自主智能(英文)最新文献_第10页

Principle of Machine Learning and Its Potential Application in Cli-mate Prediction 机器学习原理及其在气候预测中的潜在应用

自主智能(英文) Pub Date : 2021-03-11 DOI: 10.32629/jai.v4i1.483

Shengping He, Huijun Wang, Hua Li, Jiazhen Zhao

{"title":"Principle of Machine Learning and Its Potential Application in Cli-mate Prediction","authors":"Shengping He, Huijun Wang, Hua Li, Jiazhen Zhao","doi":"10.32629/jai.v4i1.483","DOIUrl":"https://doi.org/10.32629/jai.v4i1.483","url":null,"abstract":"After two “cold winters of artificial intelligence”, machine learning has once again entered the public’s vision in recent ten years, and has a momentum of rapid development. It has achieved great success in practical applications such as image recognition and speech recognition system. It is one of the main tasks and objectives of machine learning to summarize key information and main features from known data sets, so as to accurately identify and predict new data. From this perspective, the idea of integrating machine learning into climate prediction is feasible. Firstly, taking the adjustment of linear fitting parameters (i.e. slope and intercept) as an example, this paper introduces the process of machine learning optimizing parameters through gradient descent algorithm and finally obtaining linear fitting function. Secondly, this paper introduces the construction idea of neural network and how to apply neural network to fit nonlinear function. Finally, the framework principle of convolutional neural network for deep learning is described, and the convolutional neural network is applied to the return test of monthly temperature in winter in East Asia, and compared with the return results of climate dynamic model. This paper will help to understand the basic principle of machine learning and provide some reference ideas for the application of machine learning to climate prediction.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44704163","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}

引用次数: 0

Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic 基于模糊逻辑的乌尔都罗马语文本自动摘要

自主智能(英文) Pub Date : 2021-01-01 DOI: 10.32629/jai.v3i2.273

Zeshan Ali

{"title":"Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic","authors":"Zeshan Ali","doi":"10.32629/jai.v3i2.273","DOIUrl":"https://doi.org/10.32629/jai.v3i2.273","url":null,"abstract":"In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69960400","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}

引用次数: 1

A Fractional PID Controller Based on Particle Swarm Optimization Algorithm 基于粒子群优化算法的分数阶PID控制器

自主智能(英文) Pub Date : 2020-06-18 DOI: 10.32629/jai.v3i1.94

Yinglei Song

引用次数: 7

A Study of Neural Machine Translation from Chinese to Urdu 神经机器翻译汉译乌研究

自主智能(英文) Pub Date : 2020-05-06 DOI: 10.32629/jai.v2i4.82

Zeeshan Khan

引用次数: 5

Smart Outlet: Smart Electrical Outlet With Device Identification Using NFC 智能插座：使用NFC进行设备识别的智能插座

自主智能(英文) Pub Date : 2020-03-31 DOI: 10.32629/jai.v2i4.81

S. H. M. S. Andrade, J. M. Alves, Johan S. L. Barbosa, Rafaela R. Souza

引用次数: 2

Data Analytics to Increase Performance in the Human Resources Area 数据分析提高人力资源领域的绩效

自主智能(英文) Pub Date : 2020-03-31 DOI: 10.32629/jai.v2i4.80

S. H. M. S. Andrade

引用次数: 1

Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning 基于深度学习的运动图像脑电信号关键技术研究

自主智能(英文) Pub Date : 2020-02-24 DOI: 10.32629/jai.v2i4.60

Zhuozheng Wang, Zhuo Ma, Du Xiuwen, Yingjie Dong, Wei Liu

{"title":"Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning","authors":"Zhuozheng Wang, Zhuo Ma, Du Xiuwen, Yingjie Dong, Wei Liu","doi":"10.32629/jai.v2i4.60","DOIUrl":"https://doi.org/10.32629/jai.v2i4.60","url":null,"abstract":"Brain-computer interface (BCI) is an emerging area of research that establishes a connection between the brain and external devices in a completely new way. It provides a new idea about the rehabilitation of brain diseases, human-computer interaction and augmented reality. One of the main problems of implementing BCI is to recognize and classify the motor imagery Electroencephalography(EEG) signals effectively. This paper takes the motor imagery feature data of EEG as the research object to conduct the research of multi-classification method. In this study, we use the Emotiv helmet with 16 biomedical sensors to obtain EEG signal, adopt the fast independent component analysis and the fast Fourier transform to realize signal preprocessing and select the common spatial pattern algorithm to extract the features of the motor imagery EEG signal. In order to improve the accuracy of recognition of EEG signal, a new deep learning network is designed for multi-channel self-acquired EEG data set which is named as min-VGG-LSTMnet in this paper. The network combines Long Short-Term Memory Network with convolutional neural network VGG and achieves the four-class task of the left-hand, right-hand, left-foot and right-foot lifting movements based on motor imagery. The results show that the accuracy of the proposed classification method is at least 8.18% higher than other mainstream deep learning methods.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48127880","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}

引用次数: 6

Outdoor temperature estimation using ANFIS for soft sensors 使用ANFIS对软传感器进行室外温度估计

自主智能(英文) Pub Date : 2019-12-04 DOI: 10.32629/jai.v2i3.58

Zahra Pezeshki, S. M. Mazinani, E. Omidvar

{"title":"Outdoor temperature estimation using ANFIS for soft sensors","authors":"Zahra Pezeshki, S. M. Mazinani, E. Omidvar","doi":"10.32629/jai.v2i3.58","DOIUrl":"https://doi.org/10.32629/jai.v2i3.58","url":null,"abstract":"In recent years, several studies using smart methods and soft computing in the field of HVAC systems has been provided. In this paper, we propose a framework which will strengthen the benefits of the fuzzy logic and neural fuzzy systems to estimate outdoor temperature. In this regard, ANFIS is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The neural fuzzy system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43882392","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}

引用次数: 2

Pedestrian Detection in Driver Assistance Using SSD and PS-GAN 基于SSD和PS-GAN的驾驶辅助行人检测

自主智能(英文) Pub Date : 2019-12-03 DOI: 10.32629/jai.v2i3.57

Zheng Kun, Mengfei Wei, Li Shenhui, Dong Yang, Xudong Liu

{"title":"Pedestrian Detection in Driver Assistance Using SSD and PS-GAN","authors":"Zheng Kun, Mengfei Wei, Li Shenhui, Dong Yang, Xudong Liu","doi":"10.32629/jai.v2i3.57","DOIUrl":"https://doi.org/10.32629/jai.v2i3.57","url":null,"abstract":"Pedestrian detection is a critical challenge in the field of general object detection, the performance of object detection has advanced with the development of deep learning. However, considerable improvement is still required for pedestrian detection, considering the differences in pedestrian wears, action, and posture. In the driver assistance system, it is necessary to further improve the intelligent pedestrian detection ability. We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection. Firstly, we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics. Secondly, we propose a novel network architecture, namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector. Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent. At last, we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle. The results establish the validity of the approach.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41477706","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}

引用次数: 0

Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment 强化学习环境下智能体通信的组合基础语言

自主智能(英文) Pub Date : 2019-11-15 DOI: 10.32629/jai.v2i3.56

K. Lannelongue, M. Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt

{"title":"Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment","authors":"K. Lannelongue, M. Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt","doi":"10.32629/jai.v2i3.56","DOIUrl":"https://doi.org/10.32629/jai.v2i3.56","url":null,"abstract":"In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41951841","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}

引用次数: 1