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2022年第1期共收录49

1. Navigation Technology of Following AGV Based on Multi-sensor Fusion

Accession number: 20220811679460

Title of translation: AGV

Authors: Qian, Xiaoming (1); Huang, Yuxuan (1); Lou, Peihuang (1); Sun, Tian (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing; 210016, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 14-22 and 32

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the current Leader-Follower AGVs cooperative operation, in addition to obtaining environmental information, the positioning and navigation of the following AGV also need to observe the position and attitude of the leading AGV for path tracking, which has higher requirements for accuracy and stability of navigation and location. In order to improve the navigation accuracy of following AGV, an integrated navigation method combining inertial navigation and multi-vision was proposed. Aiming at the problem of multi-sensor data fusion, an optimal pose estimation method based on adaptive unscented Kalman filter was proposed. The output signal of inertial navigation sensor was used to follow the AGV attitude prediction; the path tracking navigation and RGB-D navigation constituted the multi-vision navigation, which was used as the system observation to correct the accumulated offset of inertial navigation. The experimental results showed that the compound navigation scheme had faster convergence speed, more stable path tracking state and formation maintenance.This method improved the real-time performance and robustness of the two AGVs cooperative handling system. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 21

Main heading: Kalman filters

Controlled terms: Air navigation? - ?Inertial navigation systems? - ?Sensor data fusion

Uncontrolled terms: Compound navigation? - ?Cooperative operation? - ?Current leaders? - ?Environmental information? - ?Leader-follower? - ?Multi visions? - ?Multi-AGV coordinated? - ?Multi-sensor fusion? - ?Navigation technology? - ?Path tracking

Classification code: 431.5 Air Navigation and Traffic Control? - ?723.2 Data Processing and Image Processing

DOI: 10.6041/j.issn.1000-1298.2022.01.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

2. Spatial Stratified Heterogeneity and Driving Force of Anoplophora glabripennis in North China

Accession number: 20220811679243

Title of translation:

Authors: Liu, Deqing (1, 2); Zhang, Tianyuan (3); Zhang, Xiaoli (1, 2); Zong, Shixiang (1); Huang, Jixia (1, 2)

Author affiliation: (1) Key Laboratory for Silviculture and Conservation, Ministry of Education, Beijing Forestry University, Beijing; 100083, China; (2) Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing; 100083, China; (3) State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing; 100875, China

Corresponding authors: Huang, Jixia(huangjx@bjfu.edu.cn); Huang, Jixia(huangjx@bjfu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 215-223 and 369

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Anoplophora glabripennis is widely distributed in China as the main pest of forest trees, and the distribution area is constantly expanding. It is important to study the spatial correlation of insect infestation and analyze the factors that affect its distribution. Using geodetector, the effects of environmental factors on the Anoplophora glabripennis disaster were analyzed. The results showed that the most seriously damaged areas were in the northern part of Shanxi Province, while the incidence rates in Henan, Shandong, and southern Hebei were relatively low. The main meteorological factors affecting the spatial distribution of the incidence rate were precipitation and temperature, and the main social and economic factors were population and regional GDP. The effect of temperature on the value of the first and second industries was significantly different from that of other factors. Economic development had significant positive effect on the control of local insect pests. The interaction detection showed that the combination of any two variables can explain the spatial heterogeneity of Anoplophora glabripennis disaster more effectively. The interpretation ability of incidence rate was increased significantly after interaction between precipitation and control rate. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 34

Main heading: Disasters

Uncontrolled terms: Anoplophora glabripennis? - ?Distribution area? - ?Driving forces? - ?Geodetector? - ?Incidence rate? - ?Insect infestations? - ?North China? - ?Spatial autocorrelations? - ?Spatial correlations? - ?Spatial stratified heterogeneity

DOI: 10.6041/j.issn.1000-1298.2022.01.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

3. Densely Connected BiGRU Neural Network Based on BERT and Attention Mechanism for Chinese Agriculture-related Question Similarity Matching

Accession number: 20220811677626

Title of translation: BERT-Attention-DenseBiGRU

Authors: Wang, Haoriqin (1, 2); Wang, Xiaomin (1, 3); Miao, Yisheng (1, 3); Xu, Tongyu (4); Liu, Zhichao (1, 3); Wu, Huarui (1, 3)

Author affiliation: (1) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (2) College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao; 028043, China; (3) Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (4) School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang; 110866, China

Corresponding authors: Wu, Huarui(wuhr@nercita.org.cn); Wu, Huarui(wuhr@nercita.org.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 244-252

