A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

Identification of Lubricating Oil Additives Using XGBoost and Ant Colony Optimization Algorithms

To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。

A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm

This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft’s attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.

Study of Stochastic Subspace System Identification Method

Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.

Damage Identification of A TLP Floating Wind Turbine by Meta-Heuristic Algorithms

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring（SHM）. In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP（Tension-Leg Platform） floating wind turbine structure. The Genetic Algorithms（GA）, Artificial Immune System（AIS）, Particle Swarm Optimization（PSO）, and Artificial Bee Colony（ABC） are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine（TLPFWT） is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.

Generalized Yule-walker and two-stage identification algorithms for dual-rate systems

In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

Application of Genetic Algorithms in Identification ofLinear Time-Varying System

By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

PEMFC Fractional-order Subspace Identification Model

A proton exchange membrane fuel cell(PEMFC)is a new type of hydrogen fuel cell that plays an indispensable role in an energy network.However,the multivariable and fractional-order characteristics of PEMFC make it difficult to establish a practical model.Herein,a fractional-order subspace identification model based on the adaptive monarch butterfly optimization algorithm with opposition-based learning(ALMBO)algorithm is proposed for PEMFC.Introducing the fractional-order theory into the subspace identification method by adopting a Poisson filter for with input and output data,a weight matrix is proposed to improve the identification accuracy.Additionally,the ALMBO algorithm is employed to optimize the parameters of the Poisson filter and fractional order,which introduces an opposition-based learning strategy into the migration operator and incorporates adaptive weights to improve the optimization accuracy and prevent falling into a locally optimal solution.Finally,the PEMFC fractional-order subspace identification model is established,which can accurately describe the dynamic process of PEMFC.

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.

Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection,the detection of crop diseases directly impacts the income of farmers.To address the problems of low crop-disease identification precision and detection abilities,a new method of detection is proposed based on improved genetic algorithm and extreme learning machine.Taking five different typical diseases with common crops as the objects,this method first preprocesses the images of crops and selects the optimal features for fusion.Then,it builds a model of crop disease identification for extreme learning machine,introduces the hill-climbing algorithm to improve the traditional genetic algorithm,optimizes the initial weights and thresholds of the machine,and acquires the approximately optimal solution.And finally,a data set of crop diseases is used for verification,demonstrating that,compared with several other common machine learning methods,this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.

An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems

A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.