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MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

MMA-Net: Multiple Morphology-Aware Network for Automated Cob...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Zhengxuan Qiu Jie Yang Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18°, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement.

关键词： Training Image segmentation Sensitivity Scoliosis Noise Morphology Reliability

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Performance Analysis of Structural Defect Classification Applied to Concrete Cracks Using Light-Weight Machine Learning Models

Performance Analysis of Structural Defect Classification App...

引用

robotics, automation and Artificial intelligence (RAAI), ieee international Conference on

作者： Shreyas Sanjeev Desai Shashank Hegde Vidya Kalyandurg Rohan V Shruthi M L J Department of ECE PES University Bengaluru Karnataka India

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Detection of concrete cracks plays a vital role in structural defect analysis. Classification using machine learning techniques is one of the ways to perform the process. This paper discusses the performance analysis of light-weight models used to detect concrete cracks. The Concrete Crack Images for Classification (CCIC) dataset is used in the training and testing phases of NASNetMobile., MobileNet V2., DenseNet121., and VGG-19. Modifications in the networks include layers for binary classification of the data. The quantitative analysis proves the ability of NASNetMobile as a light-weight model while MobileNet V2 can be opted if training time is the constraint. The experimental results validate a trade-off between the usage of light-weight models., especially., NASNetMobile as against regular networks. To prove the tradeoff., the performance of light-weight models is compared with VGG-19 which is a regular model. Analysis proves to waive off the usage of regular models in comparison with the NASNetMobile model.

关键词： Training Analytical models Accuracy computational modeling Machine learning Real-time systems Performance analysis

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An Improved YOLOv5 Model for Detecting Citrus Shoots in Complex Orchard Environments

An Improved YOLOv5 Model for Detecting Citrus Shoots in Comp...

引用

robotics, automation and Artificial intelligence (RAAI), ieee international Conference on

作者： Feng Xiao Haibin Wang Tianqi Huang Yueqin Xu College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

The development of citrus shoots is of great significance for the growth, flowering, fruiting, and pest and disease control of citrus trees. In order to achieve accurate recognition of citrus shoots in complex orchard environments, an improved YOLOv5 model, named YOLOv5-RepVGG-SE, was proposed. Firstly, in order to enhance the feature extraction capability, the Rep VGG modules are used to replace the Conv modules in the backbone networks of the YOLOv5 model. Second, the Squeeze-Excitation (SE) attention modules are added to the neck networks of the YOLOv5 model to improve the feature fusion capability. A dataset of citrus shoots was established to evaluate the effectiveness of the improved YOLOv5 model. The experimental results show that the improved YOLOv5 model achieves a precision ( $P$ ) of 81.1 %, a recall ( $R$ ) of 76.8%, and a mean average precision (mAP) of 71.0% at a threshold of 0.5. These values are 0.6%, 9.20/0, and 9.4% higher, respectively, than those of the YOLOv5 model. This research can serve as a reference for the development of smart devices in citrus orchards.

关键词： YOLO Accuracy Phenotypes computational modeling Vegetation Feature extraction Neck

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Behavior based mobile robot in automatic assembly

Behavior based mobile robot in automatic assembly

引用

ieee international symposium on Assembly and Manufacturing (ISAM)

作者： B. Jerbac B. Vranje Z. Kunica Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia

This work deals with the development of intelligent autonomous robot using behavior based control approach. The hypothesis is that intelligence, as something what assumes the understanding, creativity and self-improving, should rely on the learning ability. Planning of intelligent robot behavior addresses three main issues: finding task solutions in unknown situations, learning from experience and recognizing the similarity of problem paradigms. The presented behavior based model integrates perception, recognition, problem solving and learning capabilities. The reinforcement learning method is used here to evaluate robot behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience which can be used in solving similar future problems. To provide the recognition of problem similarities, the adaptive fuzzy shadowed (AFS) neural network is applied. This behavior based approach to the robot intelligence is simulated on the mobile robot model and verified on the Pioneer 2DX, the real mobile robot, using primarily the sonar perception of working environment to manage its performance in unknown surroundings for given task

关键词： Mobile robots Robotic assembly Intelligent robots robotics and automation Automatic control Problem-solving Learning Fuzzy neural networks Neural networks Sonar

