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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Ueda, Masanori Yamada, Ryunosuke Tsuji, Tokuo Okada, Kaihei Nishimura, Takehiro Shimizu, Ryota Otsu, Yuya Yoshino, Kaori Suzuki, Yosuke Nishimura, Toshihiro Watanabe, Tetsuyou Kanazawa University Graduate School of Natural Science and Technology Kakuma-machi Ishikawa Kanazawa-shi920-1192 Japan Kanazawa University Faculty of Frontier Engineering Institute of Science and Engineering Kakuma-machi Ishikawa Kanazawa-shi920-1192 Japan

ISBN: (纸本)9798331531614

We propose an assembly system involving two robotic arms with flexible mechanisms. These flexible joints provide passive compliance, allowing the robots to absorb positional errors between assembly parts and their respective destinations. Our machine learning-based object detection system shows robustness against variations in color, shape, and ambient lighting, achieving high positional accuracy. This system achieves the execution of diverse assembly tasks using a single setup, facilitated by a universal gripper and machine learning capabilities. Furthermore, the flexible joints minimize the need for precise trajectory planning. In our study, we implemented both an instruction support system and a position error correction system using object detection. We conducted real-world assembly experiments to evaluate these systems, focusing on tasks such as screw tightening and belt drive assembly, with alignment achieved through vision feedback and force feedback control. © 2025 IEEE.

关键词： Belt drives

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Explore human parsing modality for action recognition

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1623-1633页

作者： Jinfu Liu Runwei Ding Yuhang Wen Nan Dai Fanyang Meng Fang-Lue Zhang Shen Zhao Mengyuan Liu School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina Peng Cheng Laboratory ShenzhenChina State Key Laboratory of General Artificial Intelligence Peking UniversityShenzhen Graduate SchoolShenzhenChina Changchun University of Science and Technology ChangchunChina Victoria University of Wellington WellingtonNew Zealand

Multimodal-based action recognition methods have achieved high success using pose and RGB ***,skeletons sequences lack appearance depiction and RGB images suffer irrelevant noise due to modality *** address this,the authors introduce human parsing feature map as a novel modality,since it can selectively retain effective semantic features of the body parts while filtering out most irrelevant *** authors propose a new dual-branch framework called ensemble human parsing and pose network(EPP-Net),which is the first to leverage both skeletons and human parsing modalities for action *** first human pose branch feeds robust skeletons in the graph convolutional network to model pose features,while the second human parsing branch also leverages depictive parsing feature maps to model parsing features via convolutional *** two high-level features will be effectively combined through a late fusion strategy for better action *** experiments on NTU RGB t D and NTU RGB t D 120 benchmarks consistently verify the effectiveness of our proposed EPP-Net,which outperforms the existing action recognition *** code is available at https://***/liujf69/EPP-Net-Action.

关键词： action recognition human parsing human skeletons

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Generation of Photo Slideshow with Song based on Closeness between Concept of Lyrics and That of Images

Generation of Photo Slideshow with Song based on Closeness b...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mei Hashimoto Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes a method that allows users to easily convert a large number of still images into movies by displaying photos in sync with memories or favorite songs, which offers a new media viewing method for users to look back at the vast number of photos in their photo folders. We assume here that users select a song and have images stored in local PC. One method of synchronization involves selecting images based on elapsed time of song. However, since lyrics convey meaning, it can be better to display the images that align with both lyrics’ meaning and images’ meaning matched. In order to do this, we need the space which shares the concept of both lyrics and images, and select colors as the concept. Displayed images are determined based on the distance between points that are mapped from words and images to color space. Specifically, the color concepts from words are extracted using the Color Image Scale, and that from images are determined as the most frequent colors by cluster analysis. Experimental results show that it is possible to easily create movies where images with colors that match the mood of the lyrics are displayed.

