共查询到20条相似文献，搜索用时 15 毫秒
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Majdy M. Eltahir Adil Yousif Fadwa Alrowais Mohamed K. Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A. Aziz Manar Ahmed Hamza 《计算机、材料和连续体（英文）》2023,75(2):3239-3255
The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%. 相似文献
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邓芋蓝;林飞振;林雁波;孙涛;黄锋 《计量科学与技术》2024,68(11):63-70
为提升压力表的检定效率并减少读数误差,研制了一套全智能化压力表检定系统,该系统由一体化AI识别模型、控制软件和检定装置组成,实现了压力表检定流程的全智能化、自动化。系统的识别模型是基于深度学习网络框架,融合YOLO检测模型、 Paddle OCR模型、文本分类器以及相邻角度读数算法组成的一体化AI模型,不仅可识别压力表图像中指针的读数信息,还可识别压力表的生产厂家、生产编号、精确度等级和单位等基本信息;控制软件基于多线程、异步通信的结构而设计,支持同时与检定装置的多个硬件通信,控制多个压力表同时检定,且支持检定图像和数据的保存,便于后期复核和追溯,还支持将检定结果同步至OA系统,自动化打印检定证书。通过实验验证,结果表明该系统能够准确可靠地同时检定1～6台压力表,相比于人工检定和其他自动化检定系统,该系统智能化程度更大、检定效率更高、读数误差更小,具有实际应用和推广意义。 相似文献
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In recent years, the number of exposed vulnerabilities has grown rapidly andmore and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect the malicious payload and identify their categories with high accuracy, we proposed a packet-based malicious payload detection and identification algorithm based on object detection deep learning network. A dataset of malicious payload on code execution vulnerability has been constructed under the Metasploit framework and used to evaluate the performance of the proposed malware detection and identification algorithm. The experimental results demonstrated that the proposed object detection network can efficiently find and identify malicious payloads with high accuracy. 相似文献
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机器视觉作为代替人工检测轮毂表面缺陷的重要手段,是目前该领域的主要研究方向,因此针对汽车轮毂表面缺陷检测技术的研究现状进行了综述与分析。首先,从轮毂表面缺陷的类别和人工检测流程入手,阐述了基于机器视觉的轮毂表面缺陷检测技术的要求和难点。其次,分析了基于机器视觉的智能检测算法的发展历程,包括传统的机器视觉方法在缺陷图像预处理、缺陷定位和特征提取、缺陷分类识别中的应用;卷积神经网络(CNN)等深度学习方法在缺陷检测、分割以及其他方面的应用。最后,介绍了现有轮毂型号识别装置、轮毂缺陷X射线图像采集装置、轮毂表面缺陷图像采集装置,并在分析当前基于机器视觉的智能检测装置在实际应用中的局限性及需要解决的若干关键技术问题的基础上,提出了3种智能检测实验装置设计方案,为全自动快速检测装置的研制与性能提升提供理论与技术支撑。 相似文献
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目的 交通标志识别作为智能驾驶、交通系统研究中的一项重要内容,具有较大的理论价值和应用前景.尤其是文本型交通标志,其含有丰富的高层语义信息,能够提供极其丰富的道路信息.因此通过设计并实现一套新的端到端交通标志文本识别系统,达到有效缓解交通拥堵、提高道路安全的目的.方法 系统主要包括文本区域检测和文字识别两个视觉任务,并基于卷积神经网络的深度学习技术实现.首先以ResNet-50为骨干网络提取特征,并采用类FPN结构进行多层特征融合,将融合后的特征作为文本检测和识别的共享特征.文本检测定位文本区域并输出候选文本框的坐标,文字识别输出词条对应的文本字符串.结果 通过实验验证,系统在Traffic Guide Panel Dataset上取得了令人满意的结果,行识别准确率为71.08％.结论 端到端交通标志文本识别非常具有现实意义.通过卷积神经网络的深度学习技术,提出了一套端到端交通标志文本识别系统,并在开源的Traffic Guide Panel Dataset上证明了该系统的优越性. 相似文献
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张纯何君儒周宇轩林莹 《工程力学》2023,40(7):137-144
合理的弹性橡胶密封垫断面形状是保障盾构隧道管片接缝防水设计性能的关键。密封垫断面优化设计时,需要反复进行材料大变形、接触分析等复杂的非线性计算,极大限制了优化效率。为此,以闭合压力与有效接触压力占比为双控目标,提出了一种结合深度神经网络代理模型的结构优化算法。在遗传算法框架下,深度神经网络代理模型可以实现由断面形状到接触应力场的快速映射。同时,迁移学习的引入实现了不同类型断面形状代理模型的知识复用,仅利用小样本即可建立高精度的接触应力预测模型,从而有效提高了闭合压力约束条件下的密封垫结构断面优化效率。 相似文献
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JIANG Yueyu;XIA Ling;JIANG Bingyue;HAN Wei;WANG Kang 《测试技术学报》2024,38(4):435-440
