Pub Date : 2018-09-01DOI: 10.23919/IConAC.2018.8748964
WA Khan, S. Chung, Ching-Yuen Chan
Cascading correlation learning (CasCor) is a constructive algorithm which determines its own network size and typology by adding hidden units one at a time based on covariance with output error. Its generalization performance and computational time depends on the cascade architecture and iteratively tuning of the connection weights. CasCor was developed to address the slowness of backpropagation (BP), however, recent studies have concluded that in many applications, CasCor generalization performance does not guarantee to be optimal. Apart from BP, CasCor learning speed can be considered slow because of iterative tuning of connection weights by numerical optimization techniques. Therefore, this paper addresses CasCor bottlenecks and introduces a new algorithm with improved cascade architecture and tuning free learning to achieve the objectives of better generalization performance and fast learning ability. The proposed algorithm determines input connection weights by orthogonally transforming a set of correlated input units into uncorrelated hidden units and output connection weights by considering hidden units and the output units in a linear relationship. This research work is unique in that it does not need a random generation of connection weights. A comparative study on nonlinear classification and regression tasks has proven that the proposed algorithm has better generalization performance and learns many times faster than CasCor.
{"title":"Cascade Principal Component Least Squares Neural Network Learning Algorithm","authors":"WA Khan, S. Chung, Ching-Yuen Chan","doi":"10.23919/IConAC.2018.8748964","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748964","url":null,"abstract":"Cascading correlation learning (CasCor) is a constructive algorithm which determines its own network size and typology by adding hidden units one at a time based on covariance with output error. Its generalization performance and computational time depends on the cascade architecture and iteratively tuning of the connection weights. CasCor was developed to address the slowness of backpropagation (BP), however, recent studies have concluded that in many applications, CasCor generalization performance does not guarantee to be optimal. Apart from BP, CasCor learning speed can be considered slow because of iterative tuning of connection weights by numerical optimization techniques. Therefore, this paper addresses CasCor bottlenecks and introduces a new algorithm with improved cascade architecture and tuning free learning to achieve the objectives of better generalization performance and fast learning ability. The proposed algorithm determines input connection weights by orthogonally transforming a set of correlated input units into uncorrelated hidden units and output connection weights by considering hidden units and the output units in a linear relationship. This research work is unique in that it does not need a random generation of connection weights. A comparative study on nonlinear classification and regression tasks has proven that the proposed algorithm has better generalization performance and learns many times faster than CasCor.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132896673","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749023
Pir Masoom Shah, Adnan Zeb, Uferah Shafi, Syed Farhan Alam Zaidi, M. A. Shah
Parkinson Disease (PD) is one of the most critical progressive neurological diseases which mainly affects the motor system. The accurate diagnosis of PD has been a challenge to date, mainly due to the close relevance of PD to other neurological diseases. These close characteristics are the reasons that cause 25% inaccurate manual diagnosis of PD. In this paper, we present a Convolutional Neural Network (CNN) based automatic diagnosis system which accurately classifies PD and healthy control (HC). Parkinson's Progression Markers Initiative (PPMI) provides publically available benchmark T2-weighted Magnetic Resonance Imaging (MRI) for both PD and HC. The mid-brain slices of 500, T2-weighted MRI are selected and aligned using image registration technique. The performance of the proposed technique is evaluated using accuracy, sensitivity, specificity and AUC (Area Under Curve). The detailed comparison in the result section shows that the CNN archived a better performance from 3%–9% in terms of accuracy, sensitivity, specificity, and AUC when compared to the some existing techniques.
