IPSO-based hybrid approaches for reliability-redundancy allocation problems

The problem of maximizing system reliability through component reliability choices and component redundancy is called tell-ability-redundancy allocation problem （RAP）, and it is a difficult but realistic nonlinear mixed-integer optimization prob- lem. For the RAP. we pay attention to an improved particle swarm optimization （IPSO）, and introduce four hybrid approaches for combining the IPSO with other conventional search techniques, such as harmony search （HS） and LXPM （a real coded GA）. The basic structure of the hybrid approaches includes two phases. After devising an initial solution by the HS or LXPM technique in the first phase, the IPSO performs an optimal search in the next phase. In addition, a new procedure by using golden search, named GS, is developed for further improving the solutions obtained by IPSO. Consequently, four ISPO-based hybrid approaches are proposed including HS-IPSO, LXPM-IPSO, HS-IPSO-GS, and LXPM-IPSO-GS. In order to validate the per-formance of proposed approaches, five nonlinear mixed-integer RAPs are investigated where both the number of re- dundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously. As shown, the proposed approaches are all superior in terms of both optimal solutions and robustness to those by IPSO. Especially the pro-posed LXPM-IPSO-GS has shown more excellent performance than other typical approaches in the literature.

A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses

El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to improve understanding of ENSO processes,and different models for ENSO predictions have been developed,including linear statistical models based on principal oscillation pattern(POP)analyses,convolutional neural networks(CNNs),and so on.Here,we develop a novel hybrid model,named as POP-Net,by combining the POP analysis procedure with CNN-long short-term memory(LSTM)algorithm to predict the Niño-3.4 sea surface temperature(SST)index.ENSO predictions are compared with each other from the corresponding three models:POP model,CNN-LSTM model,and POP-Net,respectively.The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise.Consequently,an improved prediction is achieved in the POP-Net relative to others.The POP-Net shows a high-correlation skill for 17-month lead time prediction(correlation coefficients exceeding 0.5)during the 1994-2020 validation period.The POP-Net also alleviates the spring predictability barrier(SPB).It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.

Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

Review of Experimental-Numerical Methodologies and Challenges for Floating Offshore Wind Turbines

Due to the dissimilar scaling issues,the conventional experimental method of FOWTs can hardly be used directly to validate the full-scale global dynamic responses accurately.Therefore,it is of absolute necessity to find a more accurate,economic and efficient approach,which can be utilized to predict the full-scale global dynamic responses of FOWTs.In this paper,a literature review of experimental-numerical methodologies and challenges for FOWTs is made.Several key challenges in the conventional basin experiment issues are discussed,including scaling issues;coupling effects between aero-hydro and structural dynamic responses;blade pitch control strategies;experimental facilities and calibration methods.Several basin experiments,industrial projects and numerical codes are summarized to demonstrate the progress of hybrid experimental methods.Besides,time delay in hardware-in-the-loop challenges is concluded to emphasize their significant role in real-time hybrid approaches.It is of great use to comprehend these methodologies and challenges,which can help some future researchers to make a footstone for proposing a more efficient and functional hybrid basin experimental and numerical method.

Estimation of Shallow S-Wave Velocity Structure of Two Practical Sites from Microtremors Array Observation in Tangshan Area

Microtremors array observation for estimating S-wave velocity structure from phase velocities of Rayleigh and Love wave on two practical sites in Tangshan area by a China-US joint group are researched.The phase velocities of Rayleigh wave are estimated from vertical component records and those of Love wave are estimated from three-component records of microtremors array using modified spatial auto-correlation method.Haskell matrix method is used in calculating Rayleigh and Love wave phase velocities,and the shallow S-wave velocity structure of two practical sites are estimated by means of a hybrid approach of Genetic Algorithm and Simplex.The results are compared with the PS logging data of the two sites,showing it is feasible to estimate the shallow S-wave velocity structure of practical site from the observation of microtremor array.

Technical Note on a Track-pattern-based Model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

Data-driven prognostics and remaining useful life estimation for lithium-ion battery: A Review

As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL)estimation for lithium-ion batteries attach more and more attractions.Especially,the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status,that makes these methods flexible and applicable in actual lithium-ion battery applications.At first,the related concepts and definitions are introduced.And the degradation parameters identification and extraction is presented,as the health indicator and the foundation of RUL prediction for the lithium-ion batteries.Then,data-driven methods used for lithium-ion battery RUL estimation are summarized,in which several statistical and machine learning algorithms are involved.Finally,the future trend for battery prognostics and RUL estimation are forecasted.

