scholarly journals Customized Mobile LiDAR System for Manhole Cover Detection and Identification

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2422 ◽  
Author(s):  
Zhanying Wei ◽  
Mengmeng Yang ◽  
Liuzhao Wang ◽  
Hao Ma ◽  
Xuexia Chen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Detection System ◽  
Strong Support ◽  
Assessment System ◽  
Support Vector ◽  
Detection Accuracy ◽  
Machine Method ◽  
Efficient System ◽  
Exposure System ◽  
Detection And Identification

Manhole covers, which are a key element of urban infrastructure management, have a direct impact on travel safety. At present, there is no automatic, safe, and efficient system specially used for the intelligent detection, identification, and assessment of manhole covers. In this work, we developed an automatic detection, identification, and assessment system for manhole covers. First, we developed a sequential exposure system via the addition of multiple cameras in a symmetrical arrangement to realize the joint acquisition of high-precision laser data and ultra-high-resolution ground images. Second, we proposed an improved histogram of an oriented gradient with symmetry features and a support vector machine method to detect manhole covers effectively and accurately, by using the intensity images and ground orthophotos that are derived from the laser points and images, respectively, and apply the graph segmentation and statistical analysis to achieve the detection, identification, and assessment of manhole covers. Qualitative and quantitative analyses are performed using large experimental datasets that were acquired with the modified manhole-cover detection system. The detected results yield an average accuracy of 96.18%, completeness of 94.27%, and F-measure value of 95.22% in manhole cover detection. Defective manhole-cover monitoring and manhole-cover ownership information are achieved from these detection results. The results not only provide strong support for road administration works, such as data acquisition, manhole cover inquiry and inspection, and statistical analysis of resources, but also demonstrate the feasibility and effectiveness of the proposed method, which reduces the risk involved in performing manual inspections, improves the manhole-cover detection accuracy, and serves as a powerful tool in intelligent road administration.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

scholarly journals Vehicles Detection System at Different Weather Conditions

2021 ◽  
pp. 2040-2052
Author(s):  
Mustafa Najm Abdullah ◽  
Yousra Hussein Ali
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Detection System ◽  
Weather Condition ◽  
Weather Conditions ◽  
Histogram Equalization ◽  
Transportation Systems ◽  
Support Vector ◽  
Detection Accuracy ◽  
Gaussian Blur

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not.  As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.

scholarly journals Pear Defect Detection Method Based on ResNet and DCGAN

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 397
Author(s):  
Yan Zhang ◽  
Shiyun Wa ◽  
Pengshuo Sun ◽  
Yaojun Wang
Keyword(s):  
Defect Detection ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Detection System ◽  
Implementation Process ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Generation Network ◽  
Low Efficiency

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.

Human Detection Based on SVM and Improved Histogram of Oriented Gradients

2013 ◽  
Vol 380-384 ◽  
pp. 3862-3865 ◽  
Author(s):  
Li Hong Zhang
Keyword(s):  
Support Vector Machine ◽  
Linear Function ◽  
Detection System ◽  
Features Extraction ◽  
The Body ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Feature Description

Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

scholarly journals Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4335
Author(s):  
Goran Šeketa ◽  
Lovro Pavlaković ◽  
Dominik Džaja ◽  
Igor Lacković ◽  
Ratko Magjarević
Keyword(s):  
Large Scale ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Data Segmentation ◽  
Post Impact

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

Sinkhole Attack Detection-Based SVM In Wireless Sensor Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 16-31
Author(s):  
Sihem Aissaoui ◽  
Sofiane Boukli Hacene
Keyword(s):  
Network Performance ◽  
Ad Hoc ◽  
Detection System ◽  
Special Kind ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Sybil Attack ◽  
Wireless Medium

Wireless sensor network is a special kind of ad hoc network characterized by high density, low mobility, and the use of a shared wireless medium. This last feature makes the network deployment easy; however, it is prone to various types of attacks such as sinkhole attack, sybil attack. Many researchers studied the effect of such attacks on the network performance and their detection. Classification techniques are some of the most used end effective methods to detect attacks in WSN. In this paper, the authors focus on sinkhole attack, which is one of the most destructive attacks in WSNs. The authors propose an intrusion detection system for sinkhole attack using support vector machines (SVM) on AODV routing protocol. In the different experiments, a special sinkhole dataset is used, and a comparison with previous techniques is done on the basis of detection accuracy. The results show the efficiency of the proposed approach.

scholarly journals FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Javad Hassannataj Joloudari ◽  
Mohammad GhasemiGol ◽  
Hamid Saadatfar ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Large Scale ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Classification Methods

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.

scholarly journals Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 460 ◽  
Author(s):  
Zuojun Liu ◽  
Cheng Xiao ◽  
Tieling Zhang ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Cost Effective ◽  
Support Vector ◽  
Detection Accuracy ◽  
Machine Method ◽  
Scada System

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

scholarly journals SISTEM DETEKSI EMOSI MENGGUNAKAN SINYAL EEG “EMOCLASS”

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ngarap Imanuel Manik ◽  
Antonius Ivan
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Computer Program ◽  
Detection System ◽  
Support Vector ◽  
Emotional Experiences ◽  
Analysis Model ◽  
Eeg Signals ◽  
Emotion Model

An emotional detection system has been developed using EEG signals with the help of a computer program. The results of this development are an important step in progress in learning the classification of emotional detection because it can be obtained more quickly. This study uses a support vector machine approach with a statistical analysis model that can be used to classify emotions into the Russell Emotion Model. Emotions included are Amused, Fear, Calm, Sad, and Neutral. With some assumptions, this system can provide benefits to the multimedia sector by producing applications that automatically detect human emotional experiences.

scholarly journals Intrusion detection system based on machine learning techniques

2021 ◽  
Vol 23 (2) ◽  
pp. 953
Author(s):  
Musaab Riyadh ◽  
Dina Riadh Alshibani
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Special Kind ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Malicious Activity

Recently, the data flow over the internet has exponentially increased due to the massive growth of computer networks connected to it. Some of these data can be classified as a malicious activity which cannot be captured by firewalls and anti-malwares. Due to this, the intrusion detection systems are urgent need in order to recognize malicious activity to keep data integrity and availability. In this study, an intrusion detection system based on cluster feature concepts and KNN classifier has been suggested to handle the various challenges issues in data such as in complete data, mixed-type and noise data. To streng then the proposed system a special kind of patterns similarity measures are supported to deal with these types of challenges. The experimental results show that the classification accuracy of the suggested system is better than K-nearest neighbor (KNN) and support vector machine classifiers when processing incomplete data set, inspite of droping down the overall detection accuracy.

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