scholarly journals FAILURES DETECTION METHODS IN CHEMICAL PROCESS USING ARTIFICIAL INTELLIGENCE

2019 ◽  
Vol 16 (32) ◽  
pp. 61-68
Author(s):  
Athus Costa TELES ◽  
Ana Clara de Pádua FREITAS ◽  
Antonio Cruz RODRIGUES
Keyword(s):  
Artificial Intelligence ◽  
Computational Effort ◽  
Classification Systems ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Good Precision ◽  
The Times ◽  
The One

Any atypical change in a procedure can be characterized as a “failure”. Consequently, it may result in economic losses and/or a rise of the operational cost, because most of the times the process will need to be interrupted. Therefore, the concern with the quality and security of the processes has stimulating studies of diagnosis and monitoring failures in industrial equipments. In light of this, the present article has as purpose to apply three different methods (Artificial Neural Networks - ANN, Fuzzy Logic – FL and Support Vector Machine – SVM). All of those were applied as detection and classification systems of failure in the processes of a case study in order to diagnose these artificial intelligence techniques so that the efficiency of each method can be compared. All investigation is done by modeling a reactor of Van der Vusse’s kinetic causing four types of failures, in the concentration of a reagent (failure 1), in the sensor which measures the concentration of the interested product and temperature (failure 2 and 3), and in the valve locking (failure 4). The data used in this methodology is based in quantitative and qualitative historical information. All methods are able to detect failures, but in different times. ANN is the one which detects faster all the failures. SVM detects some minutes later, however with good precision, even though this method uses less computational effort compared to ANN. Fuzzy, in the most of the cases studied, takes hours to detect any change in the system, which makes this one the less effective among the ones studied.

scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

Artificial intelligence exploitation in facility management using deep learning

2020 ◽  
Vol 20 (4) ◽  
pp. 609-624
Author(s):  
Mohamed Marzouk ◽  
Mohamed Zaher
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Life Cycle Cost ◽  
Facility Management ◽  
Support Vector ◽  
Maintenance Costs ◽  
Content Type ◽  
Protection Systems ◽  
Future Work

Purpose This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team. Design/methodology/approach This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance. Findings The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system. Research limitations/implications The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work. Originality/value This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

scholarly journals A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8280
Author(s):  
Giovanni Mezzina ◽  
Valerio F. Annese ◽  
Daniela De Venuto
Keyword(s):  
Artificial Intelligence ◽  
Psychological Safety ◽  
Computer Interface ◽  
Support Vector ◽  
The Internet ◽  
Transmission Channel ◽  
Computer Interfaces ◽  
Eeg Data ◽  
The Internet Of Things

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.

scholarly journals A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

2022 ◽  
Vol 14 (1) ◽  
pp. 211
Author(s):  
Pengxiang Zhao ◽  
Zohreh Masoumi ◽  
Maryam Kalantari ◽  
Mahtab Aflaki ◽  
Ali Mansourian
Keyword(s):  
Landslide Susceptibility ◽  
Variable Importance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Case Study Area ◽  
Causative Factors ◽  
Importance Analysis

Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.

scholarly journals Evaluation of Water Retention Curves by Regression and Machine Learning Methods

2021 ◽  
Vol 1203 (3) ◽  
pp. 032088
Author(s):  
Milan Cisty ◽  
Barbora Povazanova
Keyword(s):  
Bulk Density ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Support Vector ◽  
Data Set ◽  
Retention Curve ◽  
Vector Machines ◽  
The One

Abstract The paper presents two methods that simplify the estimation of the water retention curves. The case study is evaluated for the soils of Záhorská lowland in the paper. These methods are based on the supposed dependence of the soil water content on the percentage content of the 1st, 2nd, 3rd and 4th Kopecký grain categories, and the dry bulk density. The representative set of the drying branch of water retention curves was measured using soil samples from the Záhorská lowland region in a laboratory. Particle size distribution and dry bulk density were also determined. In this paper support vector machines and multiple linear regression is compared to estimate the pedotransfer functions that can be used for the prediction of the drying branch of the water retention curve. Both methods were verified on other data set of measured water retention curves than the one which was used for building the models with a close agreement to measured results.

scholarly journals Ketika Demokrasi Menjadi Ancaman?: Kasus Jerinx dan Refleksi Kritis Bagi Pelaksanaan Demokrasi di Indonesia

