The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated their great advantages. General vector machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we apply it in electricity load forecasting. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we propose many methods to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.
{"title":"A novel Monte Carlo-based neural network model for electricity load forecasting","authors":"Q. Zhou, Binbin Yong, Fucun Li, Jianqing Wu, Zijian Xu, Jun Shen, Huaming Chen","doi":"10.1504/ijes.2020.107631","DOIUrl":"https://doi.org/10.1504/ijes.2020.107631","url":null,"abstract":"The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated their great advantages. General vector machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we apply it in electricity load forecasting. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we propose many methods to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"3 3","pages":"522-533"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141206590","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 : 2020-05-05DOI: 10.1504/ijes.2020.10029261
Wenbo Fu
At present, the data classification based on SOA data exchange method of internet of things (IoT) data is not perfect, the effectiveness of data filtering is low, and the security of data exchange is poor. In this paper, the mass data of IoT are classified by transfer-boost method. The auxiliary training data are used to help source training data and build a reliable classifier to make the classifier more accurate in the test data. Hedge grammar is used to process massive data of heterogeneous IoT. The buffer mechanism is introduced to deal with the unstable data flow in the IoT, so as to enhance the effectiveness of data filtering, and realise the secure data exchange through modules such as server request, identity authentication and receiving data. Experimental results showed that the proposed model can improve the classification accuracy and data filtering effect, and achieve a more secure data exchange effect.
{"title":"Mass internet of things data security exchange model under heterogeneous environment","authors":"Wenbo Fu","doi":"10.1504/ijes.2020.10029261","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029261","url":null,"abstract":"At present, the data classification based on SOA data exchange method of internet of things (IoT) data is not perfect, the effectiveness of data filtering is low, and the security of data exchange is poor. In this paper, the mass data of IoT are classified by transfer-boost method. The auxiliary training data are used to help source training data and build a reliable classifier to make the classifier more accurate in the test data. Hedge grammar is used to process massive data of heterogeneous IoT. The buffer mechanism is introduced to deal with the unstable data flow in the IoT, so as to enhance the effectiveness of data filtering, and realise the secure data exchange through modules such as server request, identity authentication and receiving data. Experimental results showed that the proposed model can improve the classification accuracy and data filtering effect, and achieve a more secure data exchange effect.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122214145","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 : 2020-04-29DOI: 10.1504/ijes.2020.10029007
Ping Ren, Jingzhao Li, Dayu Yang
Aiming at the advantages of internet of things in coal mine application, in this paper, we propose a new method of mobile opportunity perception, which makes full use of underground mine mobile resources. Without adding extra sensing devices, the collaborative working mechanism of mobile nodes and fixed nodes is established. Accordingly, it builds environment opportunity perception and information transmission of the whole place in coal mine and achieves a comprehensive perception goal. By analysing the loss and redundancy perception probability between the mobile and fixed nodes, a novel sparse heterogeneous fusion network and its mixed node arrangement strategy and low-order error detection model between nodes are established. On this basis, to verify the superiority of strategy, the hardware design and experiments are carried out, which provides a new approach and idea for mining enterprises in sensing layer and transport layer application of internet of things.
{"title":"A new mobile opportunity perception network strategy and reliability research in coal mine","authors":"Ping Ren, Jingzhao Li, Dayu Yang","doi":"10.1504/ijes.2020.10029007","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029007","url":null,"abstract":"Aiming at the advantages of internet of things in coal mine application, in this paper, we propose a new method of mobile opportunity perception, which makes full use of underground mine mobile resources. Without adding extra sensing devices, the collaborative working mechanism of mobile nodes and fixed nodes is established. Accordingly, it builds environment opportunity perception and information transmission of the whole place in coal mine and achieves a comprehensive perception goal. By analysing the loss and redundancy perception probability between the mobile and fixed nodes, a novel sparse heterogeneous fusion network and its mixed node arrangement strategy and low-order error detection model between nodes are established. On this basis, to verify the superiority of strategy, the hardware design and experiments are carried out, which provides a new approach and idea for mining enterprises in sensing layer and transport layer application of internet of things.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"463 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132630435","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 : 2020-04-29DOI: 10.1504/ijes.2020.10029028
Wei Sun, Hongji Du, Guangyi Ma, Shunshun Shi, Xiaorui Zhang, Yang Wu
It is difficult with traditional methods to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of "ghosting" phenomenon that occurs in traditional ViBe method and realise accurate and complete detection of the moving vehicle in video.
