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arXiv 2024年

作者： Islam, Sharfin He, Zhanpeng Ciocarlie, Matei Dept. of Mechanical Engineering Dept. of Computer Science Columbia University New York United States

Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining an appropriate set of hardware parameters for this class of mechanisms, therefore, is difficult - even for traditional taskbased co-optimization methods. In this paper, our goal is to implement a task-based design and policy co-optimization method for underactuated, tendon-driven manipulators. We first formulate a general model for an underactuated, tendon-driven transmission. We then use this model to co-optimize a three-link, two-actuator kinematic chain using reinforcement learning. We demonstrate that our optimized tendon transmission and control policy can be transferred reliably to physical hardware with real-world reaching experiments. © 2024, CC BY-SA.

关键词： Actuators

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Early Detection of Type 2 Diabetes Using Oversampling-Based Ensemble Learning for Classification of Imbalanced Dataset

Early Detection of Type 2 Diabetes Using Oversampling-Based ...

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2nd International Conference on Signal Processing, Communication, Power and Embedded systems, SCOPES 2024

作者： Rajasekhar, P. Mourya, Sridhar Bathala, Narasimha Reddy Lavanya, G. Gayatri, S. Shaik, Munni Vignan's Institute of engineering for women Dept. of Computer Science Engineering Visakhapatnam India Malla Reddy University Dept. of Information Technology Hyderabad India Malla Reddy University Dept. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798331506452

Diabetes is marked by high blood glucose levels and adversely affects various body systems, notably cardiovascular, renal, and visual. Early diagnosis via automated screening is vital for operative treatment. Advances in machine learning (ML), particularly have enhanced diabetes diagnosis but one of the major issues when working with medical datasets is class imbalance. Oversampling is crucial in the medical field to address class imbalance, ensuring that models can effectively learn patterns from minority classes, such as rare diseases or conditions. While feature selection and hyperparameter tuning optimize model performance, oversampling directly improves the model's ability to detect and predict critical, but less frequent, medical events, reducing bias toward the majority class. This paper introduces an oversampling based approach for using ensemble learning (EL) with decision tree as meta-estimators to address data imbalance. To balance the classes and enhance the accuracy of classification performance KMeans SMOTE Boost, SMOTE Bagging, Over Boost, and Over Bagging classifier strategies are been used on training data. This study identifies the most effective combinations of oversampling techniques and classifiers, and finds that using oversampling methods consistently leads to better classification results. These results highlight the effectiveness of oversampling with EL system in diabetes classification on Type 2 Diabetes dataset. © 2024 IEEE.

关键词： Medical problems

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New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shunt Active Power Filter

New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shu...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Houqing Wang Yue Zhao Hui Cao Darvish Peyman Dongfang Han Zhuxuan Ma Jinlai Zhang Zhenqi Wang Dept. of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA Dept. of Computer Science and Technology Xinjiang University Urumqi China Dept. of Mechanical and Electrical Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter’s effectiveness in suppressing high-frequency harmonics and introducing power losses. In this paper, a new passive damping method is proposed for the three-phase four-leg shunt active power filter (SAPF) system, by adding resistors between the neutral line and the connection point of the three-phase lines. The selection of the damping resistor is guided by criteria that aim to minimize power losses while ensuring good stability characteristics. The SAPF system with the proposed passive damping demonstrates excellent attenuation of switching frequency harmonics and significant mitigation of interphase switching interference, enhancing harmonic compensation for nonlinear loads. The mathematical model based on single-phase equivalent circuit and control strategy of three-phase four-leg SAPF are analyzed respectively. Simulations and experiments with imbalanced loads are constructed to validate the theoretical analysis.

关键词： Damping Resistors Power filters Passive filters Stability criteria Interference Active filters Harmonic analysis Mathematical models Power harmonic filters

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Artificial Intelligence and Machine Learning Algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development

Artificial Intelligence and Machine Learning Algorithms to A...

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

作者： Mithra C Showkat A. Dar Preethi A Dept. of Computer Science and Engineering School of Computing (SOC) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science and Engineeing Githam University India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. This system is built on the techniques of advanced machine Learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. The proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. This GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. The study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Machine learning algorithms Accuracy Prediction algorithms Nutrients Classification algorithms Sustainable development Crop yield Recommender systems Fertilizers Graphical user interfaces

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation

Bridging the Modality Gap: Generative Adversarial Networks f...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mayank Pandey Naga Sai Shreya Kunda Pranave Kc Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Dept. of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information.

关键词： Deep learning Magnetic resonance imaging Scalability Magnetic resonance Medical services Transforms Generative adversarial networks Medical diagnostic imaging

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Inner Layer Hyperparametric Meta-Learning Based on Feature Reuse 2

Inner Layer Hyperparametric Meta-Learning Based on Feature R...

