The velocity of a particle detector in granular flow can be regarded as the combination of rolling and sliding *** study of the contribution of rolling velocity and sliding velocity provides a new explanation to the r...
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The velocity of a particle detector in granular flow can be regarded as the combination of rolling and sliding *** study of the contribution of rolling velocity and sliding velocity provides a new explanation to the relative motion between the detector and the local granular *** this study,a spherical detector using embedded inertial navigation technology is placed in the chute granular flow to study the movement of the detector relative to the granular *** is shown by particle image velocimetry(PIV)that the velocity of chute granular flow conforms to Silbert’s *** the velocity of the detector is greater than that of the granular flow around *** decomposing the velocity into sliding and rolling velocity,it is indicated that the movement of the detector relative to the granular flow is mainly caused by *** rolling detail shown by DEM simulation leads to two potential mechanisms based on the position and drive of the detector.
Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are esse...
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The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In pra...
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There is an immediate threat to the highway transportation system from road accidents, which can cause death, serious injury, and property damage. Accidents involving motor vehicles cause significant injury or death t...
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With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method...
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Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the ...
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Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.
Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syn...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.
December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic tha...
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Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with comp...
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Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge,...
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Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge, this paper harnesses the potential of formal methods and deep reinforcement learning (DRL) for a more comprehensive solution that integrates Generalized Reactivity(1) (GR(1)) synthesis with DRL. The GR(1) synthesis module takes care of high-level task planning, ensuring a vehicle follows a correct-by-construction and verifiable plan for its mission. On the other hand, the DRL model operates as the low-level motion controller, allowing the vehicle to learn from experience and adjust its actions based on real-time sensor feedback. Therefore, the resulting controller for autonomous vehicles is not only guaranteed to finish its designated tasks but also intelligent to handle complex environments. Through comparative experimental studies, we demonstrate that the control program generated by the proposed approach outperforms the ones generated independently utilizing GR(1) reactive synthesis and DRL. IEEE
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