Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification

Energy efficiency is an important aspect of increasing production capacity, minimizing environmental impact, and reducing energy usage in the petrochemical industries. However, in practice, data quality can be degraded by measurement malfunction throughout the operation, leading to unreliable and inaccurate prediction results. Therefore, this paper presents a transfer learning fault detection and identification-energy efficiency predictor (TFDI-EEP) model formulated using long short-term memory. The model aims to predict the energy efficiency of the petrochemical process under uncertainty by using the knowledge gained from the uncertainty detection task to improve prediction performance. The transfer procedure resolves weight initialization by applying partial layer freezing before fine-tuning the additional part of the model. The performance of the proposed model is verified on a wide range of fault variations to thoroughly examine the maximum contribution of faults that the model can tolerate. The results indicate that the TFDI-EEP achieved the highest r-squared and lowest error in the testing step for both the 10% and 20% fault variation datasets compared to other conventional methods. Furthermore, the revelation of interconnection between domains shows that the proposed model can also identify strong fault-correlated features, enhancing monitoring ability and strengthening the robustness and reliability of the model observed by the number of outliers. The transfer parameter improves the prediction performance by 9.86% based on detection accuracy and achieves an r-squared greater than 0.95 on the 40% testing fault variation.

Detection and Identification of a Novel Quinone Ketoxy Radical Produced by Metal-independent Decomposition of Hydroperoxides by Halogenated Quinones

We have shown recently that halogenated quinones could enhance the decomposition of hydroperoxides and formation of alkoxyl/hydroxyl radicals independent of transition

Magnetic Detection Method for Seabed Cable in Marine Engineering Surveying

The detection and identification of the seabed cable is becoming an important task in the marine engineering. The features of the magnetic anomaly can be used to detect the existence of the seabed cable. The magnetic field model is presented, and the consistency of the magnetic anomaly distribution between the simulation of the model and the observed data is verified. The comparison shows that the seabed cable can be effectively detected and identified with reasonable method.

Distributed Fault Detection for Consensus in Second-Order Discrete-Time Multiagent Systems with Adversary

This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.

傅里叶变换用于地下电缆故障检测(英文)

An analysis of underground power cables is performed using Fourier analysis with the objective of detecting fault and average life of the cables.Three types of cables are used in this experiment:a normal cable,a shorted cable, and a cable with holes.The impedance in each case is computed and Fourier transformation is applied so that the resulting impedance magnitude and impedance phase can be examined in the frequency domain.Various windowing techniques are applied to the experimental data to eliminate any interference.Fourier analysis is then applied to the impedance data calculated from both the sending end voltage and differential voltage.This analysis reveals differences in the frequency response of the three different types of a cable and can eventually be used as a measure for fault detection. Preliminary results reveal the differences in the frequency response.Accordingly,Fourier type methods can be effectively used as low cost and viable solutions to identify and detect faults in underground cables.

Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.