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2nd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2023

作者： Cao, Jihong Sun, Xiangpeng Dept. of Mathematical Sciences University of Electronic Science and Technology of China 2006 West Source Avenue Sichuan Chengdu611731 China Dept. of Computer Science and Engineering University of Electronic Science and Technology of China 2006 West Source Avenue Sichuan Chengdu611731 China

ISBN: (纸本)9781510664968

Few-shot learning requires fast learning and adaptation during the learning process, which has been seen as a very challenging problem because of its stringent training requirements. With the development of deep learning, meta-learning is increasingly applied to solve few-shot problems. Model-agnostic meta-learning (MAML) is applied to model-agnostic tasks by finding common initialization parameters applicable to the model through both inner and outer loops. However, the generalization performance of MAML is not strong, and its inner loop is not fully functional. Therefore, we add learnable hyperparameters to the inner loop to make the model training results more relevant to the current task and reduce the number of layers of gradient updates without unnecessary computation. Experimental results show that the inner loop hyperparameter learning based on feature reuse (HLFR), finds better initialization parameters compared to MAML. © 2023 SPIE.

关键词： Deep learning

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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud 5

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jesy Janet Kumari, J. Thangam, S. Saleem Raja, A. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas It Department Oman

ISBN: (纸本)9798350385649

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony system Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption. © 2024 IEEE.

关键词： Quality of service

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Hybridization of Sparrow Search Algorithm and Sine Cosine Algorithm for Epileptic Seizure Detection 3

Hybridization of Sparrow Search Algorithm and Sine Cosine Al...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Mohapatra, Sumant Kumar Swain, Kaliprasanna Mallik, Puspanjali Mali, Sabita Ali, Farida A. Trident Academy of Technology Odisha Bhubaneswar India Shailabala Women's Autonomous College Dept. of Computer Science Cuttack India Iter Soa University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

An imbalance in electrical signal flow among neurons causes epilepsy, a complex brain disease that affects other parts of the body and results in seizures. Researchers and neurologists have put their efforts into devising powerful electroencephalogram devices to address the importance of health issues. In this context, machine learning algorithms are used to accurately detect the presence of a seizure signal. This experiment uses the hybridization of two meta-heuristic optimization algorithms: the sparrow search algorithm and the sine cosine algorithm. To conduct the research, this experiment uses the publicly available CHB-MIT EEG dataset, and for feature extraction, it uses the discrete wavelet transform (DWT). ANOVA We use feature selection to identify similar groups. This study uses three classifiers: support vector machines (SVM), Adaptive Neuro Fuzzy Inference system (ANFIS), and Extreme Learning ANFIS (ELANFIS). We got 99.8% accuracy, 98.35% sensitivity, 99.02% specificity, 98% positive predictive value, and 1 for area-under-curve for SSA-SCA-DWT-ELANFIS. This shows that it works better than SSA-SCA-DWT-SVM, SSA-SCA-DWT-ANFIS, and other methods that are already out there. In the future, we can integrate this study with the Internet of Things (IoT). © 2024 IEEE.

关键词： Support vector machines

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Transformed Deep Spatio Temporal-Features with Fused Distance for Efficient Video Retrieval 4

Transformed Deep Spatio Temporal-Features with Fused Distanc...

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4th International Conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Banerjee, Alina Kumar, Ela Ravinder, M. Indira Gandhi Delhi Technical University for Women Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665499026

For the goal of video retrieval, this research proposes wavelet transformations on deep spatiotemporal characteristics. The component-wise similarities between the query video feature and prototype video feature are calculated because level 1 wavelets extract two components from any signal or feature vector. The ultimate dissimilarity for determining the top 1 and top 5 accuracy is created by fusing these differences. The outcomes demonstrate that the suggested technique performs better than a baseline strategy. The following strategy for improvement can be investigated further by employing fast learning networks that are trained on the training sets of both data sets to provide better classification of the query as well as the prototype feature vectors, which would enhance the retrieval accuracy. © 2022 IEEE.

关键词： Wavelet transforms

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Human age prediction from bone X-ray Scan images by using advanced Deep CNN 2

Human age prediction from bone X-ray Scan images by using ad...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Ashraf, Mohd Umair Choudhary, Jaytrilok Singh, Dhirendra Pratap Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal462003 India

ISBN: (纸本)9781665437899

The pace of innovation has enabled the development of computerized systems that do have a variety of uses in the medical field, including pediatrics. Fully automated bone age calculation from left-hand X-ray scanning has been one of the applications, which enables radiologists and pediatricians to make decisions about the skeletal bone growth status of youngsters. However, one of the most difficult aspects of building a computerized system is determining the best technique for getting more accurate estimation, especially in dealing with massive volumes of data. The earlier conventional techniques use hand skeletal bone scans and depend upon an optical examination of the bone picture scans. Conventional approaches like these have a lot of variation within observers, are exceedingly time-consuming, and depend upon an experienced domain expert who assesses skeletal bone age by taking an age annotated bone scan as a reference. Various automatic systems have been employed to reduce such variation. Neural network algorithms such as Convolution neural network (CNN) algorithms and their altered versions are employed to address this, resulting in much more accurate outcomes in significantly less time. Efficient Net is one such architecture that we use in this study. This design substantially replaces the usual Convolution architectures with the considerably more efficient Compound scaled architectures which optimize both accuracy and efficiency and result in drastically decreases in time it takes to generate a trained model. In this article, we have used a collection of left-hand scans provided by the RSNA. The testing results demonstrate a 6.03 month disparity in age in the bone estimation process. © 2022 IEEE.

关键词： Pediatrics

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