scispace - formally typeset
Search or ask a question

Answers from top 6 papers

More filters
Papers (6)Insight
A control-oriented SCR model is thus indispensable for SCR control systems.
Numerical method as a complementary tool to bench tests can help to optimize the design and shorten the development cycle of SCR system.
Findings The findings show that the proposed system can enhance all three dimensions of SCR.
This ratio is important in the SCR process since the performance of the SCR catalyst is dependent on the composition of the reducing agents.
Proceedings ArticleDOI
15 Jun 2003
13 Citations
Advantages of the proposed SCR-based circuit include low cost and ruggedness.
Closed-loop control of SCR dosing enables the SCR system to be robust against disturbances and to meet conformity of production (COP) and in-use compliance norms.

See what other people are reading

Are there examples of unsupervised machine learning used in gait analysis?
5 answers
Unsupervised machine learning has been applied in gait analysis, particularly in the context of Unsupervised Gait Recognition (UGR). Research has introduced methods like cluster-based contrastive learning and Selective Fusion techniques to address challenges in UGR. Additionally, studies have explored Unsupervised Domain Adaptive Gait Recognition (UDA-GR) using uncertainty estimation and regularization methods to adapt gait identification models from indoor to outdoor scenes. Furthermore, the use of self-supervised training regimes with transformer models like ViT, CaiT, CrossFormer, Token2Token, and TwinsSVT has shown promise in gait recognition tasks, enabling learning without costly manual annotations. These approaches showcase the effectiveness of unsupervised machine learning in enhancing gait analysis methodologies.
What are the limitations of using spatial or temporal deepfake detectors for deepfake videos?
5 answers
Spatial or temporal deepfake detectors face limitations due to various factors. Current methods struggle with open data, re-compression susceptibility, and reliance on pixel-level abnormalities. Additionally, the spatio-temporal modeling of existing detectors lacks in capturing subtle temporal changes, concealing minor inconsistencies, and addressing transient inconsistencies alongside persistent motions. Moreover, detectors are vulnerable to attacks from advanced generative models like Denoising Diffusion Models, leading to decreased accuracy without visible changes. Furthermore, detecting low-quality deepfake videos remains a challenge, as many models degrade in performance with heavily compressed or low-resolution videos. These limitations highlight the need for more robust and comprehensive approaches to effectively detect deepfake videos.
How do distribution stations contribute to the efficiency and reliability of the power system?
5 answers
Distribution stations play a crucial role in enhancing the efficiency and reliability of the power system. They are essential components responsible for voltage transformation and power distribution within the distribution network. By implementing strategies like spare transformer sharing policies, distribution stations can reduce the number of spare transformers required while maintaining high reliability levels. Moreover, integrating distributed generation (DG) units at optimal locations within the distribution system can significantly enhance reliability by reducing outage durations and improving system performance. Additionally, the incorporation of energy storage systems can help balance power supply reliability with economic considerations, especially in situations where locally generated power exceeds demand, thus preventing voltage issues and network losses. These measures collectively contribute to ensuring a stable and reliable operation of the power system.
What are the current trends in on-device AI?
5 answers
Current trends in on-device AI involve optimizing AI models for edge devices to enhance sustainability, focusing on deploying Transformer models for time-series tasks on embedded hardware like FPGAs. Additionally, there is a shift towards moving intelligence services from cloud servers to on-device systems, offering benefits like privacy preservation, reduced latency, and cost savings. To address the challenges posed by varying hardware resources, a stream pipeline framework called NNStreamer is being utilized for on-device AI systems, aiming to expand device types and applications while ensuring atomic and re-deployable AI services shared among devices from different vendors. These trends highlight the ongoing efforts to make on-device AI more efficient, sustainable, and widely accessible across diverse hardware platforms.
What are the specific technical and economic benefits of implementing a distribution system in power generation?
5 answers
Implementing a distribution system in power generation offers various technical and economic benefits. From a technical perspective, integrating distributed generation (DG) optimally into distribution networks enhances system reliability, reduces power losses, and improves voltage profiles. Additionally, the use of renewable energy sources like wind power can alleviate stress on conventional power plants, decrease peak demands, and contribute to environmental sustainability. Economically, the installation of DG and capacitor banks can be optimized to maximize benefits, such as increased energy revenue, deferred investments, and reduced environmental pollution, ultimately adding economic value to society. Furthermore, the economic cost and benefit analysis of DG integration can guide investment decisions for independent power producers and stimulate economic development.
What is the recommended machine learning algorithm for chatbots?
5 answers
