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Heating and Cooling Curves02:44

Heating and Cooling Curves

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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Electrical Energy01:10

Electrical Energy

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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Mass Analyzers: Overview01:13

Mass Analyzers: Overview

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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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Energy Diagrams - II01:10

Energy Diagrams - II

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Energy diagrams are important to understand the dynamics of a system. The topology of an energy diagram helps illustrate the equilibrium points of the system.
The point in the energy diagram at which the system’s potential energy is the lowest is known as the local minima. The system tends to stay in this position indefinitely unless acted upon by a net force. The slope of the potential energy diagram at the local minima is zero, indicating that zero net force is acting on the system. The...
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Power and Energy01:12

Power and Energy

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The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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EnergiQ: デバイスのエネルギー消費パターンを解釈するための規範的な大型言語モデル駆動型インテリジェントプラットフォーム

Christoforos Papaioannou1, Ioannis Tzitzios1, Alexios Papaioannou1

  • 1Management Science and Technology Department, Democritus University of Thrace, 65404 Kavala, Greece.

Sensors (Basel, Switzerland)
|August 28, 2025
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まとめ

EnergiQはスマートセンサーと大型言語モデル (LLM) を使用して,容易に理解できるエネルギー洞察を提供します. このインテリジェント・プラットフォームは 家庭のエネルギー効率と パーソナライズされたフィードバックによる ユーザーエンゲージメントを向上させます

キーワード:
IoTベースのエネルギーモニタリングアノマリー検出機器レベルのエネルギープロファイリング人間中心のエネルギーフィードバック大型言語モデルスマートセンサー

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科学分野:

  • スマートホーム技術
  • 人工知能
  • エネルギー管理システム

背景:

  • スマートセンサは 住居のエネルギー管理に チャンスと複雑さをもたらします
  • 現在のエネルギー管理システム (EMS) は,解釈性,適応性,およびユーザーエンゲージメントが欠けている.
  • 複雑なエネルギー分析とユーザー理解の間のギャップを埋めることが重要です.

研究 の 目的:

  • エネルギー管理の強化のためのセンサーとLLMを統合したインテリジェントプラットフォームであるEnergiQを紹介する.
  • 自然言語のフィードバックを通じて,エネルギー消費データに対するユーザーの理解を向上させる.
  • エネルギー効率と持続可能な家庭の実践に消費者の積極的な関与を促す.

主な方法:

  • スマートプラグベースのIoTセンシングとタイムシリーズの機械学習 (ML) の統合
  • 装置識別のためのXGBoost分類器と異常検出のためのCNN-LSTMオートエンコーダの利用.
  • パーソナライズされたフィードバックのための指示調整モデルを持つLLM推論層の実装.

主要な成果:

  • 統計的特徴に基づくXGBoostで達成された高い機器分類精度 (94%).
  • CNN-LSTMオートエンコーダを使用した様々なデバイスで有効な異常検出が実証されました.
  • LLM層は,専門家によるエネルギー節約の勧告に91%以上の同意を示した.

結論:

  • EnergiQは複雑なエネルギーデータを 直感的で実用的な洞察力へと 翻訳しています
  • プラットフォームは消費者にエネルギー使用を積極的に管理し,効率を高めることを可能にします.
  • LLMの統合は,EMSの解釈性とユーザーエンゲージメントを大幅に改善します.