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Control Modes Comparative Analysis of Floating Offshore Wind Turbines

Control Modes Comparative Analysis of Floating Offshore Wind...

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2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Shangmao, Ai Jiayin, Su College of Shipbuilding Engineering Harbin Engineering University Heilongjiang Province Harbin China

ISBN: (纸本)9798350309614

Previous most studies have been using a constant power control method to obtain the maximum efficiency of power generation, and have not considered the impact of the control method on the floating platform and the mooring system. In order to further explore the relationships between different control methods and power coefficients on the lower foundation of the floating offshore wind turbine, a coupled model of the DTU-10MW semi-submersible floating offshore wind turbine was developed in the simulation software FAST and OrcaFlex, and the floating offshore wind turbine system was analyzed using a blade pitch controller. The relationship between the power coefficient and the platform response was investigated, and then the mooring system design scheme was discussed to enhance the characteristics. The results show that the optimal power coefficient can be obtained under low wind speed conditions, and the increased and stable power output of the floating offshore wind turbine can result from the small surge and pitch of the floating platform. In addition, compared to different control methods, constant torque control leads to the high-power output and small motion response of the platform under steady wind. © 2023 IEEE.

关键词： Constant torque control Independent pitch control Mooring analysis Motion response Power coefficient

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An Eagle-Eye Vision: Advancements in Avian Species Classification 2

An Eagle-Eye Vision: Advancements in Avian Species Classific...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Kanaga Priya, P. Vinu, M.S. PrasannaBlessy, M. Kirupa, P. Gayathri, R. Selvakumar, N. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering RVS Technical Campus Coimbatore India Department of Computer Science and Engineering SNS College of Engineering Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350340235

Avian species taxonomy is an important activity in the domain of ornithology, wildlife preservation, and ecological research. Convolutional Neural Networks (CNNs) a widely used tool for automating categorization jobs in recent years. This abstract provides an overview of applying Eagle-Eye CNN to the classification of bird species. The methodology originates with image pre-processing, comprising scaling, normalization, and augmentation, to strengthen model resilience. Subsequently, the feature extraction is accomplished inside the CNN architecture layers. Moreover, the use of transfer learning harnesses the proposed CNN model, fine-tuned for the particular job of the Eagle-Eye vision engine for bird species classification. The proposed pre-trained model can also be used for other classification tasks. This technique attains a remarkable accuracy of 89% due to its large dataset and holds potential for further applications across avian taxonomy. In summary, CNNs offer an effective avenue for automated bird or avian classification, permitting more efficient and exact bird behavior study thus enhancing avian conservation attempts. © 2023 IEEE.

关键词： Accuracy Avian classification Bird Convolutional Neural Network Eagle-Eye vision engine

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Machine Learning-based Automatic Language Identification forIndian Languages 2

Machine Learning-based Automatic Language Identification for...

引用

2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Uchoi, Enjula Solanki, Utkarsh Bhandral, Udit Patiyal, Sahil Dogra, Sahil Khera, Hargun Singh Lovely Professional University Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350368109

In multilingual civilizations like India, where many languages coexist, language identification is an essential problem in natural language processing. This study investigates the effectiveness of supervised learning techniques for language identification across a variety of Indian languages, including English, Hindi, Tamil, Punjabi, Kannada, Kangri, Assamese, Kokborok, Telugu, Marathi, Nepali, Malayalam, Bengali, and Oriya. These techniques include N-gram models, Naive Bayes (NB), Support Vector Machines, and Convolutional Neural Networks. The study makes use of a corpus that comprises representative text samples from every language and assesses how well the previously discussed techniques work in correctly categorizing text into the appropriate language. The results show that the various algorithms are not all that effective, with CNN showing promise in managing the intricacies of Indian languages. In addition, the study looks into how N-gram size affects classification accuracy and considers possible improvements to boost language recognition systems' effectiveness in multilingual settings. Overall, by providing insights into efficient methods for automatic language identification across Indian languages, this research advances language technology in a variety of linguistic environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Expert Initialized Reinforcement Learning with Application to Robotic Assembly 18

Expert Initialized Reinforcement Learning with Application t...

