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UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

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Signal reconstruction via sampled-data control with multirate filter banks

Signal reconstruction via sampled-data control with multirat...

引用

IEEE Conference on Decision and Control

作者： Y. Yamamoto H. Fujioka P.P. Khargonekar Department of Applied Systems Science Graduate Schoold of Engineering Kyoto University Kyoto Japan Department of Computer-Controled Machinery. Faculty of Engineering Osaka University Osaka Japan Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

An approach to multirate digital signal reconstruction via modern sampled-data control theory is presented. The central emphasis is upon continuous-time sampled-data H/sup /spl infin// design with delay. It is shown that the problem is reducible, in spite of the delay element, to a finite-dimensional single-rate problem under some mild conditions.

关键词： Signal reconstruction Channel bank filters Filter bank Delay Control theory Digital signal processing Encoding Books Digital filters Frequency response

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Archimedean Spiral Multi-resonator for Chipless RFID Tag Using Frequency-Coded Decimal Method

Archimedean Spiral Multi-resonator for Chipless RFID Tag Usi...

引用

SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference

作者： Hillner de P. A. Ferreira Alexandre J. R. Serres Georgina Karla de F. Serres Francisco M. de Assis Alfredo Gomes Neto Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Group of Telecom. and Applied Electromagnetism Federal Inst. of Educ. Science and Technology of Paraiba

ISBN: (纸本)9781467394932

A compact electromagnetic multi-resonator is presented in this paper using a new technique for chipless RFID tag data encoding. Employing Archimedean spirals, the information is applied into the spectral signature shifting resonant frequencies in several positions. The multi-resonator tag proposed operates in microwave band (between 3.0-7.0GHz) and has a coding data capacity of up to 100 decimal numbers for two resonant frequencies. The simulated and measured results show a good concordance and a great perspective to use in low-cost RFID chipless tag applications.

关键词： RFID chipless Frequency-Coded Decimal Archimedean spiral Multi-resonator

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A Whole Crow Search Algorithm for Solving Data Clustering

A Whole Crow Search Algorithm for Solving Data Clustering

引用

International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Ze-Xue Wu Ko-Wei Huang Abba Suganda Girsang Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan R.O.C Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

Data clustering is a well-known data mining approach that usually used to minimizes the intra distance but maximizes inter distance of each data center. The cluster problem has been proved to be an NP-hard problem. In this paper, a hybrid algorithm based on Whole optimization algorithm (WOA) and Crow search algorithm (CSA) is proposed, namely HWCA. The HWCA algorithm has the advantages of the search strategy of the WOA and CSA. In addition to, there are two operators used to improve the quality of solution, namely hybrid individual operator and enhance diversity operator. The hybrid individual operator is used to exchanges individuals from the WOA and CSA systems by using the roulette wheel approach. In other hand, the HWCA performs enhance diversity operator to improve the quality of each system. More over, the HWCA is incorporated with center optimization strategy to enhance diversity of each system. In the performance evaluation, the proposed MPGO algorithm was comparison WOA and CSA algorithm with six well-known UCI benchmarks. The results show that the proposed algorithm has a higher measure of accuracy rate with comparison algorithms.

关键词： Clustering algorithms Optimization Whales Search problems Wheels Benchmark testing Random variables

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Machine learning approaches for fault detection and diagnosis of induction motors

Machine learning approaches for fault detection and diagnosi...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Lamia Belguesmi Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

关键词： Induction motors Machine learning algorithms Fault detection Machine learning Feature extraction Monitoring Principal component analysis

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Multi-hole defect photonic crystals with enhanced surface area for biosensing applications

Multi-hole defect photonic crystals with enhanced surface ar...

引用

Laser science, LS 2013

作者： Hu, S. Kang, C. Zhao, Y. Mares, J.W. Vlasov, Y.A. Assefa, S. Weiss, S.M. Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN 37235 United States IBM T.J. Watson Research Center Yorktown Heights NY 10536 United States

Multi-hole defect (MHD) photonic crystal cavities functionalized with in situ synthesized DNA bioreceptors are demonstrated for biosensing applications. By significantly increasing light interaction with target biomol... 详细信息

ISBN: (纸本)9781557529879

关键词： Photonic crystals

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Efficient fault detection and diagnosis of wind energy converter systems

Efficient fault detection and diagnosis of wind energy conve...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.

关键词： Fault diagnosis Fault detection Machine learning Feature extraction Wind turbines Monitoring Principal component analysis

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Photonic crystal defects with increased surface area for improved refractive index sensing

Photonic crystal defects with increased surface area for imp...

引用

Conference on Lasers and Electro-Optics, CLEO 2010

作者： Kang, Christopher Christopher Phare Weiss, Sharon M. Vlasov, Yurii A. Assefa, Solomon Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University Tennessee 37235 United States IBM T.J. Watson Research Center Yorktown Heights NY 10536 United States

Photonic crystal cavities with tunable surface area via multiple-hole defects were investigated for increased resonance wavelength shifts upon exposure to variable-index analytes. Sensitivity was improved by 10% compa... 详细信息

ISBN: (纸本)9781557528896

关键词： Refractive index

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Photonic crystal defects with increased surface area for improved refractive index sensing

Photonic crystal defects with increased surface area for imp...

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Conference on Lasers and Electro-Optics

作者： Kang, Christopher Phare, Christopher Weiss, Sharon M. Vlasov, Yurii A. Assefa, Solomon Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States Department of Physics and Astronomy Vanderbilt University Nashville TN 37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University 37235 United States IBM. T.J. Watson Research Center Yorktown Heights NY 10536 United States

Photonic crystal cavities with tunable surface area via multiple-hole defects were investigated for increased resonance wavelength shins upon exposure to variable-index analytes. Sensitivity was improved by 10% compar... 详细信息

ISBN: (纸本)9781557528902

关键词： Surface defects

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Fault detection in photovoltaic systems using machine learning technique

Fault detection in photovoltaic systems using machine learni...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khadija Attouri Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.

关键词： Support vector machines Photovoltaic systems Maximum power point trackers Fault detection Feature extraction Robustness Principal component analysis

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