To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an obj...To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.展开更多
To improve overall equipment efficiency(OEE) of a semiconductor wafer wet-etching system,a heuristic tabu search scheduling algorithm is proposed for the wet-etching process in the paper,with material handling robot c...To improve overall equipment efficiency(OEE) of a semiconductor wafer wet-etching system,a heuristic tabu search scheduling algorithm is proposed for the wet-etching process in the paper,with material handling robot capacity and wafer processing time constraints of the process modules considered.Firstly,scheduling problem domains of the wet-etching system(WES) are assumed and defined,and a non-linear programming model is built to maximize the throughput with no defective wafers.On the basis of the model,a scheduling algorithm based on tabu search is presented in this paper.An improved Nawaz,Enscore,and Ham(NEH) heuristic algorithm is used as the initial feasible solution of the proposed heuristic algorithm.Finally,performances of the proposed algorithm are analyzed and evaluated by simulation experiments.The results indicate that the proposed algorithm is valid and practical to generate satisfied scheduling solutions.展开更多
This paper addresses the studies carried out on an I-beam to reveal the wave propagation characteristics and tackle the multi-mode propagation of Lamb waves. The experimental setup consisted of a new 3D Scanning Laser...This paper addresses the studies carried out on an I-beam to reveal the wave propagation characteristics and tackle the multi-mode propagation of Lamb waves. The experimental setup consisted of a new 3D Scanning Laser Doppler Vibrometer manufactured by Polytec (3D-SLDV) and was used to acquire high resolution time-space Lamb waves that were propagating in the I-beam. A high power and pulsed Nd:YAG laser was used to emit the required Lamb waves. The emission and sensing of the waves were carried out simultaneously. The wave propagation data was recorded by scanning the surface of the I-beam in a sequential manner. The measured data was used to construct the wave patterns that were propagating in the I-beams at different time instants. Furthermore, as the waves in an I-Beam propagate with multiple modes even at low frequency range, filtering was carried out in the frequency-wavenum- ber domain in order to decompose the modes. The results presented thereby confirm that the new 3D-SLDV possesses tremendous capability in revealing the wave propagation characteristics and its interaction with defect. The results could be the first time that the waves propagating in a real I-beam can be visually observed, whilst in the past, it can only be visualized through simulation. The capability of using such totally laser-based 3D inspection system to reveal the characteristics of Lamb wave and its interaction with defects are substantial.展开更多
In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based faul...In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed,and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data.Domain generalization(DG)aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training.The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained.In this article,for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years.Specifically,we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations.Then,the recent trend of DG for fault diagnosis is also analyzed.Finally,the existing problems and future prospect is performed.展开更多
Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined compo...Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.展开更多
Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characterist...Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.展开更多
The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing...The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include： multi-time scale, spacetime scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.展开更多
Some novel applications and pragmatic variations of knapsack problem （KP） are presented and constructed, which are formulated and developed from a model initiated in this paper on profit allocation from partition of...Some novel applications and pragmatic variations of knapsack problem （KP） are presented and constructed, which are formulated and developed from a model initiated in this paper on profit allocation from partition of jobs in terms of two-person discrete cooperation game.展开更多
In this paper, we reported the benefits of using eXtended Markup Language （XML） to support financial knowledge management and discussed number of issues associated with developing an XML-based financial knowledge ma...In this paper, we reported the benefits of using eXtended Markup Language （XML） to support financial knowledge management and discussed number of issues associated with developing an XML-based financial knowledge management system. Current searching engines do not provide sufficient performance in terms of recall, precision, and extensibility for financial knowledge management, because the data represented in HTML format cannot support fmancial knowledge management effectively. On the other hand, XML provides a vendor-neutral approach to structure and organize contents as XML authors are allowed to create arbitrary tags to describe the format or structure of data. A prototype of XML-based ELectronic Financial Filing System （ELFFS-XML） is developed, and value-added ated informationservices such as automatic tag generation and cross-linking rel from different data sources are provided to enable knowledge representation and knowledge generation. We compared the XML-based ELFFS with the original HTML-based ELFFS and SEDAR - an electronic filing system used in Canada, and we found that ELFFS-XML is able to provide much more functionalities to support knowledge management. We also compared our automatic tag generation result with the experts＇ and investors＇ choices, and recommended some directions for future development of similar electronic filing systems.展开更多
Continuous-time Markowitz＇s by parameterizing a critical quantity. It mean-variance efficient strategies are modified is shown that these parameterized Markowitz strategies could reach the original mean target with a...Continuous-time Markowitz＇s by parameterizing a critical quantity. It mean-variance efficient strategies are modified is shown that these parameterized Markowitz strategies could reach the original mean target with arbitrarily high probabilities. This, in turn, motivates the introduction of certain stopped strategies where stock holdings are liquidated whenever the parameterized Markowitz strategies reach the present value of the mean target. The risk aspect of the revised Markowitz strategies are examined via expected discounted loss from the initial budget. A new portfolio selection model is suggested based on the results of the paper.展开更多
