检索结果-内蒙古大学图书馆

Electronic Information technology and Smart agriculture (ICEITSA), International Conference on

作者： Xiangkai Xu Xuan Yang Yuan Huang Yingjing Qian Yibo Niu College of Information Science and Engineering Jishou University Jishou Hunan P.R. China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665413015

With the development of drone technology, carrying cameras, Lidar and other sensors has gradually become a hot research topic in recent years. However, at this stage, the dron e power grid inspection mainly relies on manual, requires staff to spend a lot of time to shoot, and because the manual operating standards are not uniform, for the follow-up inspection dat a processing has brought great inconvenience. Therefore, base d on the image-speed-angle speed three-stage PID control algo rithm, the autonomous patrol drone is designed. The system o btains position feedback and foreign object information throu gh OpenMV camera, and then obtains the data input image P D controller, the controller output expects speed to angle serial controller, and finally the output is fed back to the drone mot or to achieve the purpose of the patrol line, and at the same ti me, the foreign object recognition algorithm is designed to real ize the identification of the cable foreign body in the process of patrol. The experimental results show that the stability and fo reign object recognition accuracy are high in the course of dro ne patrol.

关键词： PI control Stability criteria Process control Inspection Cameras Sensors Object recognition

来源：评论

学校读者我要写书评

暂无评论

Improved Low Complexity Sum-Rate Maximization Algorithm for Multiuser mmWave Massive MIMO Systems

Improved Low Complexity Sum-Rate Maximization Algorithm for ...

引用

Electronic Information technology and Smart agriculture (ICEITSA), International Conference on

作者： Jian Liu Yuan Huang Xuan Yang Yingjing Qian Renmin Zhang College of Information Science and Engineering Jishou University Jishou Hunan P.R. China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665413015

In the multiuser millimeter wave (mmWave) massive multi-input multi-output (MIMO) system, a key factor causing the system sum-rate change is its interference plus noise. Based on this, on the premise that both the base station (BS) and the user side adopt the hybrid beamforming architecture and the downlink channel is the ideal channel, an improved low complexity hybrid beamforming grouping sum-rate maximization algorithm (HBG-SRM) is proposed, which aims to optimize the sum-rate in multiuser scenarios. The algorithm first predefines a relevant threshold value, which is used to group multiusers, then uses the maximum likelihood (ML) criterion to identify the optimal beam and estimate its beamforming gain within each group. Finally, the user compares all the candidate optimal beams gain within the divided group to confirm this the optimal beam. The simulation consequences also verify the superiority of the proposed algorithm's sum-rate over other algorithms.

关键词： Maximum likelihood estimation Base stations Array signal processing Millimeter wave technology Interference Massive MIMO Downlink

来源：评论

学校读者我要写书评

暂无评论

Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network 6th

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

引用

6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Lin, Jing Mi, Chunqiao School of Computer Science and Engineering Huaihua University HuaihuaHunan418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities HuaihuaHunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province HuaihuaHunan418000 China

ISBN: (纸本)9789811579837

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

intelligent control of Agricultural Irrigation Based on Reinforcement Learning

Intelligent Control of Agricultural Irrigation Based on Rein...

引用

作者： Ni Zhou Huaihua College Hunan Provincial Key Laboratory of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area

In the traditional agricultural irrigation control methods,flood irrigation and manual control are generally used to irrigate the land,and the effective utilization rate of water is only 20%-35%.With the advancement and development of science and technology,especially with the rapid development and application of sensor technology,wireless communication technology,reinforcement learning and deep learning technology,and intelligent terminals,intelligent control of agricultural irrigation integrating these high and new technologies has been adopted to improve water resources in agricultural *** utilization efficiency has become an inevitable trend and fundamental requirement for the development of precision agriculture and facility *** paper proposes an intelligent control method for agricultural irrigation based on reinforcement *** constructing a deep learning network to extract features from the raw sensor data and construct Q-learning features,using deep reinforcement learning powerful data learning capabilities,the precision of agricultural irrigation control can be effectively *** effectiveness of this method is verified by algorithm training and testing in a greenhouse plantation of a company in hunan.

关键词： intelligent control of Agricultural Irrigation Based on Reinforcement Learning

来源：评论

学校读者我要写书评

暂无评论

A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

引用

Computers, Materials & Continua 2020年第9期64卷 1959-1975页

作者： Dunhong Yao Shijun Li Ang Li Yu Chen School of Computer Science and Engineering Huaihua UniversityHuaihua418000China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China School of Computer Wuhan UniversityWuhan430072China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA School of Computer Science and Engineering Yulin Normal UniversityYulin537000China

There is no reasonable scientific basis for selecting the excellent teachers of the school’s *** solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of ***,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation ***,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation ***,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse *** results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching *** recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse *** proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.

关键词： Highly sparse dataset normalized models teaching recommendation factorization machines excellent teacher recommendation

来源：评论

学校读者我要写书评

暂无评论

Research on Crop Disaster Stress Risk Mapping System Based on agriculture Big Data

Research on Crop Disaster Stress Risk Mapping System Based o...

