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2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献

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Predicting Network Traffic Anomalies in Denial-of-Service Attacks - A Nonlinear Approach 预测拒绝服务攻击中的网络流量异常——一种非线性方法
Ding-Wei Lau, Y. Leau, S. Tan, Po-Hung Lai
The amount of data moving across the network at any given time is referred to as network traffic. It is the data units that are encapsulated in packets and sent over a network. Denial-of-Service (DDoS) attacks are various attempts to disrupt typical network, service, or server traffic. DDoS attacks attempt to disrupt legitimate users' work and data transfers by sending large packets or traffic. Various network traffic prediction techniques are investigated in this study, and a nonlinear time series method, Multilayer Perceptron Neural Network (MLPNN), has been chosen to evaluate network traffic prediction. The results with the NSL-KDD dataset show that the approach can improve prediction accuracy by up to 98.87%. With 2.26%, it outperforms other models such as Sequential Minimal Optimization (SMO).
在任何给定时间通过网络移动的数据量称为网络流量。它是被封装在数据包中并通过网络发送的数据单元。拒绝服务(DDoS)攻击是破坏典型网络、服务或服务器流量的各种尝试。DDoS攻击试图通过发送大量数据包或流量来破坏合法用户的工作和数据传输。本文研究了各种网络流量预测技术,并选择了非线性时间序列方法多层感知器神经网络(MLPNN)来评估网络流量预测。在NSL-KDD数据集上的结果表明,该方法可将预测精度提高98.87%。它以2.26%的准确率优于顺序最小优化(SMO)等其他模型。
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引用次数: 0
Adaptive Route Optimization for Mobile Robot Navigation using Evolutionary Algorithm 基于进化算法的移动机器人导航自适应路径优化
Kit Guan Lim, Yoong Hean Lee, M. K. Tan, H. Yoong, Tianlei Wang, K. Teo
As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning.
随着技术的进步,对智能移动机器人的需求也在增加。在自主机器人设计中,研究人员面临的主要问题是移动机器人的路径规划。过去已经介绍了各种各样的路径规划算法,但没有一种算法比其他算法具有绝对优势。人工势场、网格搜索、视觉方法等经典方法由于其适应性和从过去的错误或经验中学习的能力,很容易被人工智能所取代。例如,蚁群优化算法(Ant Colony Optimization, ACO)是一种基于群体智能的优化算法,被广泛用于解决路径规划问题。然而,蚁群算法的性能在很大程度上取决于其参数的选择。本文提出的自适应蚁群算法在常规蚁群算法中引入异常蚁群和随机蚁群两种不同的蚁群,以提高蚁群算法的全局搜索能力,降低蚁群算法的高收敛速度。对比了传统蚁群算法和自适应蚁群算法,结果表明自适应蚁群算法在路径规划方面优于传统蚁群算法。
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引用次数: 0
Fuzzy Image Enhancement Based on Algebraic Function and Cycloid Arc Length 基于代数函数和摆线弧长的模糊图像增强
Libao Yang, S. Zenian, R. Zakaria
Fuzzy image enhancement is an important method in the process of image processing. In this paper, we present two intensifier operators in fuzzy image enhancement process based on algebraic function and cycloid arc length respectively. The first method directly uses the algebraic function as a membership intensifier operator. The second method also using a intensifier operator which established established by the cycloid arc length as the independent variable. In the last section, the test image is experimentally analyzed, and the results show that the method we proposed can improve enhance the contrast of the image.
模糊图像增强是图像处理过程中的一种重要方法。本文分别基于代数函数和摆线弧长给出了模糊图像增强过程中的两种增强算子。第一种方法直接使用代数函数作为成员增强算子。第二种方法也采用了以摆线弧长为自变量建立的增强算子。最后对测试图像进行了实验分析,结果表明我们提出的方法可以提高图像的对比度。
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引用次数: 3
Feature Selection using Pre-clustering via Affinity Propagation for Speech Classification in Low-resource Languages 基于亲和力传播的预聚类特征选择在低资源语言语音分类中的应用
Parabattina Bhagath, Komal Bharti, Abhishek Kotiya, P. Das
Speech analysis is an active research field where different feature extraction techniques are studied for solving various issues. Such studies help to improve the time complexity of solutions by understanding necessary clues to select the features. Choosing essential features by removing irrelevant information is a significant step in feature engineering. Perceptual Linear Predictive (PLP) modeling concentrates on understanding the speech signals by focusing on the features perceived at the listener end. They have been used successfully in many speech processing applications. The selection of the order of PLP coefficients for efficient classification of spoken units plays a crucial role in the recognition task. A conventional speech processing system requires a huge training process to develop an Automatic Speech Recognition system. Such systems are efficient for the languages that have enough resources i.e. data. But, low-resource languages especially Asian languages haven't been developed to provide the data sufficient for such tasks. In this context, alternative methods and techniques are encouraged to enhance or optimize the development process with less amount of data. This paper proposes a pre-clustering technique to improve the classification rate with low resources.
