- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A data-driven model for weld bead monitoring during the laser welding assisted by magnetic field
摘要： In this research, a data-driven model is developed to monitor the seam during the laser beam welding under the influence of an external magnetic field (LBW-AMF). Firstly, a visible LBW-AMF system is built for tracking the laser melting pool and keyhole. Then, the features of the laser melting pool and keyhole are extracted with image processing techniques. The approach for an ensemble of different neural networks which includes radial basis function neural network, back-propagation neural network, and generalized regression neural network is proposed to establish the correlations of the characteristics of the laser melting pool and keyhole and the welding seam. Finally, LBW-AMF experimental results are obtained to validate the performance of the proposed data-driven model. Results illustrate that the developed model can provide a reliable result for monitoring the weld bead, which could give guidance for controlling the processing parameters in real time to improve the weld quality for practical LBW-AMF.
关键词： Image processing,Laser beam welding,Neural networks,Online monitoring,Data-driven model
更新于2025-09-23 15:21:01
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Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
摘要： A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a uni?ed methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More speci?cally, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (arti?cial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the ?nal performance accuracy evaluation. The results showed that the application of linear regression coe?cients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.
关键词： Performance,Forecasting,Machine learning,Photovoltaic,Arti?cial neural networks,Clustering
更新于2025-09-23 15:21:01
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Data-driven uncertainty analysis of distribution networks including photovoltaic generation
摘要： This paper investigates residential distribution networks with uncertain loads and photovoltaic distributed generation. An original probabilistic modeling of consumer demand and photovoltaic generation is presented that is based on the analysis of large set of data measurements. It is shown how photovoltaic generation is described by complex non-standard distributions that can be described only numerically. Probabilistic analysis is performed using an enhanced version of the Polynomial Chaos technique that exploits a proper set of polynomial basis functions. It is described how such functions can be generated from the numerically available data. Compared to other approximate methods for probabilistic analysis, the novel technique has the advantages of modeling accurately truly nonlinear problems and of directly providing the detailed Probability Density Function of relevant observable quantities affecting the quality of service. Compared to standard Monte Carlo method, the proposed technique introduces a simulation speedup that depends on the number of random parameters. Numerical applications to radial and weakly meshed networks are presented where the method is employed to explore overvoltage, unbalance factor and power loss, as a function of photovoltaic penetration and/or network configuration.
关键词： Photovoltaic generation,Data-driven models,Polynomial chaos,Unbalanced distribution networks,Probabilistic load flow,Uncertainty Analysis
更新于2025-09-23 15:21:01
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[IEEE 2018 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo) - Odessa, Ukraine (2018.9.10-2018.9.14)] 2018 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo) - Spatial-Energy Characteristics of Focused Modes of Metallic Terahertz Laser Resonator
摘要： Cellular networks are currently experiencing a tremendous growth of data traffic. To cope with this demand, a close cooperation between academic researchers and industry/standardization experts is necessary, which hardly exists in practice. In this paper, we try to bridge this gap between researchers and engineers by providing a review of current standard-related research efforts in wireless communication systems. Furthermore, we give an overview about our attempt in facilitating the exchange of information and results between researchers and engineers, via a common simulation platform for 3GPP long term evolution (LTE) and a corresponding webforum for discussion. Often, especially in signal processing, reproducing results of other researcher is a tedious task, because assumptions and parameters are not clearly specified, which hamper the consideration of the state-of-the-art research in the standardization process. Also, practical constraints, impairments imposed by technological restrictions and well-known physical phenomena, e.g., signaling overhead, synchronization issues, channel fading, are often disregarded by researchers, because of simplicity and mathematical tractability. Hence, evaluating the relevance of research results under practical conditions is often difficult. To circumvent these problems, we developed a standard-compliant open-source simulation platform for LTE that enables reproducible research in a well-defined environment. We demonstrate that innovative research under the confined framework of a real-world standard is possible, sometimes even encouraged. With examples of our research work, we investigate on the potential of several important research areas under typical practical conditions, and highlight consistencies as well as differences between theory and practice.
关键词： MIMO,pilot power allocation,LTE,Heterogeneous networks,distributed antenna systems,reproducible research,frequency synchronization,multiuser gains
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) - YILAN, Taiwan (2019.5.20-2019.5.22)] 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) - A Design of Portable Solar-Powered Air-Quality Monitor with Cloud-enabled Safety Watch Smartphone APP
摘要： The increase in multimedia services has put energy saving on the top of current demands for mobile devices. Unfortunately, batteries’ lifetime has not been as extended as it would be desirable. For that reason, reducing energy consumption in every task performed by these devices is crucial. In this work, a novel opportunistic routing protocol, called JOKER, is introduced. This proposal presents novelties in both the candidate selection and coordination phases, which permit increasing the performance of the network supporting multimedia traffic as well as enhancing the nodes’ energy efficiency. JOKER is compared in different-nature test-benches with BATMAN routing protocol, showing its superiority in supporting a demanding service such as video-streaming in terms of QoE, while achieving a power draining reduction in routing tasks.
