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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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IEEE International Conference on robotics and Biomimetics

作者： Jingchen Huang Yun Fang Weichao Guo Zhijian Qiao Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering and the Meta Robotics Institute Shanghai Jiao Tong University Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design.

关键词： Vibrations Solid modeling Virtual reality Amplitude modulation Robot sensing systems Motors Stability analysis Skin Haptic interfaces Spatiotemporal phenomena

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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2025 IEEE International Conference on Big Data and Smart Computing, BigComp 2025

作者： Hwang, Sooyeon Gweon, Gahgene Kwon, Eunseong Seoul National University Interdisciplinary Program in Artificial Intelligence Korea Republic of Seoul National University Graduate School of Convergence Science and Technology Korea Republic of POSTECH Dept. of Computer Science and Engineering Korea Republic of

关键词： Contrastive Learning

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Financial Forecasting Through Hybrid Algorithms of Machine Learning & Deep Learning 3

Financial Forecasting Through Hybrid Algorithms of Machine L...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Raman, Ramakrishnan Kafila Gehlot, Anita Shah, Heli Sanjay, C.P. Ponnusamy, R. Institute of Business Management Maharashtra Pune India SR University School of Business Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics PIT India Sri Balaji University Department of IT& Business Analytics SBUP Pune India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

This article serves as a primer where integration of different techniques related to machine learning in the realm of economic prediction, planning, and analysis (FP&A). With its ability to extract insights quickly and automatically from massive datasets, machine learning looks to be a natural fit for supporting FP&*** this article, we highlight the caveats of utilizing classic machine learning approaches for scheduling and data allocation, since most of these methods are designed for forecasting (prediction) (causal inference). Newly designed double machine cognitive approach may answer causal concerns of interest, although naïve use of machine learning often fails in this context. In this paper, we survey the existing literature on the use of machine learning to financial planning and analysis, and we utilize a case study from the field of simulation research to demonstrate the utility of this technique for both decision making and planning. We also look at how better predicting and planning may be achieved with more data sets. © 2023 IEEE.

关键词： Data mining

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

Intelligent Accident Recognition and Notification System usi...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： P. Sathish Kumar Ramesh Singh Rawat Manish Shrimali Swapnil Parikh Harshal Patil Ramya Maranan Department of Robotics & Automation United Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and IT Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur Rajasthan India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

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.

关键词： Deep learning Training Cloud computing Accuracy Roads Surveillance Feature extraction Safety Accidents Graphical user interfaces

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Neural Network Accelerated Force Estimation Rate for RMIS

Neural Network Accelerated Force Estimation Rate for RMIS

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IEEE International Conference on Robotic Computing (IRC)

作者： Dylan R. Ochoa Kevin Huang Yun-Hsuan Su Viterbi School of Engineering University of Southern California Los Angeles CA Smith College Picker Engineering Program Northampton MA Department of Computer Science Mount Holyoke College South Hadley MA

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

This paper presents a neural network approach for upsampling force data in vision-based force estimation for robot-assisted minimally invasive surgery (RMIS). This interpolation enables sampling frequencies upwards of 500Hz, dramatically increased compared to modern vision-based estimation methods alone. With greater sampling frequency, force estimation can support direct force feedback options for RMIS (compared to current, indirect methods, e.g. visual or auditory), which may increase situational awareness and thus surgical performance. The proposed network is optimized and evaluated for its viability in two ablation studies with promising results, achieving sampling rates upwards of five times the accepted minimum rate of 500Hz for direct force feedback and absolute errors within maximum force granularity of 0.06N.

关键词： Visualization Minimally invasive surgery Force Force feedback Neural networks Estimation Robot sensing systems Manipulators Frequency estimation Topology

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Active Generalized Category Discovery

arXiv

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arXiv 2024年

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github. com/mashijie1028/ActiveGCD Copyright © 2024, The Authors. All rights reserved.

关键词： Conformal mapping

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Design of Low-Cost Approximate CMOS Full Adders

Design of Low-Cost Approximate CMOS Full Adders

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Aibin Yan Shaojie Wei Zhixing Li Jie Cui Zhengfeng Huang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

Many applications have an inherent tolerance for insignificant inaccuracies. Full adders are key arithmetic functions for many error-tolerant applications. Approximate full adders are considered an efficient technique to trade off energy relative to performance and accuracy. In this paper, we propose four approximate full adders with low overhead. The proposed and the existing approximate full adders are classified into two groups according to their error distances. Simulation results show that, compared with the existing approximate full adders, in the first group, the proposed ones can reduce power-area-delay product (PADP) by 61.83%, power by 54.15%, area by 44.67%, and delay by 22.78 % on average; in the second group, the proposed ones can reduce PADP by 97.01%, power by 93.43%, area by 24.98%, and delay by 36.14% on average.

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Research Progress in Cell Classification Detection Based on Deep Learning

Research Progress in Cell Classification Detection Based on ...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Dongze Gai Chuang Zhang Yiwei Zhang Jiaduo Guo Qi Zhang Hengshen Qin Dan Dang School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Mechanical Engineering Shenyang Jianzhu University Shenyang China School of Science Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

This paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. This article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced.

关键词： Deep learning YOLO Biomedical optical imaging Accuracy Image analysis Optical imaging Feature extraction Labeling Image preprocessing Medical diagnostic imaging

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