Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckle...
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Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.
This paper presents a comparative study of three types of the models to solve the river forecasting problem of the daily flow forecast of the Black water river in the USA. They are, the conventional Auto-Regression (A...
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This paper presents a comparative study of three types of the models to solve the river forecasting problem of the daily flow forecast of the Black water river in the USA. They are, the conventional Auto-Regression (AR) model and the two soft-computing models namely the Feed forward neural network (NN) and the fuzzy logic (FL) models. The soft computing models were trained, using the actual daily flow of six water years, and tested, using the actual daily flow of one water year. Data was collected to model the dynamics of the Black River flow models from 01 Oct 1990 to 30 Sept 1996. A comparison of the proposed models and techniques are presented for predicting one water year ahead, from 01 Oct 1996 to 30 Sept 1997. Simulation results indicate that the fuzzy model outperform the neural network and the auto-regression models. Hence, fuzzy logic is recommended as a tool for river flow forecasting.
The non-negativity property of the CIR process driven by a G-Brownian motion makes it avaluable tool for term structure modeling in the fields of finance and insurance *** paper focuses on two specific types of guaran...
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Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text u...
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The Patmos instruction-set architecture is designed for real-time systems. As such, it has features that increase the predictability of code running on it. One important feature is its dual-issue pipeline: instruction...
ISBN:
(数字)9781728169583
ISBN:
(纸本)9781728169590
The Patmos instruction-set architecture is designed for real-time systems. As such, it has features that increase the predictability of code running on it. One important feature is its dual-issue pipeline: instructions may be organized in bundles of two that are issued and executed in parallel. This increases the throughput of the processor in a predictable manner, but only if the compiler makes use of ***-path code is a code-generation technique that produces predictable executions by always following the same trace of instructions. The Patmos compiler can already produce single-path code, but it does not use the second issue slot available in the processor. This is less than ideal because the single-path transformation results in code that has a high degree of instruction-level *** this paper, we present a single-path code generator that can produce bundled instructions. It includes generic support for bundling algorithms, such that implementing them is simple and does not require changing other parts of the *** also present one such bundling algorithm plugged into the single-path code generator. With it, we show that we can produce dual-issue instructions to improve performance.
Performance increase with general-purpose processors has come to a halt. We can no longer depend on Moore’s Law to increase computing performance. The only way to achieve higher performance or lower energy consumptio...
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We present the first comprehensive and large-scale evaluation of classical (NN), fuzzy (FNN) and fuzzy rough (FRNN) nearest neighbour classification. We standardise existing proposals for nearest neighbour weighting w...
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Reservation and adaptation are two well-known and effective techniques for enhancing the end-to-end performance of network applications. However, both techniques also have limitations, particularly when dealing with h...
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Reservation and adaptation are two well-known and effective techniques for enhancing the end-to-end performance of network applications. However, both techniques also have limitations, particularly when dealing with high-bandwidth, dynamic flows: fixed-capability reservations tend to be wasteful of resources and hinder graceful degradation in the face of congestion, while adaptive techniques fail when congestion becomes excessive. We propose an approach to quality of service (QoS) that overcomes these difficulties by combining features of reservations and adaptation. In this approach, a combination of online control interfaces for resource management, a sensor permitting online monitoring, and decision procedures embedded in resources enable a rich variety of dynamic feedback interactions between applications and resources. We describe a QoS architecture, GARA, that has been extended to support these mechanisms, and use three examples of application-level adaptive strategies to show how this framework can permit applications to adapt both their resource requests and behavior in response to online sensor information.
This report deals with the problem of determining the suboptimal measurement scheduling for a stochastic discrete-time distributed parameter systems (DPS) described by a non-linear partial differential equations. The ...
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This report deals with the problem of determining the suboptimal measurement scheduling for a stochastic discrete-time distributed parameter systems (DPS) described by a non-linear partial differential equations. The discrete-scanning observations are realized by the suboptimal selection of measurement data from spatially-fixed sensors. The problem statement is in such a form so that the stochastic matrix principle of Pontryagin can be applied to obtain the necessary conditions for optimality. Based on successive approximation of the suboptimal filtering equations, a computationally advantageous approach is developed to obtain the algorithm without solving the non-linear two-point boundary-value problem CTPBVP). An illustrative numerical exemple for a air pollution process is given.
This study is concerned with the use of antisemitic language on loosely moderated extremist alt-right social media. It compares, contrasts, and combines human-and machinebased approaches for discovering antisemitic co...
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