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Journal articles on the topic 'On-line classification'

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Author: Grafiati

Published: 10 March 2023

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1

HIRAJIMA, Tsuyoshi, Kenji KATAOKA, Takuji NISHIDA, Ryutaro TOSHIMA, and Masami TSUNEKAWA. "On-line Monitoring for Air Classification." Shigen-to-Sozai 118, no. 10/11 (2002): 681–86. http://dx.doi.org/10.2473/shigentosozai.118.681.

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2

Kim, Daehak, KwangSik Oh, and Jooyong Shim. "On Line LS-SVM for Classification." Communications for Statistical Applications and Methods 10, no. 2 (August 1, 2003): 595–601. http://dx.doi.org/10.5351/ckss.2003.10.2.595.

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3

Borghese, N. Alberto, and Manuele Fomasi. "Automatic Defect Classification on a Production Line." Intelligent Industrial Systems 1, no. 4 (July 14, 2015): 373–93. http://dx.doi.org/10.1007/s40903-015-0018-5.

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4

Kijima, Hiroaki, Shin Yamada, Natsuo Konishi, Hitoshi Kubota, Hiroshi Tazawa, Takayuki Tani, Norio Suzuki, et al. "The Reliability of Classifications of Proximal Femoral Fractures with 3-Dimensional Computed Tomography: The New Concept of Comprehensive Classification." Advances in Orthopedics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/359689.

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The reliability of proximal femoral fracture classifications using 3DCT was evaluated, and a comprehensive “area classification” was developed. Eleven orthopedists (5–26 years from graduation) classified 27 proximal femoral fractures at one hospital from June 2013 to July 2014 based on preoperative images. Various classifications were compared to “area classification.” In “area classification,” the proximal femur is divided into 4 areas with 3 boundary lines: Line-1 is the center of the neck, Line-2 is the border between the neck and the trochanteric zone, and Line-3 links the inferior borders of the greater and lesser trochanters. A fracture only in the first area was classified as a pure first area fracture; one in the first and second area was classified as a 1-2 type fracture. In the same way, fractures were classified as pure 2, 3-4, 1-2-3, and so on. “Area classification” reliability was highest when orthopedists with varying experience classified proximal femoral fractures using 3DCT. Other classifications cannot classify proximal femoral fractures if they exceed each classification’s particular zones. However, fractures that exceed the target zones are “dangerous” fractures. “Area classification” can classify such fractures, and it is therefore useful for selecting osteosynthesis methods.

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5

Zhao, Yaqin. "A Novel On-line Paper Defect Classification Method Based on Multi-representatives Classification." Journal of Information and Computational Science 11, no. 8 (May 20, 2014): 2585–92. http://dx.doi.org/10.12733/jics20103543.

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6

Paek, Eung Gi, John R. Wullert II, and J. S. Patel. "Holographic On-Line Learning Machine for Multicategory Classification." Japanese Journal of Applied Physics 29, Part 2, No. 7 (July 20, 1990): L1332—L1334. http://dx.doi.org/10.1143/jjap.29.l1332.

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7

Wang, Hongxing, Zheng Huang, Bin Liu, Xiang Huang, Wei Han, and Hongchen Li. "Transmission Line Scene Classification Based on Light-VGGNet." Journal of Physics: Conference Series 1631 (September 2020): 012038. http://dx.doi.org/10.1088/1742-6596/1631/1/012038.

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8

Lee, Young-Hak, Hyung Dae Jin, and Chonghun Han. "On-Line Process State Classification for Adaptive Monitoring." Industrial & Engineering Chemistry Research 45, no. 9 (April 2006): 3095–107. http://dx.doi.org/10.1021/ie048969+.

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9

Gaouda, A. M., S. H. Kanoun, and M. M. A. Salama. "On-line disturbance classification using nearest neighbor rule." Electric Power Systems Research 57, no. 1 (January 2001): 1–8. http://dx.doi.org/10.1016/s0378-7796(00)00120-6.

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10

Qing, Jianjun, Hong Huo, and Tao Fang. "Pattern classification based on k locally constrained line." Soft Computing 15, no. 4 (April 2010): 703–12. http://dx.doi.org/10.1007/s00500-010-0602-2.

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11

Saidina Omar, Abdul Malek, Muhammad Khusairi Osman, Mohammad Nizam Ibrahim, Zakaria Hussain, and Ahmad Farid Abidin. "Fault classification on transmission line using LSTM network." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 231. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp231-238.

