Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for inp...Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.展开更多
In order to efficiently and realistically capture microscopic features of fluid surface,a fast and stable surface feature simulation approach for particle-based fluids is presented in this paper.This method employs a ...In order to efficiently and realistically capture microscopic features of fluid surface,a fast and stable surface feature simulation approach for particle-based fluids is presented in this paper.This method employs a steady tension and adhesion model to construct surface features with the consideration of the adsorption effect of fluid to solid.Molecular cohesion and surface area minimization are appended for surface tension,and adhesion is added to better show the microscopic characteristics of fluid surface.Besides,the model is integrated to an implicit incompressible smoothed particle hydrodynamics(SPH)method to improve the efficiency and stability of simulation.The experimental results demonstrate that the method can better simulates surface features in a variety of scenarios stably and efficiently.展开更多
The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects ov...The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.展开更多
In order to eliminate the energy waste caused by the traditional static hardware multithreaded processor used in real-time embedded system working in the low workload situation, the energy efficiency of the hardware m...In order to eliminate the energy waste caused by the traditional static hardware multithreaded processor used in real-time embedded system working in the low workload situation, the energy efficiency of the hardware multithread is discussed and a novel dynamic multithreaded architecture is proposed. The proposed architecture saves the energy wasted by removing idle threads without manipulation on the original architecture, fulfills a seamless switching mechanism which protects active threads and avoids pipeline stall during power mode switching. The report of an implemented dynamic multithreaded processor with 45 nm process from synthesis tool indicates that the area of dynamic multithreaded architecture is only 2.27% higher than the static one in achieving dynamic power dissipation, and consumes 1.3% more power in the same peak performance.展开更多
Industrial wireless sensor networks adopt a hierarchical structure with large numbers of sensors and routers. Time Division Multiple Access (TDMA) is regarded as an efficient method to reduce the probability of confli...Industrial wireless sensor networks adopt a hierarchical structure with large numbers of sensors and routers. Time Division Multiple Access (TDMA) is regarded as an efficient method to reduce the probability of confliction. In the intra-cluster part, the random color selection method is effective in reducing the retry times in an application. In the inter-cluster part, a quick assign algorithm and a dynamic maximum link algorithm are proposed to meet the quick networking or minimum frame size requirements. In the simulation, the dynamic maximum link algorithm produces higher reductions in the frame length than the quick assign algorithm. When the number of routers is 140, the total number of time slots is reduced by 25%. However, the first algorithm needs more control messages, and the average difference in the number of control messages is 3 410. Consequently, the dynamic maximum link algorithm is utilized for adjusting the link schedule to the minimum delay with a relatively high throughput rate, and the quick assign algorithm is utilized for speeding up the networking process.展开更多
In order to use mathematical methods to study how cognitive reappraisal strategies affect the output state of emotions,Gross's cognitive reappraisal strategy is transformed into a quantitative parameter which is p...In order to use mathematical methods to study how cognitive reappraisal strategies affect the output state of emotions,Gross's cognitive reappraisal strategy is transformed into a quantitative parameter which is proposed to describe the general perception of emotional events on the basis of the emotion regulation.According to Gross's emotional regulation model,the Finite State Machine(FSM) model is used for describing the process of emotional state transition and the Likert 5 grading scale is introduced to study the level of an individual's reappraisal according to the participant's self-evaluation.The experimental results verify that the algorithm can effectively describe the relationship between the reappraisal strategy,emotional events and an emotiongenerative process.There are multiple dimensions of a human's emotional state.Thus,in the field of human-computer interaction,further research requires the development of a specific algorithm which can be implemented by a computer for the emotion regulation process.展开更多
To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A mac...To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.展开更多
基金supported by the National Key Technologies R&D Program of China under Grant No. 2015BAK38B01the National Natural Science Foundation of China under Grant Nos. 61174103 and 61603032+4 种基金the National Key Research and Development Program of China under Grant Nos. 2016YFB0700502, 2016YFB1001404, and 2017YFB0702300the China Postdoctoral Science Foundation under Grant No. 2016M590048the Fundamental Research Funds for the Central Universities under Grant No. 06500025the University of Science and Technology Beijing - Taipei University of Technology Joint Research Program under Grant No. TW201610the Foundation from the Taipei University of Technology of Taiwan under Grant No. NTUT-USTB-105-4
文摘Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.
