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.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

E-Commerce Supply Chain Process Optimization Based on Whole-Process Sharing of Internet of Things Identification Technology

With in-depth development of the Internet of Things(IoT)in various industries,the informatization process of various industries has also entered the fast lane.This article aims to solve the supply chain process problem in e-commerce,focusing on the specific application of Internet of Things technology in e-commerce.Warehousing logistics is an important link in today’s e-commerce transactions.This article proposes a distributed analysis method for RFID-based e-commerce warehousing process optimization and an e-commerce supply chain management process based on Internet of Things technology.This article first introduces the advantages and disadvantages of shared IoT identification technology and the IoT resource sharing platform based on the three-layer abstract data model and representational state transfer(REST)style.Combining actual IoT applications and the characteristics of an existing platform,a REST-based IoT resource sharing platform is proposed.Combined with actual projects,a REST-based IoT resource sharing platform was built,and key technology experiments were conducted for verification.Finally,optimizing the e-commerce supply chain management process under Internet of Things technology and explaining the advantages of optimized e-commerce supply chain management are discussed.Research on this subject provides a theoretical basis for the application of the Internet of Things in e-commerce and has practical significance and practical value for managing service capabilities and service levels in e-commerce.

Species Identification by Means of Mitochondrial Cytochrome <i>b</i>DNA Sequencing in Processed Anchovy, Sardine and Tuna Products

Identifying the contents of processed food products is essential to correct labelling. In processed foodstuffs, species identification through morphological analysis is difficult. Several factors hinder the identification of fish species in processed foods: proteins or other materials subjected to analysis may be denatured during heat treatments;the presence of other ingredients (e.g., olive and other vegetable oils) may interfere with the analysis. Consequently, possible frauds perpetrated by replacing valuable species with less precious ones may go undetected. In most processed samples (e.g. canned products), DNA is degraded into small fragments, which considerably reduces the sensitivity of molecular analysis. The main goal of our research was to develop an analytical method able to identify fish species in highly processed products, such as canned fish. The assay was developed by combining an effective method of DNA recovery from samples with the detection of small-sized sequences of the mitochondrial Cytb gene. This method appears particularly suitable when morphological characterization is difficult, to carry out such as in canned products where DNA is degraded or present in small quantities. We have analyzed 60 samples of seafood commercial products identifying 3 different genera and five different species. All analyzed samples revealed a correct species declaration, for one sample we highlighted important commercial fraud. We also used bio-informatic identification systems for the Sequence Alignment and the construction of phylogenetic tree to better confirm the revealed fraud.

Consequence Identification for Maloperation in Batch Process

Batch processes are important in chemical industry,in which operators usually play a major role and hazards may arise by their inadvertent acts.In this paper,based on hazard and operability study and concept of qualitative simulation,an automatic method for adverse consequence identification for potential maloperation is proposed.The qualitative model for production process is expressed by a novel directed graph.Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords.The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations.The method is illustrated with a simple batch process and a batch reaction process.The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations.After analysis for possible plant maloperations,some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.

An input signal used in process identification - chaos sequence

The sequences which consist of any segment of a chaos sequence are called asC-sequences. These sequences could be used as a kind of input signals to replace M-sequences in theprocess identification. This substitution is theoretically proved to be feasible. InverseC-sequences are created in a way similar to inverse M-sequences to solve the problem thatC-sequences have non-ideal balance property, that is, the numbers of '0' and '1' are unequal.Besides its good pseudo-random property, the sequences have other advantages such as easy togenerate, varieties of the segment and adjustable cycle time.