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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

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12

Kara, A. H., and F. M. Mahomed. "Classification of first-order Lagrangians on the line." International Journal of Theoretical Physics 34, no. 11 (November 1995): 2267–74. http://dx.doi.org/10.1007/bf00673841.

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13

Baldi, Charles A. "Classification of audio signals on a telephone line." Journal of the Acoustical Society of America 94, no. 3 (September 1993): 1752. http://dx.doi.org/10.1121/1.408112.

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14

Šprysl, M., J. Čítek, R. Stupka, L. Vališ, and M. Vítek. "The accuracy of FOM instrument used in on-line pig carcass classification in the Czech Republic." Czech Journal of Animal Science 52, No. 6 (January 7, 2008): 149–58. http://dx.doi.org/10.17221/2314-cjas.

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The objective of this study was to document the accuracy of the classification equipment used in the Czech Republic with respect to measurement errors in lean meat percentage prediction such as point of measurement error, equipment error as well as operator error. To this end, a total of 720 pigs were measured in one abattoir. One can say from the results that the correlations between correct and surrogate measurements of fat depth are high (0.95−0.98), for muscle thickness they are lower ranging from 0.49 to 0.88, and for lean meat percentage they are in the range of 0.85−0.95. The lowest correlation (0.49) was calculated for muscle depth measurement between the 2nd and 3rd last rib when the place of measurement was moved 1 cm in the caudal direction, which influenced the level of the FOM correlation. It was further demonstrated that for the second insertion the differences in lean meat percentage prediction ranged from −6.07% to +9.29%. It was also demonstrated that various instruments provided identical measurements of fat depth (<i>r</i> = 0.57−0.97), while for muscle depth the performance was worse (<i>r</i> = 0.38â0.78), which caused a fluctuation in the prediction of lean meat percentage with differences ranging from −2.56% to +2.81%. It can also be concluded that a high agreement between operators was demonstrated for the determination of lean meat percentage (<i>r</i> = 0.71−0.80).

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15

Haibo, Tan, Luo Bingxiang, Liu Mouhai, Guo Guang, and Xie Xiong. "Study on Line Loss Status Classification Based on Decision Tree." IOP Conference Series: Earth and Environmental Science 585 (November 4, 2020): 012121. http://dx.doi.org/10.1088/1755-1315/585/1/012121.

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16

Li, Li, Xunjian Xu, Jun Guo, and Zhou Jian. "Research on Micro-terrain Classification of Transmission Line Galloping." IOP Conference Series: Earth and Environmental Science 898, no. 1 (October 1, 2021): 012014. http://dx.doi.org/10.1088/1755-1315/898/1/012014.

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Abstract Micro-terrain and micro-weather have an important impact on transmission line galloping. In order to carry out galloping prediction of micro-terrain, the classification of galloping micro-terrain is studied in this work. Firstly, we collect historical data of 1537 galloping points from the State Grid Corporation of China, and select 208 galloping points located in the micro-terrain area by analyzing the altitude and the topographic relief characteristics around each galloping point. Then the galloping micro-terrain types are extracted by Empirical Orthogonal Function method, the first four spatial modes of galloping micro-terrain are the windward slope of east-west mountain area, the windward slope of north-south mountain area, the independent hill, and the saddle back of mountain/hill. Finally, the regional characteristics of typical micro-terrain are analyzed according to the actual lines.

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17

MIAO, Quan, Chenbo SHI, Long MENG, and Guang CHENG. "On-Line Rigid Object Tracking via Discriminative Feature Classification." IEICE Transactions on Information and Systems E99.D, no. 11 (2016): 2824–27. http://dx.doi.org/10.1587/transinf.2016edl8098.

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18

KRAMER, PETER. "BRAVAIS CLASSIFICATION OF NON-PERIODIC QUASILATTICES ON THE LINE." Modern Physics Letters B 02, no. 03n04 (May 1988): 605–11. http://dx.doi.org/10.1142/s0217984988000230.

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The five Bravais lattices Y in IE 2 are interpreted as Euclidean cell complexes in the language of algebraic topology. A metrical dual Y* is constructed for each lattice Y. The unit cell of Y is rearranged into a set of klötze, i.e. cells with boundaries parallel 01 perpendicular to a fixed line. The five Bravais lattices yield eight types of non-periodic quasi-crystal models on the line.

