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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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Manipulation of unknown objects via contact configuration regulation 39

Manipulation of unknown objects via contact configuration re...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Doshi, Neel Taylor, Orion Rodriguez, Alberto MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

We present an approach to robotic manipulation of unknown objects through regulation of the object's contact configuration: the location, geometry, and mode of all contacts between the object, robot, and environment. A contact configuration constrains the forces and motions that can be applied to the object;however, synthesizing these constraints generally requires knowledge of the object's pose and geometry. We develop an object-agnostic approach for estimation and control that circumvents this need. Our framework directly estimates a set of wrench and motion constraints which it uses to regulate the contact configuration. We use this to reactively manipulate unknown planar objects in the gravity plane. A video describing our work can be found on our project page: http://mcube. ***/research/***.

关键词： Geometry Sequential analysis Shape Kinematics Regulation Space exploration Planning

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Modeling and analysis of the Segmented Gas Finger Rehabilitation Training Actuator Based on ABAQUS 5

Modeling and Analysis of the Segmented Gas Finger Rehabilita...

引用

5th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Zhang, Zhilin Calderon, Aldrin D. School of Mechanical Manufacturing and Energy Engineering Mapúa University Manila Philippines

ISBN: (纸本)9798350357950

Stroke patients need a finger rehabilitation training driver, among them, the pneumatic soft body drive is the most commonly used. However, the traditional pneumatic soft actuator has only one air cavity. When applying air pressure control, the pneumatic soft actuator can only perform the overall bending motion and cannot perform the sectional bending motion. In this way, in the finger rehabilitation training, due to the bending position and the finger joint dislocation, easy to hurt the finger. To better simulate finger joint movements in humans, this paper presents a segmented pneumatic cavity finger rehabilitation training driver. Referring to the joint structure of human fingers, the air cavity is reasonably divided into three independent parts, with each air cavity applying a different air pressure for control. Then the mathematical modeling was established, and finally, through the ABAQUS simulation analysis, the bending movement of the joints of the actuator was tested. The experiment proved that the drive designed in this paper can achieve segmented bending control, which improved the safety of stroke patients in finger rehabilitation training. © 2023 IEEE.

关键词： ABAQUS bionics Segmented gas cavity soft actuator

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Efficiency analysis of Convolutional Neural Netwoks in Multiclass Recognition of Skin Lesions via Dermoscopy Images 10

Efficiency Analysis of Convolutional Neural Netwoks in Multi...

引用

10th international conference on Artificial intelligence and robotics, QICAR 2024

作者： Monajemi, Mahsa Shahdi, Seyed Omid Haji Nasiri, Seyed Saeed Shojaedini, Seyed Vahab Department of Biomedical Engineering Qazvin Branch Islamic Azad University Qazvin Iran Biomedical Engineering Department Iranian Research Organization for Science & Technology Tehran Iran

ISBN: (纸本)9798350348873

Although melanoma is considered as one of the deadliest types of cancer, however, detecting this disease in its early stages may lead to saving the patient's life. Biopsy is the most accurate method to diagnose this type of cancer, but due to its invasiveness trying to use non-invasive diagnostic modalities has always been of interest to researchers in this field. Dermoscopy is one of the common non-invasive modalities, in which it is determined whether the patient is healthy or cancerous by analyzing the images captured from the patient's skin. However, there may be several lesions on the skin that, despite their appearance, most of them are not cancerous and even harmless. In this way, recognizing melanoma lesions is the most important challenge in the field of analyzing dermoscopic images especially when it is desired to determine the type of other lesions in this classification process. In this article, the performance of the deep learning paradigm in the classification of multi-type lesions in dermoscopic images is evaluated and the challenges facing deep neural networks in this domain are analyzed. For this purpose, two well-known datasets of dermoscopy images known as ISIC2019 and HAM10000, which have 8 and 7 types of labeled lesions, respectively, were selected and the detection rate of lesions in them was evaluated by applying deep neural networks. The obtained results indicate that although the results obtained from the application of deep learning in the separation of lesions in both of examined datasets are significantly better than the conventional methods, but the resultant accuracy is highly dependent on the volume of data and balance between several classes of lesions. In this way, the use of data augmentation and data balancing methods should be considered as a priority for conducting any research in order to improve the analysis of dermoscopic images by using deep learning. ©2024 IEEE.

