Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

In this study,an optimized long short-term memory(LSTM)network is proposed to predict the reliability and remaining useful life(RUL)of rolling bearings based on an improved whale-optimized algorithm(IWOA).The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing.To provide covariates for reliability assessment,a kernel principal component analysis is used to reduce the dimensionality of the features.A Weibull distribution proportional hazard model(WPHM)is used for the reliability assessment of rolling bearing,and a beluga whale optimization(BWO)algorithm is combined with maximum likelihood estimation(MLE)to improve the estimation accuracy of the model parameters of the WPHM,which provides the data basis for predicting reliability.Considering the possible gradient explosion by training the rolling bearing lifetime data and the difficulties in selecting the key network parameters,an optimized LSTM network called the improved whale optimization algorithm-based long short-term memory(IWOA-LSTM)network is proposed.As IWOA better jumps out of the local optimization,the fitting and prediction accuracies of the network are correspondingly improved.The experimental results show that compared with the whale optimization algorithm-based long short-term memory(WOA-LSTM)network,the reliability prediction and RUL prediction accuracies of the rolling bearing are improved by the proposed IWOA-LSTM network.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.

Improvement of Cardiac Function by Dry Weight Optimization Based on Interdialysis Inferior Vena Caval Diameter (1)

In hemodialysis (HD) patients, the diameter of the inferior vena cava (IVC) serves for evaluation of the amount of body fluid.

Improvement of light uniformity by lighting arrangement for standardized crop production

In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these methods, lightings are implemented without considering specific optical characteristics of lighting or material characteristics of each component that constructs a cultivation system, resulting in an amount of light that becomes irregular. The amount of lighting is closely related with the growth and quality of crops, and the deviation between points where cultivated crops are located causes quality difference in the produced crops, thus impairing the economic feasibility of a plant factory. In this regard, a simulation to figure out an optimum lighting layout was performed. Arrangements based on the spectrum distribution of light source and reflector materials were implemented to ascertain the distance between lighting and height of lighting and gather information in the pre-treatment process to improve the uniformity of light in the plant cultivation system. Improvement of around 15% in light uniformity is achieved compared with the existing system after the simulation is carried out. This result would reduce the deviation in crop growth to make uniform quality crop production possible.

Improved Shark Smell Optimization Algorithm for Human Action Recognition

Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma video.An effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video surveillance.Machine Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature fusion.The accuracy and effectiveness of these models continue to be the biggest challenge in this field.In this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted features.This proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search space.The proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of FV.Once local optimal features are selected,a global search is conducted to further optimize these features.The proposed iSSO algorithm is employed on nine(9)selected CNN models.These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition.To evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation.The authors present computer vision techniques for detecting and classifying fruit leaf diseases.Examples of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and classification.The following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy.This paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature selection.In the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced dataset.The next step is to use a pre-trained deep model named Darknet53 and fine-tune it.Next,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling layer.Finally,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning classifiers.The experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry diseases.The overall average achieved accuracy is 99.7%,higher than previous techniques.

Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm

Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.