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To allow fast and automatic detection of the same semantic agriculture-related questions, a method based on BERT-Attention-DenseGRU (gated recurrent unit) was proposed. According to the agriculture question characteristics, twelve layers of the Chinese BERT model method were applied to process and analyze the text data and compare it with the Word2Vec, Glove, and TF-IDF methods, effectively solving the problem of high dimension and sparse data in the agriculture-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results showed that agriculture-related question similarity matching based on BERT-Attention-DenseBiGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the agriculture-related question dataset. The precision and F1 values of the proposed model were 97.2% and 97.6%. Compared with six other kinds of question similarity matching models, a state-of-the-art method with the agriculture-related question dataset was presented. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 37

Main heading: Network layers

Controlled terms: Agriculture? - ?Natural language processing systems? - ?Semantics

Uncontrolled terms: Agriculture-related? - ?Agriculture-related question similarity matching? - ?Attention mechanisms? - ?Coattention mechanism? - ?Densely connected BiGRU? - ?Fast detections? - ?Network-based? - ?Neural-networks? - ?Question-and-answering community? - ?Similarity-matching

Classification code: 723 Computer Software, Data Handling and Applications? - ?723.2 Data Processing and Image Processing? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control

Numerical data indexing: Percentage 9.72E+01%, Percentage 9.76E+01%

DOI: 10.6041/j.issn.1000-1298.2022.01.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

4. Detection of Walnut Internal Quality Based on X-ray Imaging Technology and Convolution Neural Network

Accession number: 20220811679347

Title of translation: X

Authors: Zhang, Shujuan (1); Gao, Tingyao (1); Ren, Rui (1); Sun, Haixia (1)

Author affiliation: (1) College of Agricultural Engineering, Shanxi Agricultural University, Taigu; 030800, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 383-388

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problems of export mixed internal quality and not easily to detect of walnuts in China, X-ray imaging technology combined with convolution neural network was proposed to quickly detect the internal quality of walnut. Using X-ray transmittance, X-ray images containing internal information were obtained. Firstly, X-ray images of walnut were preprocessed and data expanded. Then, four transfer learning models, including GoogLeNet, ResNet 101, MobileNet v2 and VGG 19, were used to construct convolutional neural networks to train walnut data sets. The model was analyzed through prediction set accuracy, loss value, test set accuracy and running time, and the model parameters were optimized. Finally, the walnut internal quality detection and sorting system was developed and applied to model verification. The results showed that among the four different transfer learning models, GoogLeNet model had the highest prediction accuracy. When the learning rate of GoogLeNet model was set to 0.001 and the epoch was set to 25, the prediction effect was the best, and the prediction accuracy was 96.67%. The results of system verification showed that the discriminant accuracy of shell walnut reached 100%, and the average discriminant accuracy was 96.39%. The system could realize the non-destructive testing and sorting of walnut internal quality, and provide further theoretical basis and technical reference for the equipment research and development. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 25

Main heading: Nondestructive examination

Controlled terms: Convolution? - ?Convolutional neural networks? - ?Deep learning? - ?Forecasting? - ?Sorting

Uncontrolled terms: Convolution neural network? - ?Convolutional neural network? - ?Detection system? - ?Internal quality? - ?Learning models? - ?Prediction accuracy? - ?Transfer learning? - ?Walnut? - ?X-ray image? - ?X-ray imaging technologies

Classification code: 461.4 Ergonomics and Human Factors Engineering? - ?716.1 Information Theory and Signal Processing

Numerical data indexing: Percentage 1.00E+02%, Percentage 9.639E+01%, Percentage 9.667E+01%

DOI: 10.6041/j.issn.1000-1298.2022.01.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

5. Research Progress on Obtaining Cultivated Land Quality Evaluation Indexes by Remote Sensing

Accession number: 20220811678172

Title of translation:

Authors: Zhang, Chao (1, 2); Gao, Lulu (1); Yun, Wenju (2, 3); Li, Li (1); Ji, Wenjun (1); Ma, Jiani (1)

Author affiliation: (1) College of Land Science and Technology, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Land Quality Monitoring and Control, Ministry of Natural Resources, Beijing; 100083, China; (3) Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing; 100035, China

Corresponding authors: Yun, Wenju(yunwenju@vip.sina.com); Yun, Wenju(yunwenju@vip.sina.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 1-13

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The data acquisition of cultivated land quality index (CLQI) is the basis of cultivated land quality (CLQ) evaluation. Remote sensing (RS) provides a new method for rapid and large-scale monitoring of CLQ data. Firstly, the connotation and function of CLQ were analyzed. On this basis, bibliometric method was used to summarize the research of CLQI in recent five years. Combined with the research status of CLQ and soil quality, CLQI system was established based on RS, which was divided into three dimensions: topographic conditions, soil properties and field utilization status. Secondly, the research status of various index acquisition methods in different dimensions was analyzed, and the commonly used RS analysis methods and corresponding technical principles were summarized. The basic data can be effectively obtained for field slope, field condition, field road accessibility, forest network degree and other indicators, and the large-scale acquisition method of soil properties were needed further research. Finally, aiming at the problems to be solved in RS monitoring of CLQ, the following suggestions and prospects were put forward: mining the remote sensing characteristics of different scales of CLQI; strengthening the research on automatic extraction of remote sensing information of CLQI; and building a remote sensing big data platform for CLQ evaluation, so as to promote the application of RS in CLQ evaluation. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 111

