Pub Date : 2021-05-12DOI: 10.1504/IJCSE.2021.115112
B. D. Martino, Maria Graziano
Architectural patterns, a concept devised by the Viennese architect Christopher Alexander, have inspired the world of patterns, especially in software engineering. Two objectives have been addressed in this work: to realise a semantic representation of Alexander's patterns by developing an OWL ontology, and to devise a rule-based system for discovering the patterns into a concrete building model, represented in IFC format, one of the pillars of the BIM - an approach now widely spread in the architectural and civil engineering design world. This paper shows how semantic and logical inference rules have been applied to discover Alexander's pattern on a generic IFC model of a building.
{"title":"Semantic techniques for discovering architectural patterns in building information models","authors":"B. D. Martino, Maria Graziano","doi":"10.1504/IJCSE.2021.115112","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115112","url":null,"abstract":"Architectural patterns, a concept devised by the Viennese architect Christopher Alexander, have inspired the world of patterns, especially in software engineering. Two objectives have been addressed in this work: to realise a semantic representation of Alexander's patterns by developing an OWL ontology, and to devise a rule-based system for discovering the patterns into a concrete building model, represented in IFC format, one of the pillars of the BIM - an approach now widely spread in the architectural and civil engineering design world. This paper shows how semantic and logical inference rules have been applied to discover Alexander's pattern on a generic IFC model of a building.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916683","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115105
S. Kajkamhaeng, C. Phongpensri
Convolutional neural networks have been popularly used for image recognition tasks. It is known that deep convolutional neural network can yield high recognition accuracy while training it can be very time-consuming. AlexNet was one of the very first networks shown to be effective for the tasks. However, due to its large kernel sizes and fully connected layers, the training time is significant. SqueezeNet has been known as smaller network that yields the same performance as AlexNet. Based on SqueezeNet, we are interested in exploring the effective insertion of the squeeze-and-excitation (SE) module into SqueezeNet that can further improve the performance and cost efficiency. The promising methodology and pattern of module insertion have been explored. The experimental results for evaluating the module insertion show the improvement on top1 accuracy by 1.55% and 3.32% while the model size is enlarged by up to 16% and 10% for CIFAR100 and ILSVRC2012 datasets respectively.
{"title":"SE-SqueezeNet: SqueezeNet extension with squeeze-and-excitation block","authors":"S. Kajkamhaeng, C. Phongpensri","doi":"10.1504/IJCSE.2021.115105","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115105","url":null,"abstract":"Convolutional neural networks have been popularly used for image recognition tasks. It is known that deep convolutional neural network can yield high recognition accuracy while training it can be very time-consuming. AlexNet was one of the very first networks shown to be effective for the tasks. However, due to its large kernel sizes and fully connected layers, the training time is significant. SqueezeNet has been known as smaller network that yields the same performance as AlexNet. Based on SqueezeNet, we are interested in exploring the effective insertion of the squeeze-and-excitation (SE) module into SqueezeNet that can further improve the performance and cost efficiency. The promising methodology and pattern of module insertion have been explored. The experimental results for evaluating the module insertion show the improvement on top1 accuracy by 1.55% and 3.32% while the model size is enlarged by up to 16% and 10% for CIFAR100 and ILSVRC2012 datasets respectively.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125596939","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115099
Kanu Goel, Shalini Batra
In machine learning and predictive analytics, the underlying data distributions tend to change with the course of time known as concept drift. Accurate labelling in case of supervised learning algorithms is essential to build consistent ensemble models. However, several real-world applications suffer from drifting data concepts which leads to deterioration in the performance of prediction systems. To tackle these challenges, we study various concept drift handling approaches which identify major types of drift patterns such as abrupt, gradual, and recurring in drifting data streams. This study also highlights the need for adaptive algorithms and demonstrates comparison of various state-of-the-art drift handling techniques by analysing their classification accuracy on artificially generated drifting data streams and real datasets.
