Pub Date : 2018-10-08DOI: 10.1108/IJIUS-03-2018-0008
Tushar Jain, Meenu Gupta, H. K. Sardana
Purpose The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects. Design/methodology/approach The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts. Findings Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects. Originality/value After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.
{"title":"Unmanned machine vision system for automated recognition of mechanical parts","authors":"Tushar Jain, Meenu Gupta, H. K. Sardana","doi":"10.1108/IJIUS-03-2018-0008","DOIUrl":"https://doi.org/10.1108/IJIUS-03-2018-0008","url":null,"abstract":"\u0000Purpose\u0000The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.\u0000\u0000\u0000Design/methodology/approach\u0000The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.\u0000\u0000\u0000Findings\u0000Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.\u0000\u0000\u0000Originality/value\u0000After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.\u0000","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/IJIUS-03-2018-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45412432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-02DOI: 10.1108/IJIUS-09-2017-0011
Hamed Pourazad, J. Askari, S. Hosseinnia
Purpose Increasing commercial applications for small unmanned aircraft create growing challenges in providing safe flight conditions. The conventional measures to detect icing are either expensive, energy consuming or heavy. The purpose of this paper is to develop a fault identification and isolation scheme using unknown input observers to detect and isolate actuator and structural faults in simultaneous occurrence. Design/methodology/approach The fault detection scheme is based on a deviation in system parameters due to icing and lock-in-place (LIP), two faults from different categories with similar indications that require different reconfiguration actions. The obtained residual signals are selected to be triggered by desired faults, while insensitive to others. Findings The proposed observer is sensitive to both actuator and structural faults, and distinguishes simultaneous occurrences by insensitivity to LIP in selected residue signals. Simulation results confirm the success of the proposed system in the presence of uncertainty and disturbance. Research limitations/implications The fault detection and isolation scheme proposed here is based on the linear model of a winged aircraft, the Aerosonde. Moreover, the faults are applied to rudder and aileron in simulations, but the design procedure for other models is provided. The designed scheme could be further implemented on a non-linear aircraft model. Practical implications Applying the proposed icing detection scheme increases detection system reliability, since fault isolation enables timely reconfiguration schemes. Originality/value The observers proposed in previous papers detected icing fault but were not insensitive to actuator faults.
{"title":"Isolating observer for simultaneous structural-actuator fault detection","authors":"Hamed Pourazad, J. Askari, S. Hosseinnia","doi":"10.1108/IJIUS-09-2017-0011","DOIUrl":"https://doi.org/10.1108/IJIUS-09-2017-0011","url":null,"abstract":"\u0000Purpose\u0000Increasing commercial applications for small unmanned aircraft create growing challenges in providing safe flight conditions. The conventional measures to detect icing are either expensive, energy consuming or heavy. The purpose of this paper is to develop a fault identification and isolation scheme using unknown input observers to detect and isolate actuator and structural faults in simultaneous occurrence.\u0000\u0000\u0000Design/methodology/approach\u0000The fault detection scheme is based on a deviation in system parameters due to icing and lock-in-place (LIP), two faults from different categories with similar indications that require different reconfiguration actions. The obtained residual signals are selected to be triggered by desired faults, while insensitive to others.\u0000\u0000\u0000Findings\u0000The proposed observer is sensitive to both actuator and structural faults, and distinguishes simultaneous occurrences by insensitivity to LIP in selected residue signals. Simulation results confirm the success of the proposed system in the presence of uncertainty and disturbance.\u0000\u0000\u0000Research limitations/implications\u0000The fault detection and isolation scheme proposed here is based on the linear model of a winged aircraft, the Aerosonde. Moreover, the faults are applied to rudder and aileron in simulations, but the design procedure for other models is provided. The designed scheme could be further implemented on a non-linear aircraft model.\u0000\u0000\u0000Practical implications\u0000Applying the proposed icing detection scheme increases detection system reliability, since fault isolation enables timely reconfiguration schemes.\u0000\u0000\u0000Originality/value\u0000The observers proposed in previous papers detected icing fault but were not insensitive to actuator faults.\u0000","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/IJIUS-09-2017-0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45469849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-02DOI: 10.1108/IJIUS-02-2018-0004
K. M. Hasan, S. Newaz, Md. Shamim Ahsan