语音分析是一个活跃的研究领域,人们研究了不同的特征提取技术来解决各种问题。这样的研究通过理解必要的线索来选择特征,有助于提高解决方案的时间复杂度。通过去除不相关信息来选择基本特征是特征工程中的一个重要步骤。感知线性预测(PLP)建模的重点是通过关注听者端感知到的特征来理解语音信号。它们已成功地应用于许多语音处理应用中。在语音识别任务中,有效分类语音单元的PLP系数顺序的选择是至关重要的。传统的语音处理系统需要大量的训练才能开发出自动语音识别系统。这样的系统对于拥有足够资源(即数据)的语言是有效的。但是,资源匮乏的语言,尤其是亚洲语言,还没有开发出能够为这些任务提供足够数据的语言。在这方面,鼓励采用其他方法和技术,以较少的数据量加强或优化开发过程。为了在资源较少的情况下提高分类率,提出了一种预聚类技术。
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引用次数: 0
Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model 基于CMAC神经网络的Lugre摩擦模型双伺服电机同步
Suprapto, Taufik, A. Nasuha, E. Riyanto
Synchronization of dual servo motor control has become an important issue due to many applications in engineering fields, such as electric vehicle, robotics, electronics production machines, and others. This paper studies cerebellar model articulation controller (CMAC) neural network (NN) controller to synchronize two servo motors with dynamic LuGre friction model. CMAC is kind of NN method represented by associative memory with more powerful properties. Cross-coupling control structure is employed to synchronize two servo motors in this study. To investigates the performance, MATLAB Simulink is applied to simulate the control design of dual servo motor. The simulation results exhibit that CMAC controller has better output trajectory and works well for two servo motors with different parameters of LuGre friction model.
由于双伺服电机控制在电动汽车、机器人、电子生产机械等工程领域的广泛应用,双伺服电机的同步控制已成为一个重要的问题。本文研究了小脑模型关节控制器(CMAC)和神经网络控制器(NN)在动态LuGre摩擦模型下同步两台伺服电机。CMAC是一种以联想记忆为代表的神经网络方法,具有更强大的特性。本研究采用交叉耦合控制结构对两台伺服电机进行同步。为了研究其性能,应用MATLAB Simulink对双伺服电机的控制设计进行了仿真。仿真结果表明,CMAC控制器具有较好的输出轨迹,能够很好地控制两个不同参数的LuGre摩擦模型伺服电机。
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引用次数: 1
Advanced Fault Detection in DC Microgrid System using Reinforcement Learning 基于强化学习的直流微电网系统高级故障检测
M. K. Tan, Kar Leong Lee, Kit Guan Lim, A. Haron, P. Ibrahim, K. Teo
As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system.