Effectiveness of Bilateral Pulmonary Artery Banding in Patients with Hypoplastic Left Heart Syndrome and Congenital Heart Defects with a Functional Single Ventricle: A Single-Center Retrospective Study

Background:Bilateral banding of the branches of the pulmonary artery in patients with hypoplastic left heart syndrome(HLHS)and other duct dependent critical neonatal heart malformations can significantly reduce the incidence of severe complications in the postoperative period,especially in severely unstable patients.In our study we compared different surgical techniques of bilateral pulmonary artery banding(PAB)in respect to their success in balancing systemic and pulmonary blood flow.Methods:We included 44 neonates with a HLHS and congenital heart diseases(CHD)with a functional single ventricle underwent a hybrid operation:bilateral PAB and patent ductus arteriosus stenting.The hybrid surgery for method No.1 is performed as a one-stage procedure,together with patent ductus arteriosus(PDA)stenting.After median sternotomy,two Gore-Tex 1–2 mm wide bands with a diameter of 3–3.5 mm are put.When we apply method No.2 then the thread is used to create bands.Method No.3 is distinguished by intraoperative assessment of blood flow at the site of narrowing of the branches of the pulmonary artery and optional stenting of the PDA.The cuff for banding is made of Gore-Tex tubing.Effectiveness when applying method Nos.1 and 2 is assessed by the change in invasive blood pressure and oxygen saturation after narrowing of the branches of the pulmonary artery.Also,with these techniques PDA stenting by inserting the introducer via pulmonary artery trunk is performed.Results:HLHS with mitral or aortic valve atresia or both was present in 19 patients(43.1%),with severe left heart obstruction resulting in PDA dependent systemic circulation in 16 babies(36.4%).CHD with single ventricle physiology occurred in 9 patients(20.5%).14 babies(31.8%)undergo the procedure following the method No.1,8 patients(18.2%)method No.2 and 22 patients(50%)method No.3.Qp/Qs=1/1 was achieved in 30 patients(30/44,68.1%):as a result of the method No.1 was achieved in 5 patients(5/14,35.7%),method No.2 in 4 patients(4/8,50%),method No.3 in 21 patients(21/22,95.5%).Multivariate regression analysis revealed that method No.3 significantly increases the chances of hemodynamic efficacy operations(OR=35.0;p=0.005;CI(95%)3–411.5).Conclusion:Application of the operation technique No.3 in combination with the intraoperative assessment of blood flow parameters at the site of banding of the branches of the pulmonary artery are the most optimal criteria for achieving Qp/Qs=1/1.If there are signs of restriction at the level of the foramen ovale,atrioseptostomy should be done in the second stage after bilateral pulmonary banding.

Study on Customer Demand Forecasting Models,Stock Management,Classification and Policies for Automobile Parts Manufacturing Company N.A.C.C.(An Advance on Classical Models)

The primary intent of the current research is to provide insights regarding the management of spare parts within the supply chain,in conjunction with offering some methods for enhancing forecasting and inventory management.In particular,to use classical forecasting methods,the use of weak and unstable demand is not recommended.Furthermore,statistical performance measures are not involved in this particular context.Furthermore,it is expected that maintenance contracts will be aligned with different levels.In addition to the examination of some literature reviews,some tools will guide us through this process.The article proposes new performance analysis methods that will help integrate inventory management and statistical performance while considering decision maker priorities through the use of different methodologies and parts age segmentation.The study will also identify critical level policies by comparing different types of spenders according to the inventory management model,also with separate and common inventory policies.Each process of the study is combined with a comparative analysis of different forecasting methods and inventory management models based on N.A.C.C.parts supply chain data,allowing us to identify a set of methodologies and parameter recommendations based on parts segmentation and supply chain prioritization.

A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China

Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.

Hybrid approach used for extended image-based wavefront sensor-less adaptive optics

The stochastic paralld gradient descent （SPGD） algorithm is widely used in wavefront sensor-less adaptive optics （WSAO） systems. However, the convergence is relatively slow. Modal-based algorithms usually provide much faster convergence than SPGD; however, the limited actuator stroke of the deformable mirror （DM） often prohibits the sensing of higher-order modes or renders a closed-loop correction inapplicable. Based on a comparative analysis of SPGD and the DM-modal-based algorithm, a hybrid approach involving both algorithms is proposed for extended image-based WSAO, and is demonstrated in this experiment. The hybrid approach can achieve similar correction results to pure SPGD, but with a dramatically decreased iteration number.

PID Neural Net work Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution

For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem,a hybrid particle swarm optimization(PSO)and differential evolution(DE)algorithm(PSO-DE)is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show t hat the proposed met hod has better decoupling capabilities and control quality than the previous approaches.

An end-to-end 3D seismic simulation of underground structures due to point dislocation source by using an FK-FEM hybrid approach

Based on the domain reduction idea and artificial boundary substructure method,this paper proposes an FK-FEM hybrid approach by integrating the advantages of FK and FEM(i.e.,FK can efficiently generate high-frequency three translational motion,while FEM has rich elements types and constitutive models).An advantage of this approach is that it realizes the entire process simulation from point dislocation source to underground structure.Compared with the plane wave field input method,the FK-FEM hybrid approach can reflect the spatial variability of seismic motion and the influence of source and propagation path.This approach can provide an effective solution for seismic analysis of underground structures under scenario of earthquake in regions where strong earthquakes may occur but are not recorded,especially when active faults,crustal,and soil parameters are available.Taking Daikai subway station as an example,the seismic response of the underground structure is simulated after verifying the correctness of the approach and the effects of crustal velocity structure and source parameters on the seismic response of Daikai station are discussed.In this example,the influence of velocity structure on the maximum interlayer displacement angle of underground structure is 96.5%and the change of source parameters can lead to the change of structural failure direction.

