Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Total ionizing dose effect modeling method for CMOS digital-integrated circuit

Simulating the total ionizing dose(TID)of an electrical system using transistor-level models can be difficult and expensive,particularly for digital-integrated circuits(ICs).In this study,a method for modeling TID effects in complementary metaloxide semiconductor(CMOS)digital ICs based on the input/output buffer information specification(IBIS)was proposed.The digital IC was first divided into three parts based on its internal structure:the input buffer,output buffer,and functional area.Each of these three parts was separately modeled.Using the IBIS model,the transistor V-I characteristic curves of the buffers were processed,and the physical parameters were extracted and modeled using VHDL-AMS.In the functional area,logic functions were modeled in VHDL according to the data sheet.A golden digital IC model was developed by combining the input buffer,output buffer,and functional area models.Furthermore,the golden ratio was reconstructed based on TID experimental data,enabling the assessment of TID effects on the threshold voltage,carrier mobility,and time series of the digital IC.TID experiments were conducted using a CMOS non-inverting multiplexer,NC7SZ157,and the results were compared with the simulation results,which showed that the relative errors were less than 2%at each dose point.This confirms the practicality and accuracy of the proposed modeling method.The TID effect model for digital ICs developed using this modeling technique includes both the logical function of the IC and changes in electrical properties and functional degradation impacted by TID,which has potential applications in the design of radiation-hardening tolerance in digital ICs.

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Different El Niño Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model

A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.

Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF

Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.

Modeling and petrophysical properties of digital rock models with various pore structure types: An improved workflow

Pore structure is a crucial factor affecting the physical properties of porous materials,and understanding the mechanisms and laws of these effects is of great significance in the fields of geosciences and petroleum engineering.However,it remains a challenge to accurately understand and quantify the relationship between pore structures and effective properties.This paper improves a workflow to focus on investigating the effect of pore structure on physical properties.First,a hybrid modeling approach combining process-based and morphology-based methods is proposed to reconstruct 3D models with diverse pore structure types.Then,the characteristics and differences in pore structure in these models are compared.Finally,the varia-tion laws and pore-scale mechanisms of the influence of pore structure on physical properties(permeability and elasticity)are discussed based on the reconstructed models.The relationship models between pore structure parameters and perme-ability/elastic parameters in the grain packing model are established.The effect of pore structure evolution on permeability/elasticity and the microscopic mechanism in three types of morphology-based reconstruction models are explored.The influence degree of pore structure on elastic parameters(bulk modulus,shear modulus,P-wave velocity,and S-wave veloc-ity)is quantified,reaching 29.54%,51.40%,18.94%,and 23.18%,respectively.This work forms a workflow for exploring the relationship between pore structures and petrophysical properties at the microscopic scale,providing more ideas and references for understanding the complex physical properties in porous media.

Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

MODELING AND IMPLEMENTATION OF SPEED GOVERNOR FOR THE HYBRID ELECTRIC VEHICLE ENGINE

A speed control analysis for an in-line gasoline fueled internal combustion （IC） engine is presented for the purpose of alleviation of high frequency oscillations in engine revolutions. A dynamic cylinder-by-cylinder model is proposed, base on slider-crank mechanism, which is extended to develop a digital governor providing a high fidelity estimation of rotary speed oscillation for hybrid vehicle engines. A modified PID controller that P and I gain is placed in feedback path is also described for hybrid electric vehicle （HEV） engine speed regulation, By comparison between measured and estimated signals, it is demonstrated that a good agreement has been achieved and the governor behaves an excellent damping speed ripple.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

Randomness in the Hybrid Modeling and Simulation of Insulin Secretion Pathways in Pancreatic Islets

Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Although deterministic models of the insulin secretion pathway have been developed, the stochastic aspect of this biological pathway has not been explored. The first step in this direction presented here is a hybrid model of the insulin secretion pathway, in which the delayed rectifying KCchannels are treated as stochastic events. This hybrid model can not only reproduce the oscillation dynamics as the deterministic model does, but can also capture stochastic dynamics that the deterministic model does not. To measure the insulin oscillation system behavior, a probability-based measure is proposed and applied to test the effectiveness of a new remedy.

A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model

Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.

A fast hybrid simulation approach of ion energy and angular distributions in biased inductively coupled Ar plasmas

In this work,a two-dimensional hybrid model,which consists of a bulk fluid module,a sheath module and an ion Monte-Carlo module,is developed to investigate the modulation of ion energy and angular distributions at different radial positions in a biased argon inductively coupled plasma.The results indicate that when the bias voltage amplitude increases or the bias frequency decreases,the ion energy peak separation width becomes wider.Besides,the widths of the ion energy peaks at the edge of the substrate are smaller than those at the center due to the lower plasma density there,indicating the nonuniformity of the ion energy distribution function(IEDF)along the radial direction.As the pressure increases from 1 to 10 Pa,the discrepancy of the IEDFs at different radial positions becomes more obvious,i.e.the IEDF at the radial edge is characterized by multiple low energy peaks.When a dual frequency bias source is applied,the IEDF exhibits three or four peaks,and it could be modulated efficiently by the relative phase between the two bias frequencies.The results obtained in this work could help to improve the radial uniformity of the IEDF and thus the etching process.

A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria.Long-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two years.Five computational algorithms were considered and analyzed for the suitability of estimation.Further two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach.The accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA test.Among the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good performances.However,the newly proposed ensemble algorithms exhibited even better performance.The proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than 0.999.This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms.It is concluded that the proposed algorithms are far superior to the commonly adopted ones.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Exploring Alternatives to Create Digital Twins from and for Process Simulation

In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus(alternative 2)were used with this same aim.The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus,which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet.Aspen HYSYS was used for solving the pre-heating,reaction,and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns.Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.

A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model

The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.