引用

Electronic Information technology and Smart agriculture (ICEITSA), International Conference on

作者： Chunqiao Mi Xiaoning Peng Ling Peng Changhua Zhao Xiaowu Deng School of Computer Science and Engineering Huaihua University Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua University Huaihua Hunan China

ISBN: (纸本)9781665413015

At present, the research on crop disaster risk is mostly carried out from the perspective of numerical simulation in one factor aspect, and there is a lack of information system practice that can integrate disaster spatio-temporal analysis technologies such as environment characteristic information extraction, geographic data scale transformation, spatial analysis and map visualization. Based on agriculture big data and highlighting the spatial characteristics of crop disasters, in this study a method of crop disaster risk mapping is designed and a prototype system is implemented, which function is composed of agriculture big data cleaning, scale conversion, multi-source data fusion, feature information extraction, risk probability prediction and map visualization. A specific application of the system is also demonstrated by taking the production of thematic map of wind-induced lodging risk probability distribution during maize jointing and tasseling stage as an example, which shows great help for avoiding maize lodging risk in agriculture practice. The research results have a good role in improving the technical level of agriculture disaster mapping and analysis, and improving the efficiency and effect of disaster prevention and reduction in agriculture production. It has important academic significance for promoting the application of big data in agriculture disaster management and establishing a new perspective of crop disaster mapping, and has important practice application value for optimizing the fine zoning of crop planting area and strengthening the accurate management of agriculture disaster risk.

关键词： Crops Data visualization Prototypes Production Big Data Information retrieval agriculture

来源：评论

学校读者我要写书评

暂无评论

High Sensitivity Optical Sensor Based on Periodic Grating Waveguide Structure for Real-time Sensing

High Sensitivity Optical Sensor Based on Periodic Grating Wa...

引用

Electronic Information technology and Smart agriculture (ICEITSA), International Conference on

作者： Lijun Huang Yangming Shi Da Lei Dajiang He Xianwu Mi Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province School of Electrical and Information Engineering Huaihua University Huaihua China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9781665413015

Compared with the strip waveguide micro-ring resonator (MRR), periodic grating waveguide (PGW) MRR can achieve a large light-matter interaction. However, it is very difficult to achieve the merit when the size is further reduced due to optical absorption and radiation losses, and inconvenient fabrication. Here, we experimentally demonstrate a high sensitivity transverse electric (TE) PGW-MRR. In order to obtain a better performance, we theoretically and experimentally analyze the feature size of proposed structure, including the width and the duty cycle of silicon blocks, the gap between the MRR and the bus waveguide. Then, a proper parameter is selected. The results experimentally indicate that the maximum extinction ratio of 14.8 dB is achieved. Finally, a different concentration Glycerol in solution is employed to test the sensing performance of the proposed PWG-MRR, and the monitoring results demonstrate that a quality factor (Q) of 8650 and sensitivity (S) of ∼390.5 nm/RIU are achieved, thus showing a potential for ecological agriculture sensing applications in lab-on-Chip system.

关键词： Q-factor Strips Sensitivity Optical resonators Waveguide theory Extinction ratio Gratings

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Student Learning Performance Prediction based on RBF Neural Network

引用

International Journal of Performability Engineering 2019年第6期15卷 1560-1569页

作者： Mi, Chunqiao School of Computer Science and Engineering Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

With the expansion of college enrollment in recent years, the quality of students' learning is beginning to decline. At present, education quality governance has become the internal demand of the reform and development of higher education. Learning performance prediction is an important means to effectively resolve the academic crisis and improve the overall education quality. In this study, firstly, the current status and problems about learning performance prediction were analyzed from the perspective of basic data, evaluation indicators, and prediction methods. Secondly, driven by ten items of basic learning situation data, a learning performance prediction model based on the RBF neural network was established, which included three layers in network topology: the input layer, hidden layer, and output layer. The activation functions of the hidden layer and output layer were a Gauss radial basis function and linear function, respectively. The modeling process included three steps: forward propagation computing prediction loss, error backward propagation adjusting network parameters, and network optimization determining model hyperparameters. The obtained results showed that the trained model had small relative root mean square error values for both the training data and testing data. When comparing the original observation values and model predicted values, it was observed that most of the sample points were evenly distributed on both sides of the diagonal line of the contrast graph, which indicates that the RBF neural network model employed in this study is promising in learning performance prediction. It is of good reference significance for promoting more accurate and efficient learning performance prediction and improving the efficiency and effectiveness of education quality governance. © 2019 Totem Publishers Ltd. All rights reserved.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Statistical Movement Classification based on Gram-Schmidt Transform

Statistical Movement Classification based on Gram-Schmidt Tr...

引用

Electronic Information technology and Smart agriculture (ICEITSA), International Conference on

作者： Changhua Zhao Chunqiao Mi Yuanquan Shi School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665413015

In this work, a technique for automatic classification of finger motion signals was introduced by using Gram-Schmidt orthonormalization. Because the motion signal has nonlinear and non-stationary processing characteristics, the Gram-Schmidt orthonormalization method is used to analyze the motion signal. By batch processing the high correlation between variables, the classification results are obtained. At the same time, combined with the idea of partial least squares, a new identity measure is proposed to select the best parameters. Finally, SVM is used to classify the movement types of the research objects. According to the test sample records of two finger motion signals (thumb and pinky), the average accuracy is 96.65%. The average accuracy of data activation (thumb and pinky) was 100%. Experiments show that the operation speed of Gram-Schmidt classification method is significantly improved, and it has better interpretability and prediction ability.

关键词： Support vector machines Dimensionality reduction Thumb Transforms Predictive models Time measurement Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Student performance early warning based on data mining

引用

International Journal of Performability Engineering 2019年第3期15卷 822-833页

Student performance in higher education is related to many complicated factors and always has uncertainty, so early warning of it is a very difficult issue. In this study, a systematic review was first carried out on student performance prediction and early warning using data mining techniques, including basic data sources, evaluating factors, predicting methods, application tools, and practices. Then, insufficiencies of the related studies were discussed, including incomprehensive source data, inadaptable and unspecialized calculation methods, and lack of integrated methodology systems in practice. Finally, a solution design was proposed, consisting of learning situation big data, a systematic early warning model, and an integrated information support system. Preliminary experiment results showed that it could identify at-risk students in a timely manner and improve the overall efficiency and effectiveness of early warning education management in practice, so it is of both academic and practical significance in promoting the deep integration of information technology and early warning education. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：