关键词： Deep neural networks

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Rapid rhythmic entrainment in bio-inspired central pattern generators

Rapid rhythmic entrainment in bio-inspired central pattern g...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Szorkovszky, Alex Veenstra, Frank Glette, Kyrre Univ Oslo Dept Informat RITMO Oslo Norway Univ Oslo Dept Informat Oslo Norway

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Entrainment of movement to a periodic stimulus is a characteristic intelligent behaviour in humans and an important goal for adaptive robotics. We demonstrate a quadruped central pattern generator (CPG), consisting of modified Matsuoka neurons, that spontaneously adjusts its period of oscillation to that of a periodic input signal. This is done by simple forcing, with the aid of a filtering network as well as a neural model with tonic input-dependent oscillation period. We first use the NSGA3 algorithm to evolve the CPG parameters, using separate fitness functions for period tunability, limb homogeneity and gait stability. Four CPGs, maximizing different weighted averages of the fitness functions, are then selected from the Pareto front and each is used as a basis for optimizing a filter network. Different numbers of neurons are tested for each filter network. We find that period tunability in particular facilitates robust entrainment, that bounding gaits entrain more easily than walking gaits, and that more neurons in the filter network are beneficial for preprocessing input signals. The system that we present can be used in conjunction with sensory feedback to allow low-level adaptive and robust behaviour in walking robots.

关键词： central pattern generator spiking neuron entrainment synchronization genetic algorithm robotics open loop

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Accurate Parameter Estimation Based on the Two-Sided KZ Filter in Low Signal-to-Noise Ratio Conditions 6

Accurate Parameter Estimation Based on the Two-Sided KZ Filt...

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6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Zhang, Liang Wang, Qing-Guo Hong Kong Baptist University Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Institute of AI and Future Networks Beijing Normal University Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798331541699

System identification is the process of developing models based on data collected from dynamic systems. A significant challenge in this field is accurately estimating model parameters using input-output data. This data, typically gathered through sensors, often contains noise due to inaccuracies in the sensors and errors during communication. The presence of such noise can lead to biased estimates of parameters, which undermines the model’s effectiveness. While traditional methods strive to minimize this bias during parameter estimation, they frequently encounter difficulties when dealing with high levels of noise. An alternative strategy involves employing filtering techniques to clean the data before estimation takes place. Low-pass (LP) filters are designed to reduce frequencies above a specific threshold and are widely used for mitigating noise issues. Among these filters, the Kolmogorov–Zurbenko (KZ) filter operates as multiple iterations of a moving average (MA) filter and has shown strong capabilities in reducing noise;it is commonly applied in high-resolution signal analysis and image processing tasks. In this study, we introduce two KZ filtering approaches aimed at denoising data prior to parameter estimation. Theoretical evaluations and experimental findings indicate that the proposed KZ filtering method surpasses LP filters and wavelet denoising techniques, particularly in environments characterized by low Signal-to-Noise Ratios (SNR). © 2024 IEEE.

关键词： Low pass filters

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CTAM4SRL: A Consolidated Temporal Analytic Method for analysis of Self-Regulated Learning 24

CTAM4SRL: A Consolidated Temporal Analytic Method for Analys...

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14th Annual international conference on Learning Analytics and Knowledge (LAK) - Learning Analytics in the Age of Artificial intelligence

作者： Nath, Debarshi Gasevic, Dragan Fan, Yizhou Rajendran, Ramkumar Indian Inst Technol Monash Res Acad Mumbai Maharashtra India Monash Univ Ctr Learning Analyt Monash CoLAM Melbourne Australia Peking Univ Grad Sch Educ Beijing Peoples R China Indian Inst Technol Interdisciplinary Programme Educ Technol Mumbai Maharashtra India

ISBN: (纸本)9798400716188

Temporality in Self-Regulated Learning (SRL) has two perspectives: one as a passage of time and the other as an ordered sequence of events. Each of these conceptions is distinct and requires independent considerations. Only a single analytic method is not sufficient in adequately capturing both these facets of temporality. Yet, most research uses a single method in temporally-focused SRL research, and those that use multiple methods do not address both aspects of temporality. We propose CTAM4SRL, a consolidated temporal analytic method which combines advanced data visualisation, network analysis and pattern mining to capture both facets of temporality. We employ CTAM4SRL in a cohort of 36 learners engaged in a reading-writing activity. Using CTAM4SRL, we were able to provide a rich temporal explanation of the interplay of the self-regulatory processes of the learners. We were further able to identify differences in SRL behaviours in high and low performers in terms of their approach to learning comprising deep and surface strategies. High performers were able to more selectively and strategically combine deep and surface learning strategies when compared to low scorers- a behaviour which was only hypothesised in SRL literature previously, but now has empirical support provided by our consolidated analytic method.

关键词： Learning Analytics Self-Regulated Learning Ordered Network analysis Visualisation Techniques pattern Mining

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Data Mining and Cloud Computing for Customer pattern analysis and Value Maximization

Data Mining and Cloud Computing for Customer Pattern Analysi...