{"title":"Adaptive online learning for classification under concept drift","authors":"Kanu Goel, Shalini Batra","doi":"10.1504/IJCSE.2021.115099","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115099","url":null,"abstract":"In machine learning and predictive analytics, the underlying data distributions tend to change with the course of time known as concept drift. Accurate labelling in case of supervised learning algorithms is essential to build consistent ensemble models. However, several real-world applications suffer from drifting data concepts which leads to deterioration in the performance of prediction systems. To tackle these challenges, we study various concept drift handling approaches which identify major types of drift patterns such as abrupt, gradual, and recurring in drifting data streams. This study also highlights the need for adaptive algorithms and demonstrates comparison of various state-of-the-art drift handling techniques by analysing their classification accuracy on artificially generated drifting data streams and real datasets.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131499574","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115091
Sheng Liu, Jing Zhao, Yu Zhang
In this paper, a two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm with two parallel nested arrays is developed. Firstly, a constructor method for fourth-order cumulant (FOC) matrices is given according to the distribution of sensors. Then a pre-existing DOA estimation technique is firstly used to estimate the elevation angles and an improved unilateral array manifold matching (AMM) algorithm is used to estimate the azimuth angles. Compared with some classical 2D DOA estimation algorithms, the proposed algorithm has much better estimation performance, particularly in the case of low SNR environment. Compared with some traditional FOC-based algorithms, the proposed algorithm has higher estimation precision. Simulation results can illustrate the validity of proposed algorithm.
{"title":"Array manifold matching algorithm based on fourth-order cumulant for 2D DOA estimation with two parallel nested arrays","authors":"Sheng Liu, Jing Zhao, Yu Zhang","doi":"10.1504/IJCSE.2021.115091","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115091","url":null,"abstract":"In this paper, a two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm with two parallel nested arrays is developed. Firstly, a constructor method for fourth-order cumulant (FOC) matrices is given according to the distribution of sensors. Then a pre-existing DOA estimation technique is firstly used to estimate the elevation angles and an improved unilateral array manifold matching (AMM) algorithm is used to estimate the azimuth angles. Compared with some classical 2D DOA estimation algorithms, the proposed algorithm has much better estimation performance, particularly in the case of low SNR environment. Compared with some traditional FOC-based algorithms, the proposed algorithm has higher estimation precision. Simulation results can illustrate the validity of proposed algorithm.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165506","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115103
R. G. Tambe, S. Talbar, S. Chavan
This paper presents two satellite image fusion algorithms namely decimated/subsampled rotated wavelet transform (SSRWT) and undecimated/non-subsampled rotated wavelet transform (NSRWT) using 2D rotated wavelet filters for extracting relevant and pragmatic information from MS and PAN images. Three major visual artefacts such as colour distortion, shifting effects and shift distortion are identified in the fused images obtained using SSRWT which are addressed by using NSRWT. The proposed NSRWT algorithm preserves spatial and spectral features of the source MS and PAN images resulting fused image with better fusion performance. The final fused image provides richer information (in terms of spatial and spectral quality) than that of the original input images. The experimental results strongly reveal that undecimated fusion algorithm (NSRWT) not only performs better than decimated fusion algorithm (SSRWT) but also improves spatial and spectral quality of the fused images.
{"title":"Satellite image fusion using undecimated rotated wavelet transform","authors":"R. G. Tambe, S. Talbar, S. Chavan","doi":"10.1504/IJCSE.2021.115103","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115103","url":null,"abstract":"This paper presents two satellite image fusion algorithms namely decimated/subsampled rotated wavelet transform (SSRWT) and undecimated/non-subsampled rotated wavelet transform (NSRWT) using 2D rotated wavelet filters for extracting relevant and pragmatic information from MS and PAN images. Three major visual artefacts such as colour distortion, shifting effects and shift distortion are identified in the fused images obtained using SSRWT which are addressed by using NSRWT. The proposed NSRWT algorithm preserves spatial and spectral features of the source MS and PAN images resulting fused image with better fusion performance. The final fused image provides richer information (in terms of spatial and spectral quality) than that of the original input images. The experimental results strongly reveal that undecimated fusion algorithm (NSRWT) not only performs better than decimated fusion algorithm (SSRWT) but also improves spatial and spectral quality of the fused images.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145535","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115102
Jingzhao Li, Tengfei Li
There are many hidden safety hazards in mine ventilation process, which cannot be dealt with in time. It is because the type of coal mine and its mining conditions are complex and changeable, and the safety decision-making level is low when coal mine ventilation is abnormal. To solve these problems, this paper presents a decision system for scene ventilation safety based on intelligent perception and decision. First, grey correlation analysis and rough set theory are used to reduce the decision table horizontally and vertically. Then, the reduced data is input into the mine ventilation safety decision model based on the improved capsule network to make ventilation safety decision. Experimental results show that this system can significantly improve the accuracy of mine ventilation safety decision, has the characteristics of strong information perception ability and accurate decision, and provides an important guarantee for mine ventilation safety.
