slim – SyntaxBug

Linux executable file slimming instruction strip usage example

When developing software under a Linux system, the output executable file can be large or small. If the running environment is on a server, the resources may be sufficient, but if it is in an embedded environment, storage resources must be competed for every inch. Therefore, there will be a need to slim down the […]

Android performance optimization series apk slimming

Why APK needs to be slimmed down. The larger the APK, the more traffic they will consume during the download and installation process, and the longer the installation waiting time will be; for the product itself, it means that the download conversion rate will be lower (because users have more opportunities to choose among competing […]

[CEEMDAN-SMA-LSSVM] Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power and wind speed prediction based on CEEMD combined with the slime algorithm with matlab implementation

?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For code acquisition, paper reproduction and scientific research simulation cooperation, please send a private message. Personal homepage: Matlab Research Studio Personal credo: Investigate things to gain knowledge. For more complete Matlab code and simulation customization […]

Regression prediction | Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power and wind speed prediction based on CEEMD combined with the slime algorithm with matlab implementation

[Wind speed prediction] Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power wind speed prediction based on CEEMD combined with the slime algorithm with matlab code

Slim down the jar package deployed by your SpringBoot project!

I have written an article about maven plugin before Detailed explanation of spring-boot-maven-plugin plug-in 1. Demand background We know that the Spring Boot project can be started through java -jar package name. So why can the Spring Boot project be started through the above command, but other ordinary projects cannot? The reason is that when […]

Android APK slimming practice: How to reduce the size of the second slimming? (4M-2.9M)

Before losing weight Because size restrictions are usually taken into consideration, a lot of work has been done. The current status is listed below: 7.3M (Debug version) and 6.5M (Release version). Turn on minifyEnabled. Turn on shrinkResources. Large irrelevant libraries have been removed. Images and code have gone through a rough round of cleaning. Start […]

Fire channel occupancy detection system based on YOLOv5 based on GSConv+SlimNeck

1. Research background and significance Project ReferenceAAAI Association for the Advancement of Artificial Intelligence The smooth flow of fire escapes is crucial to the safety of personnel and property. However, in real life, due to various reasons, fire exits are often illegally occupied, resulting in fire vehicles being unable to reach the fire scene in […]

The use of ManualResetEvent and ManualResetEventSlim in C#

Starting with .NET Framework version 2.0, ManualResetEvent derives from the EventWaitHandle class. The ManualResetEvent is functionally equivalent to the EventWaitHandle created using EventResetMode.ManualReset. ManualResetEventSlim is used to achieve better performance of ManualResetEvent. The following introduces a summary of the use of ManualResetEvent and ManualResetEventSlim in .NET (C#). 1. ManualResetEvent and ManualResetEventSlim ManualResetEvent represents a thread […]

Matlab regression prediction of slime mold optimized bidirectional long short-term memory network (SMA-BILSTM)

?Abouttheauthor:AMatlabsimulationdeveloperwholovesscientificresearch.Hecultivateshismindandimproveshistechnologysimultaneously.ForcooperationonMATLABprojects,pleasesendaprivatemessage. Personalhomepage:MatlabResearchStudio Personalcredo:Investigatethingstogainknowledge. Withtherapiddevelopmentofrenewableenergy,windpowergeneration,asanimportantpartofit,hasreceivedwidespreadattention.However,duetotheuncertaintyandvolatilityofwindpowergeneration,accuratepredictionofwindpowergenerationhasbecomeanimportantissue.Inordertosolvethisproblem,manyresearchershavebeguntoexploretheuseofmachinelearningalgorithmsforwindpowerdataprediction. Amongmachinelearningalgorithms,longshort-termmemorynetwork(LSTM)isacommonlyusedrecurrentneuralnetwork(RNN)modelthatperformswellinprocessingsequencedata.LSTMnetworksareabletocapturelong-termdependenciesinsequencedata,therebyimprovingpredictionaccuracy.However,thetraditionalLSTMmodeloftenhassomeproblemswhenprocessingwindpowerdata,suchasslowmodeltrainingspeedandlowpredictionaccuracy. Inordertosolvetheseproblems,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheSlimeMoldAlgorithm(SMA).Theslimemoldalgorithmisaheuristicalgorithmthatsimulatesthebehaviorofslimemoldsintheprocessofsearchingforfood.Ithasthecharacteristicsofglobalsearchandadaptability.ByapplyingtheslimemoldalgorithmtotheoptimizationprocessoftheLSTMmodel,thetrainingspeedandpredictionaccuracyofthemodelcanbeeffectivelyimproved. Intheexperiment,weusedasetofwindpowerdatasetstotrainandtestthemodel.First,wepreprocessedtheoriginalwindpowerdata,includingdatacleaning,featureextraction,etc.Then,weusedtheslimemoldalgorithmtooptimizetheparametersoftheSMA-biLSTMmodel.Finally,wecomparedtheresultsofwindpowerdatapredictionusingthetraditionalLSTMmodelandtheoptimizedSMA-biLSTMmodel. ExperimentalresultsshowthattheoptimizedSMA-biLSTMmodelshowsobviousadvantagesinwindpowerdataprediction.ComparedwiththetraditionalLSTMmodel,theSMA-biLSTMmodelhashigherpredictionaccuracyandfastertrainingspeed.ThisshowsthattheperformanceoftheLSTMmodelinwindpowerdatapredictioncanbeeffectivelyimprovedbyintroducingtheslimemoldalgorithmforoptimization. Insummary,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheslimemoldalgorithmforwindpowerdataprediction.Experimentalresultsshowthatthemodelhasgoodperformanceinwindpowerdataprediction.Futureresearchcanfurtherexploretheapplicationofotheroptimizationalgorithmstofurtherimprovetheaccuracyandefficiencyofwindpowerdataprediction. Corecode functionhuatu(fitness,process,type) figure plot(fitness) gridon title([type,’Fitnesscurve’]) xlabel(‘Numberofiterations/times’) ylabel(‘Fitnessvalue/MSE’) figure subplot(2,2,1) plot(process(:,1)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘L1/piece’) subplot(2,2,2) plot(process(:,2)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘L2/piece’) subplot(2,2,3) plot(process(:,3)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘K/times’) subplot(2,2,4) plot(process(:,4)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘lr’) subtitle([type,’Hyperparameterschangewiththenumberofiterations’]) ?References [1]LongZhongxiu.Researchandimplementationoflandslidepredictionbasedonsoilslopedataclassificationmodel[D].SouthwestJiaotongUniversity,2020. [2]WangYongsheng.Researchonshort-termwindpoweroutputpowerpredictionbasedondeeplearning[J].[2023-09-08]. [3]WangHaiyue.Researchontheapplicationoffuzzyinferencesystembasedongranularcomputingintimeseriesdataprediction[D].ShandongNormalUniversity,2019. 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