In this paper, we consider a single-period model comprised of an original manufacturer （OM） who produces only new products and a remanufacturer who collects used products from consumers and produces remanufactured p...In this paper, we consider a single-period model comprised of an original manufacturer （OM） who produces only new products and a remanufacturer who collects used products from consumers and produces remanufactured products. The OM and the remanufacturer compete in the product market. We examine the effects of government subsidy as a means to promote remanufacturing activity. In particularly, we consider three subsidy options： subsidy to remanufacturer, subsidy to consumers, and subsidy shared by remanufacturer and consumers. We find that the introduction of government subsidy on remanufacturer or consumers always increases remanufacturing activity. We also find that subsidy to remanufacturer is the best subsidy option, because subsidy to remanufacturer results in lower price of remanufactttred products, thus leading to higher consumer surplus.展开更多
In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the worst-case performance ratio at le...In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the worst-case performance ratio at leastα=0.87856….In fact,the estimated worst-case performance ratio is dependent on the data of the problem withαbeing a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach(with the triangle inequalities added)is at leastα+δ_d if every weight is strictly positive,whereδ_d>0 is a constant depending on the problem dimension and data.展开更多
Mathematical programming problems with semi-continuous variables and cardinality constraint have many applications,including production planning,portfolio selection,compressed sensing and subset selection in regressio...Mathematical programming problems with semi-continuous variables and cardinality constraint have many applications,including production planning,portfolio selection,compressed sensing and subset selection in regression.This class of problems can be modeled as mixed-integer programs with special structures and are in general NP-hard.In the past few years,based on new reformulations,approximation and relaxation techniques,promising exact and approximate methods have been developed.We survey in this paper these recent developments for this challenging class of mathematical programming problems.展开更多
Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport,train delays often occur.For this study,a three-month dataset of weather,train delay and train schedule reco...Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport,train delays often occur.For this study,a three-month dataset of weather,train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time.We found that in severe weather train delays are determined mainly by the type of bad weather,while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains.Identifying the factors closely correlated with train delays,we developed a machine-learning model to predict the delay time of each train at each station.The prediction model is useful not only for passengers wishing to plan their journeys more reliably,but also for railway operators developing more efficient train schedules and more reasonable pricing plans.展开更多
Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)system.Its goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue pol...Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)system.Its goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue policy.Reinforcement learning(RL)is widely used to optimize this dialogue policy.In the learning process,the user is regarded as the environment and the system as the agent.In this paper,we present an overview of the recent advances and challenges in dialogue policy from the perspective of RL.More specifically,we identify the problems and summarize corresponding solutions for RL-based dialogue policy learning.In addition,we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in RL.We believe this survey can shed light on future research in DPL.展开更多
This paper optimizes the electricity and renewable energy credit （REC） purchasing process for energy distribution. Electricity is traded in deregulated time-sequential markets at fluctuating prices. Optimal electric...This paper optimizes the electricity and renewable energy credit （REC） purchasing process for energy distribution. Electricity is traded in deregulated time-sequential markets at fluctuating prices. Optimal electricity purchasing under price and demand uncertainty is a challenging task for electricity distributors, and the recently implemented renewable portfolio standards （RPS） further complicate the purchasing process. Government regulatory decisions concerning the RPS require distributors to purchase corresponding certificates, namely RECs, equivalent to a certain percentage of their electricity sales. This paper formulates and optimizes the joint purchasing process for electricity and RECs. It also analyzes the effect of RPS policy on electricity distributors.展开更多
To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage a...To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.展开更多
Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities...Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.展开更多
基金Projects(71071115,60574054) supported by the National Natural Science Foundation of China
文摘To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.
基金Supported by the National Natural Science Foundation of China(No.71071115,61273035)
文摘To improve overall equipment efficiency(OEE) of a semiconductor wafer wet-etching system,a heuristic tabu search scheduling algorithm is proposed for the wet-etching process in the paper,with material handling robot capacity and wafer processing time constraints of the process modules considered.Firstly,scheduling problem domains of the wet-etching system(WES) are assumed and defined,and a non-linear programming model is built to maximize the throughput with no defective wafers.On the basis of the model,a scheduling algorithm based on tabu search is presented in this paper.An improved Nawaz,Enscore,and Ham(NEH) heuristic algorithm is used as the initial feasible solution of the proposed heuristic algorithm.Finally,performances of the proposed algorithm are analyzed and evaluated by simulation experiments.The results indicate that the proposed algorithm is valid and practical to generate satisfied scheduling solutions.