{"title":"Moving vehicle video detection combining ViBe and inter-frame difference","authors":"Wei Sun, Hongji Du, Guangyi Ma, Shunshun Shi, Xiaorui Zhang, Yang Wu","doi":"10.1504/ijes.2020.10029028","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029028","url":null,"abstract":"It is difficult with traditional methods to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of \"ghosting\" phenomenon that occurs in traditional ViBe method and realise accurate and complete detection of the moving vehicle in video.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134133207","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 : 2020-04-29DOI: 10.1504/ijes.2020.10029025
Xiangmao Chang, Yizhen Chen, Y. Li
Data dissemination is a fundamental service in wireless sensor networks. Traditional protocols often suffer severe medium contention problem or incur significant overhead in contention resolution. Constructive interference (CI) enables concurrent transmissions to interfere constructively, so as to enhance network performance. It has been proved that leveraging CI can achieve near-optimal network flooding latency. However, recent studies show that redundant nodes transmitting simultaneously may degrade the dissemination performance and also consume extra energy. Considering the limited energy of sensor nodes, how to choose the appropriate nodes for simultaneous transmission is important for CI-based data dissemination schemes. In this paper, we formulate this problem as a multi-objective combinatorial optimisation problem theoretically. Considering the complexity of the problem, we design a distributed greedy node selection algorithm to select suitable nodes which forward simultaneously based on CI in each time slot. The main idea of the algorithm is that, by maintaining RecSet and unRecSet in the two-hop neighbour, each node selects suitable nodes from RecSet to transmit simultaneously, such that the maximum number of nodes in unRecSet can receive the packet. Numerical simulations show the efficiency of our proposed algorithm.
{"title":"Topology control for constructive interference-based data dissemination in WSN","authors":"Xiangmao Chang, Yizhen Chen, Y. Li","doi":"10.1504/ijes.2020.10029025","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029025","url":null,"abstract":"Data dissemination is a fundamental service in wireless sensor networks. Traditional protocols often suffer severe medium contention problem or incur significant overhead in contention resolution. Constructive interference (CI) enables concurrent transmissions to interfere constructively, so as to enhance network performance. It has been proved that leveraging CI can achieve near-optimal network flooding latency. However, recent studies show that redundant nodes transmitting simultaneously may degrade the dissemination performance and also consume extra energy. Considering the limited energy of sensor nodes, how to choose the appropriate nodes for simultaneous transmission is important for CI-based data dissemination schemes. In this paper, we formulate this problem as a multi-objective combinatorial optimisation problem theoretically. Considering the complexity of the problem, we design a distributed greedy node selection algorithm to select suitable nodes which forward simultaneously based on CI in each time slot. The main idea of the algorithm is that, by maintaining RecSet and unRecSet in the two-hop neighbour, each node selects suitable nodes from RecSet to transmit simultaneously, such that the maximum number of nodes in unRecSet can receive the packet. Numerical simulations show the efficiency of our proposed algorithm.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279237","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 : 2020-04-29DOI: 10.1504/ijes.2020.10029029
Bin Gu, Long Chen, Yufeng Ke, Yijie Zhou, Haiqing Yu, Kun Wang, Dong Ming
As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users often face more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studied the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributed to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.
{"title":"The effects of varying levels of mental workload on motor imagery based brain-computer interface","authors":"Bin Gu, Long Chen, Yufeng Ke, Yijie Zhou, Haiqing Yu, Kun Wang, Dong Ming","doi":"10.1504/ijes.2020.10029029","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029029","url":null,"abstract":"As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users often face more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studied the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributed to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128854517","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 : 2020-04-29DOI: 10.1504/ijes.2020.10029023
Hong Zhang, Bing Guo, Yuncheng Shen, Xuliang Duan, Xiangqian Dong, Yan Shen
In order to solve the privacy protection problem of individual big data, this paper proposes a hierarchical data trackback technique (HDTT). This technique can realise the data trackback through inter-domain and intra-domain path reconstruction without increasing the core network storage load. The main method is as follows: record the AS domain involved by data packets and IP address information with GBF data structure by use of idle part of packet header, determine the AS domain first with GBFAS data during the path reconstruction, and then determine the intra-domain router with GBFIP data to complete the data trackback. Finally, through the verification of Data Collect Treasure platform by project group, the contact ratio between inter-domain and intra-domain paths is up to over 98% and 92%, respectively, so HDTT technique can accurately reconstruct the data flow path, realise the data trackback and achieve the privacy protection of individual big data.