{"title":"Detection of Parkinson Disease in Brain MRI using Convolutional Neural Network","authors":"Pir Masoom Shah, Adnan Zeb, Uferah Shafi, Syed Farhan Alam Zaidi, M. A. Shah","doi":"10.23919/IConAC.2018.8749023","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749023","url":null,"abstract":"Parkinson Disease (PD) is one of the most critical progressive neurological diseases which mainly affects the motor system. The accurate diagnosis of PD has been a challenge to date, mainly due to the close relevance of PD to other neurological diseases. These close characteristics are the reasons that cause 25% inaccurate manual diagnosis of PD. In this paper, we present a Convolutional Neural Network (CNN) based automatic diagnosis system which accurately classifies PD and healthy control (HC). Parkinson's Progression Markers Initiative (PPMI) provides publically available benchmark T2-weighted Magnetic Resonance Imaging (MRI) for both PD and HC. The mid-brain slices of 500, T2-weighted MRI are selected and aligned using image registration technique. The performance of the proposed technique is evaluated using accuracy, sensitivity, specificity and AUC (Area Under Curve). The detailed comparison in the result section shows that the CNN archived a better performance from 3%–9% in terms of accuracy, sensitivity, specificity, and AUC when compared to the some existing techniques.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131402852","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8748971
Yanfeng Liu, Yanmeng Xu, S. Qin
Crowdsourcing, as a new business model, can effectively reduce the cost of enterprises, stimulate public participation's passion and enable enterprises to obtain multi-channel innovation. From the point of view of business model, Crowdsourcing can effectively improve the enterprise's overall value proposition, value creation, value transfer and value network construction. Although nowadays Crowdsourcing is widely applied across industries, it is still imperfect in implementing at a practical level, especially when adapt it to fit for different industries. This paper focuses on (1) the identification of key components in an innovative business model, and (2) discussion on how to create an innovative Crowdsourcing business model, which forms a framework for developing Crowdsourcing business models at different levels of detail such as types of industries, types of platforms and types of tasks.
{"title":"What are key components when creating an innovative Crowdsourcing business model","authors":"Yanfeng Liu, Yanmeng Xu, S. Qin","doi":"10.23919/IConAC.2018.8748971","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748971","url":null,"abstract":"Crowdsourcing, as a new business model, can effectively reduce the cost of enterprises, stimulate public participation's passion and enable enterprises to obtain multi-channel innovation. From the point of view of business model, Crowdsourcing can effectively improve the enterprise's overall value proposition, value creation, value transfer and value network construction. Although nowadays Crowdsourcing is widely applied across industries, it is still imperfect in implementing at a practical level, especially when adapt it to fit for different industries. This paper focuses on (1) the identification of key components in an innovative business model, and (2) discussion on how to create an innovative Crowdsourcing business model, which forms a framework for developing Crowdsourcing business models at different levels of detail such as types of industries, types of platforms and types of tasks.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131600108","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749058
Lichen Zhang
Cloud-based cyber-physical systems, like vehicle and intelligent transportation systems, are now attracting much more attentions. These systems usually include large-scale distributed sensor networks covering various components and producing enormous measurement data. Lots of modeling languages are put to use for describing cyber-physical systems or its aspects, bringing contribution to the development of cyber-physical systems. But most of the modeling techniques only focuse on software aspect so that they could not exactly express the whole cloud-based cyber-physical systems, which require appropriate views and tools in its design; but those tools are hard to be used under systemic or object-oriented methods. For example, the widest used modeling language, UML, could not fulfil the above design's requirements by using the foremer's standard form. This paper presents a method designing the cloud-based cyber-physical systems with AADL, by which we can analyse, model and apply those requirements on cloud platforms ensuring QoS in a relatively highly extensible way at the mean time.
{"title":"Modeling Cloud Based Cyber Physical Systems Based on AADL","authors":"Lichen Zhang","doi":"10.23919/IConAC.2018.8749058","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749058","url":null,"abstract":"Cloud-based cyber-physical systems, like vehicle and intelligent transportation systems, are now attracting much more attentions. These systems usually include large-scale distributed sensor networks covering various components and producing enormous measurement data. Lots of modeling languages are put to use for describing cyber-physical systems or its aspects, bringing contribution to the development of cyber-physical systems. But most of the modeling techniques only focuse on software aspect so that they could not exactly express the whole cloud-based cyber-physical systems, which require appropriate views and tools in its design; but those tools are hard to be used under systemic or object-oriented methods. For example, the widest used modeling language, UML, could not fulfil the above design's requirements by using the foremer's standard form. This paper presents a method designing the cloud-based cyber-physical systems with AADL, by which we can analyse, model and apply those requirements on cloud platforms ensuring QoS in a relatively highly extensible way at the mean time.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131652981","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749104
Kun Liu, Kai Meng, Haiyong Chen, Peng Yang
Surface defect detection based on machine vision has drawn much attention today. Traditional methods aim at uniform repetitive texture, thus can rarely handle inhomogeneous texture surfaces like solar cells'. Therefore, a self-reference scheme based on the decomposition of structural-texture is introduced here to observe solar cell's surface cracks under electroluminescence (EL) images. Firstly, the structure-texture decomposition of the original image is carried out, and the $L_{0}$ gradient minimization and the relative total variational operation are carried out on the structural component and the textural component respectively. It turns out that the small amplitude gradient information in the structural map is removed and the crack details are preserved in the textural map. Then, the discrete wavelet transform is used to process the structural component and the textural component, and a self-reference image is obtained by combination. Through finding an appropriate radius in the spectrogram of self-reference image and setting the frequency domain inside the selected circular area to zero, we can finally acquire the precise location of the defect. The proposed method has been proved of high efficiency from a large set of tests of a real production line.
