The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle discussed.A thermodynamic approach to study optimal control processes in complex non...The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle discussed.A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems applied.The results of stochastic simulation of a fuzzy intelligent control system for various types of external/internal excitations for a dynamic,globally unstable control object-extension cableless robotic unicycle based on Soft Computing(Computational Intelligence Toolkit-SCOptKBTM)technology presented.A new approach to design an intelligent control system based on the principle of the minimum entropy production(minimum of useful resource losses)determination in the movement of the control object and the control system is developed.This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle.An algorithm for entropy production computing and representation of their relationship with the Lyapunov function(a measure of stochastic robust stability)described.展开更多
The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical appr...The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.展开更多
Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effecti...Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.展开更多
Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,comp...Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available.展开更多
The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not o...The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.展开更多
Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper....Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.展开更多
In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computi...In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.展开更多
Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but ...Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.展开更多
Nowadays,when a life span of sensor nodes are threatened by the shortage of energy available for communication,sink mobility is an excellent technique for increasing its lifespan.When communicating via a WSN,the use o...Nowadays,when a life span of sensor nodes are threatened by the shortage of energy available for communication,sink mobility is an excellent technique for increasing its lifespan.When communicating via a WSN,the use of nodes as a transmission method eliminates the need for a physical medium.Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources.Unless the route is revised and changed to reflect the location of the mobile sink location,it will be inefficient for delivering data effec-tively.In the clustering strategy,nodes are grouped together to improve commu-nication,and the cluster head receives data from compactable nodes.The sink receives the aggregated data from the head.The cluster head is the central node in the conventional technique.A single node uses more energy than a node that is routed to a dead node.Increasing the number of people using a route shortens its lifespan.The proposed work demonstrates the effectiveness with which sensor node paths can be modified at a lower cost by utilising the virtual grid.The best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment(VGDRA).Only specific nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route recon-struction.According to the results,VGDRA schemes have a longer life span because of the reduced number of loops.展开更多
Soft computing is a combination of methods that complement each other when dealing with ambiguous real life decision systems. Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of clas...Soft computing is a combination of methods that complement each other when dealing with ambiguous real life decision systems. Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of classical sets to deal with incomplete knowledge and provides a mechanism for concept approximation. It uses reducts to isolate key attributes affecting outcomes in decision systems. The paper summarizes two algorithms for reduct calculation. Moreover, to automate the application of RST, different software packages are available. The paper provides a survey of packages that are most frequently used to perform data analysis based on Rough Sets. For benefit of researchers, a comparison of based on functionalities of those software is also provided.展开更多
In recent years,several studies using smart methods and soft computing in the field of HVAC systems have been provided.In this paper,we propose a framework which will strengthen the benefits of the Fuzzy Logic(FL)and ...In recent years,several studies using smart methods and soft computing in the field of HVAC systems have been provided.In this paper,we propose a framework which will strengthen the benefits of the Fuzzy Logic(FL)and Neural Fuzzy(NF)systems to estimate outdoor temperature.In this regard,Adaptive Neuro Fuzzy Inference System(ANFIS)is used in effective combination of strategic information for estimating the outdoor temperature of the building.A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory.Due to ANFIS accuracy in specialized predictions,it is an effective device to manage vulnerabilities of each experiential framework.The NF system can concentrate on measurable properties of the samples throughout the preparation sessions.Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data.This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored.The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.展开更多
The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solutio...The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.展开更多
A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a found...A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a foundation for computational intelligence presented.The application of soft computing technology(the first step of IT)allows to extract knowledge directly from the physical signal of the electroencephalogram,as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state.The possibilities of applying quantum soft computing technologies(the second step of IT)in the processes of robust filtering of electroencephalogram signals for the formation of mental commands of robotic prosthetic arm discussed.Quantum supremacy benchmark of intelligent control simulation demonstrated.展开更多
Redundant robotic arm models as a control object discussed.Background of computational intelligence IT on soft computing optimizer of knowledge base in smart robotic manipulators introduced.Soft computing optimizer is...Redundant robotic arm models as a control object discussed.Background of computational intelligence IT on soft computing optimizer of knowledge base in smart robotic manipulators introduced.Soft computing optimizer is the sophisticated computational intelligence toolkit of deep machine learning SW platform with optimal fuzzy neural network structure.The methods for development and design technology of control systems based on soft computing introduced in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data,and in the presence of stochastic noises of various physical and statistical characters.The knowledge bases formed with the application of soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object.The robustness is achieved by application a vector fitness function for genetic algorithm,whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system,and the other components describe conventional control objective functionals such as minimum control error,etc.The application of soft computing technologies(Part I)for the development a robust intelligent control system that solving the problem of precision positioning redundant(3DOF and 7 DOF)manipulators considered.Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described.展开更多
