Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,m...Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,multiple data-path stacks are required to implement the entire data-path.As more data-path stacks are integrated into SOC,data-path is becoming a critical part of the whole giga-scale integrated circuits (GSI) design.The traditional physical design methodology can not satisfy the data-path performance requirements,because it can not accommodate the data-path bit-sliced structure and the strict performance (such as timing,coupling,and crosstalk) constraints.Challenges in the data-path physical design are addressed.The fundamental problems and key technologies in data-path physical design are analysed.The corresponding researches and solutions in this research field are also discussed.展开更多
During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place i...During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.展开更多
Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the re...Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.展开更多
The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer（LC/ESI-MS）, whi...The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer（LC/ESI-MS）, which is based on a data-dependent acquisition of isotope ions and result verified by full scan mass spectrum. The distinguished advantage of data-dependent scan is rapidness because it requires minimum sample preparation, and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of complex biomatrix. As a result, four phase-Ⅰ（M1--M4） and four Phase-Ⅱ（M5--M8） metabolites of brodimoprim were identified in urine after the oral administration of hrodimoprim to Wistar rats. Their chemical structures were proposed based on the interpretation of their CID fragmentation characterizations and the metabolic pathway was exhibited in this article.展开更多
It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in dat...It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.展开更多
To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions...To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.展开更多
Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention ...Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention and control of mastitis is based on multiple principles that have been known for a long time. To implement them successfully, they should be put forward by a motivated and motivating advisor that transfers the existing knowledge to the farmer. When the changes are data-driven, applied by an encouraged farmer through a farm-specific implementation, prevention and control of mastitis will be successful and result in happy cows, happy farmers, happy advisors, happy consumers, and a happy industry. Nationwide projects focussing on communication and transfer of existing knowledge in prevention and control are very helpful in reaching high numbers of farmers and advisors and harmonizing the message brought by different parties. This paper gives an overview of multifactorial approach of mastitis management and prevention with a focus on milking, bedding and data-analysis.展开更多
This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a...This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.展开更多
In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so o...In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.展开更多
With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wirele...With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.展开更多
文摘Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,multiple data-path stacks are required to implement the entire data-path.As more data-path stacks are integrated into SOC,data-path is becoming a critical part of the whole giga-scale integrated circuits (GSI) design.The traditional physical design methodology can not satisfy the data-path performance requirements,because it can not accommodate the data-path bit-sliced structure and the strict performance (such as timing,coupling,and crosstalk) constraints.Challenges in the data-path physical design are addressed.The fundamental problems and key technologies in data-path physical design are analysed.The corresponding researches and solutions in this research field are also discussed.
基金partially supported by the National Natural Science Foundation of China(61751306,61801208,61671233)the Jiangsu Science Foundation(BK20170650)+2 种基金the Postdoctoral Science Foundation of China(BX201700118,2017M621712)the Jiangsu Postdoctoral Science Foundation(1701118B)the Fundamental Research Funds for the Central Universities(021014380094)
文摘During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.
基金supported by the National Natural Science Foundation of China(61773087)the National Key Research and Development Program of China(2018YFB1601500)High-tech Ship Research Project of Ministry of Industry and Information Technology-Research of Intelligent Ship Testing and Verifacation([2018]473)
文摘Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.
基金the National Natural Science Foundation of China(Nos.30630075 and 20675056)Natural Science Founda-tion of Tianjin City,China(No.07JCYBJC01600)
文摘The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer（LC/ESI-MS）, which is based on a data-dependent acquisition of isotope ions and result verified by full scan mass spectrum. The distinguished advantage of data-dependent scan is rapidness because it requires minimum sample preparation, and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of complex biomatrix. As a result, four phase-Ⅰ（M1--M4） and four Phase-Ⅱ（M5--M8） metabolites of brodimoprim were identified in urine after the oral administration of hrodimoprim to Wistar rats. Their chemical structures were proposed based on the interpretation of their CID fragmentation characterizations and the metabolic pathway was exhibited in this article.
基金The Innovation Program of Shanghai Municipal Education Commission under contract No.14ZZ147the Opening Project of Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University),Ministry of Education under contract No.A1-0209-15-0503-1
文摘It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
基金funding from the EU Smarter project(PEOPLE-2013-IAPP-610675)
文摘To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.
文摘Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention and control of mastitis is based on multiple principles that have been known for a long time. To implement them successfully, they should be put forward by a motivated and motivating advisor that transfers the existing knowledge to the farmer. When the changes are data-driven, applied by an encouraged farmer through a farm-specific implementation, prevention and control of mastitis will be successful and result in happy cows, happy farmers, happy advisors, happy consumers, and a happy industry. Nationwide projects focussing on communication and transfer of existing knowledge in prevention and control are very helpful in reaching high numbers of farmers and advisors and harmonizing the message brought by different parties. This paper gives an overview of multifactorial approach of mastitis management and prevention with a focus on milking, bedding and data-analysis.
基金supported by National Natural Science Foundation of China(Nos.61603114,61673135)the Fundamental Research Funds for the Central Universities of China(No.HIT.NSRIF.201826)
文摘This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.
基金supported in part by the National Key R&D Program of China(2017YFB1001804)Shanghai Science and Technology Innovation Action Plan Project(16511100900)
文摘In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.
基金the supports of the National Natural Science Foundation of China (60403027) the projects of science and research plan of Hubei provincial department of education (2003A011)the Natural Science Foundation Of Hubei Province of China (2005ABA243).
文摘With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.