Parallel Processing Based on Ship Maneuvering in Identification of Interaction Force Coefficients

The parallel processing based on the free running model test was adopted to predict the interaction force coefficients （flow straightening coefficient and wake fraction） of ship maneuvering. And the multipopulation genetic algorithm （MPGA） based on real coding that can contemporarily process the data of free running model and simulation of ship maneuvering was applied to solve the problem. Accordingly the optimal individual was obtained using the method of genetic algorithm. The parallel processing of multiopulation solved the prematurity in the identification for single population, meanwhile, the parallel processing of the data of ship maneuvering （turning motion and zigzag motion） is an attempt to solve the coefficient drift problem. In order to validate the method, the interaction force coefficients were verified by the procedure and these coefficients measured were compared with those ones identified. The maximum error is less than 5%, and the identification is an effective method.

Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.

Decentralized closed-loop identification and controller design for a kind of cascade processes

A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.

Thin-layer Identification,Extraction Process and Content Determination of Chlorogenic Acid in Laggera alata(D.Don)Sch.Bip.ex Oliv.

[Objectives]This paper aims to establish thin-layer identification and content determination method for Laggera alata(D.Don)Sch.Bip.ex Oliv.with chlorogenic acid as the index component and compare the content of chlorogenic acid in L.alata from different places in Guangxi.[Methods]Silica gel GF254 thin-layer plate was used for identification under an ultraviolet lamp(365 nm),with butyl acetate-formic acid-water(V∶V∶V=7∶2.5∶2.5)as a developing agent.The content of chlorogenic acid was determined under the following chromatographic conditions:column,Inertsil ODS-3 C18 column(4.60 mm×250 mm,5μm);mobile phase,methanol-0.1%phosphoric acid(28∶72);detection wavelength,329 nm;flow rate,1.0 mL/min;column temperature,25℃;and injection volume,10μL.[Results]Chlorogenic acid can be detected by thin layer chromatography with clear spot and good specificity.Chlorogenic acid showed a good linear relationship in the injection amount range of 0.099-0.99μg(R^(2)=0.9999).The content of chlorogenic acid in L.alata varied greatly among the 10 different producing areas in Guangxi.L.alata produced in Dee Township,Longlin,Baise,Guangxi showed the highest chlorogenic acid content,and that produced in Shangsi County and Pingle County showed the lowest chlorogenic acid content.[Conclusions]This method can effectively identify L.alata and accurately determine the content of chlorogenic acid,thereby providing a scientific basis for the development and utilization of L.alata resources.

TLC Identification of Laggerae Alatae Herba and Extraction Process of Chlorogenic Acid

[Objectives]Taking chlorogenic acid as index component,TLC and content determination method of Laggerae Alatae Herba were established.[Methods]TLC identification used silica gel G thin-layer plate,and butyl acetate∶formic acid∶water(7∶2.5∶2.5)was taken as developing agent,and it was inspected under ultraviolet lamp(365 nm).The content was determined by chromatographic column Inertsil ODS-3 C_(18)(4.60 mm×250 mm,5μm).Mobile phase:methanol-0.1%phosphoric acid(28∶72);detection wavelength:329 nm;flow speed:1.0 mL/min;column temperature:25℃;injection volume:10μL.[Results]Chlorogenic acid can be detected by TLC,with clear spots and good specificity.When injection volume of chlorogenic acid was between 0.099 and 0.990μg(R^(2)=0.9999),there was good linear relationship.In low,medium and high sample adding groups of Laggerae Alatae Herba,average recovery rates of chlorogenic acids were 98.80%(RSD=2.09%),98.24%(RSD=1.96%)and 99.65%(RSD=2.15%).[Conclusions]The method could effectively identify medicinal material Laggerae Alatae Herba,and accurately measure the content of chlorogenic acid in Laggerae Alatae Herba,thereby providing a scientific basis for developing and using medicinal resources of Laggerae Alatae Herba.