This paper introduces an efficient holistic approach to the design optimization of lightweight structures of braided fiber-reinforced plastic material. The approach aims to mitigate the paradox of making design decisi...This paper introduces an efficient holistic approach to the design optimization of lightweight structures of braided fiber-reinforced plastic material. The approach aims to mitigate the paradox of making design decisions at early development phases, when necessary information is incomplete or lacking detail so as to properly make these decisions. However, expert knowledge is available and though it is imprecise in nature, it can compensate to create useful models. Manufacturing effort for the braiding process has been described by information accumulated via interviews with braiding experts. This information is then modelled using the soft-computing approach by fuzzy-rule-based systems. The resulting models can further be efficiently integrated into the structural design optimization process. A multidisciplinary design optimization is facilitated considering several aspects including manufacturing effort and structural mechanics, which can be used in early design phases leading to more holistic designing and, thereby, unlocking lightweight and cost-reducing potentials. Benefits of this method, including viability and ease of implementation, are proven by investigations on two academic test problems before advancing to the challenging automotive engineering design problem of the roadster A-pillar.展开更多
Soft Computing denotes a set of paradigma related to cognitive modelling, which in the last years have been intensively studied, since important synergy effects among members of this set have been disclosed. Because o...Soft Computing denotes a set of paradigma related to cognitive modelling, which in the last years have been intensively studied, since important synergy effects among members of this set have been disclosed. Because of this, Soft Computing has emerged as an environment to effectively work with red world complex problems. Fuzzy Logic, Genetic Algorithms and Neural Networks are possibly the best known representatives of Soft Computing. In this paper we show how Spectral Techniques may help to further study these subjects or to improve their performance. The name Spectral Techniques comprises Methods and Applications based on Abstract Harmonic Analysis.展开更多
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and ster...The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.展开更多
DR （diabetic retinopathy） is a most probable reason of blindness in adults, but the only remedy or escape from blindness is that we have to detect DR as early. Several automated screening techniques are used to dete...DR （diabetic retinopathy） is a most probable reason of blindness in adults, but the only remedy or escape from blindness is that we have to detect DR as early. Several automated screening techniques are used to detect individual lesions in the retina. Still it takes more dependency of time and experts. To overcome those problems and also automatically detect DR in easier and faster way, we took into soft computing approaches in our proposed work. Our proposed work will discuss several amounts of soft computing algorithms, it can detect DR features （landmark and retinal lesions） in an easy manner. Processes includes are： （1） Pre-processing; （2） Optic disc localization and segmentation; （3） Localization of fovea; （4） Blood vessel segmentation; （5） Feature extraction; （6） Feature selection; Finally （7） detection of diabetic retinopathy stages （mild, moderate, severe and PDR）. Our experimental results based on Matlab simulation and it takes databases of STARE and DRIVE. Proposed effective soft computing approaches should improve the sensitivity, specificity and accuracy.展开更多
In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the un...In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.展开更多
The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the develop...The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.展开更多
文摘The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle discussed.A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems applied.The results of stochastic simulation of a fuzzy intelligent control system for various types of external/internal excitations for a dynamic,globally unstable control object-extension cableless robotic unicycle based on Soft Computing(Computational Intelligence Toolkit-SCOptKBTM)technology presented.A new approach to design an intelligent control system based on the principle of the minimum entropy production(minimum of useful resource losses)determination in the movement of the control object and the control system is developed.This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle.An algorithm for entropy production computing and representation of their relationship with the Lyapunov function(a measure of stochastic robust stability)described.
文摘The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.
文摘Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.
基金supported by High-end Foreign Expert Introduction program (No.G20190022002)Chongqing Construction Science and Technology Plan Project (2019-0045)
文摘Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available.
文摘The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.
基金This work was supported by Hong Kong Polytechnic University(No.G.45.37.T363),the National Natural Science Foundation of PRC(No.70431003,60521003).
文摘Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.
基金This research was supported by Hankuk University of Foreign Studies Research Fund of 2021.Also,This research was supported by the MIST(Ministry of Science,ICT),Korea,under the National Program for Excellence in SW),supervised by the IITP(Institute of Information&communications Technology Planing&Evaluation)in 2021”(2019-0-01816).
文摘In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.
基金supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993)the Inha University Research Grant(2022).
文摘Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.
文摘Nowadays,when a life span of sensor nodes are threatened by the shortage of energy available for communication,sink mobility is an excellent technique for increasing its lifespan.When communicating via a WSN,the use of nodes as a transmission method eliminates the need for a physical medium.Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources.Unless the route is revised and changed to reflect the location of the mobile sink location,it will be inefficient for delivering data effec-tively.In the clustering strategy,nodes are grouped together to improve commu-nication,and the cluster head receives data from compactable nodes.The sink receives the aggregated data from the head.The cluster head is the central node in the conventional technique.A single node uses more energy than a node that is routed to a dead node.Increasing the number of people using a route shortens its lifespan.The proposed work demonstrates the effectiveness with which sensor node paths can be modified at a lower cost by utilising the virtual grid.The best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment(VGDRA).Only specific nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route recon-struction.According to the results,VGDRA schemes have a longer life span because of the reduced number of loops.
