Zesheng Xi, Bo Zhang, Yuanyuan Ma, Chuan He, Yu-Na Wang
Serverless computing aims to handle all the system administration operations needed in cloud computing, thus, to provide a paradigm that greatly simplifies cloud programming. However, the security in serverless computing is regarded as an independent technology. The lack of security consideration in the initial design makes it difficult to handle the increasingly complicated attack scenario in serverless computing, especially for the vulnerabilities and backdoor based network attack. In this paper, we propose MDSC, a mimic defense enabled paradigm for serverless computing. Specifically, MDSC paradigm introduces Dynamic Heterogeneous Redundancy (DHR) structural model to serverless computing, and make fully use of features introduced by serverless computing to achieve an intrinsic security system with acceptable costs. We show the feasibility of MDSC paradigm by implementing a trial of MDSC paradigm based on Kubernetes and Knative. Analysis and experimental results show that MDSC paradigm can achieve high level security with acceptable cost.
{"title":"The MDSC paradigm design for serverless computing defense","authors":"Zesheng Xi, Bo Zhang, Yuanyuan Ma, Chuan He, Yu-Na Wang","doi":"10.1117/12.2671158","DOIUrl":"https://doi.org/10.1117/12.2671158","url":null,"abstract":"Serverless computing aims to handle all the system administration operations needed in cloud computing, thus, to provide a paradigm that greatly simplifies cloud programming. However, the security in serverless computing is regarded as an independent technology. The lack of security consideration in the initial design makes it difficult to handle the increasingly complicated attack scenario in serverless computing, especially for the vulnerabilities and backdoor based network attack. In this paper, we propose MDSC, a mimic defense enabled paradigm for serverless computing. Specifically, MDSC paradigm introduces Dynamic Heterogeneous Redundancy (DHR) structural model to serverless computing, and make fully use of features introduced by serverless computing to achieve an intrinsic security system with acceptable costs. We show the feasibility of MDSC paradigm by implementing a trial of MDSC paradigm based on Kubernetes and Knative. Analysis and experimental results show that MDSC paradigm can achieve high level security with acceptable cost.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167299","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}
Path planning algorithm is the basis of unmanned ground platform to realize unmanned driving function. Traditional path planning algorithms mostly regard path planning as a geometric problem, which has great limitations on the work of unmanned platforms in the current complex environment. The reinforcement learning algorithm focuses on online planning and has the advantage of continuing to explore and find better solutions on the basis of effective actions. This paper studies path planning of unmanned ground platform based on reinforcement learning method. Aiming at the problems of low flexibility and slow convergence of the current reinforcement learning method in path planning, this paper improves the Q-learning algorithm based on the reinforcement learning algorithm and conducts simulation experiments and analyzes the experimental results. The analysis shows that the path planning algorithm of unmanned ground platform based on reinforcement learning has obvious advantages in performance.
{"title":"Research on path planning algorithm of unmanned ground platform based on reinforcement learning","authors":"Pei Zhang, Chengye Zhang, Weilong Gai","doi":"10.1117/12.2671690","DOIUrl":"https://doi.org/10.1117/12.2671690","url":null,"abstract":"Path planning algorithm is the basis of unmanned ground platform to realize unmanned driving function. Traditional path planning algorithms mostly regard path planning as a geometric problem, which has great limitations on the work of unmanned platforms in the current complex environment. The reinforcement learning algorithm focuses on online planning and has the advantage of continuing to explore and find better solutions on the basis of effective actions. This paper studies path planning of unmanned ground platform based on reinforcement learning method. Aiming at the problems of low flexibility and slow convergence of the current reinforcement learning method in path planning, this paper improves the Q-learning algorithm based on the reinforcement learning algorithm and conducts simulation experiments and analyzes the experimental results. The analysis shows that the path planning algorithm of unmanned ground platform based on reinforcement learning has obvious advantages in performance.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123452430","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}
Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability. For the sake to improve the accuracy of EV battery SOH prediction. Firstly, data structuring, PCA dimension reduction and data standardization were used to transform downloaded data into data that could be trained with high accuracy model. After that, the characteristic factors related to battery capacity were extracted from the battery charging data and correlation analysis was carried out. According to the method of Pearson coefficient, the features with strong correlation were left and then imported into the sample data. The factor parameters of SVR and other models were optimized by grid search algorithm, and the final prediction model was established. Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability.