A solution based on DNN-SVM is proposed for fault detection and localization in the electric power wireless mesh network system. The timely and accurate identification and localization of faults in the electric power wireless mesh network system pose challenges for maintenance and repair work. In this paper, real-time data from the electric power wireless mesh network system, including signal strength, signal quality, and PCE operating status, is collected using dedicated devices. A DNN-SVM algorithm is constructed to achieve simultaneous fault detection and localization in the wireless mesh network. The DNN is used to discriminate fault states, while the multilayer binary SVM is employed for fault-type classification. Experimental validation is conducted on an actual electric power wireless mesh network dataset. The decision time for a single data sample is in the millisecond range, and the overall average accuracy rate is 80%. 相似文献
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Osamah Taher;Kasım Özacar; 《International journal of imaging systems and technology》2024,34(3):e23084
Foot pain, particularly caused by heel spurs and Sever's disease, significantly impacts mobility and daily activities for many people. These diseases are traditionally diagnosed by orthopedic specialists using X-ray images of the lateral foot. In certain situations, the absence of specialists requires the adoption of AI-based methods; however, the lack of a dataset hinders the use of AI for the preliminary diagnosis of these diseases. Therefore, this study first presents a novel dataset consisting of 3956 annotated lateral foot X-ray images and uses the original capsule network (CapsNet) to automatically detect and classify heel bone diseases. The low accuracy of 73.99% of CapsNet due to the low extraction feature layers led us to search for a new model. For this reason, this paper also proposes a new enhanced capsule network (HeCapsNet) by adjusting the features extraction layers, adding extra convolutional layers, using “he normal” kernel initializer instead of “normal” and utilizing the “same” padding scheme to perform better with medical images. Evaluating the performance of the proposed model, higher accuracy rates are achieved, including 97.29% for balanced data, 94.19% for imbalanced data, area under the curve (AUC) of 98.69%, and a fivefold cross-validation accuracy of 95.77%. We then compared our proposed model with state-of-the-art modified CapsNet models using various datasets (MNIST, Fashion-MNIST, CIFAR10, and brain tumor). HeCapsNet performed similarly to modified CapsNets on relatively simple non-medical datasets such as MNIST and Fashion-MNIST, but performed better on more complex medical datasets. 相似文献
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Khalid Masood Mahmoud M. Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif 《计算机、材料和连续体（英文）》2023,74(3):5417-5430
Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-of-the-art techniques. 相似文献
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Sourav Kumar Tanwar;Prakash Choudhary; Priyanka;Tarun Agrawal; 《International journal of imaging systems and technology》2024,34(4):e23149
Classifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture-based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network-based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings. 相似文献