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Improved target tracking based on spatio-temporal learning

Improved target tracking based on spatio-temporal learning

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international Conference on Information and automation (ICIA)

作者： Songmin Jia Dishi Zeng Tao Xu Hui Zhang Xiuzhi Li College of Electronic and Control Engineering Beijing University of Technology Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781509041039

Object tracking is always in the core status with the develop of robotics. In this paper, we proposed a novel tracking framework based on Spatio-Temporal Context tracking (STC) and Template-Matching algorithm(TM). STC tracking method is a fast and simplified tracking method that strongly dependent on the context region (the surrounding background of the target). Therefore, it cannot correct the tracking error by itself and even lost the target during tracking. Under this circumstance, we make a closed-loop judgment whether the movement of the target between two neighboring image sequence larger than a constant or not. If the movement larger than the constant, the template matching is employed as sample detector to correct the error or re-track the target on line. The template-matching algorithm is an efficient and fast detection algorithm that find the maximum probability point in the image that similar to the template. In order to decrease the calculating time, the search region is not the whole image but the context region of STC tracking method. What's more, after tracking and detecting we update the scale parameter to adapt the change of the target's appearance. Finally, experimental result demonstrate that our tracking method have improve the robustness of tracking.

关键词： Target tracking Context Context modeling computational modeling Robustness Mathematical model

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Towards Ubiquitous Production Systems and Enterprises

Towards Ubiquitous Production Systems and Enterprises

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ieee international symposium on Industrial Electronics (ISIE)

作者： G. Putnik C. Cardeira P. Leitao F. Restivo J. Santos A. Sluga P. Butala Universidade do Minho Braga Portugal IDMEC Laboratory at Instituto Superior Técnico Technical University of Lisbon Lisboa Portugal Polytechnic Institute of Bragança Braganca Portugal Faculdade de Engenharia (FEUP) Universidade do Porto Porto Portugal Cambus Universitário de Santiago Universidade de Aveiro Aveiro Portugal Faculty of Mechanical Engineering University of Ljubljana Ljubljana Slovenia

In the evolvable manufacturing world we are moving towards systems made of small intelligent interacting units which make their own decisions independently from a centralized decision maker. System intelligence will be the collective intelligence resulting from a large number of individual decision, each one based on its local knowledge. in our paper we present the ubiquitous production systems and enterprises (UPSE) whose foundations can be found in two main scientific areas/disciplines, (1) ubiquitous computing systems and (2) virtual and networked enterprises and organizations. We present the concept and the underlying formal model as well as our positioning in the state of the art of similar concepts. Finally we present some ongoing experiments that will work as a testbed for the UPSE concept.

关键词： Production systems Ubiquitous computing Pervasive computing Robot sensing systems Radio frequency Physics computing Assembly systems robotics and automation Flexible manufacturing systems Intelligent manufacturing systems

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AI Adoption in Healthcare: Addressing Challenges and Change Management

AI Adoption in Healthcare: Addressing Challenges and Change ...

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international symposium on computational intelligence and Informatics

作者： Vivien Leuba Noémi Piricz Innovation Management Doctoral School Óbuda University Budapest Hungary Keleti Károly Faculty of Business and Management Óbuda University Budapest Hungary

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

The rapid advancement of Artificial intelligence (AI) presents new opportunities and challenges for companies, particularly in the areas of automation and innovation. However, the success of AI-technology implementation largely depends on effective change management, which aims to address employee resistance and also influences the organizational culture. This article highlights the importance of change management by examining its key success factors, the impact of leadership, and potential failure points, such as inadequate communication or insufficient resources. Furthermore, it explores the most effective models for AI technology implementation, with special focus on how change management practices facilitate successful integration. Additionally, the article explains the problems and challenges identified in the literature related to AI-adoption and change management, such as the lack of technological competencies, and ethical concerns surrounding new technologies. Finally, this paper proposes solutions that, when applied as strategies, can lead to successful implementation, including continuous education and training, clear goal setting and leadership commitment.