Main heading: Soils

Controlled terms: Data acquisition? - ?Land use? - ?Quality control? - ?Remote sensing

Uncontrolled terms: Cultivated land qualities? - ?Evaluation index? - ?Field utilization status? - ?Quality evaluation? - ?Quality evaluation indices? - ?Quality indices? - ?Remote-sensing? - ?Research status? - ?Soil property? - ?Topographic conditions

Classification code: 403 Urban and Regional Planning and Development? - ?483.1 Soils and Soil Mechanics? - ?723.2 Data Processing and Image Processing? - ?913.3 Quality Assurance and Control

DOI: 10.6041/j.issn.1000-1298.2022.01.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

6. Detection Method of Double Side Breakage of Population Cotton Seed Based on Improved YOLO v4

Accession number: 20220811678647

Title of translation: YOLO v4

Authors: Wang, Qiaohua (1, 2); Gu, Wei (1); Cai, Peizhong (1); Zhang, Hongzhou (1)

Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 389-397

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Computer vision is one of the commonly used technical methods in the field of cotton seed detection. It has been widely used in the field of non-destructive inspection of agricultural products. However, in most cases, it is difficult for researchers to use computer vision to detect small-sized objects such as cotton seeds on both sides. The detection effect is not good. Aiming at this problem, a type of cotton seed detection and sorting device was designed, which used the transparent characteristics of the acrylic plate under strong light and white background to slide the cotton seed into the groove of the transparent acrylic plate through the feeding device. With the rotation of the turntable, the front and back images of the same batch of cotton were collected by two CCD cameras at different positions. The improved YOLO v4 target detection algorithm was used to detect damaged cotton seeds. The experimental results showed that the model established by this method can detect damaged and intact cotton seeds in the population cotton seeds with an accuracy of 95.33%, recall rate of 96.31%, and missed detection rate of 0. The detection effect was better than that of the original YOLO v4 network, respectively. The proposed method realized the identification of the damage of double-sided group cotton seed, and provided technical support for the subsequent research and development of related delinted cotton seed intelligent detection equipment. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 28

Main heading: CCD cameras

Controlled terms: Computer vision? - ?Cotton? - ?Damage detection? - ?Image recognition? - ?Nondestructive examination? - ?Object detection

Uncontrolled terms: Acrylic plates? - ?Breakage detection? - ?Cotton seeds? - ?Delinted cotton seed? - ?Detection device? - ?Detection effect? - ?Detection methods? - ?Double CCD camera? - ?Double sides? - ?YOLO v4

Classification code: 714.2 Semiconductor Devices and Integrated Circuits? - ?723.2 Data Processing and Image Processing? - ?723.5 Computer Applications? - ?741.2 Vision? - ?742.2 Photographic Equipment? - ?821.4 Agricultural Products

Numerical data indexing: Percentage 9.533E+01%, Percentage 9.631E+01%

DOI: 10.6041/j.issn.1000-1298.2022.01.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

7. Structure and Functional Properties of Soybean Protein Isolate-Dextran Non-covalent Polymer

Accession number: 20220811679319

Title of translation: -

Authors: Zhu, Xiuqing (1); Du, Xiaoqian (1); Hu, Miao (1); Liu, Guannan (1); Qi, Baokun (1); Li, Yang (1, 2)

Author affiliation: (1) College of Food Science, Northeast Agricultural University, Harbin; 150030, China; (2) Heilongjiang Institute of Green Food Science, Harbin; 150023, China

Corresponding authors: Li, Yang(yangli@neau.edu.cn); Li, Yang(yangli@neau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 398-405

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the aim to reveal the interaction between soybean protein isolates (SPI) and dextran (Dex) in the mixed system and the effect of dextran concentration (1%, 3%, 5% and 7%) on the structure and functional properties of SPI, fluorescence spectrum, ultraviolet spectrum and Fourier transform infrared spectrum were used to characterize the conformational changes of the SPI-Dex non-covalent polymer, and the effect of dextran concentration on the functional properties of SPI was analyzed through particle size, surface hydrophobicity, turbidity, solubility, emulsification activity, emulsification stability and antioxidant activity. The results showed that SPI and Dex can interact through two non-covalent forces: hydrophobic interaction and hydrogen bond under neutral conditions, thereby changed the structure and functional properties of SPI. The addition of Dex can prevent the exposure of tryptophan and tyrosine residues, and form a tighter tertiary structure compared with SPI alone. When the addition of Dex in the mixed system was less than 5%, with the increase of Dex, the particle size, surface hydrophobicity, and turbidity of SPI-Dex polymer were decreased significantly, and the solubility, emulsification, and antioxidant activity of SPI-Dex polymer were improved significantly. When the concentration of Dex in the mixed system was 5%, the improvement effect on the functional properties of SPI was the most significant, the solubility, emulsifying activity index and antioxidant activity of SPI were increased by 16.35%, 18.71% and 11.30%, respectively. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 28