关键词： JOKER,QoE,Opportunistic routing,ad-hoc networks
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Conference on Information and Communication Technology (CICT) - Allahabad, India (2019.12.6-2019.12.8)] 2019 IEEE Conference on Information and Communication Technology - LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture
摘要： Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identi?cation from the challenging images taken from a height.
关键词： Computer Vision,LED Pattern Identi?cation,UAVs,Wireless Sensor Networks,Precision Agriculture,Remote Sensing
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Designing with Luminescent Solar Concentrator Photovoltaics
摘要： In a pro?le matchmaking application of mobile social networks, users need to reveal their interests to each other in order to ?nd the common interests. A malicious user may harm a user by knowing his personal information. Therefore, mutual interests need to be found in a privacy preserving manner. In this paper, we propose an ef?cient privacy protection and interests sharing protocol referred to as PRivacy-aware Interest Sharing and Matching (PRISM). PRISM enables users to discover mutual interests without revealing their interests. Unlike existing approaches, PRISM does not require revealing the interests to a trusted server. Moreover, the protocol considers attacking scenarios that have not been addressed previously and provides an ef?cient solution. The inherent mechanism reveals any cheating attempt by a malicious user. PRISM also proposes the procedure to eliminate Sybil attacks. We analyze the security of PRISM against both passive and active attacks. Through implementation, we also present a detailed analysis of the performance of PRISM and compare it with existing approaches. The results show the effectiveness of PRISM without any signi?cant performance degradation.
关键词： privacy,Mobile social networks,pro?le matchmaking,interests
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
摘要： Spectrum sensing is used to detect spectrum holes and find active primary users while randomly selecting channel for sensing lead to secondary user’s low throughput in high traffic cognitive radio networks. Spectrum prediction forecasts future channel states on the basis of historical information. A new frame structure is proposed in this letter for the imperfect spectrum prediction, resulting to select channels for sensing only from the channels predicted to be idle. Simulation results show that secondary user’s throughput is significantly enhanced by imperfect spectrum prediction. The impacts of traffic intensity, prediction errors, and channel number on the throughput are also investigated in this study.
关键词： frame structure,Imperfect spectrum prediction,cognitive radio networks
更新于2025-09-23 15:21:01
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[IEEE NAECON 2019 - IEEE National Aerospace and Electronics Conference - Dayton, OH, USA (2019.7.15-2019.7.19)] 2019 IEEE National Aerospace and Electronics Conference (NAECON) - In Situ Process Monitoring for Laser-Powder Bed Fusion using Convolutional Neural Networks and Infrared Tomography
摘要： Additive Manufacturing (AM) is a growing field for various industries of avionics, biomedical, automotive and manufacturing. The onset of Laser Powder Bed Fusion (LPBF) technologies for metal printing has shown exceptional growth in the past 15 years. Quality of parts for LPBF is a concern for the industry, as many parts produced are high risk, such as biomedical implants. To address these needs, a LPBF machine was designed with in-situ sensors to monitor the build process. Image processing and machine learning algorithms provide an efficient means to take bulk data and assess part quality, validating specific internal geometries and build defects. This research will analyze infrared (IR) images from a Selective Laser Melting (SLM) machine using a Computer Aided Design (CAD) designed part, featuring specific geometries (squares, circles, and triangles) of varying sizes (0.75-3.5 mm) on multiple layers for feature detection. Applying image processing to denoise, then Principal Component Analysis (PCA) for further denoising and applying Convolution Neural Networks (CNN) to identify the features and identifying a class which does not belong to a dataset, where a dataset are created from CAD images. Through this automated process, 300 geometric elements detected, classified, and validated against the build file through CNN. In addition, several build anomalies were detected and saved for end-user inspection.
关键词： Laser Powder Bed Fusion (LPBF),Principal Component Analysis (PCA),infrared image (IR),Convolution Neural Networks (CNN),Additive Manufacturing (AM),Computer Aided Design (CAD)
更新于2025-09-23 15:21:01
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Design, Simulation and Fabrication of a High Gain Low Sidelobe Level Waveguide Slot Array Antenna at X-band with Zero Beam Tilts in Both Azimuth and Elevation Directions
摘要： This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
关键词： microgrid,Adaptive dynamic programming,reinforcement learning,evolutionary computing,dynamic energy management system (DEMS),renewable energy,neural networks
更新于2025-09-23 15:21:01