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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.
Design and dSPACE Implementation of a Simplified Fuzzy Control of a DC-DC Three-Level Converter
The design of an efficient DC-DC converter depends critically on its suitable control. In this paper, a new simplified output tracking control strategy for a DC-DC three-level boost converter is presented. The proposed strategy is characterized by its good tracking performances, its simplicity of design, and the stability that is ensured over the entire operating range. Thanks to (i) the adopted Takagi–Sugeno (TS) fuzzy approach; (ii) the small-signal model derived under the large domain of operating conditions, and (iii) the proportional-integral (PI) controllers’ merit. After introducing the three-level boost converter topology, the operating principles and mathematical modeling are addressed. Then, the proposed output control strategy is developed based on the PI control and the TS fuzzy approximation. A controller ensuring the capacitor voltages balancing has been also introduced in this paper. Experimental results using dSPACE (DS1104) and a laboratory prototype of three-level boost converter demonstrate the flexibility of the proposed controller, its reference tracking capability, and its ability to satisfy the performance specification over the whole operating range of the system.
Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis
Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of the CNN model. For this research, nearly 2400 specimens (600 images in each class) were accessed for training purposes. This developed model is implemented using python version 3.7.3 and the model is equipped on the deep learning package called Keras, TensorFlow backed, and Jupyter which are used as the developmental environment. This model achieved an accuracy of 96.4% for identifying classes of leaf disease and pests in cotton plants. This revealed the feasibility of its usage in real-time applications and the potential need for IT-based solutions to support traditional or manual disease and pest’s identification.