machine learning

摘要： Fatigue failure phenomena of the concrete structures under long-term low amplitude loading have attractedmore attention.Some structures,such as wind power towers,offshore platforms,and high-speed railways,may resist millions of cycles loading during their intended lives.Over the past century,analytical methods for concrete fatigue are emerging.It is concluded that models for the concrete fatigue calculation can fall into four categories:the empirical model relying on fatigue tests,fatigue crack growth model in fracture mechanics,fatigue damage evolution model based on damage mechanics and advanced machine learning model.In this paper,a detailed review of fatigue computing methodology for concrete is presented,and the characteristics of different types of fatigue models have been stated and discussed.

摘要： Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.

摘要： Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote devices.Taskoffloading has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely.The offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational environment.This study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence offloading.Full offloading and partial offloading strategies are the two types of offloading strategies.The algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning algorithms.We examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine learning.Under the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.

摘要： The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

摘要： Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

摘要： At present,iron and steel enterprises mainly use“after spot test ward”to control final product quality.However,it is impossible to realize on-line quality predetermining for all products by this traditional approach,hence claims and returns often occur,resulting in major eco-nomic losses of enterprises.In order to realize the on-line quality predetermining for steel products during manufacturing process,the predic-tion models of mechanical properties based on deep learning have been proposed in this work.First,the mechanical properties of deep drawing steels were predicted by using LSTM(long short team memory),GRU(gated recurrent unit)network,and GPR(Gaussian process regression)model,and prediction accuracy and learning efficiency for different models were also discussed.Then,on-line re-learning methods for transfer learning models and model parameters were proposed.The experimental results show that not only the prediction accuracy of optimized trans-fer learning models has been improved,but also predetermining time was shortened to meet real time requirements of on-line property prede-termining.The industrial production data of interstitial-free(IF)steel was used to demonstrate that R2 value of GRU model in training stage reaches more than 0.99,and R2 value in testing stage is more than 0.96.

摘要： Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.

摘要： The entry into a hyper-connected society increases the generalization of communication using SNS.Therefore,research to analyze big data accumulated in SNS and extractmeaningful information is being conducted in various fields.In particular,with the recent development of Deep Learning,the performance is rapidly improving by applying it to the field of Natural Language Processing,which is a language understanding technology to obtain accurate contextual information.In this paper,when a chatbot system is applied to the healthcare domain for counseling about diseases,the performance of NLP integrated withmachine learning for the accurate classification ofmedical subjects from text-based health counseling data becomes important.Among the various algorithms,the performance of Bidirectional Encoder Representations from Transformers was compared with other algorithms of CNN,RNN,LSTM,and GRU.For this purpose,the health counseling data of Naver Q&A service were crawled as a dataset.KoBERT was used to classify medical subjects according to symptoms and the accuracy of classification results was measured.The simulation results show that KoBERTmodel performed high performance by more than 5%and close to 18%as large as the smallest.

摘要： Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.

摘要： Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.