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提出了一种基于CBAMTL-MobileNet V3的车载网络入侵检测方法。该方法使用轻量级模型MobileNet V3,减少其层数加快模型的训练和检测速度;将模型中的SE模块置换为注意力模块(CBAM)使模型更聚焦于特定特征,提高特征提取能力,进而提高检测攻击的精确度;引入迁移学习对模型权重进行微调,减少参数和内存资源的消耗,缩短了训练时间,使模型表现出更快的运算速度。仿真结果表明:所提模型的各项检测指标都优于MobileNet V3模型。与其他模型相比,所提模型既具备轻量级模型的高效性,同时又高于其他复杂模型的检测精度,识别各类别攻击的性能最优。 相似文献
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针对\"大数据\"时代如何利用数据对房颤进行智能、高效的诊断问题,提出了基于一维卷积神经网络的智能诊断方法,以避免传统算法依赖人工特征提取和先验知识的问题。首先,分别构建一维LeNet-5和AlexNet神经网络模型,合理设置网络结构参数;然后,在采集的实验数据基础上针对心电信号的特点进行一系列的数据处理,随机构建训练样本和测试样本;最后,将训练样本分别输入上述2个神经网络模型中训练学习,再将训练好的模型用于房颤的诊断。实验结果表明:一维Le Net-5网络模型存在\"过拟合\"现象,而一维Alex Net网络模型在避免了上述现象的同时,诊断精度达到了95. 34%,较传统方法有了较大提升,为房颤诊断提供了有效的手段。 相似文献
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Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient’s data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage. 相似文献
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The reinforcement learning (RL) is being used for scheduling to improve the adaptability and flexibility of an automated production line. However, the existing methods only consider processing time certain and known and ignore production line layouts and transfer unit, such as robots. This paper introduces deep RL to schedule an automated production line, avoiding manually extracted features and overcoming the lack of structured data sets. Firstly, we present a state modelling method in discrete automated production lines, which is suitable for linear, parallel and re-entrant production lines of multiple processing units. Secondly, we propose an intelligent scheduling algorithm based on deep RL for scheduling automated production lines. The algorithm establishes a discrete-event simulation environment for deep RL, solving the confliction of advancing transferring time and the most recent event time. Finally, we apply the intelligent scheduling algorithm into scheduling linear, parallel and re-entrant automated production lines. The experiment shows that our scheduling strategy can achieve competitive performance to the heuristic scheduling methods and maintains stable convergence and robustness under processing time randomness. 相似文献
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针对多种定位因素存在复杂关联且不易准确提取的问题,提出了以完整双耳声信号作为输入的、基于深度学习的双耳声源定位算法。首先,分别采用深层全连接后向传播神经网络(Deep Back Propagation Neural Network,D-BPNN)和卷积神经网络(Convolutional Neural Network, CNN)实现深度学习框架;然后,分别以水平面 15°、30°和 45°空间角度间隔的双耳声信号进行模型训练;最后,采用前后混乱率、定位准确率与训练时长等指标进行算法有效性分析。模型预测结果表明,CNN模型的前后混乱率远低于 D-BPNN;D-BPNN模型的定位准确率能够达到87%以上,而 CNN模型的定位准确率能够达到 98%左右;在相同实验条件下,CNN模型的训练时长大于 D-BPNN,且随着水平面角度间隔的减小,两者训练时长之间的差异愈发显著。 相似文献
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R. Gopi Mahantesh Mathapati B. Prasad Sultan Ahmad Fahd N. Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal 《计算机、材料和连续体（英文）》2022,72(1):141-156
Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network. 相似文献
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目的 解决定制化木门尺寸规格不统一、表面纹理多样而导致的堆垛分类困难、搬运效率低下等问题。方法 提出采用深度学习方法进行定制式木门工件检测,以YOLOV3网络为基本框架开展机器人工件识别方法研究。首先,通过图像数据增强和预处理,扩充定制式木门数据;然后,进行YOLO V3损失函数改进,并根据木门特征进行定制式木门数据集锚框尺度的重新聚类;最后,应用空间金字塔池化层进行YOLO V3中特征金字塔网络改进,并通过随机选取的测试集验证本文方法的有效性。结果 测试数据集的平均检测准确率均值达到98.05%,检测每张图片的时间为137 ms。结论 研究表明,本文方法能够满足木门生产线对准确率和实时性的要求,可大大提高定制化木门转线及堆垛效率。 相似文献