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Study on prediction model of grain post-harvest loss

Procedia computer science 2017年 122卷 122-129页

作者： Hanxiao Yu Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

With the arrival of the information age, a great deal of data has been produced in a series of process of grain post-harvest. The rational use of these data allows us to obtain more intelligent, in-depth and valuable information. In this paper, we set up a variety of prediction models for the consumption of grain post-harvest loss, and select the appropriate classifier through comparison. On this basis, the dimension reduction is processed and the confusion matrix is used as the evaluation index to evaluate the prediction effect. Make correlation analysis of the data, obtained the main influencing factors of post-harvest consumption loss link. Finally, visualize the results of the process.

关键词： Prediction model Evaluating indicator Visualization

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Analysis of Grain Storage Loss Based on Decision Tree Algorithm

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Procedia computer science 2017年 122卷 130-137页

作者： Xueli Liu Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

Different grain storage factors will cause different degrees of grain loss. In this paper, the data mining method is used to study the loss of grain storage, and the grain loss analysis and forecasting model based on decision tree algorithm is proposed. The paper analyzes and predicts the grain loss caused by different grain storage factors. And the influence of model parameters on model fitting and accuracy is verified by the verification curve. Then the decision tree model is optimized by the method of grid search and cross validation, which improves the prediction accuracy of the decision tree model to analyze the grain loss.

关键词： Decision tree Grid search Cross validation Validation curve Grain storage loss

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Analysis of the grain loss in harvest based on logistic regression

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Procedia computer science 2017年 122卷 698-705页

作者： Tingkai Huang Bingchan Li Dongqin Shen Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

In this paper, the grain loss assessment was studied based on logistic regression, and 5400 samples of 31 provinces in our country in the year 2012-2014 were selected, and the 7 typical provinces among them were respectively tested. Using the logistic regression model to predict the loss rate of grain harvesting step, the prediction result is 86.25%. The stochastic gradient descent algorithm is used to optimize the parameters of the model, when the learning_rate is 0.1, the prediction results of grain harvest losses of up to 92.53%, which further improves the prediction accuracy of grain harvest step loss.

关键词： Logistic regression stochastic gradient descent algorithm grain harvest step loss

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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The design of agricultural machinery autonomous navigation system based on Linux-ARM

The design of agricultural machinery autonomous navigation s...

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2016 IEEE Advanced Information Management,Communicates,Electronic and automation Control Conference(IMCEC 2016)

作者： Maoli Wang Yongwei Tang Huijuan Hao Fengqi Hao Junfei Ma Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) School of Automation Engineering Qingdao University of Technology

ISBN: (纸本)9781509001668

The design of agricultural machinery navigation autopilot system for practical problems includes double RTKDGPS receiver,navigation tracking controller,intelligent control terminal,the steering Angle sensor,automatic steering control valve group and other *** to the functional requirements,system adopts hierarchical control strategy,through the embedded Linux software and ARM development board,for modular design such as high precision positioning and automatic operation control,wireless communication,display,integrated data communication,navigation path tracking,vehicle key parameter setting,data storage,playback and other *** engine test results show that the positioning accuracy is 4.5 cm,lateral deviation error operation in field trials have shown straight line within 5cm,meet the expected requirement.

关键词： navigation of agricultural machinery automatic pilot embedded software modular design self-correction

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Micro-blog opinion leader selection method using emotional contribution model

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Journal of computers (Taiwan) 2017年第3期28卷 159-173页

作者： Li, Ya-Xing Wang, Zhao-Kai Liu, Li-Jun Feng, Xu-Peng Liu, Li Huang, Qing-Song Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Educational Technology and Network Center Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

To solve the false correlation caused by negative influence in selecting opinion leader, a micro-blog opinion leader selection method using emotional contribution model is proposed. When the retweeters and reviewers of the tracked object is made, there is not all positive content but kinds of contents of negative, thus they are divided into effective influence or ineffective, which means the all contents made by retweeters and reviewers are taken as an emotional contribution model. In the process of object tracking by using emotional contribution model, messages dissemination occurs mostly in the early, so the early spread the greater influence. As the result, when the tracked object has negative forwarding or reply, the proposed method can infer the polarity of emotion based on variant LSTM. Then, we get the effective influence ranking of opinion leader by each object's coverage rate. The experimental results show coverage rate of proposed model has improved 4.9% than the Page Rank algorithm.

关键词： Long short-term memory

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Acoustic Model Compression with Knowledge Transfer

Acoustic Model Compression with Knowledge Transfer

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第十四届全国人机语音通讯学术会议

作者： Jiangyan Yi Jianhua Tao Zhengqi Wen Ya Li Hao Ni National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of Sciences

Mobile devices have limited computing power and limited ***,large deep neural network(DNN) based acoustic models are not well suited for application on mobile *** order to alleviate this problem,this paper proposes to compress acoustic models by using knowledge *** approach forces a large teacher model to transfer generalized knowledge to a small student *** student model is trained with a linear interpolation of hard probabilities and soft probabilities to learn generalized knowledge from the teacher *** hard probabilities are generated from a Gaussian mixture model hidden Markov model(GMM-HMM) *** soft probabilities are computed from a teacher model(DNN or RNN).Experiments on AMI corpus show that a small student model obtains 2.4%relative WER improvement over a large teacher model with almost 7.6 times compression ratio.

关键词： model compression knowledge transfer deep neural networks automatic speech recognition

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Learning non-local image diffusion for image denoising

arXiv

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arXiv 2017年

作者： Qiao, Peng Dou, Yong Feng, Wensen Chen, Yunjin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing

Image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, the diffusion behavior of which is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local selfsimilarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many nonlocal methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-theart image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Copyright © 2017, The Authors. All rights reserved.

关键词： Image denoising

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Event-triggered H∞ control for networked control system subject to sensor and actuator saturations

Event-triggered H∞ control for networked control system sub...

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第29届中国控制与决策会议

作者： Wenlin Zou Jie Cao Liuwen Li Shumin Fei School of Computer Science and Engineering Nanjing University of Science and Technology School of Mathematics and Information Technology Jiangsu Second Normal University Key Laboratory of Measurement and Control of CSE(Ministry of Education) School of AutomationSoutheast University

ISBN: (纸本)9781509046584

This paper is devoted to the event-triggered H control problem for networked control systems with sensor and actuator saturations.A new event-triggered scheme in consideration of saturation constraint is introduced for the networked control *** constructing an appropriate Lyapunov-Krasovskii functional associated with the LMI technique,criteria for the exponential stability with an H norm bound and H control design have been obtained for the networked control system with proper event-triggered parameters.A simulation example is employed to show the effectiveness of the proposed control method.

关键词： event-triggered scheme sensor and actuator saturation network-induced delay networked control systems

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H∞ Filter for Sampled-data Systems with Sensor Saturation

H∞ Filter for Sampled-data Systems with Sensor Saturation

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第29届中国控制与决策会议

ISBN: (纸本)9781509046584

The problem of H filtering problem for sampled-data system in the presence of sensor saturation has been *** utilizing discontinuous Lyapunov function as well as linear matrix inequality,sufficient conditions of asymptotical stability and prescribed H performance for the filtering error system are *** on the conditions,the H filter exists if certain matrix inequahties are ***,a simulation example is employed to show the effectiveness of the proposed H filter design method.

关键词： sampled-data systems sensor saturation H_∞ filter time-delay

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