AI-Optimized Sensor Network and Signal Processing Model for Advanced Manufacturing and Green Energy Applications
AI最適化センサーネットワークと信号処理モデルによる高度製造とグリーンエネルギー応用 (AI 翻訳)
Sarvaree Bano, Priyanka Gupta
🤖 gxceed AI 要約
日本語
本研究は、AIを活用したセンサーネットワークと信号処理モデルにより、先進製造業とグリーンエネルギーシステムの効率性と持続可能性を向上させる手法を提案する。予知保全や再生可能エネルギー発電所のリアルタイム最適化にAIを適用し、稼働率向上と資源浪費削減を実証。従来手法より高い性能を示し、コスト削減と環境負荷低減に貢献する。
English
This study proposes an AI-optimized sensor network and signal processing model to enhance efficiency and sustainability in advanced manufacturing and green energy systems. It applies AI for predictive maintenance and real-time optimization of renewable energy plants, demonstrating improved uptime and resource efficiency. Results show cost reduction and environmental benefits, highlighting AI's role in smarter industrial practices.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は日本の工場や再生可能エネルギー設備におけるAI活用の可能性を示すが、具体的な日本の政策や制度（GXリーグ、SSBJ等）との連携はない。一般的な技術効率化の観点で参考になる。
In the global GX context
The paper contributes to the global discourse on AI-driven industrial efficiency and green energy optimization, but lacks direct links to climate disclosure frameworks like TCFD or ISSB. It offers technical insights for manufacturing and renewable sectors aiming to reduce operational emissions.
👥 読者別の含意
🔬研究者:AIとセンサーネットワークのグリーンエネルギー応用に関する実証的研究として参考になる。
🏢実務担当者:予知保全や再生可能エネルギー効率化の具体的なAI適用事例として、導入検討に活用できる。
📄 Abstract（原文）
The Study discusses the application of AI optimization of sensor networks and signal processing models to achieve efficiency and sustainability in advanced manufacturing and green energy systems. This research investigates how artificial intelligence-powered tools improve efficiency in monitoring equipment by enabling rapid predictions about its condition and enhancing overall functionality. In the production stage, the document presents the application of artificial intelligence methods, such as supervised, unsupervised, and reinforcement learning, in predictive maintenance plans in order to minimise equipment breakdowns by ensuring that the imminent failure of machines is detected in time. In the context of sustainable technology, artificial intelligence can be used to improve the operation of renewable energy stations such as photovoltaic arrays and hydroelectric power plants by providing real-time adjustments to the existing activities based on instant measurements of the environment, thereby increasing the overall performance of the facilities and reducing the wastage of resources. The approach entails selecting suitable sensors and data collection techniques, and designing advanced signal-processing models for noise elimination, feature removal, and anomaly detection. The performance of the AI algorithms used can be tested, with the AI algorithms proving better at predicting uptime and energy generation than traditional algorithms. The results indicate that adopting AI leads to high operational efficiency, cost reduction, and sustainability. The paper concludes with implications for the two industries, recommendations for future research, and the adoption of AI-driven technologies. The paper can bring a lot of lessons on why AI is important to simplify sensor networks to promote smarter and greener industrial practices.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/iciccs67901.2026.11502884first seen 2026-05-15 20:18:42 · last seen 2026-06-09 04:53:58
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。