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19

Peleg, K., A. Korem, and M. Shneider. "Automated classification of electrical appliances on a production line." NDT International 21, no. 6 (December 1988): 438–45. http://dx.doi.org/10.1016/0308-9126(88)90290-8.

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20

Cerchiello, Paola, and Paolo Giudici. "Non parametric statistical models for on-line text classification." Advances in Data Analysis and Classification 6, no. 4 (October 13, 2012): 277–88. http://dx.doi.org/10.1007/s11634-012-0122-2.

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21

Peleg, K. "Automated classification of electrical appliances on a production line." NDT & E International 21, no. 6 (December 1988): 438–45. http://dx.doi.org/10.1016/0963-8695(88)90172-7.

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22

Wang, Xiangming, and Zhongkai Zhang. "On-line Defect Detection and Classification of Latex Gloves." Journal of Physics: Conference Series 1575 (June 2020): 012103. http://dx.doi.org/10.1088/1742-6596/1575/1/012103.

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23

TAJIMA, J., and H. KONO. "Natural Object/Artifact Image Classification Based on Line Features." IEICE Transactions on Information and Systems E91-D, no. 8 (August 1, 2008): 2207–11. http://dx.doi.org/10.1093/ietisy/e91-d.8.2207.

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24

Matthews, Clare E., Ludmila I. Kuncheva, and Paria Yousefi. "Classification and comparison of on-line video summarisation methods." Machine Vision and Applications 30, no. 3 (January 18, 2019): 507–18. http://dx.doi.org/10.1007/s00138-019-01007-x.

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25

Anschütz, Johannes. "A Tannakian classification of torsors on the projective line." Comptes Rendus Mathematique 356, no. 11-12 (November 2018): 1203–14. http://dx.doi.org/10.1016/j.crma.2018.10.006.

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26

Straat, Michiel, Fthi Abadi, Christina Göpfert, Barbara Hammer, and Michael Biehl. "Statistical Mechanics of On-Line Learning Under Concept Drift." Entropy 20, no. 10 (October 10, 2018): 775. http://dx.doi.org/10.3390/e20100775.

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We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e., the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

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27

Fong, Li Wei, Pi Ching Lou, and Kung Ting Lin. "On-Line Bayesian Classifier Design for Measurement Fusion." Advanced Materials Research 461 (February 2012): 826–29. http://dx.doi.org/10.4028/www.scientific.net/amr.461.826.

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A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Network (RBFN). By incorporating Markov chain into Bayesian estimation scheme, a RBFN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is developed that can handle the switching plant in the multi-sensor environment. The simulation results are presented which demonstrate the effectiveness of the proposed method.

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28

Lv, Chunyu. "Fault Classification on Transmission Line of 10kV Rural Power Grid." International Journal of Sciences 2, no. 01 (2016): 1–3. http://dx.doi.org/10.18483/ijsci.894.

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29

Dragut, Andreea B., and Codrin M. Nichitiu. "A Monotonic On-Line Linear Algorithm for Hierarchical Agglomerative Classification." Information Technology and Management 5, no. 1/2 (January 2004): 111–41. http://dx.doi.org/10.1023/b:item.0000008078.09272.89.

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30

Oh, Janghoon, Jinwoo Jeong, Yeonsoo Jang, Jaeyoon Lee, and Dongweon Yoon. "Blind Classification of Line-Coding Schemes Based on Characteristic Features." IEEE Access 5 (2017): 9562–67. http://dx.doi.org/10.1109/access.2017.2708739.

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31

Chen, Yud-Ren, Roy Winfield Huffman, Bosoon Park, and Minh Nguyen. "Transportable Spectrophotometer System for On-Line Classification of Poultry Carcasses." Applied Spectroscopy 50, no. 7 (July 1996): 910–16. http://dx.doi.org/10.1366/0003702963905583.

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This paper describes a transportable spectrophotometer system developed for real-time classification of poultry carcasses on-site at slaughter plants. The system measures the spectral reflectance of poultry carcasses in the visible/near-infrared regions (471 to 963.7 nm). An optimal neural network classifier for real-time classification of poultry carcasses into normal, septicemic, and cadaver classes with an average accuracy of 93% was obtained. When the classifier was used to classify the carcasses into two classes, normal and abnormal (septicemic and cadaver), the average accuracy was 97.4%. The percentages of the false positive and the false negative error rates were 2.4 and 2.9%, respectively. This paper also proposes implementing the system at the slaughter plants as a poultry carcass screening system (PCSS). Using two visible/NIR spectrophotometer systems, the PCSS tests both sides of the breast of each bird. With the PCSS, the inspection-passed-bird and inspection-rejected-bird error rates by the spectrophotometer systems would be minimal, and less than 5% of the incoming birds would require further inspection by human inspectors.