{"title":"A decision system based on intelligent perception and decision for scene ventilation safety","authors":"Jingzhao Li, Tengfei Li","doi":"10.1504/IJCSE.2021.115102","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115102","url":null,"abstract":"There are many hidden safety hazards in mine ventilation process, which cannot be dealt with in time. It is because the type of coal mine and its mining conditions are complex and changeable, and the safety decision-making level is low when coal mine ventilation is abnormal. To solve these problems, this paper presents a decision system for scene ventilation safety based on intelligent perception and decision. First, grey correlation analysis and rough set theory are used to reduce the decision table horizontally and vertically. Then, the reduced data is input into the mine ventilation safety decision model based on the improved capsule network to make ventilation safety decision. Experimental results show that this system can significantly improve the accuracy of mine ventilation safety decision, has the characteristics of strong information perception ability and accurate decision, and provides an important guarantee for mine ventilation safety.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132847139","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 : 2021-05-12DOI: 10.1504/IJCSE.2021.115100
Shu-Juan Peng, Liang Zhang, Xin Liu
Skeletal motion transition is of crucial importance to the animation creation. In this paper, we propose a hybrid deep learning framework that allows for efficient human motion transition. First, we integrate a convolutional restricted Boltzmann machine with deep belief network to extract the spatio-temporal features of each motion style, featuring on appropriate detection of transition points. Then, a quadruples-like data structure is exploited for motion graph building, motion splitting and indexing. Accordingly, the similar frames fulfilling the transition segments can be efficiently retrieved. Meanwhile, the transition length is reasonably computed according to the average speed of the motion joints. As a result, different kinds of diverse motions can be well transited with satisfactory performance. The experimental results show that the proposed transition approach brings substantial improvements over the state-of-the-art methods.
{"title":"Flexible human motion transition via hybrid deep neural network and quadruple-like structure learning","authors":"Shu-Juan Peng, Liang Zhang, Xin Liu","doi":"10.1504/IJCSE.2021.115100","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115100","url":null,"abstract":"Skeletal motion transition is of crucial importance to the animation creation. In this paper, we propose a hybrid deep learning framework that allows for efficient human motion transition. First, we integrate a convolutional restricted Boltzmann machine with deep belief network to extract the spatio-temporal features of each motion style, featuring on appropriate detection of transition points. Then, a quadruples-like data structure is exploited for motion graph building, motion splitting and indexing. Accordingly, the similar frames fulfilling the transition segments can be efficiently retrieved. Meanwhile, the transition length is reasonably computed according to the average speed of the motion joints. As a result, different kinds of diverse motions can be well transited with satisfactory performance. The experimental results show that the proposed transition approach brings substantial improvements over the state-of-the-art methods.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131440173","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 : 2021-03-03DOI: 10.1504/IJCSE.2021.113633
Yang Yong, Li Jing, Zhang Jing, Liu Yang, Zhao Li, Guo Ruxue
Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydro-chemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source types online, and further to strive to create more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on light gradient boosting machine (LightGBM). This method combined light gradient boosting (GB) with the decision tree (DT) to improve the network integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO3 components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective way to recognise source types of water in a mine online.