{"title":"Battery health analysis of electric vehicle based on EL-SVR","authors":"Ling Zhong, X. Liu","doi":"10.1117/12.2671142","DOIUrl":"https://doi.org/10.1117/12.2671142","url":null,"abstract":"Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability. For the sake to improve the accuracy of EV battery SOH prediction. Firstly, data structuring, PCA dimension reduction and data standardization were used to transform downloaded data into data that could be trained with high accuracy model. After that, the characteristic factors related to battery capacity were extracted from the battery charging data and correlation analysis was carried out. According to the method of Pearson coefficient, the features with strong correlation were left and then imported into the sample data. The factor parameters of SVR and other models were optimized by grid search algorithm, and the final prediction model was established. Lithium-ion battery has become an indispensable energy storage component in our life because of its environmental protection and high energy characteristics. The battery SOH is the decisive factor to ensure its stability.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125016157","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}
Wenteng Liang, Shang Dai, Yizhen You, Kang Yang, Jianan Zhang, Tai Sun, Ruyi Li, Yue Zhang, linxi zou
In order to improve the accuracy of power dispatching text analysis and the ability to guide the operation of the power grid, a power dispatch text entity recognition method is proposed based on Bidirectional Encoder Representations from Transformers-Conditional Random Field (BERT-CRF). Taking the power grid fault handling plan text as the research object, the entity marking method of the fault handling plan is proposed. The word vector of the plan entity is calculated based on the BERT pre-training model, the characterization ability of the professional entity of the plan is enhanced by fine-tuning the initial BERT parameters, and the recognition ability of the plan text sequence is improved from the overall situation to access the CRF layer in the neural network. Thus, an entity recognition model of fault handling plan is established based on the BERT-CRF. Through the verification of a power grid fault handling plan, the proposed method has higher power dispatch entity and event recognition accuracy compared with other algorithms.
{"title":"The entity and event recognition method of power dispatching text information based on BERT-CRF","authors":"Wenteng Liang, Shang Dai, Yizhen You, Kang Yang, Jianan Zhang, Tai Sun, Ruyi Li, Yue Zhang, linxi zou","doi":"10.1117/12.2671453","DOIUrl":"https://doi.org/10.1117/12.2671453","url":null,"abstract":"In order to improve the accuracy of power dispatching text analysis and the ability to guide the operation of the power grid, a power dispatch text entity recognition method is proposed based on Bidirectional Encoder Representations from Transformers-Conditional Random Field (BERT-CRF). Taking the power grid fault handling plan text as the research object, the entity marking method of the fault handling plan is proposed. The word vector of the plan entity is calculated based on the BERT pre-training model, the characterization ability of the professional entity of the plan is enhanced by fine-tuning the initial BERT parameters, and the recognition ability of the plan text sequence is improved from the overall situation to access the CRF layer in the neural network. Thus, an entity recognition model of fault handling plan is established based on the BERT-CRF. Through the verification of a power grid fault handling plan, the proposed method has higher power dispatch entity and event recognition accuracy compared with other algorithms.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116046658","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}
Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the Generative Adversarial Network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model.
{"title":"GAN-based algorithm for efficient image inpainting","authors":"Zheng Han, Zehao Jiang, Yuan Ju","doi":"10.1117/12.2671788","DOIUrl":"https://doi.org/10.1117/12.2671788","url":null,"abstract":"Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the Generative Adversarial Network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116125058","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}
Diversity among the members of classifiers is deemed to be a key point in classifier ensemble. However, there doesn’t exist a widely accepted diversity measure and construct. In this paper, we propose a sample and feature double random construction of training sample variability. A support vector machine is used as the base classifier to construct the difference by distinguishing the regularization term C and the kernel function. Based on the negative correlation theory, the base classifier generalization error and disparity judgment functions are proposed, and the base classifier is integrated by ranking according to the judgment functions, which could achieve a higher accuracy rate by the support vector machine ensemble.
