Wavelet Denoising Method Research of Soybean Straw Cellulose Near Infrared Rapid Detection

Weizheng Shen,Nan Ji,Haoran Du,Hongbin Li,Sida Ma,Qingming Kong 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.1

In this paper,we made a research for soybean straw hemicellulose rapid detection by establishing a quantitative analysis model based on near-infrared spectroscopy. At first,146 samples were collected from varieties of soybean straws are gathered in different areas of Heilongjiang province, then made chemical testing of components and spectral scanning to soybean straw, the 140 samples were classified to two groups, in which 100 samples were chosen as calibration set and the remaining 40 samples were chosen as verification set. Wavelet transform was used to deal with the noise spectrum, selected DBN wavelet, Haar wavelet and Symlet wavelet in different layers under penalty threshold, Bridge-massart threshold, and default global threshold for spectral signal decomposition and reconstruction, compared with other traditional noise reduction methods,Symlet2-2 layer decomposition wavelet basis for hemicellulose spectral processing possessed better effect with the determination coefficient of validation set rising from 0.462524 to 0.6314158 after processing.

Research on Analysis Model of Soybean Straw Component

Weizheng Shen,Jianbo Wang,Qingming Kong,Jing Guan,Jin Cui,Ziqing Liu 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.6

To achieve the rapid detection of soybean straw component, the key lies in establishing a quantitative analysis model with higher prediction accuracy which is rapid, stable and reliable. In order to establish the optimal Near-infrared (NIR) analysis model of cellulose and hemicellulose content in soybean straw, this paper uses NIR transmission technology by applying interval Partial Least Squares (iPLS) on the optimization of characteristic spectrum range of cellulose and hemicellulose spectrum. During the optimized characteristic spectrum range, prediction models of Partial Least Squares Regression (PLSR) and the Back Propagation Neural Network (BPNN) are built in the cellulose and hemicellulose contents respectively. The results show that the best modeling band of the Cellulose content is 5615-5731cm-1, and the optimal coefficient of determination of prediction model, PredictionR2(P-R2) reaches 0.9179266; And the best modeling band of the hemicellulose content is 5615-5731cm-1 ,the P-R2 is 0.920407. After the selection of iPLS optimal band, the quantitative analysis model of cellulose and hemicelluloses established by adopting the PLSR and BP Neural Network is more concise and has higher prediction accuracy and faster data computing speed. It also provides a theoretical basis for the optimization of characteristic spectrum range for the design of small dedicated NIR analytical instruments.

Research on Milk Conductivity Real-time Online Monitoring System

Weizheng Shen,Wenxiao Yu,Qingming Kong,Yu Zhang,Guanting Liu,Qi Wang 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.5

Bovine mastitis is one of the common diseases in the dairy industry, and is also caused by the loss in the dairy industry. Timely and effective detection of bovine mastitis has important significance. By monitoring the milk conductivity values and other parameters, is to automate the monitoring cow health, is the basis of the prevention of bovine mastitis infection. This design is based single-chip microcomputer processing core control sensor measurement unit, data transmission unit, display and control unit, online monitoring system to achieve real-time data collection, transmission, display and other control functions. For monitoring the conductivity values were piecewise linear model of the relationship between the conductivity and the feedback voltage power function curve model comparison with simulation experiment. Simulation results show that the conductivity measurement piecewise linear model is a better, fitter factor, the average relative error is 2.31% lower, measuring range is 0.7 ms / cm-11.7ms / cm. The error of measuring the overall system is of relative low ratio, but the accuracy is higher, and better meets the needs of real-time monitoring, for cow health management and prevention of bovine mastitis or other diseases provide effective technical means.

