共查询到20条相似文献，搜索用时 15 毫秒
1.
Suliman Aladhadh Hidayat Ur Rehman Ali Mustafa Qamar Rehan Ullah Khan 《计算机、材料和连续体（英文）》2021,69(3):3399-3411
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In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate). 相似文献
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Abdu Gumaei Mabrook Al-Rakhami Hussain AlSalman Sk. Md. Mizanur Rahman Atif Alamri 《计算机、材料和连续体（英文）》2020,65(2):1033-1057
Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy. In order to evaluate the proposed framework, we conducted a comprehensive set of experiments to validate the applicability of DL-HAR. Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models. 相似文献
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Guangyong Yang Jianqiu Zeng Mengke Yang Yifei Wei Xiangqing Wang Zulfiqar Hussain Pathan 《计算机、材料和连续体（英文）》2020,63(2):769-785
A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model. Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages. 相似文献
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Zhaoquan Gu Yu Su Chenwei Liu Yinyu Lyu Yunxiang Jian Hao Li Zhen Cao Le Wang 《计算机、材料和连续体（英文）》2020,65(2):1437-1452
The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper, we modify some classic methods to generate adversarial examples that could mislead the LPRS. We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples. In addition, we show that the generated images could also attack the black-box systems; we show some examples that the Baidu LPR system also makes incorrect recognitions. We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks. 相似文献
6.
Muhammad Hasnain Seung Ryul Jeong Muhammad Fermi Pasha Imran Ghani 《计算机、材料和连续体（英文）》2020,64(2):729-752
Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy. The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics. 相似文献
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运用Mawhin连续性定理研究具分布时滞的周期运动细胞神经网络周期解的存在性,假设行为函数位于一带型区域内,激活函数位于两线性函数所夹的区域内。 相似文献
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The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning network that masks the steps of extracting the optical flow in the network and greatly speeds up the action recognition. Based on the two-stream method, the two-stream collaborative learning model captures the interaction of the temporal and spatial features to greatly enhance the accuracy of recognition. Our proposed method is highly capable of achieving the balance of efficiency and precision on large-scale video action recognition datasets. 相似文献
10.
Hu XiongChen Zhaoneng Tong Dechun Zhu JimeiShanghai Maritime UniversityShanghai Jiaotong University Podong Dadao Shanghai Huashan Rod Shanghai P.R.ChinaP.R. China 《国际设备工程与管理》1998,(1)
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Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces. The purpose of our work is to study, design and develop a parking-availability predictor that extracts the knowledge from human mobility data, based on the anonymized human displacements of an urban area, and also from weather conditions. Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution. However, access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions. To overcome this limitation, our proposal uses urban areas that are defined at very low spatial and temporal resolution. We conducted several experiments using three Artificial Neural Networks: Multilayer Perceptron, Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs. Several metrics are provided for the sake of comparison within our study and between other studies. The solution has been evaluated in a real-world testbed in the city of Murcia (Spain) integrating an open human-mobility dataset showing high accuracy. A MAPE between 4% and 10% was reported in horizons of 1 to 3 h. 相似文献
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语言模型是语音识别系统的重要组成部分,目前的主流是n-gram模型。然而n-gram模型存在一些不足,对语句中长距信息描述差、数据稀疏是影响模型性能的两个重要因素。针对不足,研究者提出循环神经网络(Recurrent Neural Network,RNN)建模技术,在英语语言模型建模上取得了较好的效果。根据汉语特点将RNN建模方法应用于汉语语言建模,并结合两种模型的优点,提出了模型融合构建方法。实验结果表明:相比传统的n-gram语言模型,采用RNN训练的汉语语言模型困惑度(Per PLexity,PPL)有了下降,在对汉语电话信道的语音识别上,系统错误率也有下降,将两种语言模型融合后,系统识别错误率更低。 相似文献
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储层物性参数和储层特性的预测模型 总被引：1，自引：0，他引：1
本文利用神经网络计算方法，建立了一个具有自适应、复杂非线性的储层预测模型，对储层的物性参数（孔隙度、渗透率）进行预测和储层特性的识别，并对其预测精度进行检验，获得了明显优于常规解释方法的地质效果。 相似文献
15.
Fangzhen Hu Chenkai Cao Sancan Han Ding Wang Xi Chen 《Advanced Materials Technologies》2024,9(7):2301814
Transferring the concept of chemical-driven responses into artificial intelligence technology holds the key to mimicking olfactory for neuromorphic computing of chemical recognition. Currently, artificial olfactory systems are designed based on chemical sensor arrays. Time-dependent responses of the sensor arrays are processed by artificial neural networks for recognition. However, the sensors generate instantly volatile responses, and algorithms for the processing of the time-dependent responses have not been involved. The recognition accuracy and speed are severely impeded. A sensor array can only achieve an accuracy of 90% after at least 5 training epochs. Herein an artificial olfactory chemical-resistant synapse consisting of 3D hierarchical WO3@WO3 nanofibers are demonstrated. The nanofibers exhibit persistent resistance responses through chemical exposures due to the strong chemisorption of water molecules. Typical synaptic behaviors including paired-pulse facilitation, long-term −1 short-term memory, and learning experience have been achieved. Next, a recurrent neural network that is committed to processing the time-dependent data is used to identify gas-phase chemicals of 3-hydroxy-2-butanone, triethylamine, and trimethylamine. Training-free gas recognition has been realized by a WO3@WO3 nanofiber synapse only, in which the accuracy is above 90% at the first epoch. The results have great potential to satisfy stringent performance requirements on artificial perception systems. 相似文献
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Real object recognition using moment invariants 总被引：1，自引：0，他引：1
Moments and functions of moments have been extensively employed as invariant global features of images in pattern recognition.
In this study, a flexible recognition system that can compute the good features for high classification of 3-D real objects
is investigated. For object recognition, regardless of orientation, size and position, feature vectors are computed with the
help of nonlinear moment invariant functions. Representations of objects using two-dimensional images that are taken from
different angles of view are the main features leading us to our objective. After efficient feature extraction, the main focus
of this study, the recognition performance of classifiers in conjunction with moment-based feature sets, is introduced 相似文献
18.
Muneeb Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Faisal Abdulaziz Alfouzan Nouf M. Alzahrani Jawad Ahmad 《计算机、材料和连续体（英文）》2022,70(3):4675-4689
Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM. 相似文献
19.
利用数字图像处理的方法开发了轮胎胎号字符识别系统。首先通过CCD摄像头获取轮胎胎号图像,再对得到的图像进行预处理和特征提取,最后用人工神经网络的方法识别出图像中的字符信息。实验结果表明该方法是有效的。 相似文献
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提出了适用于嵌入式系统并融合深度可分离卷积神经网络与双向门控循环单元的DSConvBiGRU网络模型,将其用于动态手势序列的分类,设计并实现了一种使用低分辨率热电堆阵列传感器的动态手势识别解决方案,构建了动态手势数据集并在公开网站发布,完成了预训练网络模型在Raspberry Pi边缘端的部署。系统对传感器输出的连续20个温度矩阵进行区间映射、背景减除、Lanczos插值和Otsu二值化预处理得到单个动态手势序列,再由预训练的DSConvBiGRU网络进行分类。实验结果表明：网络模型在测试集上识别准确率为99.291%,在边缘端预处理耗时5.513 ms,推理耗时8.231 ms,该系统满足低功耗、高精度和实时性的设计需求。 相似文献