{"title":"Evaluate the performance of the support vector machines ensemble","authors":"Bowen Liu, Yihui Qiu","doi":"10.1117/12.2671159","DOIUrl":"https://doi.org/10.1117/12.2671159","url":null,"abstract":"Diversity among the members of classifiers is deemed to be a key point in classifier ensemble. However, there doesn’t exist a widely accepted diversity measure and construct. In this paper, we propose a sample and feature double random construction of training sample variability. A support vector machine is used as the base classifier to construct the difference by distinguishing the regularization term C and the kernel function. Based on the negative correlation theory, the base classifier generalization error and disparity judgment functions are proposed, and the base classifier is integrated by ranking according to the judgment functions, which could achieve a higher accuracy rate by the support vector machine ensemble.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"46 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116312198","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}
In recent years, stock price prediction has become a research hotspot. The price of the stock market is unstable, which often rises or falls sharply due to the national policies, which makes it difficult for investors to achieve stable returns in the stock market. With the rapid rise of artificial intelligence, computers have become flexible in dealing with mathematical problems. Therefore, the extraordinary computing power of computers has been used to analyze and predict the trend of the stock market. More and more computer professionals began to enter the financial market and use neural network to study the trend of the stock market. This paper uses BP neural network and LSTM neural network to learn and predict the stock data of Shanghai Composite Index from January 2012 to June 2022. LSTM is a kind of RNN, but it is superior to other neural networks. It can effectively deal with data forgetting and gradient explosion problems and bring reliability to the prediction results of the model. The two models are evaluated by analyzing MAE, MSE and the time required for model training. The results show that LSTM model can not only learn longer time span than BP model, but also better than BP model in MAE and MSE indexes, which provides some reference and guidance for the prediction of medium and long-term stocks.
{"title":"A comparative study of stock price prediction based on BP and LSTM neural network","authors":"Shujia Huang, Ben Wang, Lingbo Hao, Zebin Si","doi":"10.1117/12.2671216","DOIUrl":"https://doi.org/10.1117/12.2671216","url":null,"abstract":"In recent years, stock price prediction has become a research hotspot. The price of the stock market is unstable, which often rises or falls sharply due to the national policies, which makes it difficult for investors to achieve stable returns in the stock market. With the rapid rise of artificial intelligence, computers have become flexible in dealing with mathematical problems. Therefore, the extraordinary computing power of computers has been used to analyze and predict the trend of the stock market. More and more computer professionals began to enter the financial market and use neural network to study the trend of the stock market. This paper uses BP neural network and LSTM neural network to learn and predict the stock data of Shanghai Composite Index from January 2012 to June 2022. LSTM is a kind of RNN, but it is superior to other neural networks. It can effectively deal with data forgetting and gradient explosion problems and bring reliability to the prediction results of the model. The two models are evaluated by analyzing MAE, MSE and the time required for model training. The results show that LSTM model can not only learn longer time span than BP model, but also better than BP model in MAE and MSE indexes, which provides some reference and guidance for the prediction of medium and long-term stocks.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126628622","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}
When an abnormal flight occurs, if the previous flight cannot take off as planned, it will affect the subsequent flight, resulting in a downward impact. Therefore, airlines often adopt different recovery measures (including flight delays, flight cancellations, aircraft swaps, etc.) to eliminate or mitigate the downward impact. When evaluating the pros and cons of the recovery plan, the loss of delay, loss of flight cancellation and loss of aircraft exchange are generally considered. However, in fact, many complex factors are ignored when measuring these losses, such as food, transportation and accommodation costs of crew and passengers caused by flight delay, and compensation for delay, etc. Expert systems are suitable for situations where no or little data is available and the business logic is complex, and their introduction into flight disruption impact assessment is an exploration of artificial intelligence in civil aviation. The evaluation of the impact of flight disruptions by an expert system not only quantifies the benefits of recovery solutions, but also provides some reference for evaluating the advantages and disadvantages of existing models and algorithms.