Research on MW-IPLS in Wavelength Selection based on NIR of Rice Moisture

Weizheng Shen,Jingjing Wang,Fengzhu Hu,Qingming Kong 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.12

The moisture content of rice is of great significance for the eating quality and food safety. Therefore, it is very necessary to establish a rapid, stable, reliable and high prediction accuracy quantitative analysis model that can be used in on-line detection. In this study, the quantitative analysis technique of near infrared diffuse reflectance spectroscopy was used to detect the moisture content in rice. We combine the algorithm (MW-IPLS) based on MWPLS (Moving Window Partial Least Squares) with IPLS (Interval Partial Least Squares) to optimize the characteristic wavelength. Then we establish partial least squares regression in the preferred characteristic wavelength range. The experimental results show that the model of quantitative analysis using the MW-IPLS algorithm to optimize the characteristic wavelength is optimal comparing with the whole spectrum and single method such as MWPLS and IPLS. The numbers of Factors, R2P, RMSECV and RMSEP are 6, 0.8597, 0.2523 and 0.2753 respectively. Therefore, using the WM-IPLS algorithm to optimize the characteristic wavelength can reduce the processing capacity of the data and make the model more concise. In addition, it also provides a new method for the analysis of near infrared spectral characteristic wavelength selection.

The Design of System about Cow Activity Based on SVM

Weizheng Shen,Congcong Chen,Shuang Zheng,Shanjun He,Mingda Li 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.3

In view of the cow extensive farming, which lacks modern management tools, the cow estrus determine are often laid off. In this paper, a cow behavioral characteristic is designed. The system collects X, Y, Z axis acceleration data through triaxial accelerometer ADXL345, by ZigBee wireless network and RS485 sent to the information center, it’s based on the support vector of binary tree to classify their behavior. The result shows, the algorithm for cow’s stationary and moving classification accuracy was 93.26%, a slight and sharp motion accuracy was 84.81%, It provides an effective basis in judging cows in heat or in physical abnormalities. That system contributes to the refinement of cows feeding and health culture,and has great significance for the stable and healthy development of the dairy industry.

Design and Implementation of Livestock House Environmental Perception System Based on Wireless Sensor Networks

Weizheng Shen,Guanting Liu,Zhongbin Su,Rongyu Su,Yu Zhang 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.5

In order to monitor the six factors which is the most crucial influence on livestock production in livestock house, this paper designs a set of livestock house(LH) environmental perception system based on wireless sensor networks. This system is composed by a number of data acquisition nodes(DAN) connected by using Wireless Sensor Networks. Inside of DAN is equipped with six type of sensor which in charge of collecting Real-time environmental data in LH, including carbon dioxide sensor, ammonia gas sensor, illumination sensor, relative humidity sensor and so on. The communication mode between upper-computer and central node is GPRS, to research on remotely control environmental parameters in LH in the future. The upper-computer could treat data which is uploaded by several central nodes through some way, for example, data analysis based on established protocol, threshold judgment, etc. Then, the real-time data is stored by database. The stability and veracity of the system was verified by field tests which set technical grade monitors as control groups. Comparing with industrial-grade high precision monitoring instruments, the error of collecting data and wireless communication network is within normal error range, By SPSS, the result shows that there is no significant difference of P-Value(P>0.05) in different groups at same time.

Near-infrared Detection of Straw Cellulose by Orthogonal Signal Correction and Partial Least Squares

Qingming Kong,Zhongbin Su,Weizheng Shen,Mingming Han,Bohan Li,Xudong Wang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.5

Orthogonal signal correction (OSC)and partial least squares(PLS)were used during the pretreatment of straw to reduce environmental noise and prediction models were established for near-infrared detection of straw cellulose. Tests were run with soybean stalk as the object of study. Test results indicated that compared to a model established using a traditional denoising method, the determination coefficients for calibration set models established by second derivative+smoothing and OSC were 0.9318595 and 0.9328905 respectively while the root mean square error for calibration (RMSEC) were 0.6762902 and 0.6696454. For an OSC-PLS regression model with a factor of 8, the relative standard deviation of a prediction model was less than 5%. In the OSC denoising process, the root mean square error fluctuated with the increasing number of PLS factors. Compared to the second derivative-smoothing denoising, OSC-PLS denoising removed the non-correlated variation from spectra and improved interpretation ability of variation while the analysis and convergence were expedited. It was therefore concluded that OSC-PLS denoising could be used to realize the rapid and accurate near-infrared detection of straw cellulose.

