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检索条件"任意字段=11th International Conference on Computer-Aided Design and Computer Graphics"

共 8485 条记录，以下是1141-1150 订阅

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e-Cattie: A Versatile Climbing Quadrupedal Robot

e-Cattie: A Versatile Climbing Quadrupedal Robot

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： Emmanouil Papadakis Markos Sigalas Michail Vangos Panos Trahanias Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) Heraklion Greece Department of Computer Science University of Crete Heraklion Greece

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

this paper introduces e-Cattie, a novel quadrupedal robotic system equipped for climbing and traversing surfaces of various inclinations and roughness, from planar terrains to vertical walls and even upside-down surfaces. designed with autonomy and versatility at its core, e-Cattie is inspired by the agility and climbing prowess of cats, embodying dynamic posture and gait adaptability to meet diverse operational needs while maintaining significant payload capacity. the system integrates three key components: a highly agile quadrupedal robotic module, a vacuum system for effective surface attachment, and an innovative suction cup module for secure and adaptable surface adhesion. Experimental evaluations demonstrate e-Cattie's capabilities in walking and climbing across different inclinations, highlighting its potential in maintenance, surveillance, and search-and-rescue applications. the experiments further assess the robot's efficiency with respect to the holding force across all orientations, showcasing the practical implications of its design for real-world applications. e-Cattie's introduction marks a significant step towards developing autonomous robotic systems capable of navigating challenging terrains, paving the way for future advancements in robotic climbing and traversal technologies.

关键词： Legged locomotion Vacuum systems Mechatronics Navigation Surveillance Surface roughness Rough surfaces Maintenance Quadrupedal robots Payloads

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Predicting Future Event via Small Data (e.g., 4 Data) by ASF and Curve Fitting Methods 11

Predicting Future Event via Small Data (e.g., 4 Data) by ASF...

引用

11th international conference on Intelligent Control and Information Processing, ICICIP 2021

作者： Zhang, Yunong Chen, Jielong Lu, Haosen Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China

ISBN: (纸本)9781665425155

Future prediction is a branch of information processing. An attempt to predict some future event year is presented in this work via combining the addition-subtraction frequency (ASF) method, i.e., ASF algorithms with 3 inputs, and multiple mathematical modeling methods (e.g., polynomial curve fitting, exponential curve fitting, and smoothing spline). the 3-input ASF algorithms using full-traversal, equal-half-traversal, and unequal-half-traversal are applied in the numerical experiments. the difficult challenge we face is that the raw data set size is small, i.e., only 4. thus, we process the limited information in a variety of ways, i.e., we handle the small data set by using multiple methods. We finally predict that 2021, 2022, or 2027 is of relatively high possibility to be a future year of such a small-data sequence. there may be errors of one to two years, and it may be avoided if some proper measures are taken. © 2021 IEEE.

关键词： Curve fitting

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Play that Trunky Music: Development of an Auditory Enrichment Device for Elephants in Zoos 24

Play That Trunky Music: Development of an Auditory Enrichmen...

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11th international conference on Animal computer Interaction

作者： Mastali, Arianna Mayo, Benjamin Ramey, Charles Elgart, Nate Miller, Kirby Jackson, Melody Georgia Inst Technol Atlanta GA 30332 USA Zoo Atlanta Atlanta GA USA

ISBN: (纸本)9798400711756

Elephants are a quintessential animal for zoos and wildlife parks. these zoo-housed animals serve as ambassadors to educate the public about their respective species as well as wildlife more broadly. As with any zoo-housed animal, physical and cognitive engagement and exercise are crucially important to the well-being of zoo-housed elephants. A key component of cognitive stimulation for elephants is a complex and variable environment. We designed and deployed an instrumented enrichment device for African elephants (Loxodonta africana) at Zoo Atlanta, augmenting their existing foodbased environmental enrichment with audio cognitive enrichment. To gauge elephant interest in our device, we compared usage of the existing food-based enrichment before and after augmentation with audio. the device was installed for 7 days and 10 hours and had a positive impact on frequency and retention time with the existing enrichment, increasing frequency of usage by 81 instances and retention time by 3 hours, 28 minutes, and 23 seconds. While our audio enrichment device was successful at collecting data with 88.14% accuracy, improvements could be made to the sensing methods to reduce the rate of false actuations. Overall, the study is an example of successfully collecting longitudinal data with elephants and showed that these elephants responded positively to sound enrichment.

关键词： animal computer interaction zoo enrichment elephant enrichment

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Deep Reinforcement Learning-based Computing Offloading and Resource Allocation Algorithm in Internet of Vehicles

Deep Reinforcement Learning-based Computing Offloading and R...