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32

Stanford, K., R. J. Richmond, S. D. M. Jones, W. M. Robertson, M. A. Price, and A. J. Gordon. "Video image analysis for on-line classification of lamb carcasses." Animal Science 67, no. 2 (October 1998): 311–16. http://dx.doi.org/10.1017/s1357729800010079.

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AbstractVideo image analysis (VIA), carcass shape and colour data were collected for 1211 lambs of known gender, breed type and carcass weight over a 1-week period using the VIAscan® system developed by the Australian Meat Research Corporation. Classification data (thickness of soft tissue over the 12th rib (GR measurement) and subjective conformation scores on a five-point scale of the leg, loin and shoulder) were assessed by an Agriculture and Agri-Food Canada grader after carcasses had chilled at 5°C for 3 to 6 h. Dissections into saleable meat yield (no. = 58) were performed after carcasses had chilled an additional 24 h. The timing of this study, which was dependent on availability of the VIA equipment, influenced the age and type of lambs available for analysis. The majority of lambs evaluated were wool-breed wethers, age > 10 months, of average GR (15·7 (s.d. 0·2) mm) and muscle conformation (3·0, s.d. 0·1). VIA improved the prediction of saleable meat yield (R2 = 0·71, residual s.d. = 14g/kg) compared with the current classification system (R2 = 0·52, residual s.d. = 18 g/kg). Although prediction ofGR measurement by VIA resulted in a large residual error (residual s.d. = 2·4 mm), the proportion of waste fat (perirenal and subcutaneous) and bone dissected from the carcass was accurately predicted (R2 = 0·62, residual s.d. = 11 g/kg). Proportions of leg (R2 = 0·71, residual s.d. = 7 g/kg) and shoulder (R2 = 0·62, residual s.d. = 9 g/kg) primals were also accurately predicted by VIA, although there were no significant predictors for the proportion of the loin (P > 0·15). VIA improved the prediction of saleable meat yield compared with the current classification system. However collection of additional data including some from extremely lean or well muscled animals would be required before VIA could be recommended to classify lamb carcasses

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33

Kvalheim, Olav M. "On-line classification and updating of disjoint principal component models." Chemometrics and Intelligent Laboratory Systems 3, no. 3 (March 1988): 243–47. http://dx.doi.org/10.1016/0169-7439(88)80054-6.

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34

CHEN, Y. R., B. PARK, R. W. HUFFMAN, and M. NGUYEN. "CLASSIFICATION OF ON-LINE POULTRY CARCASSES WITH BACKPROPAGATION NEURAL NETWORKS." Journal of Food Process Engineering 21, no. 1 (February 1998): 33–48. http://dx.doi.org/10.1111/j.1745-4530.1998.tb00437.x.

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35

Kim, Hang Joon, Jong Wha Jung, and Sang Kyoon Kim. "On-line Chinese character recognition using ART-based stroke classification." Pattern Recognition Letters 17, no. 12 (October 1996): 1311–22. http://dx.doi.org/10.1016/0167-8655(96)00078-5.

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36

Xi, Yanhui, Weijie Zhang, Feng Zhou, Xin Tang, Zewen Li, Xiangjun Zeng, and Pinghua Zhang. "Transmission line fault detection and classification based on SA-MobileNetV3." Energy Reports 9 (December 2023): 955–68. http://dx.doi.org/10.1016/j.egyr.2022.12.043.

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37

Calderoni, Filippo, David Marker, Luca Motto Ros, and Assaf Shani. "Anti-classification results for groups acting freely on the line." Advances in Mathematics 418 (April 2023): 108938. http://dx.doi.org/10.1016/j.aim.2023.108938.

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38

Kulcke, A., C. Gurschler, G. Spöck, R. Leitner, and M. Kraft. "On-Line Classification of Synthetic Polymers Using near Infrared Spectral Imaging." Journal of Near Infrared Spectroscopy 11, no. 1 (February 2003): 71–81. http://dx.doi.org/10.1255/jnirs.355.