{"title":"Flight disruption impact assessment based on expert system","authors":"Bingjie Liang, Fujun Wang, Jun Bi","doi":"10.1117/12.2671099","DOIUrl":"https://doi.org/10.1117/12.2671099","url":null,"abstract":"When an abnormal flight occurs, if the previous flight cannot take off as planned, it will affect the subsequent flight, resulting in a downward impact. Therefore, airlines often adopt different recovery measures (including flight delays, flight cancellations, aircraft swaps, etc.) to eliminate or mitigate the downward impact. When evaluating the pros and cons of the recovery plan, the loss of delay, loss of flight cancellation and loss of aircraft exchange are generally considered. However, in fact, many complex factors are ignored when measuring these losses, such as food, transportation and accommodation costs of crew and passengers caused by flight delay, and compensation for delay, etc. Expert systems are suitable for situations where no or little data is available and the business logic is complex, and their introduction into flight disruption impact assessment is an exploration of artificial intelligence in civil aviation. The evaluation of the impact of flight disruptions by an expert system not only quantifies the benefits of recovery solutions, but also provides some reference for evaluating the advantages and disadvantages of existing models and algorithms.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916251","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}
Face recognition has been widely used in daily life, but the existing model systems use processed high-quality datasets in training, while the face pictures in real scenes usually contain the influence of blurring, lighting, obscuring and other factors, thus making the existing face recognition models cannot perform well, and secondly, the existing face datasets have less data of Asian descent, resulting in the distribution learned by the models with the actual application. There is a certain error in the actual application. We propose a method to train face recognition models for realistic scenes by image augment of local face data to improve the classification accuracy of the models for low-quality images, and we demonstrate the feasibility of our method through experiments. Our method improves 0.619% and 0.414% in classifying images with added illumination and added random squares, respectively, compared to the current state-of-the-art methods.
{"title":"A training method for face representation models in realistic scenarios","authors":"C. Li","doi":"10.1117/12.2671250","DOIUrl":"https://doi.org/10.1117/12.2671250","url":null,"abstract":"Face recognition has been widely used in daily life, but the existing model systems use processed high-quality datasets in training, while the face pictures in real scenes usually contain the influence of blurring, lighting, obscuring and other factors, thus making the existing face recognition models cannot perform well, and secondly, the existing face datasets have less data of Asian descent, resulting in the distribution learned by the models with the actual application. There is a certain error in the actual application. We propose a method to train face recognition models for realistic scenes by image augment of local face data to improve the classification accuracy of the models for low-quality images, and we demonstrate the feasibility of our method through experiments. Our method improves 0.619% and 0.414% in classifying images with added illumination and added random squares, respectively, compared to the current state-of-the-art methods.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121467372","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}
Peipei Xu, Lianxiang Jiang, Bingui Xu, Mingxiang Li, Fei Wang
Low-cost, intelligence and short development cycle has become its trend of small satellites. A hybrid on-board avionics topology based on CAN bus and router was proposed. The telemetry was collected by On-Board Computer (OBC) via CAN bus, while the router integrated RS422, LVDS, Ethernet, Camera Link and TLK2711 interfaces, which support data rate varying from 1Mbps to 10Gbps and usually used by payloads, so it makes regular payloads integrated into the avionics much easier. The OBC used the PowerPC MPC8548 processor, which run at 1GHz. Plug and play mechanism was adopted to make the OBC recognize the devices dynamically when they powered on, which accelerated the system integration; furthermore, the software modules were also allowed to install or uninstall dynamically on-line for flexibility. For the modular and various interfaces supported, payload modules such as GNSS-R receiver, ADS-B receiver and camera electronics was easily integrated into the avionics box, so the signaling were transferred via the backplane instead of cables.
{"title":"Highly integrated modular avionics from platform to payload for micro-satellites","authors":"Peipei Xu, Lianxiang Jiang, Bingui Xu, Mingxiang Li, Fei Wang","doi":"10.1117/12.2671425","DOIUrl":"https://doi.org/10.1117/12.2671425","url":null,"abstract":"Low-cost, intelligence and short development cycle has become its trend of small satellites. A hybrid on-board avionics topology based on CAN bus and router was proposed. The telemetry was collected by On-Board Computer (OBC) via CAN bus, while the router integrated RS422, LVDS, Ethernet, Camera Link and TLK2711 interfaces, which support data rate varying from 1Mbps to 10Gbps and usually used by payloads, so it makes regular payloads integrated into the avionics much easier. The OBC used the PowerPC MPC8548 processor, which run at 1GHz. Plug and play mechanism was adopted to make the OBC recognize the devices dynamically when they powered on, which accelerated the system integration; furthermore, the software modules were also allowed to install or uninstall dynamically on-line for flexibility. For the modular and various interfaces supported, payload modules such as GNSS-R receiver, ADS-B receiver and camera electronics was easily integrated into the avionics box, so the signaling were transferred via the backplane instead of cables.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"188 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120830357","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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