Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e....Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.展开更多
It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily ...It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.展开更多
At present,artificial intelligence computing platforms are usually based on cloud hosts for services,which have the characteristics of fast training speed and a wide variety of model types.However,the online models of...At present,artificial intelligence computing platforms are usually based on cloud hosts for services,which have the characteristics of fast training speed and a wide variety of model types.However,the online models of such platforms mostly adopt the form of downloading model files,which is difficult to integrate into traditional software system systems.In response to existing problems,this paper takes the relevant theoretical technologies of next-generation intelligent computing platforms as the development framework,and conducts research on the diversity of multi-level intelligent computing requirements,by implementing a universal algorithm model construction and automatic integration mechanism;Build a multi domain and multi-level application algorithm library for different application scenarios;Design a personalized algorithm recommendation based on knowledge reasoning and object-oriented approach,and build an emerging intelligent computing platform for analyzing and understanding real-world data,meeting the needs of complex engineering application software such as heavy backend,light frontend,loose coupling,microservices,etc.,providing theoretical and technical support for innovative big data services and applications with diverse computing requirements.展开更多
Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison ...Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison with electronic computing.The optical implementation of the fundamental building blocks of a digital computer,i.e.logic gates,has been investigated extensively in the past few decades.Optical logic gate computing is an alternative approach to various analogue optical computing architectures.In this paper,the latest development of optical logic gate computing with different kinds of implementations is reviewed.Firstly,the basic concepts of analogue and digital computing with logic gates in the electronic and optical domains are introduced.And then a comprehensive summary of various optical logic gate schemes including spatial encoding of light field,semiconductor optical amplifiers(SOA),highly nonlinear fiber(HNLF),microscale and nanoscale waveguides,and photonic crystal structures is presented.To conclude,the formidable challenges in developing practical all-optical logic gates are analyzed and the prospects of the future are discussed.展开更多
A survey on agents, causality and intelligence is presented and an equilibrium-based computing paradigm of quantum agents and quantum intelligence (QAQI) is proposed. In the survey, Aristotle’s causality principle an...A survey on agents, causality and intelligence is presented and an equilibrium-based computing paradigm of quantum agents and quantum intelligence (QAQI) is proposed. In the survey, Aristotle’s causality principle and its historical extensions by David Hume, Bertrand Russell, Lotfi Zadeh, Donald Rubin, Judea Pearl, Niels Bohr, Albert Einstein, David Bohm, and the causal set initiative are reviewed;bipolar dynamic logic (BDL) is introduced as a causal logic for bipolar inductive and deductive reasoning;bipolar quantum linear algebra (BQLA) is introdused as a causal algebra for quantum agent interaction and formation. Despite the widely held view that causality is undefinable with regularity, it is shown that equilibrium-based bipolar causality is logically definable using BDL and BQLA for causal inference in physical, social, biological, mental, and philosophical terms. This finding leads to the paradigm of QAQI where agents are modeled as quantum enssembles;intelligence is revealed as quantum intelligence. It is shown that the enssemble formation, mutation and interaction of agents can be described as direct or indirect results of quantum causality. Some fundamental laws of causation are presented for quantum agent entanglement and quantum intelligence. Applicability is illustrated;major challenges are identified in equilibriumbased causal inference and quantum data mining.展开更多
基金The authors appreciate the financial support provided by the Natural Science Foundation of China(No.41807294)This study was also financially supported by China Geological Survey Project(Nos.DD20190716 and 0001212020CC60002)。
文摘Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.
基金Project(KJZD-M202000801) supported by the Major Project of Chongqing Municipal Education Commission,ChinaProject(2016YFE0205600) supported by the National Key Research&Development Program of China+1 种基金Project(CXQT19023) supported by the Chongqing University Innovation Group Project,ChinaProjects(KFJJ2018069,1853061,1856033) supported by the Key Platform Opening Project of Chongqing Technology and Business University,China。
文摘It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.
文摘At present,artificial intelligence computing platforms are usually based on cloud hosts for services,which have the characteristics of fast training speed and a wide variety of model types.However,the online models of such platforms mostly adopt the form of downloading model files,which is difficult to integrate into traditional software system systems.In response to existing problems,this paper takes the relevant theoretical technologies of next-generation intelligent computing platforms as the development framework,and conducts research on the diversity of multi-level intelligent computing requirements,by implementing a universal algorithm model construction and automatic integration mechanism;Build a multi domain and multi-level application algorithm library for different application scenarios;Design a personalized algorithm recommendation based on knowledge reasoning and object-oriented approach,and build an emerging intelligent computing platform for analyzing and understanding real-world data,meeting the needs of complex engineering application software such as heavy backend,light frontend,loose coupling,microservices,etc.,providing theoretical and technical support for innovative big data services and applications with diverse computing requirements.
基金National Key R&D Program of China (2018AAA0102100)Hunan Provincial Department of Education Outstanding Youth Project (22B0385)2022 Disciplinary Construction “Revealing the List and Appointing Leaders” Project(22JBZ051)。
基金supported by the National Key Research and Development Program of China(Grants No.2021YFA1401500)the National Natural Science Foundation of China(12022416)+3 种基金the Department of Natural Resources of Guangdong Province(No.GDNRC[2022]22)Department of Science and Technology of Guangdong Province(No.2021A0505080002)Intelligent Laser Basic Research Laboratory(No.PCL2021A14-B1)the Hong Kong Research Grants Council(16306220).
文摘Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison with electronic computing.The optical implementation of the fundamental building blocks of a digital computer,i.e.logic gates,has been investigated extensively in the past few decades.Optical logic gate computing is an alternative approach to various analogue optical computing architectures.In this paper,the latest development of optical logic gate computing with different kinds of implementations is reviewed.Firstly,the basic concepts of analogue and digital computing with logic gates in the electronic and optical domains are introduced.And then a comprehensive summary of various optical logic gate schemes including spatial encoding of light field,semiconductor optical amplifiers(SOA),highly nonlinear fiber(HNLF),microscale and nanoscale waveguides,and photonic crystal structures is presented.To conclude,the formidable challenges in developing practical all-optical logic gates are analyzed and the prospects of the future are discussed.
文摘A survey on agents, causality and intelligence is presented and an equilibrium-based computing paradigm of quantum agents and quantum intelligence (QAQI) is proposed. In the survey, Aristotle’s causality principle and its historical extensions by David Hume, Bertrand Russell, Lotfi Zadeh, Donald Rubin, Judea Pearl, Niels Bohr, Albert Einstein, David Bohm, and the causal set initiative are reviewed;bipolar dynamic logic (BDL) is introduced as a causal logic for bipolar inductive and deductive reasoning;bipolar quantum linear algebra (BQLA) is introdused as a causal algebra for quantum agent interaction and formation. Despite the widely held view that causality is undefinable with regularity, it is shown that equilibrium-based bipolar causality is logically definable using BDL and BQLA for causal inference in physical, social, biological, mental, and philosophical terms. This finding leads to the paradigm of QAQI where agents are modeled as quantum enssembles;intelligence is revealed as quantum intelligence. It is shown that the enssemble formation, mutation and interaction of agents can be described as direct or indirect results of quantum causality. Some fundamental laws of causation are presented for quantum agent entanglement and quantum intelligence. Applicability is illustrated;major challenges are identified in equilibriumbased causal inference and quantum data mining.