{"title":"A research on hierarchical trackback technique for individual big data","authors":"Hong Zhang, Bing Guo, Yuncheng Shen, Xuliang Duan, Xiangqian Dong, Yan Shen","doi":"10.1504/ijes.2020.10029023","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029023","url":null,"abstract":"In order to solve the privacy protection problem of individual big data, this paper proposes a hierarchical data trackback technique (HDTT). This technique can realise the data trackback through inter-domain and intra-domain path reconstruction without increasing the core network storage load. The main method is as follows: record the AS domain involved by data packets and IP address information with GBF data structure by use of idle part of packet header, determine the AS domain first with GBFAS data during the path reconstruction, and then determine the intra-domain router with GBFIP data to complete the data trackback. Finally, through the verification of Data Collect Treasure platform by project group, the contact ratio between inter-domain and intra-domain paths is up to over 98% and 92%, respectively, so HDTT technique can accurately reconstruct the data flow path, realise the data trackback and achieve the privacy protection of individual big data.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129488293","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 : 2020-04-28DOI: 10.1504/ijes.2020.10029031
Wei Sun, Hui Xu, Xiaorui Zhang, Aiguo Song
Automatic melanoma diagnosis based on image processing can give more objective results. To facilitate examination for patients at home, we propose a new automatic melanoma diagnosis framework based on common images. Firstly, we use illumination assessment based on variational framework for Retinex (VFR) to filter the images with illumination problem caused by variation of capturing cameras viewpoint and ambient light. Secondly, the GrabCut algorithm based on colour difference is used to segment lesion area. It can complete segmentation automatically and efficiently. Thirdly, we use convolutional neural network (CNN) to extract high-level features and choose support vector machine (SVM) classifier to complete melanoma classification. Compared to hand-craft features, CNN can acquire deep information of images. Because of the lack of medical images, the SVM classifier is better than other classifiers. Finally, we validated our approach from different perspectives and the accuracy is increased by about 5% over other methods.
{"title":"Automatic melanoma diagnosis framework based on common image feature learning","authors":"Wei Sun, Hui Xu, Xiaorui Zhang, Aiguo Song","doi":"10.1504/ijes.2020.10029031","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029031","url":null,"abstract":"Automatic melanoma diagnosis based on image processing can give more objective results. To facilitate examination for patients at home, we propose a new automatic melanoma diagnosis framework based on common images. Firstly, we use illumination assessment based on variational framework for Retinex (VFR) to filter the images with illumination problem caused by variation of capturing cameras viewpoint and ambient light. Secondly, the GrabCut algorithm based on colour difference is used to segment lesion area. It can complete segmentation automatically and efficiently. Thirdly, we use convolutional neural network (CNN) to extract high-level features and choose support vector machine (SVM) classifier to complete melanoma classification. Compared to hand-craft features, CNN can acquire deep information of images. Because of the lack of medical images, the SVM classifier is better than other classifiers. Finally, we validated our approach from different perspectives and the accuracy is increased by about 5% over other methods.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125925700","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 : 2020-04-28DOI: 10.1504/ijes.2020.10029022
Ning Cao, Guofu Li, Hua Yu, Yingying Wang, Mei Wu, Chenjing Gong
Wireless sensor networks have been pushed to the forefront in recent decades owing to the advent of the internet of networks. There exist some crucial parameters which affect the reliability and lifetime performance of typical applications in wireless sensor networks. In this paper, we implement the single-hop protocol with J-Sim simulation tool. Then, we analyse the closure relationships among the density, radius, reliability and lifetime and disclose the trade-off analysis results among them. Next, we propose that two intelligent evaluation models can be applied to under such situations. Thus, wireless sensor network users can predict the lifetime and reliability directly and simulations will be not necessary. This paper also discusses the disadvantages of this approach.
{"title":"Analysis of single-hop routing protocol evaluation models in wireless sensor networks","authors":"Ning Cao, Guofu Li, Hua Yu, Yingying Wang, Mei Wu, Chenjing Gong","doi":"10.1504/ijes.2020.10029022","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029022","url":null,"abstract":"Wireless sensor networks have been pushed to the forefront in recent decades owing to the advent of the internet of networks. There exist some crucial parameters which affect the reliability and lifetime performance of typical applications in wireless sensor networks. In this paper, we implement the single-hop protocol with J-Sim simulation tool. Then, we analyse the closure relationships among the density, radius, reliability and lifetime and disclose the trade-off analysis results among them. Next, we propose that two intelligent evaluation models can be applied to under such situations. Thus, wireless sensor network users can predict the lifetime and reliability directly and simulations will be not necessary. This paper also discusses the disadvantages of this approach.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127442664","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 : 2020-04-28DOI: 10.1504/ijes.2020.10029024
Qi Wang, Chang-song Yang, Yu-xiang Wang
Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.
{"title":"Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle","authors":"Qi Wang, Chang-song Yang, Yu-xiang Wang","doi":"10.1504/ijes.2020.10029024","DOIUrl":"https://doi.org/10.1504/ijes.2020.10029024","url":null,"abstract":"Strapdown inertial navigation system (SINS) initial alignment error model is presented under moving base. SINS navigation error models and the integrated navigation error models are proposed. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with same integration and different integration with same vehicle movements respectively. Simulation experiments show that the observability degree of states variables can be improved under linear or rotation movement, furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables accordingly. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133512162","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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