2021 ◽  
Vol 1 (1) ◽  
pp. 98-107
Author(s):  
Fatih Abdulbari
Keyword(s):  
Hate Speech ◽  
Freedom Of Expression ◽  
Interesting Case ◽  
Electronic Transactions ◽  
The Media ◽  
Fundamental Value ◽  
The Times ◽  
The One ◽  
The Right

The most important and fundamental value in democracy is freedom of expression. This freedom is considered a part of human rights and is the most important feature of democracy. In the times, on the one hand, the media to speak out is increasingly numerous and varied, but on the other hand there is a dilemma where this freedom is actually used to sow and spread false information or conspiracy theories without evidence. In addition, the concept of freedom of opinion has not developed much following the latest developments, so this concept is increasingly abstract because there are no clear boundaries for freedom of expression. In Indonesia, the emergence of the Law on Information and Electronic Transactions (UU ITE) is actually used as a threat to criminalize individuals whose opinions are considered to be disturbing and attack others.  The Jerinx case is a very interesting case study of how freedom of opinion has actually created a counterfactual narrative. He was convicted in 2020 for making hate speech on his social media accounts. The ITE Law which allows arrests for expressing opinions is problematic because it clearly contradicts the main principle of democracy, namely freedom of expression. This research will critically examine the Jerinx case from the perspective of democratic values to see and analyze how the right to speak and have an opinion in Indonesia. The extent to which freedom of opinion is actually facilitated is considered not to violate the rights of others, and the extent to which the democratic climate has a place in Indonesia.

scholarly journals Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

2017 ◽  
Author(s):  
Tao Wen ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Chengyuan Lin ◽  
Chengren Xiong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Series Analysis ◽  
Trend Component ◽  
Geological Conditions ◽  
Component Displacement

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall, and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with step-like deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

2021 ◽  
Author(s):  
Chao Liang ◽  
Xiangrong Zhang ◽  
Dedong Cui ◽  
Zhengang Yan ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
Pitch Angle ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Detection ◽  
Detection Accuracy ◽  
Angle Data ◽  
Angle Deviation ◽  
Optimal Kernel ◽  
The One

Abstract The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

scholarly journals Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

2017 ◽  
Vol 17 (12) ◽  
pp. 2181-2198 ◽  
Author(s):  
Tao Wen ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Chengyuan Lin ◽  
Chengren Xiong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Series Analysis ◽  
Trend Component ◽  
Geological Conditions ◽  
Component Displacement

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

scholarly journals IoT Botnet Detection Using Various One-Class Classifiers

2020 ◽  
pp. 1-20
Author(s):  
Mehedi Hasan Raj ◽  
A. N. M. Asifur Rahman ◽  
Umma Habiba Akter ◽  
Khayrun Nahar Riya ◽  
Anika Tasneem Nijhum ◽  
...  
Keyword(s):  
Network Flow ◽  
Cost Effective ◽  
Support Vector ◽  
Good Precision ◽  
Accuracy Score ◽  
Botnet Detection ◽  
The People ◽  
Iot Devices ◽  
Set Up ◽  
The One

Nowadays, the Internet of Things (IoT) is a common word for the people because of its increasing number of users. Statistical results show that the users of IoT devices are dramatically increasing, and in the future, it will be to an ever-increasing extent. Because of the increasing number of users, security experts are now concerned about its security. In this research, we would like to improve the security system of IoT devices, particularly in IoT botnet, by applying various machine learning (ML) techniques. In this paper, we have set up an approach to detect botnet of IoT devices using three one-class classifier ML algorithms. The algorithms are: one-class support vector machine (OCSVM), elliptic envelope (EE), and local outlier factor (LOF). Our method is a network flow-based botnet detection technique, and we use the input packet, protocol, source port, destination port, and time as features of our algorithms. After a number of preprocessing steps, we feed the preprocessed data to our algorithms that can achieve a good precision score that is approximately 77–99%. The one-class SVM achieves the best accuracy score, approximately 99% in every dataset, and EE’s accuracy score varies from 91% to 98%; however, the LOF factor achieves lowest accuracy score that is from 77% to 99%. Our algorithms are cost-effective and provide good accuracy in short execution time.

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