Purpose The purpose of this paper is to demonstrate the development of an aircraft-type autonomous portable drone suitable for surveillance and disaster management. The drone is capable of flying at a maximum speed of 76 km/h. This portable drone comprises five distinct parts those are easily installable within several minutes and can be fit in a small portable kit. The drone consists of a ballistic recovery system, allowing the drone landing vertically. The integrated high-definition camera sends real-time video stream of desired area to the ground control station. In addition, the drone is capable of carrying ~1.8 kg of payload. Design/methodology/approach In order to design and develop the portable drone, the authors sub-divided the research activities in six fundamental steps: survey of the current drone technologies, design the system architecture of the drone, simulation and modeling of various modules of the drone, development of various modules of the drone and their performance analysis, integration of various modules of the drone, and real-life performance analysis and finalization. Findings Experimental results: the cruise speed of the drone was in the range between 45 and 62 km/h. The drone was capable of landing vertically using the ballistic recovery system attached with it. On the contrary, the drone can transmit real-time video to the ground control station and, thus, suitable for surveillance. The audio system of the drone can be used for announcement of emergency messages. The drone can carry 1.8 kg of payload and can be used during disaster management. The drone parts are installed within 10 min and fit in a small carrying box. Practical implications The autonomous aircraft-type portable drone has a wide range of applications including surveillance, traffic jam monitoring and disaster management. Social implications The cost of the cost-effective drone is within $700 and creates opportunities for the deployment in the least developed countries. Originality/value The autonomous aircraft-type portable drone along with the ballistic recovery system were designed and developed by the authors using their won technology.
{"title":"Design and development of an aircraft type portable drone for surveillance and disaster management","authors":"K. M. Hasan, S. Newaz, Md. Shamim Ahsan","doi":"10.1108/IJIUS-02-2018-0004","DOIUrl":"https://doi.org/10.1108/IJIUS-02-2018-0004","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to demonstrate the development of an aircraft-type autonomous portable drone suitable for surveillance and disaster management. The drone is capable of flying at a maximum speed of 76 km/h. This portable drone comprises five distinct parts those are easily installable within several minutes and can be fit in a small portable kit. The drone consists of a ballistic recovery system, allowing the drone landing vertically. The integrated high-definition camera sends real-time video stream of desired area to the ground control station. In addition, the drone is capable of carrying ~1.8 kg of payload.\u0000\u0000\u0000Design/methodology/approach\u0000In order to design and develop the portable drone, the authors sub-divided the research activities in six fundamental steps: survey of the current drone technologies, design the system architecture of the drone, simulation and modeling of various modules of the drone, development of various modules of the drone and their performance analysis, integration of various modules of the drone, and real-life performance analysis and finalization.\u0000\u0000\u0000Findings\u0000Experimental results: the cruise speed of the drone was in the range between 45 and 62 km/h. The drone was capable of landing vertically using the ballistic recovery system attached with it. On the contrary, the drone can transmit real-time video to the ground control station and, thus, suitable for surveillance. The audio system of the drone can be used for announcement of emergency messages. The drone can carry 1.8 kg of payload and can be used during disaster management. The drone parts are installed within 10 min and fit in a small carrying box.\u0000\u0000\u0000Practical implications\u0000The autonomous aircraft-type portable drone has a wide range of applications including surveillance, traffic jam monitoring and disaster management.\u0000\u0000\u0000Social implications\u0000The cost of the cost-effective drone is within $700 and creates opportunities for the deployment in the least developed countries.\u0000\u0000\u0000Originality/value\u0000The autonomous aircraft-type portable drone along with the ballistic recovery system were designed and developed by the authors using their won technology.\u0000","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/IJIUS-02-2018-0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48026454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-02DOI: 10.1108/IJIUS-03-2018-0007
D. Mikhalchenko, A. Ivin, D. Malov
Purpose Single image depth prediction allows to extract depth information from a usual 2D image without usage of special sensors such as laser sensors, stereo cameras, etc. The purpose of this paper is to solve the problem of obtaining depth information from 2D image by applying deep neural networks (DNNs). Design/methodology/approach Several experiments and topologies are presented: DNN that uses three inputs—sequence of 2D images from videostream and DNN that uses only one input. However, there is no data set, that contains videostream and corresponding depth maps for every frame. So technique of creating data sets using the Blender software is presented in this work. Findings Despite the problem of an insufficient amount of available data sets, the problem of overfitting was encountered. Although created models work on the data sets, they are still overfitted and cannot predict correct depth map for the random images, that were included into the data sets. Originality/value Existing techniques of depth images creation are tested, using DNN.
