Chemical Industry and Engineering Progress ›› 2019, Vol. 38 ›› Issue (02): 779-789.DOI: 10.16085/j.issn.1000-6613.2018-0726
• Chemical processes and equipment • Previous Articles Next Articles
Xiwei REN1(),Lifeng HE1(),Bin YAO1,Anling SONG2,Yan ZHONG1,Yanling LIU1
Received:
2018-04-09
Revised:
2018-07-02
Online:
2019-02-05
Published:
2019-02-05
Contact:
Lifeng HE
任喜伟1(),何立风1(),姚斌1,宋安玲2,钟岩1,刘艳玲1
通讯作者:
何立风
作者简介:
<named-content content-type="corresp-name">任喜伟</named-content>(1981—),男,博士研究生,研究方向为数据挖掘、模式识别与图像处理。E-mail:<email>renxiwei@126.com</email>。|何立风,教授,博士生导师,研究方向为图像处理、计算机视觉。E-mail:<email>helifeng@ist.aichi-pu.ac.jp</email>。
基金资助:
CLC Number:
Xiwei REN, Lifeng HE, Bin YAO, Anling SONG, Yan ZHONG, Yanling LIU. Clustering optimization algorithm with adaptive threshold for oil-water interface detection process[J]. Chemical Industry and Engineering Progress, 2019, 38(02): 779-789.
任喜伟, 何立风, 姚斌, 宋安玲, 钟岩, 刘艳玲. 油水界面测量过程中自适应阈值聚类优化算法[J]. 化工进展, 2019, 38(02): 779-789.
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序号 | 数据 | 序号 | 数据 | 序号 | 数据 | 序号 | 数据 |
---|---|---|---|---|---|---|---|
1 | (64) | 26 | (90) | 51 | (508) | 76 | (1028) |
2 | (54) | 27 | (109) | 52 | (553) | 77 | (1300) |
3 | (76) | 28 | (99) | 53 | (499) | 78 | (1205) |
4 | (45) | 29 | (99) | 54 | (455) | 79 | (2832) |
5 | (66) | 30 | (99) | 55 | (530) | 80 | (2708) |
6 | (77) | 31 | (96) | 56 | (570) | 81 | (3953) |
7 | (45) | 32 | (115) | 57 | (608) | 82 | (3896) |
8 | (55) | 33 | (136) | 58 | (604) | 83 | (3904) |
9 | (67) | 34 | (127) | 59 | (528) | 84 | (3296) |
10 | (74) | 35 | (158) | 60 | (553) | 85 | (3996) |
11 | (72) | 36 | (235) | 61 | (559) | 86 | (3996) |
12 | (64) | 37 | (436) | 62 | (555) | 87 | (3996) |
13 | (87) | 38 | (466) | 63 | (577) | 88 | (3996) |
14 | (78) | 39 | (476) | 64 | (564) | 89 | (3996) |
15 | (56) | 40 | (480) | 65 | (573) | 90 | (3996) |
16 | (54) | 41 | (546) | 66 | (589) | 91 | (3996) |
17 | (46) | 42 | (589) | 67 | (599) | 92 | (3996) |
18 | (66) | 43 | (554) | 68 | (576) | 93 | (3996) |
19 | (89) | 44 | (478) | 69 | (622) | 94 | (3996) |
20 | (94) | 45 | (500) | 70 | (674) | 95 | (3996) |
21 | (90) | 46 | (504) | 71 | (611) | 96 | (3996) |
22 | (109) | 47 | (490) | 72 | (613) | 97 | (3996) |
23 | (89) | 48 | (486) | 73 | (978) | 98 | (3996) |
24 | (103) | 49 | (438) | 74 | (1030) | 99 | (3996) |
25 | (107) | 50 | (504) | 75 | (1079) | 100 | (3996) |
序号 | 数据 | 序号 | 数据 | 序号 | 数据 | 序号 | 数据 |
---|---|---|---|---|---|---|---|
1 | (64) | 26 | (90) | 51 | (508) | 76 | (1028) |
2 | (54) | 27 | (109) | 52 | (553) | 77 | (1300) |
3 | (76) | 28 | (99) | 53 | (499) | 78 | (1205) |
4 | (45) | 29 | (99) | 54 | (455) | 79 | (2832) |
5 | (66) | 30 | (99) | 55 | (530) | 80 | (2708) |
6 | (77) | 31 | (96) | 56 | (570) | 81 | (3953) |
7 | (45) | 32 | (115) | 57 | (608) | 82 | (3896) |
8 | (55) | 33 | (136) | 58 | (604) | 83 | (3904) |
9 | (67) | 34 | (127) | 59 | (528) | 84 | (3296) |
10 | (74) | 35 | (158) | 60 | (553) | 85 | (3996) |
11 | (72) | 36 | (235) | 61 | (559) | 86 | (3996) |
12 | (64) | 37 | (436) | 62 | (555) | 87 | (3996) |
13 | (87) | 38 | (466) | 63 | (577) | 88 | (3996) |
14 | (78) | 39 | (476) | 64 | (564) | 89 | (3996) |