Main heading: Ultraviolet spectroscopy

Controlled terms: Amino acids? - ?Antioxidants? - ?Dextran? - ?Emulsification? - ?Hydrogen bonds? - ?Hydrophobicity? - ?Particle size? - ?Proteins? - ?Solubility? - ?Structural properties ? - ?Turbidity

Uncontrolled terms: Antioxidant activities? - ?Functional properties? - ?Mixed systems? - ?Noncovalent? - ?Particles sizes? - ?Protein isolates? - ?Soybean protein isolate? - ?Soybean proteins? - ?Structure property? - ?Surface hydrophobicity

Classification code: 408 Structural Design? - ?741.1 Light/Optics? - ?801.4 Physical Chemistry? - ?802.3 Chemical Operations? - ?803 Chemical Agents and Basic Industrial Chemicals? - ?804 Chemical Products Generally? - ?804.1 Organic Compounds? - ?804.2 Inorganic Compounds? - ?931.2 Physical Properties of Gases, Liquids and Solids? - ?951 Materials Science

Numerical data indexing: Percentage 1.00E00%, Percentage 1.13E+01%, Percentage 1.635E+01%, Percentage 1.871E+01%, Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 7.00E+00%

DOI: 10.6041/j.issn.1000-1298.2022.01.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

8. Motion State Prediction Method of CNC Machine Tools Based on Adaptive Deep Learning

Accession number: 20220811677500

Title of translation:

Authors: Du, Liuqing (1); Li, Xiang (1); Yu, Yongwei (1)

Author affiliation: (1) School of Mechanical Engineering, Chongqing University of Technology, Chongqing; 400054, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 53

Issue: 1

Issue date: January 25, 2022

Publication year: 2022

Pages: 451-458

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The feature relationship of the motion state of CNC machine tools is very complex. Realizing the prediction of the future operation state of CNC machine tools can tap the potential abnormal emergencies of machine tools and enhance the stability of machine tool processing. In view of the problem of poor adaptability and low accuracy of prediction under dynamic label of machine tool state and differential distribution data, an adaptive hybrid deep learning model was established to predict machine tool state by combining time series feature relationship and model fusion method. Firstly, by combining the nearest neighbor classifier, an adaptive updating rule based on weight accumulation was designed, and a state prediction model with data adaptability was established. On this basis, an optimization strategy of feature distance metric based on center loss function was proposed, and a comprehensive decision loss function was constructed to ensure model fusion effectively. Based on a combination convergence criterion, the BBPT method was used to train the model, and the test data was verified. The experimental results showed that the model can adapt dynamic label and differential distribution data. The prediction of the state category of CNC machine tools had strong anti-interference, fast response and high accuracy, and can better meet the requirements of machine tool state classification and prediction. The prediction accuracy and real-time performance were significantly compared with BP and LSTM classification networks, and the shortest prediction time was only 100 ms in GPU mode. ? 2022, Chinese Society of Agricultural Machinery. All right reserved.

Number of references: 25

Main heading: Machine tools

Controlled terms: Computer control systems? - ?Forecasting? - ?Long short-term memory? - ?Nearest neighbor search? - ?Timing circuits

Uncontrolled terms: Adaptive hybrid timing model? - ?CNC machine tools? - ?Deep learning? - ?K-near neighbor algorithm? - ?Loss functions? - ?Model fusion? - ?Motion state? - ?Nearest-neighbor algorithms? - ?State prediction? - ?Timing modeling

Classification code: 603.1 Machine Tools, General? - ?713.4 Pulse Circuits? - ?723.5 Computer Applications? - ?731.1 Control Systems? - ?921.5 Optimization Techniques

Numerical data indexing: Time 1.00E-01s

DOI: 10.6041/j.issn.1000-1298.2022.01.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2022 Elsevier Inc.

 

9. Multi-objective Optimization of Cylinder/Valve-plate Sealing Ring in Axial Piston Pump Based on Genetic Algorithm

Accession number: 20220811677525

Title of translation:

Authors: Ye, Shaogan (1); Ge, Jigang (1); Hou, Liang (1); Mu, Rui (1); Bu, Xiangjian (1)

Author affiliation: (1) Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen; 361021, China