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international conference on Information, Communication and Networks (ICICN)

作者： Yiping Yuan Xin Chen Libo Jiao Yijie Wang School of Control Science and Engineering Beijing Information Science & Technology University Beijing China School of Computer Science Beijing Information Science & Technology University Beijing China

With the rapid emergence of the Internet of Vehicles (IoV) and 6G networks, the exponential growth in data traffic has presented a formidable challenge to the limited computational capabilities of in-vehicle devices. To overcome this challenge, mobile edge computing (MEC) offers a viable solution by offloading computationally intensive tasks to the edge servers. In this study, we design a novel joint optimization approach that leverages deep reinforcement learning (DRL) to inform decisions on computational offloading, energy consumption, and resource allocation. Our proposed scheme considers the involvement of both the base station (BS) and road side units (RSUs) in providing computing resources to the user while simultaneously minimizing energy consumption and reducing latency of computational tasks. Our simulations unequivocally establish the efficacy of the Deep Reinforcement Learning-based computing offloading and resource allocation (DCORA) algorithm. the proposed DCORA algorithm outperforms alternative baseline schemes by approximately 15% in direct comparison.

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design of Innovation and Entrepreneurship Service Platform Based on WebGIS

Design of Innovation and Entrepreneurship Service Platform B...

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computer Technologies and Development (TechDev), international conference on

作者： Lei Liu Liuyu Yang Guangdong Polytechnic Foshan Guangdong China Guangdong Medical University Dongguan Guangdong China

under the new mode of “Internet +”, the advantages of information technology such as the Internet are used to escort innovation and entrepreneurship activities. this new mode is conducive to promoting the development of innovation and entrepreneurship. In view of the needs of the innovation and entrepreneurship service platform of Foshan Software Park, the technical framework and implementation approach of the innovation and entrepreneurship service platform are studied and formulated. the new generation of science and technology, combined with intelligent cluster technology, plug-in development technology, service-oriented SOA architecture, web service technology and GIS technology, are used to build an innovation and entrepreneurship public service platform for the government and enterprises and institutions in the district, and provide services around the construction of the platform.

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Individual temporal and spatial dynamics of learning to control central Beta activity in neurofeedback training

Individual temporal and spatial dynamics of learning to cont...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Elmeri Syrjänen Joana Silva Elaine Astrand MDU Vasterås Sweden

Neurofeedback (NFB) and Brain-computer Interface (BCI) research seldom present within-session individual learning dynamics. this is even though a large proportion of NFB and BCI users cannot learn neural self-regulation required to control the feedback. Understanding the time course and learning variability between participants might allow us to design better NFB and BCI protocols to promote learning of neural self-regulation. the importance of developing novel NFB and BCI protocols becomes apparent, considering the clinical utility of these techniques. Tuning the brain to perform optimally could provide for long-term non-pharmacological treatment without any drug-associated side effects. this paper reports the strategies used by participants and the individual dynamics of central Beta NFB downregulation training and associated mental strategies for nine participants. the results showed that all participants could learn to downregulate their central Beta power in a single session, however, the dynamics of learning differed between participants. We visually identified two learning dynamics; 1) a continual decrease in Beta power and 2) an initial decrease followed by a stable level of Beta power. Topographic plots indicated high spatial variability in Beta power decreases in participants. Responses from end-of-session debriefing indicated that all participants felt they could control the feedback. Although participants could control the feedback, an optimal mental strategy for controlling central Beta power was not revealed.

关键词： Training Protocols Neural engineering Feedback amplifiers Brain-computer interfaces Neurofeedback Tuning

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier

Handwritten Digit Classification based on Ensemble Majority ...

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international conference on Systems and Control (ICSC)

作者： Mariam Alsharif Amina Abdo Kenz A Bozed Faculty of Information Technology University of Benghazi Benghazi Libya Departemnt of Artificial Intelligence Collage of Computer Technology-Benghazi Benghazi Libya Department of Computer System Design University of Benghazi Benghazi Libya

Handwritten digit recognition is a significant challenge in the field of machine learning, particularly for pattern recognition and computer vision applications. It has found applications in various areas, such as identifying digits on utility maps, postal mail zip codes, bank check amounts, and more. Offline handwritten digits possess distinct characteristics, including size, orientation, position, and thickness. Each person's handwriting is unique, making the classification process more difficult. Additionally, the high similarity between certain digits and the over fitting problem with high-dimensional data can increase computational time and cost. Consequently, numerous researchers have developed and implemented different machine learning algorithms to effectively address the issue of recognizing handwritten digits. this paper introduces a novel approach to improve the classification performance and accuracy of handwritten digit recognition. the proposed method utilizes an ensemble majority vote classifier, which combines three classifiers in each experiment by utilizing a range of different algorithms; Naïve Bayes (NB), Adaptive Boosting Algorithm (AdaBoost), K-Nearest Neighbors (K-NN), Multi-layer Perceptron Classifier (MLP), eXtreme Gradient Boosting algorithm (XGBoost), and Decision Tree Algorithm (DT) to form a single ensemble classifier. To validate the approach, we used the MINST free public handwritten dataset, to ascertain the classifier with the highest rate of accuracy in this study. the results obtained from the experiments demonstrated that the full performance of Ensemble model classifiers MLP, XGBOOST, and DT is superior to AdaBoost, K-NN, and NB. It achieved the highest accuracy level, reaching 98% in the experiment. the experimental results of this research showed that the recognition accuracy was improved compared to previous works.