{"title":"A self-reference scheme based on structure-texture decomposition for crack defect detection with electroluminescence images","authors":"Kun Liu, Kai Meng, Haiyong Chen, Peng Yang","doi":"10.23919/IConAC.2018.8749104","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749104","url":null,"abstract":"Surface defect detection based on machine vision has drawn much attention today. Traditional methods aim at uniform repetitive texture, thus can rarely handle inhomogeneous texture surfaces like solar cells'. Therefore, a self-reference scheme based on the decomposition of structural-texture is introduced here to observe solar cell's surface cracks under electroluminescence (EL) images. Firstly, the structure-texture decomposition of the original image is carried out, and the $L_{0}$ gradient minimization and the relative total variational operation are carried out on the structural component and the textural component respectively. It turns out that the small amplitude gradient information in the structural map is removed and the crack details are preserved in the textural map. Then, the discrete wavelet transform is used to process the structural component and the textural component, and a self-reference image is obtained by combination. Through finding an appropriate radius in the spectrogram of self-reference image and setting the frequency domain inside the selected circular area to zero, we can finally acquire the precise location of the defect. The proposed method has been proved of high efficiency from a large set of tests of a real production line.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383630","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749050
Zainab Mones, D. Zhen, I. Alqatawneh, Q. Zeng, F. Gu, A. Ball
Planetary gearboxes are widely used in drivetrains of helicopters, wind turbines, and heavy industrial applications. Since the role of complex planetary gearboxes is quite important and their failure may cause a shutdown of the entire train resulting in major economic losses, condition monitoring of planetary gearboxes has received significant attention. The installation of conventional accelerometers on the machine's housing can fail to provide accuracy in diagnosing planetary gearbox faults, due to the alterations in planet gear mesh excitation with the carrier. Advances in low-cost and low-power Micro-Electro-Mechanical Systems (MEMS) technology allow the MEMS accelerometer to be installed onto the rotating shaft directly, and this implies that the rotating machine's dynamic features can be measured more accurately. This paper examines the characteristics of the rotating acceleration signals measured by on-rotor MEMS accelerometer installed on the low-speed input shaft of a planetary gearbox. The experimental results show that by investigating the frequency spectra of the on-rotor accelerometer measurements, different faults of planetary gearbox can be clearly diagnosed, thus providing a reliable and low-cost method for the condition monitoring of planetary gearbox.