15 | (56) | 40 | (480) | 65 | (573) | 90 | (3996) |
16 | (54) | 41 | (546) | 66 | (589) | 91 | (3996) |
17 | (46) | 42 | (589) | 67 | (599) | 92 | (3996) |
18 | (66) | 43 | (554) | 68 | (576) | 93 | (3996) |
19 | (89) | 44 | (478) | 69 | (622) | 94 | (3996) |
20 | (94) | 45 | (500) | 70 | (674) | 95 | (3996) |
21 | (90) | 46 | (504) | 71 | (611) | 96 | (3996) |
22 | (109) | 47 | (490) | 72 | (613) | 97 | (3996) |
23 | (89) | 48 | (486) | 73 | (978) | 98 | (3996) |
24 | (103) | 49 | (438) | 74 | (1030) | 99 | (3996) |
25 | (107) | 50 | (504) | 75 | (1079) | 100 | (3996) |
序号 | 油水界面 数据集 | 最高准确率/% | 平均迭代次数/万次 | 平均运行时间/ms·万次-1 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
传统分类统计算法 | 经典K-means 聚类算法 | 本文算法 | 传统分类统计算法 | 经典K-means聚类算法 | 本文 算法 | 传统分类 统计算法 | 经典K-means 聚类算法 | 本文算法 | ||
1 | 数据集1 | 60 | 85 | 100 | 1.00 | 3.49 | 2.00 | 44.53 | 339.23 | 50.30 |
2 | 数据集2 | 54 | 85 | 100 | 1.00 | 3.45 | 2.00 | 45.93 | 325.27 | 50.93 |
3 | 数据集3 | 42 | 82 | 100 | 1.00 | 3.22 | 2.00 | 39.19 | 282.54 | 47.28 |
4 | 数据集4 | 48 | 76 | 100 | 1.00 | 3.13 | 2.00 | 38.46 | 267.99 | 48.92 |
5 | 数据集5 | 54 | 69 | 100 | 1.00 | 3.09 | 2.00 | 37.11 | 259.21 | 50.04 |
序号 | 油水界面 数据集 | 最高准确率/% | 平均迭代次数/万次 | 平均运行时间/ms·万次-1 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
传统分类统计算法 | 经典K-means 聚类算法 | 本文算法 | 传统分类统计算法 | 经典K-means聚类算法 | 本文 算法 | 传统分类 统计算法 | 经典K-means 聚类算法 | 本文算法 | ||
1 | 数据集1 | 60 | 85 | 100 | 1.00 | 3.49 | 2.00 | 44.53 | 339.23 | 50.30 |
2 | 数据集2 | 54 | 85 | 100 | 1.00 | 3.45 | 2.00 | 45.93 | 325.27 | 50.93 |
3 | 数据集3 | 42 | 82 | 100 | 1.00 | 3.22 | 2.00 | 39.19 | 282.54 | 47.28 |
4 | 数据集4 | 48 | 76 | 100 | 1.00 | 3.13 | 2.00 | 38.46 | 267.99 | 48.92 |
5 | 数据集5 | 54 | 69 | 100 | 1.00 | 3.09 | 2.00 | 37.11 | 259.21 | 50.04 |
序号 | 油水界面 数据集 | 最高准确率/% | 平均迭代次数/万次 | 平均运行时间/ms·万次-1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R[18] | R[19] | R[20] | 本文算法 | R[18] | R[19] | R[20] | 本文算法 | R[18] | R[19] | R[20] | 本文算法 | ||
1 | 数据集1 | 89 | 100 | 100 | 100 | 4.40 | 2.00 | 2.75 | 2.00 | 520.31 | 65.48 | 195.01 | 50.30 |
2 | 数据集2 | 82 | 85 | 90 | 100 | 4.10 | 2.00 | 2.73 | 2.00 | 433.75 | 64.55 | 190.02 | 50.93 |
3 | 数据集3 | 85 | 100 | 95 | 100 | 3.40 | 2.00 | 2.77 | 2.00 | 339.99 | 66.86 | 200.15 | 47.28 |
4 | 数据集4 | 85 | 90 | 100 | 100 | 5.00 | 2.00 | 2.61 | 2.00 | 525.31 | 60.75 | 168.75 | 48.92 |
5 | 数据集5 | 82 | 95 | 90 | 100 | 3.20 | 2.00 | 3.37 | 2.00 | 320.33 | 69.68 | 304.65 | 50.04 |
序号 | 油水界面 数据集 | 最高准确率/% | 平均迭代次数/万次 | 平均运行时间/ms·万次-1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R[18] | R[19] | R[20] | 本文算法 | R[18] | R[19] | R[20] | 本文算法 | R[18] | R[19] | R[20] | 本文算法 | ||
1 | 数据集1 | 89 | 100 | 100 | 100 | 4.40 | 2.00 | 2.75 | 2.00 | 520.31 | 65.48 | 195.01 | 50.30 |
2 | 数据集2 | 82 | 85 | 90 | 100 | 4.10 | 2.00 | 2.73 | 2.00 | 433.75 | 64.55 | 190.02 | 50.93 |
3 | 数据集3 | 85 | 100 | 95 | 100 | 3.40 | 2.00 | 2.77 | 2.00 | 339.99 | 66.86 | 200.15 | 47.28 |
4 | 数据集4 | 85 | 90 | 100 | 100 | 5.00 | 2.00 | 2.61 | 2.00 | 525.31 | 60.75 | 168.75 | 48.92 |
5 | 数据集5 | 82 | 95 | 90 | 100 | 3.20 | 2.00 | 3.37 | 2.00 | 320.33 | 69.68 | 304.65 | 50.04 |
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