In today's data-driven business landscape, robust metadata and data documentation practices are essential for enterprises aiming to maximize their data assets. When integrated with Business Intelligence (BI) syste...
详细信息
Design optimization of high-power nonlinear power amplifiers requires much back-and-forth between power sweep, bias sweep, and load-pull simulations. Such manual optimizations are very slow given the large number of p...
详细信息
ISBN:
(数字)9798331540401
ISBN:
(纸本)9798331540418
Design optimization of high-power nonlinear power amplifiers requires much back-and-forth between power sweep, bias sweep, and load-pull simulations. Such manual optimizations are very slow given the large number of parameters and goals. The automation of this process holds promise for reducing time to successful designs in computer-aided design operations, especially in microwave power-amplifier design. This work proposes multidimensional image completion techniques using Generative Adversarial Networks (GAN) as a method for reducing the number of simulation queries required to characterize a power amplifier over multiple design parameters simultaneously, such as input power and load impedance. Specifically, as few as 16 simulation queries are able to accurately predict the optimal reflection coefficient over input power to within a vector error of about 0.1, leading to significant time savings over other characterization methods.
The paper discusses the numerical methods of the regulator optimization for the design of feedback locked systems. The proposed methods are based on the optimization procedure in the program VisSim 5.0/6/0 with the us...
详细信息
ISBN:
(纸本)9780889868571
The paper discusses the numerical methods of the regulator optimization for the design of feedback locked systems. The proposed methods are based on the optimization procedure in the program VisSim 5.0/6/0 with the use of original rules, structures and special criteria of the optimality (cost function).
Large Language Models (LLMs) have demonstrated remarkable adaptability in performing various tasks, including machine translation (MT), without explicit training. Models such as OpenAI’s GPT-4 and Google’s Gemini ar...
详细信息
This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primar...
详细信息
ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primary dataset of sales conversations was processed using an internal tool, which converts audio into numerical data based on 11 acoustic features such as pitch, volume, and speech rate. Traditional machine learning models, including AdaBoost, Support Vector Machine (SVM), and Random Forest, were compared with deep learning models like Dense Neural Networks (DNN) and TabNet. The optimized DNN model achieved the highest performance, with an F1-Score of 0.7298, balancing precision and recall effectively. TabNet provided competitive results with an F1-Score of 0.7237, offering additional benefits of feature interpretability through its attention mechanism. These findings suggest that while traditional models, particularly AdaBoost, perform adequately, deep learning models, especially DNN and TabNet, are better equipped to capture the complex, non-linear relationships in acoustic and emotional data. The results provide actionable insights for optimizing sales strategies through data-driven decision-making. It offers a foundation for future advancements in predictive analytics within automotive industries using voicebased datasets.
This paper discusses the activities involved in improving systems engineering (SE) on a large, mature program. It discusses the reasons for improving SE for a fielded product and then provides a description and discus...
To improve the accuracy of Deep Neural Networks (DNNs) applied to Network Intrusion Detection systems (NIDS) researchers often increase the complexity of their designed model. Given the processing limitations of resou...
详细信息
Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because tactile data is often scarce and cos...
详细信息
Multicolor microscopy and super-resolution optical microscopy are two widely used techniques that greatly enhance the ability to distinguish and resolve structures in cellular *** methods have individually transformed...
详细信息
Multicolor microscopy and super-resolution optical microscopy are two widely used techniques that greatly enhance the ability to distinguish and resolve structures in cellular *** methods have individually transformed cellular imaging by allowing detailed visualization of cellular and subcellular structures,as well as organelle ***,integrating multicolor and super-resolution microscopy into a single method remains challenging due to issues like spectral overlap,crosstalk,photobleaching,phototoxicity,and technical *** challenges arise from the conflicting requirements of using different fluorophores for multicolor labeling and fluorophores with specific properties for super-resolution *** propose a novel multicolor super-resolution imaging method called phasor-based fluorescence spatiotemporal modulation(Phasor-FSTM).This method uses time-resolved detection to acquire spatiotemporal data from encoded photons,employs phasor analysis to simultaneously separate multiple components,and applies fluorescence modulation to create super-resolution ***-FSTM enables the identification of multiple structural components with greater spatial accuracy on an enhanced laser scanning confocal microscope using a single-wavelength *** demonstrate the capabilities of Phasor-FSTM,we performed two-color to four-color super-resolution imaging at a resolution of~λ/5 and observed the interactions of organelles in live cells during continuous imaging for a duration of over 20 *** method stands out for its simplicity and adaptability,seamlessly fitting into existing laser scanning microscopes without requiring multiple laser lines for excitation,which also provides a new avenue for other super-resolution imaging technologies based on different principles to build multi-color imaging systems with the requirement of a lower budget.
Accurately predicting dry matter intake (DMI) in lactating crossbred cows under semi-arid conditions is a critical challenge in dairy farming. Precise DMI predictions are essential for optimizing nutrient utilization ...
详细信息
暂无评论