摘要 :
This paper discussed and demonstrated how simulation modeling is used to predict the results of changes to steel making or other manufacturing processes. The process is modeled accounting for complex variability, and models are ru...
展开
This paper discussed and demonstrated how simulation modeling is used to predict the results of changes to steel making or other manufacturing processes. The process is modeled accounting for complex variability, and models are run quickly in accelerated time allowing multiple scenarios to be evaluated in a short period of time. This is one example of using simulation modeling in a steel plant. Any process that involves variability and interdependencies can benefit from a simulation model when modifications are being considered to determine if the changes will obtain the desired effect or potentially cause problems in a different area of the plant.
收起
摘要 :
This presentation will discuss and demonstrate how simulation modeling can be used to predict production results associated with changes to steel making processes, operation sequences or shop layouts. By creating and running detai...
展开
This presentation will discuss and demonstrate how simulation modeling can be used to predict production results associated with changes to steel making processes, operation sequences or shop layouts. By creating and running detailed and accurate simulation models of current or proposed mill operations, multiple change scenarios can be quickly evaluated, assuring confident decisions and maximum project benefits.
收起
摘要 :
In this paper, we presented an exercise health prediction method based on thermal performance simulation of clothed human. Based on the proposed simulation framework, multi-disciplinary knowledge and multi-combined models can be i...
展开
In this paper, we presented an exercise health prediction method based on thermal performance simulation of clothed human. Based on the proposed simulation framework, multi-disciplinary knowledge and multi-combined models can be integrated together to form a simulation system, in which the pre-simulation, mathematical models’ integration, numerical solving method and simulation presentation are all included. By importing cloud computing technology into the thermal simulation of clothed human, both the average-case running time has been drastically reduced. With the cloud CAD system, users can quickly simulate, evaluate, validate, and optimize the design for the specified wearing scenario.
收起
摘要 :
Data streaming, in which a large dataset is received as a “stream” of updates, is an important model in the study of space-bounded computation. Starting with the work of Le Gall [SPAA '06], it has been known that quantum streami...
展开
Data streaming, in which a large dataset is received as a “stream” of updates, is an important model in the study of space-bounded computation. Starting with the work of Le Gall [SPAA '06], it has been known that quantum streaming algorithms can use asymptotically less space than their classical counterparts for certain problems. However, so far, all known examples of quantum advantages in streaming are for problems that are either specially constructed for that purpose, or require many streaming passes over the input. We give a one-pass quantum streaming algorithm for one of the best-studied problems in classical graph streaming-the triangle counting problem. Almost-tight parametrized upper and lower bounds are known for this problem in the classical setting; our algorithm uses polynomially less space in certain regions of the parameter space, resolving a question posed by Jain and Nayak in 2014 on achieving quantum advantages for natural streaming problems.
收起
摘要 :
Data streaming, in which a large dataset is received as a “stream” of updates, is an important model in the study of space-bounded computation. Starting with the work of Le Gall [SPAA '06], it has been known that quantum streami...
展开
Data streaming, in which a large dataset is received as a “stream” of updates, is an important model in the study of space-bounded computation. Starting with the work of Le Gall [SPAA '06], it has been known that quantum streaming algorithms can use asymptotically less space than their classical counterparts for certain problems. However, so far, all known examples of quantum advantages in streaming are for problems that are either specially constructed for that purpose, or require many streaming passes over the input. We give a one-pass quantum streaming algorithm for one of the best-studied problems in classical graph streaming-the triangle counting problem. Almost-tight parametrized upper and lower bounds are known for this problem in the classical setting; our algorithm uses polynomially less space in certain regions of the parameter space, resolving a question posed by Jain and Nayak in 2014 on achieving quantum advantages for natural streaming problems.
收起
摘要 :
The mechanism of attention control is best described by biased-competition theory (BCT), which suggests that a top-down goal state biases a competition among object representations for the selective routing of a visual input for c...