{"title":"Application of light gradient boosting machine in mine water inrush source type online discriminant","authors":"Yang Yong, Li Jing, Zhang Jing, Liu Yang, Zhao Li, Guo Ruxue","doi":"10.1504/IJCSE.2021.113633","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.113633","url":null,"abstract":"Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydro-chemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source types online, and further to strive to create more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on light gradient boosting machine (LightGBM). This method combined light gradient boosting (GB) with the decision tree (DT) to improve the network integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO3 components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective way to recognise source types of water in a mine online.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121856979","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 : 2021-03-03DOI: 10.1504/IJCSE.2021.10036006
Abhilasha Chaudhuri, T. P. Sahu
Naive Bayes (NB) is a widely used classifier in the field of machine learning. However, its conditional independence assumption does not hold true in real-world applications. In literature, various feature weighting approaches have attempted to alleviate this assumption. Almost all of these approaches consider the relationship between feature-class (relevancy) and feature-feature (redundancy) independently, to determine the weights of features. We argue that these two relationships are mutually dependent and both cannot be improved simultaneously, i.e., form a trade-off. This paper proposes a new paradigm to determine the feature weight by formulating it as a multi-objective optimisation problem to balance the trade-off between relevancy and redundancy. Multi-objective artificial bee colony-based feature weighting technique for naive Bayes (MOABC-FWNB) is proposed. An extensive experimental study was conducted on 20 benchmark UCI datasets. Experimental results show that MOABC-FWNB outperforms NB and other existing state-of-the-art feature weighting techniques.
{"title":"Feature weighting for naïve Bayes using multi objective artificial bee colony algorithm","authors":"Abhilasha Chaudhuri, T. P. Sahu","doi":"10.1504/IJCSE.2021.10036006","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.10036006","url":null,"abstract":"Naive Bayes (NB) is a widely used classifier in the field of machine learning. However, its conditional independence assumption does not hold true in real-world applications. In literature, various feature weighting approaches have attempted to alleviate this assumption. Almost all of these approaches consider the relationship between feature-class (relevancy) and feature-feature (redundancy) independently, to determine the weights of features. We argue that these two relationships are mutually dependent and both cannot be improved simultaneously, i.e., form a trade-off. This paper proposes a new paradigm to determine the feature weight by formulating it as a multi-objective optimisation problem to balance the trade-off between relevancy and redundancy. Multi-objective artificial bee colony-based feature weighting technique for naive Bayes (MOABC-FWNB) is proposed. An extensive experimental study was conducted on 20 benchmark UCI datasets. Experimental results show that MOABC-FWNB outperforms NB and other existing state-of-the-art feature weighting techniques.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125084001","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 : 2021-03-03DOI: 10.1504/IJCSE.2021.113634
Han Zhang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie, Shixiong Zhu
The autonomous decision-making capability of unmanned surface vehicles (USV) is the basis for many tasks. Most of the works ignore the variability of the scene. For example, traditional decision-making methods are not adaptable to changing weather that a USV is likely to encounter. In order to solve the low adaptability problem of a USV using single decision model in changing weather, we propose an adaptive model of USV based on human memory cognitive process. The USV first stores the perceived weather features in sensory memory. Then, it combines weather characteristics with prior knowledge to classify the weather in perceptual associative memory. Finally, USV calls different decision models stored in long-term memory based on the current weather category to make the decision. Simulated experiments are carried out on USV obstacle avoidance decision task in Unity3D. Experiments show that our model performs better than using only a single decision model.
{"title":"Unmanned surface vehicle adaptive decision model for changing weather","authors":"Han Zhang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie, Shixiong Zhu","doi":"10.1504/IJCSE.2021.113634","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.113634","url":null,"abstract":"The autonomous decision-making capability of unmanned surface vehicles (USV) is the basis for many tasks. Most of the works ignore the variability of the scene. For example, traditional decision-making methods are not adaptable to changing weather that a USV is likely to encounter. In order to solve the low adaptability problem of a USV using single decision model in changing weather, we propose an adaptive model of USV based on human memory cognitive process. The USV first stores the perceived weather features in sensory memory. Then, it combines weather characteristics with prior knowledge to classify the weather in perceptual associative memory. Finally, USV calls different decision models stored in long-term memory based on the current weather category to make the decision. Simulated experiments are carried out on USV obstacle avoidance decision task in Unity3D. Experiments show that our model performs better than using only a single decision model.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115712991","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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