关键词： Image color analysis Mood Refining Asia Information processing Color Media Motion pictures Synchronization

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Generation of target speech with speaker individuality based on accent conversion for English pronunciation learning

Generation of target speech with speaker individuality based...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Rei Hamakawa Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper first provides an overview of the English pronunciation learning support tool. The tool aims to use "accent-modified speech that retains the learner’s voice quality" as the "target speech." We propose a new conversion model based on conventional methods for this accent conversion. Specifically, we improve the conventional LSTM-based DNN model for accent conversion by adopting a transformer-based model. Our experiments investigated the model’s ability to handle the unique katakana pronunciation characteristic of Japanese speakers. The results confirmed the effectiveness of the proposed conversion method, although challenges remain, such as the scarcity of Japanese speech data and the need to improve the accuracy of speaker identity retention.

关键词： Accuracy Synthesizers Training data Symbols Switches Information processing Phonetics Transformers Data models Speech processing

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Development of Phishing Environment with Fake Web Authentication and Verification of Risk of Data Leakage

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2024年第8期144卷 851-859页

作者： Hibino, Yoshiteru Takemoto, Shu Nozaki, Yusuke Yoshikawa, Masaya Graduate School of Science and Technology Meijo University 1-501 Shiogamaguchi Tempaku-ku Aichi Nagoya468-8502 Japan Faculty of Information Engineering Meijo University 1-501 Shiogamaguchi Tempaku-ku Aichi Nagoya468-8502 Japan

In public wireless LAN services such as free WiFi, the dangers of eavesdropping and tampering with communications have been pointed out. Evil Twin attack which is one of the problems utilizes a fake access point (AP) which has the same configuration as a genuine AP. This study develops a platform for evaluating Evil Twin attacks and investigates information leakage scenarios and thier countermeasures. The information leakage scenarios in the evaluation experiment are the theft of IDs and passwords when logging in via Web authentication and the list attack that attempts to log in to other services using the stolen ID and password pairs. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Wireless local area networks (WLAN)

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Black-Box Targeted Adversarial Attack Based on Multi-Population Genetic Algorithm

Black-Box Targeted Adversarial Attack Based on Multi-Populat...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Aiza, Yuuto Zhang, Chao Yu, Jun Graduate School of Science and Technology Niigata University Niigata Japan University of Fukui Faculty of Engineering Fukui Japan Institute of Science and Technology Niigata University Niigata Japan

ISBN: (纸本)9798350337020

The fast gradient signed method (FGSM) is an efficient white-box attack method that uses the gradient information to generate adversarial examples. However, applying the classic FGSM to real-world applications is often difficult due to the challenge of obtaining the internal structure of the models. Therefore, we have made slight modifications to the conventional genetic algorithm (GA) to effectively optimize the gradient signed function of the classic FGSM and generate adversarial examples from the perspective of the black-box attack. To attack multiple given target classes simultaneously, we initialize multiple different subpopulations and ensure that each subpopulation attacks a specified target class. Additionally, we propose two different strategies to migrate successfully attacked subpopulations into unsuccessful ones to ramp up attacks on unsuccessful classes. To evaluate the performance of the proposed algorithm, we compare it with the conventional GA when attacking the well-trained VGG19-BN model on the CIFAR-10 database. Furthermore, we investigate the impact of the proposed strategies on performance and analyze their respective contributions. The experimental results confirm that the proposed algorithm can successfully attack a greater variety of classes at a faster rate. © 2023 IEEE.

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Exhaustive Deep Learning Power Analysis for Secure Block Ciphers and Its Evaluation

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2024年第1期144卷 7-14页

作者： Takemoto, Shu Ikezaki, Yoshiya Nozaki, Yusuke Yoshikawa, Masaya Graduate School of Science and Technology Meijo University 1-501 Shiogamaguchi Tempaku-ku Aichi Nagoya468-8502 Japan Faculty of Information Engineering Meijo University 1-501 Shiogamaguchi Tempaku-ku Aichi Nagoya468-8502 Japan

