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Multiagent Consensus Control Strategy considering Whole-Process Thermodynamic Characteristics of Air Conditioning Process
Due to the distributed and decentralized characteristics of air conditioning load, the distributed control strategy has advantages for the air conditioning load to participate in the demand response. However, existing approaches focus on the dynamic control performance with very few considerations on the cost. To fill this gap, this paper proposes a multiagent consensus control method considering the whole-process response cost of air conditioning. Based on the thermodynamic characteristics of air conditioning load in the load reduction process and recovery process, the cost function curve of air conditioning load is established. Then, the multiagent consensus control strategy is adopted to send the power adjustment information to each air conditioner to realize the optimal control of the air conditioning load. The simulation results verify that the proposed method can take into account the whole-process response cost of air conditioning loads and result in smaller control cost than existing methods.
A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM
Loop closure detection serves as the fulcrum of improving the accuracy and precision in simultaneous localization and mapping (SLAM). The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. Therefore, a loop closure detection strategy based on an unsupervised learning method is proposed in this paper. The main component adopts BigBiGAN to extract features and establish an original bag of words. Then, the complete bag of words is used to detect loop closing. Finally, a considerable validation check of the ORB descriptor is added to verify the result and output outcome of loop closure detection. The proposed algorithm and other compared algorithms are, respectively, applied on Autolabor Pro1 to execute the indoor visual SLAM. The experiment shows that the proposed algorithm increases the recall rate by 20% compared with ORB-SLAM2 and LSD-SLAM. And it also improves at least 40.0% accuracy than others and reduces 14% time loss of ORB-SLAM2. Therefore, the presented SLAM based on BigBiGAN does benefit much the visual SLAM in the indoor environment.
A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection
There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance.
Fault Detection and Identification for Nonlinear Process Based on Inertia-Based KEPCA and a New Combined Monitoring Index
In the present study, we introduce a new approach for the nonlinear monitoring process based on kernel entropy principal component analysis (KEPCA) and the notion of inertia. KEPCA plays double roles. First, it reduces the data in the high-dimensional space. Second, it constructs the model. Before data reduction, KEPCA transforms input data into high-dimensional feature space based on a nonlinear kernel function and automatically determines the number of principal components (PCs) based on the computation of the inertia. The retained PCs express the maximum inertia entropy of data in the feature space. Then, we use the Parzen window estimator to compute the upper control limit (UCL) for inertia-based KEPCA instead of the Gaussian assumption. Our second contribution concerns a new combined index based on the monitoring indices T2 and SPE in order to simplify the detection task of the fault and prevent any confusion. The proposed approaches have been applied to process fault detection and diagnosis for the well-known benchmark Tennessee Eastman process (TE). Results were performing.
A Tool for Energy Consumption Monitoring and Analysis of the Android Terminal
With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.
Performance Analysis in DF Energy Harvesting Full-Duplex Relaying Network with MRC and SC at the Receiver under Impact of Eavesdropper
This paper investigates the decode-and-forward (DF) full-duplex (FD) relaying system under the presence of an eavesdropper. Moreover, the relay node is able to harvest energy from a transmitter, and then it uses the harvested energy for conveying information to the receiver. Besides, both two-hop and direct relaying links are taking into consideration. In the mathematical analysis, we derived the exact expressions for intercept probability and outage probability (OP) by applying maximal ratio combining (MRC) and selection combining (SC) techniques at the receiver. Next, the Monte Carlo simulation is performed to validate the mathematical analysis. The results show that the simulation curves match the mathematic expressions, which confirms the analysis section.