Remote Monitoring of Heading Rice Growing and Nitrogen Content Based on UAV Images

Yu Zhang,Zhongbin Su,Weizheng Shen,Renshan Jia,Jiling Luan 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.7

Rice heading is the critical stage of the growth of rice, rice plants are relatively large, field canopy, poor resistance, and coincided with the high temperature and rainy weather, pests and diseases are more prone period. Therefore, the use of UAV to monitor the growth status of rice, easy to understand rice growth and nutritional status, in order to achieve high-quality, high yield, efficient purpose. This article is the field experiments under different nitrogen levels, using UAV monitoring multispectral images of rice, by reference remote sensing spectral indices, derived green normalized difference vegetation index (GNDVI) relative to other spectral index is more suitable for rice field biomass inversion modeling. At the same time, this study is based on the inversion model, implemented the rice growing and nitrogen content graded by ISODATA methods in ENVI. Achieved classification on rice growth and nitrogen remote sensing thematic map in ArcGIS, to provide timely and accurate information for rice seedling diagnosis and management decision, has reached the purpose of rice production precise management.

The Construction of the Animal Husbandry Information System Based on the Technology of Map Conflation

Yue Guo,Zhongbin Su,Weizheng Shen,Zhipeng Guo,Qingming Kong 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.3

The research aims to merge the sections of geographic information distribution of the large-scale farms information monitoring system and the farming enterprises filing system which are under the Animal Husbandry Bureau in Heilongjiang Province as a Geographic Information System (GIS) that based on the map conflation technology of topological relation. Applying a variety of algorithms of points, lines, surfaces to this study, and using optimized "Spider code" and matching algorithm based on area overlay rate to solve the map database conflation problem of two different sources but consistent geographic target. It not only improves the map accuracy and consistency, but also adds new space characteristics, and updates attribute information which associated with dataset spatial characteristics. It makes the personnel at all levels in the Animal Husbandry Bureau in Heilongjiang Province realize information sharing. It is conductive to promote the work of staff at all levels and provide decision support of data to the Animal Husbandry Bureau in Heilongjiang Province.

Research on the Real-time Monitoring System of Cow’s Rumination

Shuang Zhang,Yu Zhang,Weizheng Shen,Lu Xu,Hao Wu,Zhongbin Su 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.7

Cow’s rumination is an important process of food digestion for dairy cows which is a valuable indicator during the cow management. By measuring the cow’s rumination time, it is capable to predict the cow’s estrus, and learn the cow’s health situation. This paper uses MSP430F149 processor with a sound sensor to achieve the design of real-time monitoring system for cow’s rumination. Since the system require high accuracy of the audio signal of the cow’s rumination, the ADC acquisition module and filtering and amplification process was made a special design. For the change of the spectral characteristics of the voice when cows ruminating, this paper designed the endpoint detection algorithm and sound sequence windowed function, and FFT transform is performed on the data in the Hamming window, and then do the frequency domain analysis to the audio of cow’s rumination. Comparing the sound spectrum collected from high precision recording instrument and this system, this system has done a good job in frequency and time domain. Applying the high precision recording instrument to the simulation experiment for cow’s rumination which conduct 50 sets of different time ranging, using this system to do the real-time monitoring, the mean error was 4.38, the R2 value is 0.877, the Pearson correlation was 0.936. This system performed accurate in the acquisition of the sound signal, and more accurately in identifying cows ruminating, and it can also provide effective means for the intelligent management of the pasture on cow’s health and breeding.