Ontology type: schema:ScholarlyArticle
2021-12-02
AUTHORSVan Bang Dinh, Ngoc Le Chau, Nam T. P. Le, Thanh-Phong Dao
ABSTRACTIn precision engineering, compliant mechanisms are growingly promising mechanisms in designing micro/nano positioners and manipulators due to emerging advantages of free friction, no joint, and decreased assembly. Nevertheless, compliant mechanisms have flexible configurations with nonlinear behaviors, the design, analysis, and optimization are becoming challenges, and a systematic design method is still limited. Therefore, this paper proposes a new multi-phases optimization design method for compliant mechanisms. In the suggested method, the topology optimization is integrated with finite element method, intelligent modeling, and neural network algorithm. First, the solid isotropic material with penalization-based topology is used to design a new compliant mechanism. The numerical simulations are conducted. Next, the parameters of adaptive neuro-fuzzy inference system are optimized by the Taguchi to achieve an improved ANFIS (IANFIS) model. The IANFIS approaches are used to predict behaviors of the developed mechanism. The results confirmed that the developed IANFIS has a highly accurate prediction in comparison with other regression models. Particularly, the metric values of IANFIS models are relatively good. Particularly, the R2 value is approximately 1 while the MSE, RMSE, and SD values are approximately 0. Last, the neural network algorithm is extended to search the optimal geometry sizes for the compliant mechanism. In the size optimization, two scenarios for are taken into consideration. For the scenario 1, the displacement, rotation angle, parasitic, and stress of the mechanism are found about 1.9977 mm, 0.8232 degrees, 0.1666 mm, and 13.94 MPa, respectively. For the scenario 2, the displacement, rotation angle, the parasitic, and stress are approximately 1.8501 mm, 0.8237 degrees, 0.1429 mm, and 11.8193 MPa, respectively. The results of size optimization showed that the displacement of the mechanism is enhanced by 12.94% and the rotation angle is improved to 4.5E+11% in comparison to the initial topology. The statistic results of Friedman and Kruskal–Wallis found that the accuracy and efficiency of proposed method are superior to those of other methods with p-values less than 0.001. The proposed method is applicable to other industrial systems. More... »
PAGES1-30
http://scigraph.springernature.com/pub.10.1007/s00366-021-01552-y
DOIhttp://dx.doi.org/10.1007/s00366-021-01552-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1143582277
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam",
"id": "http://www.grid.ac/institutes/grid.448730.c",
"name": [
"Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam"
],
"type": "Organization"
},
"familyName": "Dinh",
"givenName": "Van Bang",
"id": "sg:person.010652263274.60",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010652263274.60"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam",
"id": "http://www.grid.ac/institutes/grid.448730.c",
"name": [
"Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam"
],
"type": "Organization"
},
"familyName": "Chau",
"givenName": "Ngoc Le",
"id": "sg:person.010236227624.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010236227624.21"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, Vietnam",
"id": "http://www.grid.ac/institutes/grid.513012.4",
"name": [
"Institute of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, Vietnam"
],
"type": "Organization"
},
"familyName": "Le",
"givenName": "Nam T. P.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam",
"id": "http://www.grid.ac/institutes/grid.444812.f",
"name": [
"Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam",
"Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam"
],
"type": "Organization"
},
"familyName": "Dao",
"givenName": "Thanh-Phong",
"id": "sg:person.011077663603.19",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011077663603.19"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00500-015-1883-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041419020",
"https://doi.org/10.1007/s00500-015-1883-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11081-019-09469-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1121377058",
"https://doi.org/10.1007/s11081-019-09469-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10462-017-9610-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100163009",
"https://doi.org/10.1007/s10462-017-9610-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13369-018-3445-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105876020",
"https://doi.org/10.1007/s13369-018-3445-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-016-2404-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019347449",
"https://doi.org/10.1007/s00521-016-2404-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00814-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1120405742",
"https://doi.org/10.1007/s00366-019-00814-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00158-020-02786-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1134417052",
"https://doi.org/10.1007/s00158-020-02786-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-020-00977-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1124867481",
"https://doi.org/10.1007/s00366-020-00977-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-020-00963-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1124769071",
"https://doi.org/10.1007/s00366-020-00963-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00849-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1120475015",
"https://doi.org/10.1007/s00366-019-00849-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-016-2267-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002517126",