The recommended machine learning algorithms for chatbots include transformer-based models like BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet. These transformer models have shown significant improvements in natural language processing tasks, particularly in text classification and language understanding. Additionally, machine learning algorithms such as decision trees, logistic regression, and support vector machines have been utilized for binary classification tasks in chatbot applications. Furthermore, the use of machine learning algorithms in chatbots has been explored to facilitate conversations between strangers, as seen in the development of BlahBlahBot, which suggests personalized topics based on social media posts to enhance conversation quality and user engagement. Overall, transformer-based models and traditional machine learning algorithms play crucial roles in enhancing chatbot performance and user experience across various applications.
How do distribution stations contribute to the overall efficiency and reliability of the power system?
5 answers
Distribution stations play a crucial role in enhancing the efficiency and reliability of the power system. They are composed of various components like distribution transformers, low-voltage boxes, and protection equipment, responsible for voltage transformation and power distribution. The safe and reliable operation of distribution stations is vital for ensuring the stability of the entire distribution network. Moreover, studies have shown that implementing strategies like spare transformer sharing policies can reduce the number of spare transformers required while maintaining high reliability levels. Additionally, integrating distributed generation (DG) into distribution systems can significantly enhance reliability by bringing generation closer to load centers. By addressing issues like misalignment between local generation and demand through technologies like energy storage systems, distribution stations play a key role in optimizing power distribution and ensuring system reliability.
Is pytorch better dan tensorflow for bert?
4 answers
Based on the comparative studies presented in the provided contexts, PyTorch has shown promising results compared to TensorFlow for applications involving BERT. Specifically, a study evaluated PyTorch and TensorFlow using the MNIST database and found that PyTorch exhibited better performance in terms of execution time and overall efficiency. Additionally, the analysis of neural network libraries highlighted that PyTorch outperformed TensorFlow in certain tasks, indicating its superiority for specific applications like BERT. These findings suggest that PyTorch may be a better choice than TensorFlow when working with BERT models, showcasing its potential for enhancing performance in natural language applications.
Why is generation station is important in power system?
5 answers
Power generation stations play a crucial role in power systems due to their ability to generate electric power. They incorporate various technologies like synchronous machines connected to renewable sources such as wind, solar, and hydropower generators to enhance power quality and grid stability. Additionally, power generation stations can effectively combine different power supply devices like hydraulic turbine generators, wind-driven generators, and solar cell panels to ensure continuous and efficient power generation. Some stations even utilize heat energy storage devices to supplement power generation, further enhancing their efficiency. By integrating these technologies and systems, power generation stations contribute significantly to meeting the electricity demands of modern lifestyles and industries while ensuring stable and reliable power supply.
Comparison of energy system models in Europe?
5 answers
The comparison of energy system models in Europe involves assessing various aspects such as regional aggregations, renewable generation data accuracy, computational constraints, and model harmonization. Studies highlight differences in final electricity demand, supply, and hydrogen across models. Variability in reanalysis-based datasets for wind and solar generation can lead to conflicting results and misallocation of investments. Spatial clustering methods are crucial for overcoming computational burdens and accurately representing network flows and renewables in electricity system models. The MODEX network emphasizes harmonized model applications and systematic model comparisons, enhancing methodological knowledge and transparency in comparing energy system models. Different models like ZuBer, Pandapower Pro, Energy Agents, and GridSim offer optimization potentials for scenarios like PV expansion and grid limit violations in rural grids.
Why is using waste plastic more environmentally friendly than using virgin plastic?
5 answers
Using waste plastic is more environmentally friendly than using virgin plastic due to several reasons outlined in the research papers. Waste plastics can be recycled into high-value products like building materials, reducing the need for new raw materials and minimizing environmental degradation. Additionally, waste plastics can be effectively converted into alternative fuel sources through processes like pyrolysis, offering a sustainable energy solution and reducing pollution. Furthermore, utilizing recycled plastics in construction materials can enhance thermal resistance and acoustical performance, contributing to energy efficiency and creating a more eco-friendly environment. Overall, the reuse of waste plastics not only helps in curbing pollution and reducing resource depletion but also promotes sustainable practices in various industries, making it a more environmentally friendly choice compared to virgin plastics.