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Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.

This paper reports a facile functionalization method on a metal-oxide semiconductor and a cuprous oxide (Cu2O) based chemiresistive electronic nose for the detection of volatile organic compounds (VOCs). A library of functionalized Cu2O nanospheres was developed through silanization using chemically diverse organosilanes. An electronic nose was fabricated with unmodified Cu2O nanospheres and five types of functionalized Cu2O nanospheres as the sensing elements. The electronic nose showed stable and rapid resistance responses to 25–200 ppm model VOCs, with the operating temperature of 180 °C. Single VOCs and ternary VOC mixtures could be discriminated by the electronic nose, and six types of tea leaves were also proved to be distinguishable as an illustration of the application of the electronic nose. We expected that the silanization could provide a simple approach for material diversification and the electronic nose would have further application in identification and discrimination of complex gas samples.

In this paper, the energy release mechanism of shock-induced chemical reaction (SICR) of Al/Ni composites was investigated. The Al/Ni composites with two additives, namely Teflon (PTFE) and copper (Cu), were considered in both theoretical calculations and experiments to investigate their influence on SICR characteristics of Al/Ni composites system. Assuming the SICR process is controlled by shock temperature rising in the materials, the reaction efficiency of Al/Ni composites was calculated by Arrhenius reaction rate and Avrami−Erofeev reaction models. Hugoniot curves and the temperature rise under shock compression were calculated to analyze the mechanism of the influence on SICR characteristics by additives. Impact-initiated experiments of Al/Ni composites were carried out to study SICR characters at various impact velocities. The parameters of SICR model were determined by using corresponding experimental results. The calculation results showed that for the SICR process of Al/Ni, the critical shock temperature for initiation of SICR (T cr = 452 K) and the apparent activation energy (E a = 90.9 kJ/mol) appeared much lower than the values of normal ignition process (T ig = 990 K, E iga = 351.6 kJ/mol). The additive of Cu decreased the shock temperature significantly in Al/Ni composites, which led to the increase of critical shock conditions for initiation of SICR and the decrease of reaction efficiency at the same shock pressure. On the other hand, the additive of PTFE to Al/Ni composites decreased the critical shock conditions for initiation of SICR and increased the chemical reaction efficiency by participating in the reactions.

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