"https://doi.org/10.1007/s00521-016-2267-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10846-017-0671-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092059332",
"https://doi.org/10.1007/s10846-017-0671-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00810-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1117649990",
"https://doi.org/10.1007/s00366-019-00810-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00822-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1118045038",
"https://doi.org/10.1007/s00366-019-00822-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12541-018-0213-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110203574",
"https://doi.org/10.1007/s12541-018-0213-x"
],
"type": "CreativeWork"
}
],
"datePublished": "2021-12-02",
"datePublishedReg": "2021-12-02",
"description": "In precision engineering, compliant mechanisms are growingly promising mechanisms in designing micro/nano positioners and manipulators due to emerging advantages of free friction, no joint, and decreased assembly. Nevertheless, compliant mechanisms have flexible configurations with nonlinear behaviors, the design, analysis, and optimization are becoming challenges, and a systematic design method is still limited. Therefore, this paper proposes a new multi-phases optimization design method for compliant mechanisms. In the suggested method, the topology optimization is integrated with finite element method, intelligent modeling, and neural network algorithm. First, the solid isotropic material with penalization-based topology is used to design a new compliant mechanism. The numerical simulations are conducted. Next, the parameters of adaptive neuro-fuzzy inference system are optimized by the Taguchi to achieve an improved ANFIS (IANFIS) model. The IANFIS approaches are used to predict behaviors of the developed mechanism. The results confirmed that the developed IANFIS has a highly accurate prediction in comparison with other regression models. Particularly, the metric values of IANFIS models are relatively good. Particularly, the R2 value is approximately 1 while the MSE, RMSE, and SD values are approximately 0. Last, the neural network algorithm is extended to search the optimal geometry sizes for the compliant mechanism. In the size optimization, two scenarios for are taken into consideration. For the scenario 1, the displacement, rotation angle, parasitic, and stress of the mechanism are found about 1.9977 mm, 0.8232 degrees, 0.1666 mm, and 13.94 MPa, respectively. For the scenario 2, the displacement, rotation angle, the parasitic, and stress are approximately 1.8501\u00a0mm, 0.8237 degrees, 0.1429\u00a0mm, and 11.8193\u00a0MPa, respectively. The results of size optimization showed that the displacement of the mechanism is enhanced by 12.94% and the rotation angle is improved to 4.5E+11% in comparison to the initial topology. The statistic results of Friedman and Kruskal\u2013Wallis found that the accuracy and efficiency of proposed method are superior to those of other methods with p-values less than 0.001. The proposed method is applicable to other industrial systems.",
"genre": "article",
"id": "sg:pub.10.1007/s00366-021-01552-y",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1041785",
"issn": [
"0177-0667",
"1435-5663"
],
"name": "Engineering with Computers",
"publisher": "Springer Nature",
"type": "Periodical"
}
],
"keywords": [
"compliant mechanisms",
"new compliant mechanism",
"adaptive neuro-fuzzy inference system",
"neuro-fuzzy inference system",
"design method",
"neural network algorithm",
"size optimization",
"solid isotropic material",
"optimization design method",
"finite element method",
"systematic design method",
"network algorithm",
"nano-positioner",
"free friction",
"inference system",
"element method",
"topology optimization",
"rotation angle",
"precision engineering",
"isotropic materials",
"geometry size",
"nonlinear behavior",
"intelligent modeling",
"numerical simulations",
"ANFIS model",
"industrial systems",
"MPa",
"flexible configuration",
"accurate prediction",
"displacement",
"optimization",
"suggested method",
"initial topology",
"scenario 2",
"scenario 1",
"metric values",
"angle",
"Taguchi",
"algorithm",
"friction",
"R2 values",
"manipulator",
"topology",
"positioner",
"method",
"parasitics",
"stress",
"simulations",
"system",
"engineering",
"behavior",
"materials",
"joints",
"model",
"efficiency",
"modeling",
"configuration",
"RMSE",
"design",
"results",
"promising mechanism",
"parameters",
"statistic results",
"values",
"accuracy",
"comparison",
"prediction",
"MSE",
"advantages",
"mechanism",
"scenarios",
"size",
"assembly",
"degree",
"challenges",
"consideration",
"approach",
"analysis",
"geometry optimization",
"Kruskal-Wallis",
"SD values",
"regression models",
"Friedman",
"p-value",
"paper"
],
"name": "Topology-based geometry optimization for a new compliant mechanism using improved adaptive neuro-fuzzy inference system and neural network algorithm",
"pagination": "1-30",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1143582277"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00366-021-01552-y"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00366-021-01552-y",
"https://app.dimensions.ai/details/publication/pub.1143582277"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:39",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_894.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00366-021-01552-y"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01552-y'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01552-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01552-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01552-y'
This table displays all metadata directly associated to this object as RDF triples.