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conference on Artificial intelligence and robotics and Asia-Pacific international Symposium

作者： Mohammad Sadegh Sirjani Seyed Amir Mousavi Mostafa Sadeghi Department of Computer Ferdowsi University of Mashhad Mashhad Iran Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran

In the contemporary business landscape, companies are seeking efficient methods to analyze customer behavior and extract actionable insights to foster customer relationships and drive business growth. This paper presents a novel approach that combines the strengths of cloud computing, software engineering, and data mining techniques to analyze customer patterns and optimize the value of the customer life cycle. By employing data mining techniques, including clustering, classification, association analysis, and predictive modeling, businesses can identify customer patterns and behaviors. Through the extensive analysis of customer data, organizations can uncover concealed patterns, preferences, and trends, which facilitate informed decisions regarding customer acquisition, retention, upselling, and personalized marketing strategies, ultimately maximizing the value of the customer life cycle. The integration of cloud computing, software engineering, and data mining enables businesses to leverage advanced analytics and extract valuable insights from customer data. This approach enables organizations to gain a comprehensive understanding of customer patterns and behaviors, thereby facilitating targeted marketing campaigns, personalized customer experiences, and improved customer satisfaction.

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2023 international conference on the Confluence of Advancements in robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

2023 International Conference on the Confluence of Advanceme...

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2023 international conference on the Confluence of Advancements in robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

ISBN: (纸本)9798350306088

The proceedings contain 66 papers. The topics discussed include: an innovative simplistic solar powered low-cost dry waste segregator robot;artificial intelligence for better organizational creativity: co-occurrence network analysis towards emerging themes;reduced area search (RAS): an algorithm to optimize path planning of mobile robots;an energy-efficient cluster-based routing protocol techniques for extending the lifetime of wireless sensor network;literature review on network security approaches using machine learning models;live wall: an AI based visual seeker through human tracking;glove rupture detection system using skin impedance;object recognition system for the visually impaired: leveraging ioT and remote server integration;and YOLO V5 deep learning model for dental problem detection.

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Double-frame rate-based cheat detection 3

Double-frame rate-based cheat detection

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3rd international conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Wang, HongXia Yan, XiangYu Li, Hui Xu, Fei School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9781510667563

The relationship between modern education development and artificial intelligence is getting closer and closer, but the technology of online examination and test detection needs to be improved. Most of the existing technologies are aimed at the comparison of pictures per frame, head detection, limb detection, key part detection, etc., although this effect is ideal, but at the same time the requirements for conditions are high. In this paper, for online exam detection, it is proposed to use face recognition and Euclidean distance combined with Yolov3 to complete the cheating detection. © 2023 SPIE.

关键词： Face recognition

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IPA-3D1K: A large Retail 3D Model Dataset for Robot Picking

IPA-3D1K: A large Retail 3D Model Dataset for Robot Picking

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Lindermayr, Jochen Odabasi, Cagatay Jordan, Florian Graf, Florenz Knak, Lukas Kraus, Werner Bormann, Richard Huber, Marco F. Fraunhofer IPA Dept Robot & Assist Syst D-70569 Stuttgart Germany Univ Stuttgart Fraunhofer IPA Dept Cyber Cognit Intelligence CCI D-70569 Stuttgart Germany Univ Stuttgart Inst Ind Mfg & Management IFF D-70569 Stuttgart Germany

ISBN: (纸本)9781665491907

Robotic applications like automated order picking in warehouses or retail stores, or fetch and carry tasks in hospitals, care homes, or households rely on the capability of service robots to find and handle a specific type of object. These applications are challenging as the set of objects is very large and varies over time. Despite its significance, there is no suitable universal large-scale dataset available from the retail domain, which allows for a principled analysis of all relevant robotics research aspects in that field. Hence, this paper introduces a novel dataset of more than 1,000 retail objects, including color images, 3D scans, and high-resolution textured 3D models of individual objects, synthetic scenes and real settings, which covers the specifics of the retail domain. The dataset was designed to serve researchers in all relevant robotics tasks in retail like 3D reconstruction and object modeling, large-scale object classification and instance detection including incremental learning and fine-grained detection, text reading, logo detection, semantic grounding and affordance detection, grasp analysis and manipulation planning, as well as digital twinning and virtual environments. Based on synthetic RGB images of scenes created from the 3D models, two exemplary use cases are examined in this paper to demonstrate the benefits of the dataset: we evaluate the state-of-the-art incremental object detection method InstanceNet and a few-shot fine-grained object classification method. The results prove the suitability of InstanceNet for incremental object detection on large datasets and are promising for the few-shot object classification system.

关键词： Large dataset

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