随着技术的发展,对电力供应的需求也在增加。由于不可再生能源的减少,电力需求难以得到满足。因此,引入了微电网的概念,微电网由可再生能源、储能设备和负载组成。微电网有两种类型,即交流(AC)微电网和直流(DC)微电网。各种研究表明,直流微电网比交流微电网具有更多的优势。然而,与交流微电网相比,由于缺乏对直流微电网的研究,直流微电网的应用并不广泛。此外,直流微电网还有一个尚未解决的重大问题,即直流微电网的故障。一旦发生故障,整个直流微电网将迅速受到影响。因此,本课题旨在设计一种基于人工智能的故障检测器,有效地检测故障并隔离故障。在直流微电网系统中应用基于人工智能的故障检测技术对其进行保护。比较了人工免疫系统的两种技术。结果表明,改进的变大小检测器负选择算法在直流微电网系统故障检测中具有比一般半径定大小负选择算法更好的性能。
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引用次数: 0
Mobile Application for Bird Species Identification Using Transfer Learning 使用迁移学习的鸟类物种识别移动应用程序
Srijan, Samriddhi, Deepak Gupta
Bird populations are declining worldwide, and several species have gone extinct in historical times. Hence for ornithologists and birdwatchers, exploration of rarely found bird species has become a challenging task. We have developed a deep learning based android application to help users recognize 260 Species of birds, making bird classification a lot more user-friendly. In this paper, we use Convolutional Neural Networks (CNN) pre-trained on ImageNet Dataset as freeze layers of the network, and train the last output layer, which consists of 260 different classes. CNN models such as EfficientNet-lite0, Xception, MobilenetV2, ResNet-50, InceptionV3, and InceptionResNetV2 have been compared based on the accuracy, and working of the mobile app is explained. Maximum accuracy of 99.82% on train data and 98.61% on test data is achieved.
世界范围内的鸟类数量正在下降,一些物种在历史上已经灭绝。因此,对于鸟类学家和观鸟者来说,探索罕见的鸟类物种已经成为一项具有挑战性的任务。我们开发了一个基于深度学习的android应用程序,帮助用户识别260种鸟类,使鸟类分类更加人性化。在本文中,我们使用在ImageNet数据集上预训练的卷积神经网络(CNN)作为网络的冻结层,并训练由260个不同的类组成的最后一个输出层。对CNN模型(EfficientNet-lite0、Xception、MobilenetV2、ResNet-50、InceptionV3、InceptionResNetV2)的准确率进行了比较,并对移动应用的工作原理进行了说明。列车数据和测试数据的最高准确率分别达到99.82%和98.61%。
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引用次数: 2
Mask-Vision: A Machine Vision-Based Inference System of Face Mask Detection for Monitoring Health Protocol Safety 基于机器视觉的健康协议安全监测面罩检测推理系统
Rovenson V. Sevilla, A. Alon, Mark P. Melegrito, R. Reyes, Bobby M. Bastes, Roselle P. Cimagala
To avoid adversely affecting community health and the global economy, effective ways to limit the COVID-19 pandemic require constant attention. In the absence of efficient antivirals and insufficient medical resources, WHO recommends several methods to minimize infection rates and prevent depletion of scarce healthcare resources. One of the non-pharmaceutical treatments that can be used to decrease the primary source of SARS-CoV2 droplets expelled by an infected individual is to wear a mask. Irrespective of disagreements about medical resources and mask types, all governments enforce the wearing of masks that cover the nose and mouth by the general population. In the next years, the suggested mask detection models might be a valuable tool for ensuring that safety measures are followed correctly. The YOLOv3 model, a deep transfer learning object identification state-of-the-art approach, is used to create a mask detection model in this research article. The suggested model's exceptional performance makes it ideal for video surveillance equipment. The suggested approach focuses on creating an enhanced dataset from a 300-image dataset utilizing data augmentation techniques such as image filtering. The Data augmentation-based mask detection model's mean average precision was found to be 89.8% during training and 100% during overall testing, with detection per frame accuracy ranging from 40.03% to 65.03%.