{"title":"Fault Diagnosis of Planetary Gearboxes via Processing the On-Rotor MEMS Accelerometer Signals","authors":"Zainab Mones, D. Zhen, I. Alqatawneh, Q. Zeng, F. Gu, A. Ball","doi":"10.23919/IConAC.2018.8749050","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749050","url":null,"abstract":"Planetary gearboxes are widely used in drivetrains of helicopters, wind turbines, and heavy industrial applications. Since the role of complex planetary gearboxes is quite important and their failure may cause a shutdown of the entire train resulting in major economic losses, condition monitoring of planetary gearboxes has received significant attention. The installation of conventional accelerometers on the machine's housing can fail to provide accuracy in diagnosing planetary gearbox faults, due to the alterations in planet gear mesh excitation with the carrier. Advances in low-cost and low-power Micro-Electro-Mechanical Systems (MEMS) technology allow the MEMS accelerometer to be installed onto the rotating shaft directly, and this implies that the rotating machine's dynamic features can be measured more accurately. This paper examines the characteristics of the rotating acceleration signals measured by on-rotor MEMS accelerometer installed on the low-speed input shaft of a planetary gearbox. The experimental results show that by investigating the frequency spectra of the on-rotor accelerometer measurements, different faults of planetary gearbox can be clearly diagnosed, thus providing a reliable and low-cost method for the condition monitoring of planetary gearbox.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124891409","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8748990
Lingling Chen, Zekun Yang, Cun Zhang, Jie Wang, Yaying Li
In view of the problem of continuous movement pattern recognition for above-knee prostheses control, a periodic locomotion-model recognition method was proposed based on electromyography of thigh stump. Firstly, after analyzing the surface electromyography of gluteus maximus, multi-feature sections detection algorithm was proposed based on moving windows, and multi-feature sections within a motion cycle were extracted. Secondly, random forest algorithm was applied to recognize the movement pattern of each section. Finally, a periodic pattern recognition method based on binary tree was proposed to fuse the recognition results of each section. The experiment results indicated that this method improved the recognition accuracy by about 8% with multi-feature sections fusion. The pattern recognition of periodic motion (flat walking, upstairs, and downstairs) and aperiodic motion (sitting and standing) were realized, and the recognition accuracy and real-time performance have improved obviously.
{"title":"Periodic Locomotion-model Recognition Based on Electromyography of Thigh Stump","authors":"Lingling Chen, Zekun Yang, Cun Zhang, Jie Wang, Yaying Li","doi":"10.23919/IConAC.2018.8748990","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748990","url":null,"abstract":"In view of the problem of continuous movement pattern recognition for above-knee prostheses control, a periodic locomotion-model recognition method was proposed based on electromyography of thigh stump. Firstly, after analyzing the surface electromyography of gluteus maximus, multi-feature sections detection algorithm was proposed based on moving windows, and multi-feature sections within a motion cycle were extracted. Secondly, random forest algorithm was applied to recognize the movement pattern of each section. Finally, a periodic pattern recognition method based on binary tree was proposed to fuse the recognition results of each section. The experiment results indicated that this method improved the recognition accuracy by about 8% with multi-feature sections fusion. The pattern recognition of periodic motion (flat walking, upstairs, and downstairs) and aperiodic motion (sitting and standing) were realized, and the recognition accuracy and real-time performance have improved obviously.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127799374","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749098
Zijian Guo, H. Ye, Wei Dong, Xiang Yan, Yindong Ji
Fault detection of point machine operations is discussed in this paper, which is critical for ensuring the safety of a running train. A fault detection method is proposed based on stacked autoencoders (SAE), which can be easily trained and has great expressive power. The method only requires normal samples to train the SAE model, and integrates feature extraction and fault detection into one step. The proposed method is evaluated by using the historical field data collected from a real high-speed railway. Experimental results show the effectiveness and merits of the SAE based detection method.
{"title":"A Fault Detection Method for Railway Point Machine Operations Based On Stacked Autoencoders","authors":"Zijian Guo, H. Ye, Wei Dong, Xiang Yan, Yindong Ji","doi":"10.23919/IConAC.2018.8749098","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749098","url":null,"abstract":"Fault detection of point machine operations is discussed in this paper, which is critical for ensuring the safety of a running train. A fault detection method is proposed based on stacked autoencoders (SAE), which can be easily trained and has great expressive power. The method only requires normal samples to train the SAE model, and integrates feature extraction and fault detection into one step. The proposed method is evaluated by using the historical field data collected from a real high-speed railway. Experimental results show the effectiveness and merits of the SAE based detection method.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115928856","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8748946
Xiaoli Tang, Zainab Mones, Xianghong Wang, F. Gu, A. Ball
Condition monitoring (CM) deliveries significant benefits to industries by reducing breakdown losses of machines and enhancing their safe and high-performance operations. Monitoring the machine conditions in real time using an appropriate wireless sensor network (WSN) has the advantages of the avoidance of cable usages, ease of system deployment and hence cost-effectiveness of CM implementation. One of the major challenges for WSN is the battery replacement. Generally, the batteries of sensor nodes are difficult to recharge or replace due to the inevitable layout at inaccessible or risky positions. Recently, energy harvesting (EH) applied to WSNs has increasingly caught the attention of researchers due to the ideal permanent non-maintenance requirements of the autonomous WSN nodes. This paper overviews the principles of several promising EH technologies (including photovoltaic, thermoelectric, pyroelectric, piezoelectric, electromagnetic, triboelectric EH technologies) used in various fields. In addition, the corresponding EH prototypes and fabricated products developed by various researchers are reviewed. After the discussion of the advantages and limitations of different technologies, the EH technologies are evaluated for further development of the energy harvesters to achieve a maintenance-free system for reliable monitoring machines. Finally, a discussion on challenges, applications and future developments of EH applied for machine CM is held.
