用于减少工业碳足迹的增强递归神经网络

K. Chande, Rahul Kanekar, Kiran Nair, Dina Amandykova, Supriya Addanke, Tolegen Zhaina
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引用次数: 0

摘要

目前,绿色通信技术正受到人们的广泛关注。人们对绿色通信的研究兴趣日益浓厚,这也会破坏环境。衡量各种产品,公司和流程的绿色通信强度正在全球范围内进行,遵循的规则是只有相关的影响是可管理的,这被表示为碳足迹。绿色探测正在对碳的产生产生直接的、大规模的影响。绿色倡议可以通过提高能源效率有效地减少碳的产生。综上所述,绿色发现直接影响碳产量。本研究尝试使用增强递归神经网络(Enhanced Recurrent Neural Network, ERNN)来减少碳足迹能源。从投资的角度来看,碳足迹分析可以帮助评估公司的整体表现和比较表现。它可以被用作管理和评估公司绩效的工具。有效的生产管理证明了运营的质量,并能提供显著的竞争优势。
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Enhanced Recurrent Neural Network for Reducing Carbon Foot Printing in Industry
At present, green communication technology is receiving a significant research attention. The increasing research interest on green communication can also undermine the environment. Measuring the green communication intensity of various products, companies and processes is being carried out globally by following the rule that only the related effects are manageable, which is expressed as a carbon footprint. Green detections are having a direct, large-scale impact on carbon productions. The green initiatives can effectively reduce carbon productions by improving the energy efficiency. In summary, green discovery directly affect carbon production. This research work has attempted to reduce the carbon footprint energy by using Enhanced Recurrent Neural Network (ERNN). From an investment perspective, carbon footprint analysis can assist in evaluating a company’s overall and comparative performance. It can be used as a tool to manage and evaluate the performance of a company. Effective production management demonstrates the quality of operations and can provide a significant competitive advantage.
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