文摘Soft computing is a combination of methods that complement each other when dealing with ambiguous real life decision systems. Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of classical sets to deal with incomplete knowledge and provides a mechanism for concept approximation. It uses reducts to isolate key attributes affecting outcomes in decision systems. The paper summarizes two algorithms for reduct calculation. Moreover, to automate the application of RST, different software packages are available. The paper provides a survey of packages that are most frequently used to perform data analysis based on Rough Sets. For benefit of researchers, a comparison of based on functionalities of those software is also provided.
文摘In recent years,several studies using smart methods and soft computing in the field of HVAC systems have been provided.In this paper,we propose a framework which will strengthen the benefits of the Fuzzy Logic(FL)and Neural Fuzzy(NF)systems to estimate outdoor temperature.In this regard,Adaptive Neuro Fuzzy Inference System(ANFIS)is used in effective combination of strategic information for estimating the outdoor temperature of the building.A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory.Due to ANFIS accuracy in specialized predictions,it is an effective device to manage vulnerabilities of each experiential framework.The NF system can concentrate on measurable properties of the samples throughout the preparation sessions.Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data.This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored.The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.
文摘The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
文摘A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a foundation for computational intelligence presented.The application of soft computing technology(the first step of IT)allows to extract knowledge directly from the physical signal of the electroencephalogram,as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state.The possibilities of applying quantum soft computing technologies(the second step of IT)in the processes of robust filtering of electroencephalogram signals for the formation of mental commands of robotic prosthetic arm discussed.Quantum supremacy benchmark of intelligent control simulation demonstrated.
文摘Redundant robotic arm models as a control object discussed.Background of computational intelligence IT on soft computing optimizer of knowledge base in smart robotic manipulators introduced.Soft computing optimizer is the sophisticated computational intelligence toolkit of deep machine learning SW platform with optimal fuzzy neural network structure.The methods for development and design technology of control systems based on soft computing introduced in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data,and in the presence of stochastic noises of various physical and statistical characters.The knowledge bases formed with the application of soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object.The robustness is achieved by application a vector fitness function for genetic algorithm,whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system,and the other components describe conventional control objective functionals such as minimum control error,etc.The application of soft computing technologies(Part I)for the development a robust intelligent control system that solving the problem of precision positioning redundant(3DOF and 7 DOF)manipulators considered.Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described.
文摘This paper introduces an efficient holistic approach to the design optimization of lightweight structures of braided fiber-reinforced plastic material. The approach aims to mitigate the paradox of making design decisions at early development phases, when necessary information is incomplete or lacking detail so as to properly make these decisions. However, expert knowledge is available and though it is imprecise in nature, it can compensate to create useful models. Manufacturing effort for the braiding process has been described by information accumulated via interviews with braiding experts. This information is then modelled using the soft-computing approach by fuzzy-rule-based systems. The resulting models can further be efficiently integrated into the structural design optimization process. A multidisciplinary design optimization is facilitated considering several aspects including manufacturing effort and structural mechanics, which can be used in early design phases leading to more holistic designing and, thereby, unlocking lightweight and cost-reducing potentials. Benefits of this method, including viability and ease of implementation, are proven by investigations on two academic test problems before advancing to the challenging automotive engineering design problem of the roadster A-pillar.
文摘Soft Computing denotes a set of paradigma related to cognitive modelling, which in the last years have been intensively studied, since important synergy effects among members of this set have been disclosed. Because of this, Soft Computing has emerged as an environment to effectively work with red world complex problems. Fuzzy Logic, Genetic Algorithms and Neural Networks are possibly the best known representatives of Soft Computing. In this paper we show how Spectral Techniques may help to further study these subjects or to improve their performance. The name Spectral Techniques comprises Methods and Applications based on Abstract Harmonic Analysis.
文摘The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.
文摘DR （diabetic retinopathy） is a most probable reason of blindness in adults, but the only remedy or escape from blindness is that we have to detect DR as early. Several automated screening techniques are used to detect individual lesions in the retina. Still it takes more dependency of time and experts. To overcome those problems and also automatically detect DR in easier and faster way, we took into soft computing approaches in our proposed work. Our proposed work will discuss several amounts of soft computing algorithms, it can detect DR features （landmark and retinal lesions） in an easy manner. Processes includes are： （1） Pre-processing; （2） Optic disc localization and segmentation; （3） Localization of fovea; （4） Blood vessel segmentation; （5） Feature extraction; （6） Feature selection; Finally （7） detection of diabetic retinopathy stages （mild, moderate, severe and PDR）. Our experimental results based on Matlab simulation and it takes databases of STARE and DRIVE. Proposed effective soft computing approaches should improve the sensitivity, specificity and accuracy.
文摘In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.
文摘The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.