文摘This paper addresses the studies carried out on an I-beam to reveal the wave propagation characteristics and tackle the multi-mode propagation of Lamb waves. The experimental setup consisted of a new 3D Scanning Laser Doppler Vibrometer manufactured by Polytec (3D-SLDV) and was used to acquire high resolution time-space Lamb waves that were propagating in the I-beam. A high power and pulsed Nd:YAG laser was used to emit the required Lamb waves. The emission and sensing of the waves were carried out simultaneously. The wave propagation data was recorded by scanning the surface of the I-beam in a sequential manner. The measured data was used to construct the wave patterns that were propagating in the I-beams at different time instants. Furthermore, as the waves in an I-Beam propagate with multiple modes even at low frequency range, filtering was carried out in the frequency-wavenum- ber domain in order to decompose the modes. The results presented thereby confirm that the new 3D-SLDV possesses tremendous capability in revealing the wave propagation characteristics and its interaction with defect. The results could be the first time that the waves propagating in a real I-beam can be visually observed, whilst in the past, it can only be visualized through simulation. The capability of using such totally laser-based 3D inspection system to reveal the characteristics of Lamb wave and its interaction with defects are substantial.
基金supported by the National Natural Science Foundation of China(62322315,61873237)the Zhejiang Provincial Natural Science Foundation of China(LR22F030003)+1 种基金supported by Research Grant Council of Hong Kong(11201023,11202224)Hong Kong Innovation and Technology Commission(InnoHK Project CIMDA).
文摘In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed,and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data.Domain generalization(DG)aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training.The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained.In this article,for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years.Specifically,we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations.Then,the recent trend of DG for fault diagnosis is also analyzed.Finally,the existing problems and future prospect is performed.
基金supported by the National Natural Science Foundation of China(21203067)Foundation for Distinguished Young Talents in Higher Education of Guangdong,China(LYM 11052)ITS/244/11 of Innovation and Technology Fund,HKSAR,China~~
基金Projects(City U 11201315,T32-101/15-R)supported by the Research Grants Council of the Hong Kong Special Administrative Region,China
文摘Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.
基金supported by the National Natural Science Foundation of China (Nos. 61773203, U1833126, 61304190)the Open Funds of Graduate Innovation Base (Lab) of Nanjing University of Aeronautics and Astronautics of China (No. kfjj20180703)+1 种基金the State Key Laboratory of Air Traffic Management System and Technology of China (No. SKLATM201707)the Hong Kong Research Grant Council General Research Fund of China (No. 11209717)
文摘Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.
基金partially supported by a GRF project from RGC of Hong Kong China (City U: 11207714)+2 种基金a SRG grant from City University of Hong Kong China (7004909)a National Basic Research Program of China (2011CB013104)
文摘The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include： multi-time scale, spacetime scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.
基金Supported by the Research Fund of Shenzhen University(200552).
文摘Some novel applications and pragmatic variations of knapsack problem （KP） are presented and constructed, which are formulated and developed from a model initiated in this paper on profit allocation from partition of jobs in terms of two-person discrete cooperation game.
基金supported by National Natural Science Foundation of China(71402157)the Natural Science Foundation of Guangdong Province,China(2014A030313753)+2 种基金CityU Start-up(7200399)the Center for Adaptive Super Computing Software-Multi Threaded Architectures(CASS-MT)at the U.S.Department of Energy’s Pacific Northwest National LaboratoryPacific Northwest National Laboratory Is Operated by Battelle Memorial Institute(Contract DE-ACO6-76RL01830)
文摘In this paper, we reported the benefits of using eXtended Markup Language （XML） to support financial knowledge management and discussed number of issues associated with developing an XML-based financial knowledge management system. Current searching engines do not provide sufficient performance in terms of recall, precision, and extensibility for financial knowledge management, because the data represented in HTML format cannot support fmancial knowledge management effectively. On the other hand, XML provides a vendor-neutral approach to structure and organize contents as XML authors are allowed to create arbitrary tags to describe the format or structure of data. A prototype of XML-based ELectronic Financial Filing System （ELFFS-XML） is developed, and value-added ated informationservices such as automatic tag generation and cross-linking rel from different data sources are provided to enable knowledge representation and knowledge generation. We compared the XML-based ELFFS with the original HTML-based ELFFS and SEDAR - an electronic filing system used in Canada, and we found that ELFFS-XML is able to provide much more functionalities to support knowledge management. We also compared our automatic tag generation result with the experts＇ and investors＇ choices, and recommended some directions for future development of similar electronic filing systems.
