dept. of Electrical and Computer Engineering Gifu College Gifu Japan Interdisciplinary Technology Development Gifu College Gifu Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the *** this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset. © 2022 IEEE.

关键词： Classification (of information)

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Fabric Defect Detection System using YOLO

Fabric Defect Detection System using YOLO

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Masashi Kawaguchi Yuta Morimitsu Ryusei Watanabe Chihiro Ikuta Naohiro Ishii Masayoshi Umeno Department of Electrical & Electronic Engineering Suzuka College National Institute of Technology Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Department of Electronic Engineering Chubu University Kasugai Japan

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Analog Hardware Neural Networks using Logarithmic Four-Quadrant Multiple Circuits 10

Analog Hardware Neural Networks using Logarithmic Four-Quadr...

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10th International Congress on advanced Applied Informatics, IIAI-AAI 2021

作者： Kawaguchi, Masashi Ishii, Naohiro Umeno, Masayoshi National Institute of Technology Suzuka College Department of Electrical & Electronic Engineering Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Chubu University Department of Electronic Engineering Kasugai Japan

ISBN: (纸本)9781665424202

In the artificial intelligence, machine learning and neural network fields, many practical use models have been proposed. However, these models are based on a digital Von Neumann model's computer. There are a minority of studies in the field of analog learning neural networks. In our previous study, we used multiple circuits as connecting weights. Multiple circuits calculate the product of the two input values, input signals and connecting weights. However, the working range is very small because of the semiconductor characteristics. When constructing a network, one of the disadvantages is that the input and output range is limited. Furthermore, the circuit operation becomes unstable because of the characteristics of the multiple circuit. In this study, we proposed logarithmic four-quadrant multiple circuits as a connecting weight. From the experimental result, it is shown that this circuit operates stably over a wide range. The model works using pure analog electronic circuits. The learning time is quite short compare to a digital process computer. We improved the number of units and network layers. Moreover, we suggest the possibility of the realization of the hardware implementation of the deep learning model. © 2021 IEEE.

关键词： Network layers

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One Solution to the Cost-Sharing Issue by the Sweden Game Experiment and Educational Effects

One Solution to the Cost-Sharing Issue by the Sweden Game Ex...

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advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Masashi Kawaguchi Takashi Kusaka Naohiro Ishii Masayoshi Umeno Department of Electrical & Electronic Engineering National Institute of Technology Suzuka College Suzuka Japan Department of General Education National Institute of Technology Suzuka College Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Department of Electronic Engineering Chubu University Kasugai Japan

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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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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