为避免对社区卫生和全球经济产生不利影响,需要持续关注限制COVID-19大流行的有效方法。在缺乏有效抗病毒药物和医疗资源不足的情况下,世卫组织建议几种方法来尽量减少感染率并防止耗尽稀缺的卫生保健资源。可用于减少感染者排出的SARS-CoV2飞沫主要来源的非药物治疗方法之一是戴口罩。尽管在医疗资源和口罩类型方面存在分歧,但所有政府都强制要求普通民众佩戴覆盖口鼻的口罩。在接下来的几年里,建议的口罩检测模型可能是确保正确遵循安全措施的宝贵工具。本文使用深度迁移学习对象识别技术的YOLOv3模型创建掩码检测模型。该型号的卓越性能使其成为视频监控设备的理想选择。建议的方法侧重于利用图像过滤等数据增强技术从300个图像数据集创建增强数据集。基于Data augmentation的mask检测模型在训练期间的平均准确率为89.8%,在整体测试期间的平均准确率为100%,每帧检测准确率为40.03% ~ 65.03%。
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引用次数: 13
Unique Approach to Detect Bowling Grips Using Fuzzy Logic Contrast Enhancement 使用模糊逻辑对比度增强来检测保龄球握把的独特方法
Rafeed Rahman, Sifat Tanvir, Md. Tawhid Anwar
Nowadays Cricket has become a much more competitive sport. We can see new bowlers are evolving with their unique bowling styles and variations. A bowler possesses the expertise to bowl multiple categories of bowling in a particular over and baffling the batsman completely. Despite unique bowling styles create confusion in batsmen, the grip of bowlers can reveal greatly what the bowler is trying to bowl. This research concentrates on predicting the type of delivery the bowler is trying to ball with a unique combination of Fuzzy Logic and state-of-the-art machine learning and deep learning models. For the research purpose, a grip dataset is used that contains 5573 images of grips of 13 categories of deliveries. An approach of image contrast enhancement is shown using Fuzzy logic based on the L-channel of the CIE 1976 L*a*b* color space (CIELAB) color space [L*a*b where L=Luminosity and a*b are green, red blue and yellow color] generated from RGB and then the proposed shallow Convolution Neural Network (CNN), VGG 16, KNN, Naïve Bayes, and Decision Tree were trained and the accuracies shown were remarkable.
如今,板球已经成为一项更具竞争性的运动。我们可以看到新的保龄球手正在发展他们独特的保龄球风格和变化。一个投球手拥有的专业知识,以保龄球多个类别的保龄球在一个特定的和莫名其妙的击球手完全。尽管独特的保龄球风格会让击球手感到困惑,但投球手的握持可以极大地揭示出投球手想要打的是什么。这项研究将模糊逻辑与最先进的机器学习和深度学习模型相结合,专注于预测投球手试图投球的类型。为了研究目的,我们使用了一个握力数据集,其中包含13个交付类别的5573张握力图像。基于RGB生成的CIE 1976 L*a*b*颜色空间(CIELAB)颜色空间[L*a*b,其中L=Luminosity和a*b分别为绿色、红色、蓝色和黄色]的L通道,提出了一种基于模糊逻辑的图像对比度增强方法,并对所提出的浅卷积神经网络(CNN)、VGG 16、KNN、Naïve贝叶斯和决策树进行了训练,结果表明准确率显著。
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引用次数: 1
Wi-Fi Group Monitoring System Using Free Space Propagation Model with Active Scanning 基于主动扫描自由空间传播模型的Wi-Fi群监控系统
Pang Tong, Lee-Yeng Ong, M. Leow
In these few years, Wi-Fi technology is widely used for indoor localization to overcome the limitation of global positioning system (GPS) accessibility within the indoor environment. The GPS accuracy is affected with an approximated error rate of 5 to 10 meters because the GPS signal will decrease while penetrating through the building walls or obstacles. This paper describes a low-cost and simple development of a group monitoring system with a single Wi-Fi access point using Raspberry Pi. Distance estimation methods based on the free-space propagation model and received signal strength indicator (RSSI) are implemented. The received signal strength indicator is collected during active scanning for indoor group monitoring in public spaces, such as shopping malls, retail shops, airports, railway stations, hotels, etc. A Group monitoring system with Wi-Fi active scanning opens the possibilities for the utilization of a single Wi-Fi access point to analyze the distance estimation for multiple audiences within the indoor public spaces. The functionalities of the proposed system include device detection, distance estimation, group monitoring, and social distancing alert.
近年来,Wi-Fi技术被广泛应用于室内定位,以克服全球定位系统(GPS)在室内环境下可达性的限制。由于GPS信号在穿透建筑物墙壁或障碍物时会衰减,影响GPS精度,误差率约为5 ~ 10米。本文介绍了一种使用树莓派开发的具有单个Wi-Fi接入点的低成本和简单的组监控系统。实现了基于自由空间传播模型和接收信号强度指标(RSSI)的距离估计方法。主动扫描时采集接收到的信号强度指标,用于商场、零售店、机场、火车站、酒店等公共场所的室内群体监测。带有Wi-Fi主动扫描的群监控系统为利用单个Wi-Fi接入点分析室内公共空间内多个观众的距离估计提供了可能性。该系统的功能包括设备检测、距离估计、群体监控和社交距离警报。
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引用次数: 0
期刊
2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
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