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On Improving Malaria Parasite Detection from Microscopic Images: A Comparative Analytics of Hybrid Deep Learning Models

On Improving Malaria Parasite Detection from Microscopic Ima...

引用

international conference of Information and Communication Technology (ICoICT)

作者： Antora Dev Mostafa M. Fouda Leslie Kerby Zubair Md Fadlullah Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Department of Computer Science Idaho State University Pocatello ID USA Department of Computer Science Western University London ON Canada

Malaria, a life-threatening mosquito-borne disease, contributes to a significantly high number of fatalities in tropical/sub-tropical regions due to inadequate detection technology, lack of laboratory experience, and other barriers. From the design perspective of a general-purpose point-of-care solution for detecting malaria along with other tropical diseases, malaria parasite detection from blood work needs to integrate accurate and fast detection capabilities. In this vein, in this paper, we develop three hybrid data-driven models in this paper that combine a convolutional neural network (CNN) with long short-term memory (LSTM), bi-directional LSTM (BiLSTM), and gated recurrent unit (GRU), respectively. CNN is employed in all three proposed models to extract the relevant features that are passed to two cascaded layers of Recurrent Neural Networks (RNNs) in each model that acts as a classifier. Based on the experiments conducted with a public dataset, we demonstrate that our designed CNN-GRU-GRU hybrid model outperformed the other models in terms of accuracy (96.01%), less type-I error rate (1.81%), and type-II error rate (2.18%). On the other hand, the CNN-LSTM-LSTM model was attributed to a low computing (training) time of just 4 minutes and 46 seconds. Our findings clearly elucidate the potential of combining classifiers in biomedical analytics research and pave the way for portable point-of-care devices with reasonable accuracy and fast computation times, enabling them to be used for collaborative learning for large-scale, real-time disease modeling.

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ConformalRefiner: Retinal Vessel Topology Reconstruction via Conformal Risk Control 11th

ConformalRefiner: Retinal Vessel Topology Reconstruction via...

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11th international Joint conference on Rough Sets, IJCRS 2025

作者： Pang, Xiaolong Wei, Zhipeng Shi, Jie Yue, Xiaodong Artificial Intelligence Institute of Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China VLN Lab NAVI MedTech Co. Ltd. Shanghai China

ISBN: (纸本)9783031927409

Retinal vessel segmentation has been widely applied in ophthalmic disease diagnosis. However, current deep-learning-based vessel segmentation methods still suffer from disconnected vessel structures. they struggle with noise interference and low signal-to-noise ratios in difficult-to-separate regions. We typically set fixed threshold values for class probabilities output by the network to separate the results, but this can lead to ignoring vessels in ambiguous regions. Furthermore, current threshold selection methods do not take into account the overall distribution of the sample population. To address these issues, we propose a plug-and-play vessel segmentation reconstruction network, ConformalRefiner, which employs Conformal Risk Control. Firstly, we use a threshold calibration method based on conformal risk control theory to alleviate the uncertainty in selecting an initial threshold, thereby obtaining a significant threshold value that includes more challenging vessels in the calibrated result. Additionally, we design a dual-input reconstruction network that utilizes topological calibration outcomes to guide the reconstruction of the initial segmentation mask. Finally, to tackle the issue of noise introduced by the calibration, we employ vascular topology priors to further enhance performance. Experimental results show our approach outperforms state-of-the-art methods on DRIVE and FIVE datasets, especially in topological connectivity metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Conformal risk control Deep learning Retinal vessel segmentation Topology prior

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Real-time Multi Pose Trajectory Tracking based on OpenPose Keypoints 11

Real-time Multi Pose Trajectory Tracking based on OpenPose K...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Surowka, Adam Cracow Univ Technol Fac Elect & Comp Engn Ul Warszawska 24 PL-31155 Krakow Poland

ISBN: (纸本)9781665426053

Currently, there exist several open-source computer vision libraries designed for human pose estimation from photos and videos. they are mainly focused on the possibility of detecting individuals in the image and returning their skeleton determinants. An often overlooked or underdeveloped functionality is tracking the trajectory of the detected people, which presents a serious problem in the process of design automated video surveillance systems. In this work, the author attempts to develop an algorithm for tracking multiple human poses in real-time, based on simple decision filters. the developed solution is designed to work with keypoints obtained from a selected open-source human poses recognition library. the author reveals details related to the method of processing and analysing obtained keypoints, describes the concept of decision filters and presents the results of the software implementation.

关键词： automated surveillance system human pose recognition human pose tracking body posture analysis computer vision data analysis

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