224 TRIPLES
22 PREDICATES
123 URIs
100 LITERALS
4 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s00366-021-01552-y | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0801 |
3 | ″ | schema:author | Naa0c2d1aa61f47fea9d21847dbaa8599 |
4 | ″ | schema:citation | sg:pub.10.1007/s00158-020-02786-y |
5 | ″ | ″ | sg:pub.10.1007/s00366-019-00810-4 |
6 | ″ | ″ | sg:pub.10.1007/s00366-019-00814-0 |
7 | ″ | ″ | sg:pub.10.1007/s00366-019-00822-0 |
8 | ″ | ″ | sg:pub.10.1007/s00366-019-00849-3 |
9 | ″ | ″ | sg:pub.10.1007/s00366-020-00963-7 |
10 | ″ | ″ | sg:pub.10.1007/s00366-020-00977-1 |
11 | ″ | ″ | sg:pub.10.1007/s00500-015-1883-2 |
12 | ″ | ″ | sg:pub.10.1007/s00521-016-2267-y |
13 | ″ | ″ | sg:pub.10.1007/s00521-016-2404-7 |
14 | ″ | ″ | sg:pub.10.1007/s10462-017-9610-2 |
15 | ″ | ″ | sg:pub.10.1007/s10846-017-0671-x |
16 | ″ | ″ | sg:pub.10.1007/s11081-019-09469-8 |
17 | ″ | ″ | sg:pub.10.1007/s12541-018-0213-x |
18 | ″ | ″ | sg:pub.10.1007/s13369-018-3445-2 |
19 | ″ | schema:datePublished | 2021-12-02 |
20 | ″ | schema:datePublishedReg | 2021-12-02 |
21 | ″ | schema:description | In precision engineering, compliant mechanisms are growingly promising mechanisms in designing micro/nano positioners and manipulators due to emerging advantages of free friction, no joint, and decreased assembly. Nevertheless, compliant mechanisms have flexible configurations with nonlinear behaviors, the design, analysis, and optimization are becoming challenges, and a systematic design method is still limited. Therefore, this paper proposes a new multi-phases optimization design method for compliant mechanisms. In the suggested method, the topology optimization is integrated with finite element method, intelligent modeling, and neural network algorithm. First, the solid isotropic material with penalization-based topology is used to design a new compliant mechanism. The numerical simulations are conducted. Next, the parameters of adaptive neuro-fuzzy inference system are optimized by the Taguchi to achieve an improved ANFIS (IANFIS) model. The IANFIS approaches are used to predict behaviors of the developed mechanism. The results confirmed that the developed IANFIS has a highly accurate prediction in comparison with other regression models. Particularly, the metric values of IANFIS models are relatively good. Particularly, the R2 value is approximately 1 while the MSE, RMSE, and SD values are approximately 0. Last, the neural network algorithm is extended to search the optimal geometry sizes for the compliant mechanism. In the size optimization, two scenarios for are taken into consideration. For the scenario 1, the displacement, rotation angle, parasitic, and stress of the mechanism are found about 1.9977 mm, 0.8232 degrees, 0.1666 mm, and 13.94 MPa, respectively. For the scenario 2, the