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The lack of industrially-applicable, fast polymer classification systems is currently a major stumbling block in establishing both economically- and ecologically-useful waste recycling systems. With the advent of near infrared (NIR) spectral imaging for online classification, a method capable of distinguishing between different materials while simultaneously providing reliable size and shape information became available. In particular, polymer materials can be identified by their characteristic reflection spectra in the NIR without critical interferences from varying sample sizes and colours. A dedicated laboratory-scale prototype spectral imaging system has been developed and a number of classification algorithms have been evaluated for their applicability for polymer classification. Of the investigated algorithms, the Spectral Angle Mapper algorithm, supplemented by a threshold value and applied to the first derivatives of the normalised spectra, proved to be best suited for a rapid and reliable classification of polymers. Based on these achievements, an on-line system capable of classifying polymer parts delivered on a conveyor belt in real-time has been set up, which can be used, for example, as a sensor for fully-automated industrial polymer waste sorters.

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39

Torabi, Keivan, Saed Sayad, and Stephen T. Balke. "On-line adaptive Bayesian classification for in-line particle image monitoring in polymer film manufacturing." Computers & Chemical Engineering 30, no. 1 (November 2005): 18–27. http://dx.doi.org/10.1016/j.compchemeng.2005.06.008.

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40

Li, Ke, Yi Liu, Quanxin Wang, Yalei Wu, Shimin Song, Yi Sun, Tengchong Liu, Jun Wang, Yang Li, and Shaoyi Du. "A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM." PLOS ONE 10, no. 11 (November 6, 2015): e0140395. http://dx.doi.org/10.1371/journal.pone.0140395.

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41

Ma, Junshui, James Theiler, and Simon Perkins. "Accurate On-line Support Vector Regression." Neural Computation 15, no. 11 (November 1, 2003): 2683–703. http://dx.doi.org/10.1162/089976603322385117.

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Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented.Inbothscenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

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42

Wang, Y., Q. Chen, K. Li, D. Zheng, and J. Fang. "AIRBORNE LIDAR POWER LINE CLASSIFICATION BASED ON SPATIAL TOPOLOGICAL STRUCTURE CHARACTERISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 13, 2017): 165–69. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-165-2017.

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Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.

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43

Kim, In Kyum, Jun S. Lee, Il-Yoon Choi, and Jeeha Lee. "A Study on Line Classification for Efficient Maintenance of Railway Infrastructure." Journal of the Korean society for railway 19, no. 5 (October 31, 2016): 672–84. http://dx.doi.org/10.7782/jksr.2016.19.5.672.

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44

Shin, Jun-Hyun, and Jin-O. Kim. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant." Energies 13, no. 17 (September 3, 2020): 4584. http://dx.doi.org/10.3390/en13174584.

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This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

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45

Yun, Ho-Won, Seong-Hyeon Shin, Woo-Jin Jang, and Hochong Park. "On-Line Audio Genre Classification using Spectrogram and Deep Neural Network." Journal of Broadcast Engineering 21, no. 6 (November 30, 2016): 977–85. http://dx.doi.org/10.5909/jbe.2016.21.6.977.

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46

Malt, Barbara C. "An on-line investigation of prototype and exemplar strategies in classification." Journal of Experimental Psychology: Learning, Memory, and Cognition 15, no. 4 (1989): 539–55. http://dx.doi.org/10.1037/0278-7393.15.4.539.

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47

高, 杉. "Power Line Corridor Monitoring and Image Classification Based on Hybrid UAV." Artificial Intelligence and Robotics Research 09, no. 02 (2020): 55–63. http://dx.doi.org/10.12677/airr.2020.92007.

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48

Malyutin, A. V. "Classification of the group actions on the real line and circle." St. Petersburg Mathematical Journal 19, no. 02 (February 7, 2008): 279–97. http://dx.doi.org/10.1090/s1061-0022-08-00999-0.

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49

Furao, Shen, and Osamu Hasegawa. "An incremental network for on-line unsupervised classification and topology learning." Neural Networks 19, no. 1 (January 2006): 90–106. http://dx.doi.org/10.1016/j.neunet.2005.04.006.

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Mei, Jiangping, Yabin Ding, Wenchang Zhang, and Ce Zhang. "Fast detection, position and classification of moving objects on production line." Optik 121, no. 23 (December 2010): 2176–78. http://dx.doi.org/10.1016/j.ijleo.2009.11.003.

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