{"title":"A Review on Energy Harvesting Supplying Wireless Sensor Nodes for Machine Condition Monitoring","authors":"Xiaoli Tang, Zainab Mones, Xianghong Wang, F. Gu, A. Ball","doi":"10.23919/IConAC.2018.8748946","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748946","url":null,"abstract":"Condition monitoring (CM) deliveries significant benefits to industries by reducing breakdown losses of machines and enhancing their safe and high-performance operations. Monitoring the machine conditions in real time using an appropriate wireless sensor network (WSN) has the advantages of the avoidance of cable usages, ease of system deployment and hence cost-effectiveness of CM implementation. One of the major challenges for WSN is the battery replacement. Generally, the batteries of sensor nodes are difficult to recharge or replace due to the inevitable layout at inaccessible or risky positions. Recently, energy harvesting (EH) applied to WSNs has increasingly caught the attention of researchers due to the ideal permanent non-maintenance requirements of the autonomous WSN nodes. This paper overviews the principles of several promising EH technologies (including photovoltaic, thermoelectric, pyroelectric, piezoelectric, electromagnetic, triboelectric EH technologies) used in various fields. In addition, the corresponding EH prototypes and fabricated products developed by various researchers are reviewed. After the discussion of the advantages and limitations of different technologies, the EH technologies are evaluated for further development of the energy harvesters to achieve a maintenance-free system for reliable monitoring machines. Finally, a discussion on challenges, applications and future developments of EH applied for machine CM is held.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662090","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749086
Xichun Luo
Micromanufacturing has attracted great attention as micro-components/products such as micro-displays, micro-sensors, micro-batteries, etc. are becoming established in all major areas of our daily life and can already been found across the broad spectrum of application areas especially in sectors such as automotive, aerospace, photonics, renewable energy and medical instruments. These micro-components/products are usually made of multi-materials (may include hard-to-machine materials) and possess complex shaped micro-structures but demand sub-micron machining accuracy. A number of micro-machining processes is therefore, needed to deliver such components/products. The talk introduces the concept of hybrid micro-machining process which involves integration of various micro-machining processes with the purpose of improving machinability, geometrical accuracy, tool life, surface integrity, machining rate and reducing the process forces. It uses three typical hybrid micromachining processes to demonstrate the effectiveness of hybrid micromachining process in terms of machining performance and productivity. Development a new 6-axis hybrid micro machine tool and material handling system to implement the hybrid micromachining processes is also introduced. The talk concludes with the future research focus and challenges of hybrid micromachining in the new era of smart manufacturing.
{"title":"Hybrid micromachining - a paradigm shift in micromanufacturing","authors":"Xichun Luo","doi":"10.23919/IConAC.2018.8749086","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749086","url":null,"abstract":"Micromanufacturing has attracted great attention as micro-components/products such as micro-displays, micro-sensors, micro-batteries, etc. are becoming established in all major areas of our daily life and can already been found across the broad spectrum of application areas especially in sectors such as automotive, aerospace, photonics, renewable energy and medical instruments. These micro-components/products are usually made of multi-materials (may include hard-to-machine materials) and possess complex shaped micro-structures but demand sub-micron machining accuracy. A number of micro-machining processes is therefore, needed to deliver such components/products. The talk introduces the concept of hybrid micro-machining process which involves integration of various micro-machining processes with the purpose of improving machinability, geometrical accuracy, tool life, surface integrity, machining rate and reducing the process forces. It uses three typical hybrid micromachining processes to demonstrate the effectiveness of hybrid micromachining process in terms of machining performance and productivity. Development a new 6-axis hybrid micro machine tool and material handling system to implement the hybrid micromachining processes is also introduced. The talk concludes with the future research focus and challenges of hybrid micromachining in the new era of smart manufacturing.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122996570","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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