{"title":"Obtaining depth map from 2D non stereo images using deep neural networks","authors":"D. Mikhalchenko, A. Ivin, D. Malov","doi":"10.1108/IJIUS-03-2018-0007","DOIUrl":"https://doi.org/10.1108/IJIUS-03-2018-0007","url":null,"abstract":"\u0000Purpose\u0000Single image depth prediction allows to extract depth information from a usual 2D image without usage of special sensors such as laser sensors, stereo cameras, etc. The purpose of this paper is to solve the problem of obtaining depth information from 2D image by applying deep neural networks (DNNs).\u0000\u0000\u0000Design/methodology/approach\u0000Several experiments and topologies are presented: DNN that uses three inputs—sequence of 2D images from videostream and DNN that uses only one input. However, there is no data set, that contains videostream and corresponding depth maps for every frame. So technique of creating data sets using the Blender software is presented in this work.\u0000\u0000\u0000Findings\u0000Despite the problem of an insufficient amount of available data sets, the problem of overfitting was encountered. Although created models work on the data sets, they are still overfitted and cannot predict correct depth map for the random images, that were included into the data sets.\u0000\u0000\u0000Originality/value\u0000Existing techniques of depth images creation are tested, using DNN.\u0000","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"40 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/IJIUS-03-2018-0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41303123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-03-19DOI: 10.1007/978-3-642-00264-9_5
Keigo Watanabe, Kouki Tanaka, K. Izumi, K. Okamura, R. Syam
{"title":"Discontinuous Control and Backstepping Method for the Underactuated Control of VTOL Aerial Robots with Four Rotors","authors":"Keigo Watanabe, Kouki Tanaka, K. Izumi, K. Okamura, R. Syam","doi":"10.1007/978-3-642-00264-9_5","DOIUrl":"https://doi.org/10.1007/978-3-642-00264-9_5","url":null,"abstract":"","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"36 1","pages":"83-100"},"PeriodicalIF":1.0,"publicationDate":"2009-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88175042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1007/978-3-642-00264-9
A. Budiyono, B. Riyanto, E. Joelianto
{"title":"Intelligent Unmanned Systems: Theory and Applications","authors":"A. Budiyono, B. Riyanto, E. Joelianto","doi":"10.1007/978-3-642-00264-9","DOIUrl":"https://doi.org/10.1007/978-3-642-00264-9","url":null,"abstract":"","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"35 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89916295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1007/978-3-642-00264-9_17
Ming Yu, Ming Luo, S. Arogeti, Danwei W. Wang, Xinzheng Zhang
{"title":"Fault and Mode Switching Identification for Hybrid Systems with Application to Electro-Hydraulic System in Vehicles","authors":"Ming Yu, Ming Luo, S. Arogeti, Danwei W. Wang, Xinzheng Zhang","doi":"10.1007/978-3-642-00264-9_17","DOIUrl":"https://doi.org/10.1007/978-3-642-00264-9_17","url":null,"abstract":"","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"51 1","pages":"257-274"},"PeriodicalIF":1.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90055150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1007/978-3-642-00264-9_10
A. Nassiraei, K. Ishii
{"title":"How Does \"Intelligent Mechanical Design Concept\" Help Us to Enhance Robot's Function?","authors":"A. Nassiraei, K. Ishii","doi":"10.1007/978-3-642-00264-9_10","DOIUrl":"https://doi.org/10.1007/978-3-642-00264-9_10","url":null,"abstract":"","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"81 1","pages":"155-178"},"PeriodicalIF":1.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88604660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1007/978-3-642-00264-9_4
A. Budiyono, T. Sudiyanto
{"title":"Control of Small Scale Helicopter Using s-CDM and LQ Design","authors":"A. Budiyono, T. Sudiyanto","doi":"10.1007/978-3-642-00264-9_4","DOIUrl":"https://doi.org/10.1007/978-3-642-00264-9_4","url":null,"abstract":"","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":"61 1","pages":"63-81"},"PeriodicalIF":1.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84199176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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