展开
The mechanism of attention control is best described by biased-competition theory (BCT), which suggests that a top-down goal state biases a competition among object representations for the selective routing of a visual input for classification. Our work advances this theory by making it computationally explicit as a deep neural network (DNN) model, thereby enabling predictions of goal-directed attention control using real-world stimuli. This model, which we call Deep-BCN, is built on top of an 8-layer DNN pre-trained for object classification, but has layers mapped to early visual (V1, V2/V3, V4), ventral (PIT, AIT), and frontal (PFC) brain areas that have their functional connectivity informed by BCT. Deep-BCN also has a superior colliculus and a frontal-eye field, and can therefore make eye movements. We compared Deep-BCN's eye movements to those made from 15 people performing a categorical search for one of 25 target object categories, and found that it predicted both the number of fixations during search and the saccade-distance travelled before search termination. With Deep-BCN a DNN implementation of BCT now exists, which can be used to predict the neural and behavioral responses of an attention control mechanism as it mediates a goal-directed behavior-in our study the eye movements made in search of a target goal.
收起
摘要 :
In this paper is presented an adaptive module of the learning style of the students of the Computational Platform for the Educational Model based on the Cloud Paradigm, proposed in previous work. This adaptive module redefines the...
展开
In this paper is presented an adaptive module of the learning style of the students of the Computational Platform for the Educational Model based on the Cloud Paradigm, proposed in previous work. This adaptive module redefines the learning style that has the student, in order to improve his academic performance in the platform. The learning style of the students is very important in this platform, because it defines the profile of the educational resources to be proposed to the students. The learning style defines the activities, tools and evaluations that must have the educational resources, to carry out the learning process more successful. The adaptive module carries out the automatic reconfiguration of the learning style assigned to a student, once evaluated its performance in the different courses that the student has taken so far. To assign a new style of learning, the module takes into account the academic performance of the student. If this is low, it calculates and assigns a new style. The initial learning style of the students is defined using the Felder-Silverman test. Then, this module is invoked interactively each time is finished a scholar period.
收起
摘要 :
In this paper is presented an adaptive module of the learning style of the students of the Computational Platform for the Educational Model based on the Cloud Paradigm, proposed in previous work. This adaptive module redefines the...
展开
In this paper is presented an adaptive module of the learning style of the students of the Computational Platform for the Educational Model based on the Cloud Paradigm, proposed in previous work. This adaptive module redefines the learning style that has the student, in order to improve his academic performance in the platform. The learning style of the students is very important in this platform, because it defines the profile of the educational resources to be proposed to the students. The learning style defines the activities, tools and evaluations that must have the educational resources, to carry out the learning process more successful. The adaptive module carries out the automatic reconfiguration of the learning style assigned to a student, once evaluated its performance in the different courses that the student has taken so far. To assign a new style of learning, the module takes into account the academic performance of the student. If this is low, it calculates and assigns a new style. The initial learning style of the students is defined using the Felder-Silverman test. Then, this module is invoked interactively each time is finished a scholar period.
收起
摘要 :
Click-through rate (CTR) prediction plays an important role in industrial bidding advertising. Most of the deep CTR models focus on the interaction problem of capturing features after embedding layer, such as PNN, NFM, DeepFM and ...
展开
Click-through rate (CTR) prediction plays an important role in industrial bidding advertising. Most of the deep CTR models focus on the interaction problem of capturing features after embedding layer, such as PNN, NFM, DeepFM and xDeepFM. Few models consider the importance of features before feature interaction. The ECA module proposed in CVPR2020 computer vision is an extremely lightweight plug and play module, which is used to improve the performance of various deep CNN architectures. This paper introduces the idea of ECANET to modify the model embedding layer to dynamically learn the importance of embedding features and construct a new deep CTR prediction model. ECANET and NFM model are combined to build a novel model ECANFM. The effectiveness of the model is verified by a comparative experiment on a public data set.
收起
摘要 :
Click-through rate (CTR) prediction plays an important role in industrial bidding advertising. Most of the deep CTR models focus on the interaction problem of capturing features after embedding layer, such as PNN, NFM, DeepFM and ...
展开
Click-through rate (CTR) prediction plays an important role in industrial bidding advertising. Most of the deep CTR models focus on the interaction problem of capturing features after embedding layer, such as PNN, NFM, DeepFM and xDeepFM. Few models consider the importance of features before feature interaction. The ECA module proposed in CVPR2020 computer vision is an extremely lightweight plug and play module, which is used to improve the performance of various deep CNN architectures. This paper introduces the idea of ECANET to modify the model embedding layer to dynamically learn the importance of embedding features and construct a new deep CTR prediction model. ECANET and NFM model are combined to build a novel model ECANFM. The effectiveness of the model is verified by a comparative experiment on a public data set.
收起
原文获取