Generating prototypical residential building geometry models using a new hybrid approach

Building prototyping has regularly been used in building performance analyses with statistically feasible models.The novelty of this research involves a new hybrid approach combining stratified sampling and k-means clustering to establish building geometry prototypes.The research focuses on residential buildings in Ningbo,China.Seventeen small residential districts(SRDs)containing 367 residential buildings were systemically selected for survey and data collection.The stratified sampling used building construction year as the main parameter to generate stratification.Floor numbers,shape coefficients,floor areas,and window-to-wall ratios were used as the four observations for k-means clustering.Based on this new approach,nine building geometry prototypes were identified and modelled.These statistically representative prototypes provide building geometrical information and characteristic-based evaluations for subsequent building performance analysis.

Identification and Cloning of Differentially Expressed Genes Involved in the Interaction Between Potato and Phytophthora infestans using a Subtractive Hybridization and cDNA-AFLP Combinational Approach

Using a subtractive hybridization （SH）/cDNA-AFLP combinational approach, differentially expressed genes involved in the potato-Phytophthora infestans interaction were identified. These included genes potentially controlling pathogenesis or avr genes in P. infestans as well as those potentially involved in potato resistance or susceptibility to this pathogen. Forty-one differentially expressed transcript, derived fragments （TDFs）, resulting from the interaction, were cloned and sequenced. Two TDFs, suggested as potential pathogenicity factors, have sequence similarity to N-succinyl diaminopimelate aminotransferase and a transcriptional regulator, TetR family gene, respectively. Two other TDFs, suggested as potential avr genes, have sequence similarity to an EST sequence from Avr41Cf.41Avr91Cf- 9 and a P. infestans avirulence-associated gene, respectively. Genes＇ expression and origin were confirmed using Southern blots, Northern blots and qRT-PCR, he., potential resistance gene DL81 was induced at 12 hpi in the moderately resistant cultivar, whereas it was down-regulated as early as 6 hpi in the susceptible cultivar. On the other hand, DL21 was induced at 6 hpi （3.38-fold） in response to the highly aggressive isolate （US8） and strongly up-regulated thereafter （25.13-fold at 120 hpi.）, whereas it was only slightly up-regulated in response to the weakly aggressive isolate US11 （3.82-fold at 96 hpi）, suggesting its potential involvement as a susceptibility gene.

Hybrid approach for loss recovery mechanism in OBS networks

This letter reports a study of a hybrid burst assembly and a hybrid burst loss recovery scheme （delay-based burst assembly and hybrid loss recovery （DBAHLR）） which selectively employs proactive or reactive loss recovery techniques depending on the classification of traffic into short term and long term, respectively. Traffic prediction and segregation of optical burst switching network flows into the long term and short term are conducted based on predicted link holding times using the hidden Markov model （HMM）. The hybrid burst assembly implemented in DBAHLR uses a consecutive average-based burst assembly to handle jitter reduction necessary in real-time applications, with variations in burst sizes due to the non-monotonic nature of the average delay handled by additional burst length thresholding. This dynamic hybrid approach based on HMM prediction provides overall a lower blocking probability and delay and more throughput when compared with forward segment redundancy mechanism or purely HMM prediction-based adaptive burst sizing and wavelength allocation （HMM-TP）.

Vehicle height and leveling control of electronically controlled air suspension using mixed logical dynamical approach

Vehicle height and leveling control of electronically controlled air suspension(ECAS) still poses theoretical challenges for researchers that have not been adequately addressed in prior research. This paper investigates the design and verification of a new controller to adjust the vehicle height and to regulate the roll and pitch angles of the vehicle body(leveling control) during the height adjustment procedures. A nonlinear mechanism model of the vehicle height adjustment system is formulated to describe the dynamic behaviors of the system. By using mixed logical dynamical(MLD) approach, a novel control strategy is proposed to adjust the vehicle height by controlling the on-off statuses of the solenoid valves directly. On this basis, a correction algorithm is also designed to regulate the durations of the on-off statuses of the solenoid valves based on pulse width modulated(PWM) technology, thus the effective leveling control of the vehicle body can be guaranteed. Finally, simulations and vehicle tests results are presented to demonstrate the effectiveness and applicability of the proposed control methodology.

Importance of retrieving noun phrases and named entities from digital library content

We present a novel approach for extracting noun phrases in general and named entities in particular from a digital repository of text documents.The problem of coreference resolution has been divided into two subproblems:pronoun resolution and non-pronominal resolution.A rule based-technique was used for pronoun resolution while a learning approach for nonpronominal resolution.For named entity resolution,disambiguation arises mainly due to polysemy and synonymy.The proposed approach fixes both problems with the help of WordNet and the Word Sense Disambiguation tool.The proposed approach,to our knowledge,outperforms several baseline techniques with a higher balanced F-measure,which is harmonic mean of recall and precision.The improvements in the system performance are due to the filtering of antecedents for the anaphor based on several linguistic disagreements,use of a hybrid approach,and increment in the feature vector to include more linguistic details in the learning technique.