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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zou, Weidong Cao, Weipeng Xia, Yuanqing Zhong, Bineng Li, Dachuan School of Automation Beijing Institute of Technology Beijing China Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331527471

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, RAdam, Adabound, and AdaBelief. © 2024 IEEE.

关键词： Deep neural networks

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation

arXiv

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arXiv 2025年

作者： Muttaqien, Muhammad A. Motoda, Tomohiro Hanai, Ryo Yukiyasu, Domae Automation Research Team National Institute of AIST Tokyo Japan Department of Computer Science University of Tsukuba Tsukuba Japan

This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed on utilizing gradient-based attention mechanisms and customized datasets to fine-tune performance. While CLIP, SAM, and Grad-CAM are established components, their integration within this structured pipeline represents a significant contribution to the field. The resulting segmented masks, generated through this combined approach, can be effectively utilized as inputs for robotic systems, enabling more precise and adaptive object manipulation in the context of convenience store products. © 2025, CC BY.

关键词： Robotics

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Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation

Attention-Guided Integration of CLIP and SAM for Precise Obj...

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IEEE/SICE International Symposium on System Integration

作者： Muhammad A. Muttaqien Tomohiro Motoda Ryo Hanai Domae Yukiyasu Automation Research Team National Institute of AIST Tokyo Japan Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

关键词： YOLO Image segmentation Accuracy Foundation models Scalability Pipelines System integration Robustness Robots Manipulator dynamics

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Reliable Navigation Learning in Stochastic Transportation Networks with Uncertain Topology 3

Reliable Navigation Learning in Stochastic Transportation Ne...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Guo, Hongliang Shi, Rui Deng, Yiran Chen, Qiming Zhou, Yingjie Institute for Infocomm Research Agency for Science Technology and Research Singapore Singapore School of Automation Engineering Chengdu China College of Computer Science Chengdu China College of Communication Engineering Chengdu China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

This paper studies the reliable navigation problem in stochastic transportation networks with uncertain topology. The objective is to find a dynamic routing policy, which navigates the ego vehicle to the destination with the minimal linear combination of mean and standard deviation (mean-std) of travel time. Different from almost all other stochastic routing problems, which focus on the stochasticity of travel time and assume a fixed topology of the underlying transportation network, the reliable navigation problem considered in this paper involves an additional layer of stochasticity, namely the uncertain network topology. To reach reliable navigation, we advance the canonical reinforcement learning (RL) method, which could originally find a routing policy with the minimal mean travel time, to simultaneously estimate and minimize the linear combination of the mean and standard deviation of a routing policy's travel time. Additionally, to cope with the uncertain network topology problem, we enhance RL's underlying Markov decision process (MDP) with a variational action set as well as a novel decision-list policy that outputs a list of ranked actions instead of a single one. We evaluate and compare the proposed method with state-of-the-art mean-std shortest path solutions in a range of transportation networks, and demonstrate that the proposed method achieves superior performance with the lowest response time. © 2022 IEEE.

关键词： Travel time

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Multi-station TDOA Localization Method Based on Tikhonov Regularization 7

Multi-station TDOA Localization Method Based on Tikhonov Reg...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Pan, Jianghuai Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Jiangsu Automation Research Institute Lianyungang222006 China

ISBN: (纸本)9781665478571

In this paper, we consider localization using multi-station TDOA, aiming at the invalidation of localization caused by the irrationality of the sensor distribution, proposed a new robust localization algorithm based on Tikhonov regularization method. The new method gives attention both to the resolution and the square error of the estimation results, restrained the uncertain part while keeps the certain part of the observation noise, and guarantees the validation of new method on multi-station TDOA localization even the the sensor distribution irrationally. It has a high vallue of engineering applicaton, the simulation results varify the validity of the algorithm. © 2022 IEEE.

关键词： Time difference of arrival

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Spiking Transformer with Experts Mixture 38

Spiking Transformer with Experts Mixture

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Zhaokun Lu, Yijie Jia, Yanhao Che, Kaiwei Niu, Jun Huang, Liwei Shi, Xinyu Zhu, Yuesheng Li, Guoqi Yu, Zhaofei Yuan, Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Peng Cheng Laboratory China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China Institute of Automation Chinese Academy of Sciences China Deep NeuroCognition Lab I2R and CFAR Agency for Science Technology and Research Singapore

Spiking Neural Networks (SNNs) provide a sparse spike-driven mechanism which is believed to be critical for energy-efficient deep learning. Mixture-of-Experts (MoE), on the other side, aligns with the brain mechanism of distributed and sparse processing, resulting in an efficient way of enhancing model capacity and conditional computation. In this work, we consider how to incorporate SNNs' spike-driven and MoE's conditional computation into a unified framework. However, MoE uses softmax to get the dense conditional weights for each expert and TopK to hard-sparsify the network, which does not fit the properties of SNNs. To address this issue, we reformulate MoE in SNNs and introduce the Spiking Experts Mixture Mechanism (SEMM) from the perspective of sparse spiking activation. Both the experts and the router output spiking sequences, and their element-wise operation makes SEMM computation spike-driven and dynamic sparse-conditional. By developing SEMM into Spiking Transformer, the Experts Mixture Spiking Attention (EMSA) and the Experts Mixture Spiking Perceptron (EMSP) are proposed, which performs routing allocation for head-wise and channel-wise spiking experts, respectively. Experiments show that SEMM realizes sparse conditional computation and obtains a stable improvement on neuromorphic and static datasets with approximate computational overhead based on the Spiking Transformer baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-childhood Education

AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-c...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Asif Mahmud Tahsin Faisal M. Akhtaruzzaman Jamal Uddin Tanvin Sachitra Halder Fahim Shahryer Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The smart Dialogic Alphabet Zone Board (DAZ-Board) aims to address the issue of learning process for pre-childhood education among the children. It is importance to facilitate a healthy and effective environment for early childhood education in shaping a child’s cognitive development, language acquisition, and literacy skills. Traditional teaching methods often fail to engage young learners effectively, leading to limited understanding and retention. On the other hand, dependencies on mobile devices raise the physical and mental health issues. This study introduces an interactive, auditory, and multisensory game board providing a wonderful and healthy experiences in tailoring the developmental needs of children. The objective of this research is to design and develop an interactive alphabet board with necessary sensors, electronics, and controller modules. Moreover, designing a Machine Learning (ML) model for voice-based alphabet recognition is one of the important contributions of this research. Among the results of different ML models, Naïve Bayes Classifier outperforms the most with accuracy ≈ . %. The proposed system will facilitate the preschoolers to learn in modern and interactive way, while keeping away from being addicted to the mobile devices.

关键词： Pediatrics Visualization Accuracy Scalability Education Games Speech recognition Sensors Smart phones Systematic literature review

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