In order to realize a sustainable society that integrates cyber and physical space (Society 5.0), it is important to construct secure Cyber-Physical System (CPS). Devices in industrial systems must be energy efficient for environmental protection and low latency for production efficiency. On the other hand, CPS has issues such as huge energy consumption of the entire system and delays caused by communication processing due to frequent network connections of many devices. Lightweight block ciphers are one of the key technologies to solve these problems and improve the confidentiality of highly secret communication data. PRINCE with low latency and Midori128 with low energy operation are both computationally secure. Furthermore, they have been reported to have tamper-resistant circuits with improved security against the threat of power analysis attacks, which use the power consumption of cryptographic operations to guess secret keys. However, deep learning power analysis attacks have been proposed in recent years, focusing on deep learning, which has dramatically improved performance. Therefore, it is very important to evaluate the tamper resistance of PRINCE and Midori128. Against this background, this study examines the threat of deep learning power analysis attack methods against several implementations of PRINCE and Midori128. The proposed method achieves efficient analysis by generating deep learning training data oriented to implementation types and target ciphers. Evaluation experiments using actual equipment showed that the proposed method is able to analyze all partial keys of tamper-resistant circuits, which were difficult to analyze with conventional attack methods. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Energy utilization

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LiDAR-based Pedestrian Tracking Adapting to Sparse Point Cloud Utilizing Interacting Multiple Model

LiDAR-based Pedestrian Tracking Adapting to Sparse Point Clo...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Imoto, Masanori Muhammad, Haziq Sekiguchi, Kazuma Ismail, Zool Hilmi Nonaka, Kenichiro Tokyo City University Graduate School of Integrative Science and Engineering Setagaya Tokyo Japan Tokyo City University Faculty of Science and Engineering Setagaya Tokyo Japan Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798331531614

The vehicle navigating through narrow and crowded environments requires detailed shape information of the surrounding pedestrians for collision avoidance. While Light Detection and Ranging (LiDAR) is highly effective at measuring the position of objects, its performance diminishes as the distance between the LiDAR and the objects increases. The number of data points acquired decreases, leading to a less informative reconstruction of the object's pose and shape. To address this issue, this study proposes a pedestrian tracking method that involves constructing multiple pedestrian models and estimating the appropriate model parameters from likelihoods using the Interacting Multiple Model, based on the number of point clouds. We prepare three models: ellipse, bounding box, and point cloud center of gravity models. The ellipse and bounding box models estimate pose and size, while the point cloud center of gravity model estimates pose. The elliptical model uses Random Sample Consensus to determine model parameters that suppress arm swing and body sway during walking. Through experimental validation, this method effectively demonstrated its ability to continuously track pedestrians, including those with only a few acquired data points from a pedestrian located far from the LiDAR, while accurately estimating their pose and size. © 2025 IEEE.

关键词： Object tracking

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Production of mesenchymal stem cell derived-secretome as cell-free regenerative therapy and immunomodulation:A biomanufacturing perspective

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BIOCELL 2022年第8期46卷 1885-1891页

作者： FUAD GANDHI TORIZAL FRANSISCUS FIANO ANTHONY KERANS ANNISA KHUMAIRA Department of Biotechnology Faculty of Science and TechnologyUniversitas'Aisyiyah YogyakartaYogyakarta55292Indonesia Department of Bioengineering and Department of Chemical Systems Engineering Graduate School of EngineeringThe University of TokyoTokyo1138654Japan Department of Physiology and Biochemistry Faculty of Medicine and Health ScienceWarmadewa UniversityBali80239Indonesia

The potential of mesenchymal stem cells(MSCs)in regenerative medicine has been largely known due to their capability to induce tissue regeneration in vivo with minimum inflammation during *** adult stem cell type exhibit unique features of tissue repair mechanism and immune modulation mediated by their secreted factors,called ***,the utilization of secretome as a therapeutic agent provided new insight into cell-free ***,a sufficient amount of secretome is necessary to realize their applications for translational medicine which required a proper biomanufacturing *** factors related to their production need to be considered to produce a clinical-grade secretome as a biological therapeutic *** viewpoint highlights the current challenges and considerations during the biomanufacturing process of MSCs secretome.

关键词： Secretome MSCs Culture system

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A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jedidah Mwaura Shunsuke Araki Ken’Ichi Kakizaki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology FuKuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices.

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