基金supported by the National Natural Science Foundation of China (10571167)the National Basic Research Program of China (973 Program, 2007CB814902)+2 种基金the Science Fund for Creative Research Groups (10721101)supported by the Nomura Centrefor Mathematical Finance and the Oxford–Man Institute of Quantitative Financea start-up fund of the University of Oxford
文摘Continuous-time Markowitz＇s by parameterizing a critical quantity. It mean-variance efficient strategies are modified is shown that these parameterized Markowitz strategies could reach the original mean target with arbitrarily high probabilities. This, in turn, motivates the introduction of certain stopped strategies where stock holdings are liquidated whenever the parameterized Markowitz strategies reach the present value of the mean target. The risk aspect of the revised Markowitz strategies are examined via expected discounted loss from the initial budget. A new portfolio selection model is suggested based on the results of the paper.
基金The authors thank the anonymous referees for their comments and suggestions. This research was supported by the Natural Science Foundation of China （Nos.71231007, 71373222, 71501149）.
文摘In this paper, we consider a single-period model comprised of an original manufacturer （OM） who produces only new products and a remanufacturer who collects used products from consumers and produces remanufactured products. The OM and the remanufacturer compete in the product market. We examine the effects of government subsidy as a means to promote remanufacturing activity. In particularly, we consider three subsidy options： subsidy to remanufacturer, subsidy to consumers, and subsidy shared by remanufacturer and consumers. We find that the introduction of government subsidy on remanufacturer or consumers always increases remanufacturing activity. We also find that subsidy to remanufacturer is the best subsidy option, because subsidy to remanufacturer results in lower price of remanufactttred products, thus leading to higher consumer surplus.
基金This work was supported by the National Natural Science Foundation of China(Grant No.10401038)Startup Grant for Doctoral Research of Beijing University of Technology and Hong Kong RGC Earmarked Grant CUHK4242/04E
文摘In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the worst-case performance ratio at leastα=0.87856….In fact,the estimated worst-case performance ratio is dependent on the data of the problem withαbeing a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach(with the triangle inequalities added)is at leastα+δ_d if every weight is strictly positive,whereδ_d>0 is a constant depending on the problem dimension and data.
基金supported by the National Natural Science Foundation of China grants(Nos.11101092,10971034)the Joint National Natural Science Foundation of China/Research Grants Council of Hong Kong grant(No.71061160506)the Research Grants Council of Hong Kong grants(Nos.CUHK414808 and CUHK414610).
文摘Mathematical programming problems with semi-continuous variables and cardinality constraint have many applications,including production planning,portfolio selection,compressed sensing and subset selection in regression.This class of problems can be modeled as mixed-integer programs with special structures and are in general NP-hard.In the past few years,based on new reformulations,approximation and relaxation techniques,promising exact and approximate methods have been developed.We survey in this paper these recent developments for this challenging class of mathematical programming problems.
文摘Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport,train delays often occur.For this study,a three-month dataset of weather,train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time.We found that in severe weather train delays are determined mainly by the type of bad weather,while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains.Identifying the factors closely correlated with train delays,we developed a machine-learning model to predict the delay time of each train at each station.The prediction model is useful not only for passengers wishing to plan their journeys more reliably,but also for railway operators developing more efficient train schedules and more reasonable pricing plans.
基金Innovation and Technology Fund(ITF),Government of the Hong Kong Special Administrative Region(HKSAR),China(No.PRP-054-21FX).
文摘Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)system.Its goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue policy.Reinforcement learning(RL)is widely used to optimize this dialogue policy.In the learning process,the user is regarded as the environment and the system as the agent.In this paper,we present an overview of the recent advances and challenges in dialogue policy from the perspective of RL.More specifically,we identify the problems and summarize corresponding solutions for RL-based dialogue policy learning.In addition,we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in RL.We believe this survey can shed light on future research in DPL.
文摘This paper optimizes the electricity and renewable energy credit （REC） purchasing process for energy distribution. Electricity is traded in deregulated time-sequential markets at fluctuating prices. Optimal electricity purchasing under price and demand uncertainty is a challenging task for electricity distributors, and the recently implemented renewable portfolio standards （RPS） further complicate the purchasing process. Government regulatory decisions concerning the RPS require distributors to purchase corresponding certificates, namely RECs, equivalent to a certain percentage of their electricity sales. This paper formulates and optimizes the joint purchasing process for electricity and RECs. It also analyzes the effect of RPS policy on electricity distributors.
基金the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption,2018YFB0904200)eponymous Complement S&T Program of State Grid Corporation of China(SGLNDKOOKJJS1800266)。
文摘To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.
基金supported in part by the National Natural Science Foundation of China (90924302, 91024030, 71025001, 70890084, and 60921061)the US Defense Advanced Research Projects through two seedling grants to Rensselaer Polytechnic Institutethe US National Science Foundation support for EAGER (IIS-1143585)
文摘Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.
