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Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.

In order to reduce the peak load in the period of power shortage through power demand side response, it is necessary to evaluate the peak cutting potential of major industries in this region. Based on electricity information acquisition system and big data mining analysis technology, this paper proposes a peak-clipping potential assessment method and process for power demand side response based on the characteristics of different industries, and evaluates and ranks the peak-clipping potential of 17 major industries in Henan Province. The evaluation method and results provide a theoretical basis for studying and judging the peak cutting scale and target in advance.

The lead‐acid battery has been widely used in various fields. In civil aviation aircraft, it plays a vital role in the power system to maintain normal operation during the flight mission. Thus, an effective abnormal detection system for monitoring and diagnosing the status of aircraft lead‐acid battery is essential to ensure its safety and reliability. This paper aims to effectively identify aircraft battery faulty using unsupervised anomaly detection techniques. It introduces state‐of‐the‐art anomaly detection algorithms and evaluates their performance on a large real civil aviation battery data. The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.