引用

IEEE 18th international conference on automation Science and engineering (IEEE CASE)

作者： Langaa, Jeppe Sloth, Christoffer Univ Southern Denmark Maersk McKinney Moller Inst Odense Denmark

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

This paper investigates the advantages and boundaries of actor-critic reinforcement learning algorithms in an industrial setting. We compare and discuss Cycle of Learning, Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy Gradient with respect to performance in simulation as well as on a real robot setup. Furthermore, it emphasizes the importance and potential of combining demonstrated expert behavior with the actor-critic reinforcement learning setting while using it with an admittance controller to solve an industrial assembly task. Cycle of Learning and Twin Delayed Deep Deterministic Policy Gradient showed to be equally usable in simulation, while Cycle of Learning proved to be best on a real world application due to the behavior cloning loss that enables the agent to learn rapidly. The results also demonstrated that it is a necessity to incorporate an admittance controller in order to transfer the learned behavior to a real robot.

关键词： Training Robotic assembly computer aided software engineering automation Service robots Cloning Reinforcement learning

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Deep Learning for Malware Detection and Classification 4

Deep Learning for Malware Detection and Classification

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Avhad, Parimal Kolse, Shivam Pangavhane, Parth Gadekar, Tushar Sangale, Abhimanyu Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Electronics and Computer Engineering Maharashtra Pune India Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Computer Science Engineering Maharashtra Pune India

ISBN: (纸本)9798350393248

Malicious software, or malware, poses a persistent and evolving threat to cybersecurity. Traditional detection methods based on signatures and heuristics are challenged by the rapid proliferation of polymorphic and zero-day attacks. This research paper explores the application of deep learning, specifically Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in the realm of malware detection and classification. CNNs excel in capturing spatial patterns in binary data, treating it akin to images, while RNNs are adept at handling sequential information. Through rigorous experimentation and evaluation, this study provides a comparative analysis of the performance of these deep learning architectures against traditional methods. Furthermore, the paper delves into the interpretability of deep learning models, shedding light on their decision-making process in the context of malware detection. The findings of this research not only demonstrate the efficacy of deep learning in bolstering cybersecurity measures but also contribute valuable insights into the evolving landscape of malware defense strategies. © 2023 IEEE.

关键词： Recurrent neural networks

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AI Based Data Augmentation Process Used to Find the Defect in Injection Molding

AI Based Data Augmentation Process Used to Find the Defect i...

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Rebekah, Repalle Deepthi Crestose Laxmaiah, G. Raj, S Solomon Singh, Samit Kumar Hazela, Bramah Verma, Manish Kumar Ravindra College of Engineering for Women Department of Computer Science and Engineering Andhra Pradesh Kurnool India Chaitanya Bharathi Institute of Technology Department of Mechanical Engineering Hyderabad India Bhoj Reddy Engineering College for Women Department of Mechanical Engineering Hyderabad India Amity University Amity School of Engineering & Technology Department of Computer Science and Engineering Lucknow India Lovely Professional University Department of Psychology Phagwara India

ISBN: (纸本)9798350317060

Automatic quality examination is now receiving attention as a vital element of the industry 4.o. However, because of the variety of products, challenges associated with creating uniformly high-quality product pictures, and short cycle durations, injection molding technologies have not gotten much attention in this field of study. In this work, we suggested an injection molding defect inspection method for edge intelligence. The data imbalance and scarcity issues faced by small and medium-sized businesses (SMEs) are addressed via data augmentation, implemented the unique technique of the injection process, and evaluated effectiveness of the created AI model. The suggested model's accuracy was more than 90%, demonstrating the system's viability for use in the real world. © 2023 IEEE.

关键词： Injection molding

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Human-Like Multimodal Perception and Purposeful Manipulation for Deformable Objects 18

Human-Like Multimodal Perception and Purposeful Manipulation...

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IEEE 18th international conference on automation Science and engineering (IEEE CASE)

作者： Kaur, Upinder Ma, Xin Huang, Yuanmeng Voyles, Richard M. Purdue Univ Sch Engn Technol Purdue Polytech Inst W Lafayette IN 47907 USA

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Humans possess the uncanny ability to perceive and manipulate deformable objects with agility. Deformable objects, such as a ziplock bag, with their infinite configurations, are particularly challenging. Manipulating deformable objects is hard as deciding corrective action demands great familiarity with its state transitions. Humans learn to manipulate such objects by leveraging multimodal perception capabilities wherein they build a rich understanding by exploration and active manipulation. For robots, such adaptability is lacking as multimodal perception is still limited. In this work, we emulate human-like learning by extending multimodal perception to assist in exploratory manipulation-to build a rich understanding of the deformable object- and purposeful manipulation- to learn to achieve the desired state based on the knowledge of the object. To validate this framework, we undertake the task of closing a ziplock bag as it is a challenging perception task with a simple state structure demanding multi-sensory modes. The bag's parameters are learned in 10mins during exploratory manipulation. With the multimodal inputs of the three sensors, the accuracy of state detection improves significantly across the different types of bags, illustrating the adaptability. The multimodal perception achieves a maximum state detection accuracy of 95.5%. The bags are closed fully in all 50 trials with the purposeful manipulation framework.