关键词： Resistance Training Technological innovation Leadership Ethics Navigation Companies Medical services Artificial intelligence Informatics

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Facilitating Processor-Based DPR Systems for non-DPR Experts

Facilitating Processor-Based DPR Systems for non-DPR Experts

引用

Annual ieee symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Edward Chen William Gruver Dorian Sabaz Lesley Shannon School of Engineering Science Simon Fraser University Burnaby BC Canada Intelligent Robotics Corporation North Vancouver BC Canada

Currently, only Xilinx field programmable gate arrays (FPGAs) support dynamic partial reconfiguration (DPR). While there is currently some computer aided design (CAD) tool support for ISE-based DPR designs, none exists for microprocessor-based designs created in EDK. Creating DPR systems with the limited tool support currently available for ISE-based systems is already a challenging and complex process for novice DPR designers. These difficulties are severely compounded for potential microprocessor-based designs requiring a significant learning curve for novice DPR designers before they can successfully create their first working DPR system. This paper presents preliminary work towards extending the automation in Xilinx®'s current DPR design flow to include microprocessor based systems. The objective is to abstract low level details for novice designers, allowing them to focus on learning how to improve the quality of their design as opposed to how to perform the necessary manual transformations to generate a preliminary functional design. A case study demonstrated that the learning curve required to implement a first working design could be reduced by more than a factor of 15 times by improving the current automation available for microprocessor-based EDK designs.

关键词： Design automation Field programmable gate arrays Aerodynamics Microprocessors Intelligent robots Process design Fabrics Machine intelligence Service robots Orbital robotics

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Thai Language Sentence Segmentation Based on N-Gram Context Model

Thai Language Sentence Segmentation Based on N-Gram Context ...

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ieee international symposium on Parallel and Distributed Processing with Applications and ieee international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Hongbin Wang Zhiju Zhang Qiang Shen Yantuan Xian Yafei Zhang Cunli Mao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781728152103

Segmentation of Thai language's sentences is the basic task of natural language processing of Thai language and has important significance on studying lexical analysis, syntactic analysis and machine translation relating to Thai language. Combined with statistical machine learning methods and features of Thai language, a segmentation method of Thai language's sentences based on n-gram context model was proposed. The Orchid1997 corpuses were expanded through this method based on the segmentation tasks of Thai language's sentences. And then, the symbols during Thai language's paragraphs (such as words and space, etc.) were marked using n-gram algorithm depend on the lexical features and syntactic features of Thai language. The segmentation of Thai language's sentences was implemented through identifying the space character at the end of the sentence. The experimental results show that this method can realize reliable segmentation of Thai language's sentences by utilized language characteristics effectively. The comprehensive segmentation performance of space and comprehensive performance F value of sentence segmentation is easy to reach 85.43% and 71.67%, respectively.

关键词： computational linguistics learning (artificial intelligence) natural language processing natural languages text analysis Natural Language Processing natural languages computational LINGUISTICS text processing Context modeling Tai languages

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An Averaging Ensemble Method for Multiclass Brain Tumor Classification Using Transfer Learning

An Averaging Ensemble Method for Multiclass Brain Tumor Clas...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 ieee international Conference on

作者： Shahajalal Hossain Shahin Sk. Shariful Alam Shah Muhammad Azmat Ullah Dept. of Electronics & Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

A brain tumor is a disease that is caused by the abnormal growth of cells inside the brain or central spinal canal. It originates from the brain or, may spread to the brain from the other parts of the human body. While the exact causes of brain tumors remain unknown, environmental exposure and mutations in genes are the primary factors that significantly influence their progression. It is very difficult to diagnose whether the tumor is cancerous or not by analyzing the MRI images with bare eyes. For this, we need to use computer-aided diagnoses (CAD) to make the treatment easier. We have proposed an ensemble model that consists of the prediction of two deep learning models- Xception & InceptionV3 with accuracy of 97.89% and 91.56% respectively. We have used the data augmentation technique here. The proposed model gives an accuracy of 98.82%. We have also used the DenseNet121 for comparison purposes but among them, the proposed model performs better and gives high accuracy for the dataset contains about 7023 human brain MRI images which are categorized into 4 classes: glioma, meningioma, no-tumor & pituitary. The model averaging ensemble method results demonstrate that utilizing it can successfully categorize the samples that are incorrectly classified by the individual models.

关键词： Solid modeling Accuracy Magnetic resonance imaging computational modeling Transfer learning Brain tumors Medical services Predictive models Brain modeling Ensemble learning

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