Styloid Process Is the Robust Landmark for Identification of the Facial Nerve during Parotid Surgery

Objective: To identify the facial nerve trunk (FNT) very easily. quickly and accurately considering the styloid process as the robust anatomically consistent landmark during parotid surgery. Patients and Methods: This prospective study was conducted between January 2010 to December 2022 at a Tertiary care hospital. It included 130 patients: 84 (65%) males and 46 (35%) females with a mean age of 43.52 ± 15.63 years. Patients presented with pleomorphic adenoma (90), chronic sialectasis (05), parotid cyst (03), parotid sinus (01), mucoepidermoid carcinoma (28), adenoid cystic carcinoma (02) and squamous cell carcinoma (01) were subjected to superficial parotidectomy and total parotidectomy. The outcome was evaluated regarding the clinical success of facial nerve function that considered the location of the styloid process in the triangle technique for identification of the FNT during parotid surgery. Results: All 130 patients underwent superficial and total parotidectomy. The facial nerve trunk was successfully identified as the location of the styloid process in the triangle technique in all the patients with no intra-operative complications. Operative time ranged from 50 to 180 minutes with a mean time 90.70 ± 15.68 minutes. Dysfunction of the angle of the mouth due to the deficit of the marginal mandibular nerve (MMN) was noted in 5 patients (3.8%), 02 had difficulty in eye closure (1.5%). No patient reported surgical site infection, skin flap necrosis and hematoma in this series. Conclusion: Styloid process is the prominent robust landmark in the triangle technique for the identification of facial nerve trunk with relative ease, safety and accuracy in the parotid surgical procedure. This can be a very useful method to locate the FNT and minimize facial nerve injury during parotid surgery.

Research on Volume Identification with the Method of Discharging at the Velocity of Sound

The method of volume identification in pneumatics was studied through theoretical analysis and experimental investigation. Regarding discharging from a container as a thermodynamic process with invariable index the dependence of the container’s volume on the pressure in the container and the index, during discharging at the velocity of sound, is deduced. Then through a lot of experiments, the value of index n of the process is found with a given precision and a specified volume range. Furthermore, the feasibility and practicability of this method are verified by experiments.

Application of Computer Vision Technique to Maize Variety Identification

Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.

Similarity identification method on formational interfaces and application in general granite

An instrumented drilling system can be applied for the acquisition of drilling process parameters. The system can provide continuous and huge data for geotechnical engineering. However, due to the complexity of ground strata, the variation in the drilling parameters with stratigraphical characteristics is great and the correlation between likely comparable parameters is not high, which limits the use of conventional correlation approaches in this field. How to use the data for engineering and how to get a reasonable interpretation for the relationships among the drilling parameters, as well as between a drilling parameter and formational characteristics, become a technical choke point for the development and application of the instrumented drilling system. Based on similarity criteria, the extraction of sample data and characteristics, the pretreatment of data and feature matching algorithms have been analyzed and an approach of slope coefficient searching identification has been established. A case study was carried out for the similarity between the rotational speed of the drill bit, flushing pressure, and effective thrust force graphics in general weathered granite. The result shows that the similarity coefficients between the rotational speed of the drill bit, flushing pressure, and effective thrust force are 0.72 and 0.83, respectively. Although there are differences between the distances of the graphics, the curves of both rotational speed and flushing pressure agree with the effective thrust curve in shape, which provides a possible method for the identification of various formations by use of the similarity between feature drilling parameters.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

Continuous-time System Identification with Nuclear Norm Minimization and GPMF-based Subspace Method

To improve the accuracy and effectiveness of continuous-time (CT) system identification, this paper introduces a novel method that incorporates the nuclear norm minimization (NNM) with the generalized Poisson moment functional (GPMF) based subspace method. The GPMF algorithm provides a simple linear mapping for subspace identification without the timederivatives of the input and output measurements to avoid amplification of measurement noise, and the NNM is a heuristic convex relaxation of the rank minimization. The Hankel matrix with minimized nuclear norm is used to determine the model order and to avoid the over-parameterization in subspace identification method (SIM). Furthermore, the algorithm to solve the NNM problem in CT case is also deduced with alternating direction methods of multipliers (ADMM). Lastly, two numerical examples are presented to evaluate the performance of the proposed method and to show the advantages of the proposed method over the existing methods. © 2014 Chinese Association of Automation.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.