关键词： computer aided software engineering automation Sensors Task analysis Robots

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Economic Operation Analysis of Energy Storage System in Smart Grid

Economic Operation Analysis of Energy Storage System in Smar...

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2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Yan, Jing Yang, Jingxian Yang, Zhen Hong, Yufan College of Electrical Engineering Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Lanzhou China

ISBN: (纸本)9798350309614

Regarding the continuing increase of renewable energy in smart grid, energy storage system (ESS) has play an important role in deal with the fluctuation of new energy, such as PV and wind. However, the application of ESS in smart grid is limited to its expensive cost, therefore, how to configure a reasonable capacity in a comprehensive scenario of smart grid has become a key concern for researchers. To address this issue, this article first uses a fuzzy clustering algorithm to generate scenarios of wind and PV, and builds an economic operation model for ESS based on profit margin analysis for solving the optimal capacity configuration of ESS. At the same time, an economic criterion for investment of ESS considering the life loss in smart grid is proposed and sensitivity analysis is introduced to study subsidy policies, such as electricity prices and initial investment unit prices. © 2023 IEEE.

关键词： Economic criterion Energy storage system Profit margin analysis Smart grid

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Research on Key Technologies of Ceramic MEMS Platinum Rhodium 10-platinum Thermocouple

Research on Key Technologies of Ceramic MEMS Platinum Rhodiu...

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2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Zhang, Hongquan Xu, Dexin Yu, Guangbin Yu, Junchang School of Intelligent Science and Engineering Harbin Engineering University Harbin China Nanhai Institute of Harbin Engineering University Harbin Engineering University Sanya China School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350309614

In order to solve many technical problems of high temperature thermocouple for gas turbine temperature detection, such as large heat capacity, slow response time, and inability to mount table, a ceramic MEMS high temperature thermocouple with redundant function was developed based on ceramic MEMS technology. Based on the micromachining technology, the thermocouple base was made, and the sensitive heating electrode pattern of platinum rhodium 10-platinum thermocouple (S-type thermocouple) was formed by the film process. The flexible thermocouple was formed by the sandwich packaging of the sensitive element, and the high temperature thermocouple calibration test system was developed. The experimental results show that the designed MEMS high temperature thermocouple has the characteristics of fast response, and can be installed on a smooth surface. The output sensitivity of the thermocouple is compared with that of the secondary standard platinum rhodium 10-platinum thermocouple in the temperature range of 0 ∼ 1000 °C, the deviation is ±0.3%, and the 90% response time is 22 ms. © 2023 IEEE.

关键词： ceramic micromachining Fast response flexibility Platinum rhodium 10 platinum thermocouple Redundancy

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Electric Vehicles Routing Including Smart-Charging Method and Energy Constraints 18

Electric Vehicles Routing Including Smart-Charging Method an...

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IEEE 18th international conference on automation Science and engineering (IEEE CASE)

作者： Estil-les, Maria A. Del Cacho Fanti, Maria Pia Mangini, Agostino M. Roccotelli, Michele Polytech Univ Bari Bari Italy

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

This paper presents an optimization-based approach for the Electric Vehicle Routing Problem considering Smart-Charging methods. The objective, based on the application of the model, is to obtain the shortest route for each of the electric vehicles that have to deliver freight to a set of customers minimizing the charging/discharging cost. Based on the Smart-Charging method, in which vehicles can charge/discharge energy from/to the grid, the power grid limits, and balancing needs are considered. In this way, both the charging points and the energy districts are prevented from exceeding the maximum allowed energy peak. A real case study in the Apulia Italian region (Italy) shows the effectiveness of the proposed optimization model.

关键词： Costs computer aided software engineering automation Electric vehicles Routing Power grids Optimization

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