基金Supported by the National Natural Science Foundation of China(61873299,61702036,61572075)Fundamental Research Funds for the Central Universities of China(FRF-TP-17-012A1)China Postdoctoral Science Foundation(2017M620619)
文摘In order to efficiently and realistically capture microscopic features of fluid surface,a fast and stable surface feature simulation approach for particle-based fluids is presented in this paper.This method employs a steady tension and adhesion model to construct surface features with the consideration of the adsorption effect of fluid to solid.Molecular cohesion and surface area minimization are appended for surface tension,and adhesion is added to better show the microscopic characteristics of fluid surface.Besides,the model is integrated to an implicit incompressible smoothed particle hydrodynamics(SPH)method to improve the efficiency and stability of simulation.The experimental results demonstrate that the method can better simulates surface features in a variety of scenarios stably and efficiently.
基金supported by the National Natural Science Foundation of China under Grants No.61172049,No.61003251the National High Technology Research and Development Program of China(863 Program)under Grant No.2011AA040101the Doctoral Fund of Ministry of Education of Chinaunder Grant No.20100006110015
文摘The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.
基金supported partially by the National High Technical Research and Development Program of China (863 Program) under Grants No. 2011AA040101, No. 2008AA01Z134the National Natural Science Foundation of China under Grants No. 61003251, No. 61172049, No. 61173150+2 种基金the Doctoral Fund of Ministry of Education of China under Grant No. 20100006110015Beijing Municipal Natural Science Foundation under Grant No. Z111100054011078the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science under Grant No. Z121101002812005
文摘In order to eliminate the energy waste caused by the traditional static hardware multithreaded processor used in real-time embedded system working in the low workload situation, the energy efficiency of the hardware multithread is discussed and a novel dynamic multithreaded architecture is proposed. The proposed architecture saves the energy wasted by removing idle threads without manipulation on the original architecture, fulfills a seamless switching mechanism which protects active threads and avoids pipeline stall during power mode switching. The report of an implemented dynamic multithreaded processor with 45 nm process from synthesis tool indicates that the area of dynamic multithreaded architecture is only 2.27% higher than the static one in achieving dynamic power dissipation, and consumes 1.3% more power in the same peak performance.
基金supported by Beijing Education and Scientific Research Programthe National High Technical Research and Development Program of China (863 Program) under Grant No. 2011AA040101+2 种基金the National Natural Science Foundation of China under Grants No. 61173150, No. 61003251Beijing Science and Technology Program under Grant No. Z111100054011078the State Scholarship Fund
文摘Industrial wireless sensor networks adopt a hierarchical structure with large numbers of sensors and routers. Time Division Multiple Access (TDMA) is regarded as an efficient method to reduce the probability of confliction. In the intra-cluster part, the random color selection method is effective in reducing the retry times in an application. In the inter-cluster part, a quick assign algorithm and a dynamic maximum link algorithm are proposed to meet the quick networking or minimum frame size requirements. In the simulation, the dynamic maximum link algorithm produces higher reductions in the frame length than the quick assign algorithm. When the number of routers is 140, the total number of time slots is reduced by 25%. However, the first algorithm needs more control messages, and the average difference in the number of control messages is 3 410. Consequently, the dynamic maximum link algorithm is utilized for adjusting the link schedule to the minimum delay with a relatively high throughput rate, and the quick assign algorithm is utilized for speeding up the networking process.
基金supported by the National Natural Science Foundation of China under Grants No.61170115,No. 61170117,No. 61105120the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science under Grant No. Z121101002812005
文摘In order to use mathematical methods to study how cognitive reappraisal strategies affect the output state of emotions,Gross's cognitive reappraisal strategy is transformed into a quantitative parameter which is proposed to describe the general perception of emotional events on the basis of the emotion regulation.According to Gross's emotional regulation model,the Finite State Machine(FSM) model is used for describing the process of emotional state transition and the Likert 5 grading scale is introduced to study the level of an individual's reappraisal according to the participant's self-evaluation.The experimental results verify that the algorithm can effectively describe the relationship between the reappraisal strategy,emotional events and an emotiongenerative process.There are multiple dimensions of a human's emotional state.Thus,in the field of human-computer interaction,further research requires the development of a specific algorithm which can be implemented by a computer for the emotion regulation process.
基金partially supported by the National Key Technologies R&D Program of China under Grant No.2015BAK38B01the National Natural Science Foundation of China under Grant Nos.61174103 and 61272357the Fundamental Research Funds for the Central Universities under Grant No.06500025
文摘To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.