displacement, rotation angle, the parasitic, and stress are approximately 1.8501 mm, 0.8237 degrees, 0.1429 mm, and 11.8193 MPa, respectively. The results of size optimization showed that the displacement of the mechanism is enhanced by 12.94% and the rotation angle is improved to 4.5E+11% in comparison to the initial topology. The statistic results of Friedman and Kruskal–Wallis found that the accuracy and efficiency of proposed method are superior to those of other methods with p-values less than 0.001. The proposed method is applicable to other industrial systems. |
22 | ″ | schema:genre | article |
23 | ″ | schema:inLanguage | en |
24 | ″ | schema:isAccessibleForFree | false |
25 | ″ | schema:isPartOf | sg:journal.1041785 |
26 | ″ | schema:keywords | ANFIS model |
27 | ″ | ″ | Friedman |
28 | ″ | ″ | Kruskal-Wallis |
29 | ″ | ″ | MPa |
30 | ″ | ″ | MSE |
31 | ″ | ″ | R2 values |
32 | ″ | ″ | RMSE |
33 | ″ | ″ | SD values |
34 | ″ | ″ | Taguchi |
35 | ″ | ″ | accuracy |
36 | ″ | ″ | accurate prediction |
37 | ″ | ″ | adaptive neuro-fuzzy inference system |
38 | ″ | ″ | advantages |
39 | ″ | ″ | algorithm |
40 | ″ | ″ | analysis |
41 | ″ | ″ | angle |
42 | ″ | ″ | approach |
43 | ″ | ″ | assembly |
44 | ″ | ″ | behavior |
45 | ″ | ″ | challenges |
46 | ″ | ″ | comparison |
47 | ″ | ″ | compliant mechanisms |
48 | ″ | ″ | configuration |
49 | ″ | ″ | consideration |
50 | ″ | ″ | degree |
51 | ″ | ″ | design |
52 | ″ | ″ | design method |
53 | ″ | ″ | displacement |
54 | ″ | ″ | efficiency |
55 | ″ | ″ | element method |
56 | ″ | ″ | engineering |
57 | ″ | ″ | finite element method |
58 | ″ | ″ | flexible configuration |
59 | ″ | ″ | free friction |
60 | ″ | ″ | friction |
61 | ″ | ″ | geometry optimization |
62 | ″ | ″ | geometry size |
63 | ″ | ″ | industrial systems |
64 | ″ | ″ | inference system |
65 | ″ | ″ | initial topology |
66 | ″ | ″ | intelligent modeling |
67 | ″ | ″ | isotropic materials |
68 | ″ | ″ | joints |
69 | ″ | ″ | manipulator |
70 | ″ | ″ | materials |
71 | ″ | ″ | mechanism |
72 | ″ | ″ | method |
73 | ″ | ″ | metric values |
74 | ″ | ″ | model |
75 | ″ | ″ | modeling |
76 | ″ | ″ | nano-positioner |
77 | ″ | ″ | network algorithm |
78 | ″ | ″ | neural network algorithm |
79 | ″ | ″ | neuro-fuzzy inference system |
80 | ″ | ″ | new compliant mechanism |
81 | ″ | ″ | nonlinear behavior |
82 | ″ | ″ | numerical simulations |
83 | ″ | ″ | optimization |
84 | ″ | ″ | optimization design method |
85 | ″ | ″ | p-value |
86 | ″ | ″ | paper |
87 | ″ | ″ | parameters |
88 | ″ | ″ | parasitics |
89 | ″ | ″ | positioner |
90 | ″ | ″ | precision engineering |
91 | ″ | ″ | prediction |
92 | ″ | ″ | promising mechanism |
93 | ″ | ″ | regression models |
94 | ″ | ″ | results |
95 | ″ | ″ | rotation angle |
96 | ″ | ″ | scenario 1 |
97 | ″ | ″ | scenario 2 |
98 | ″ | ″ | scenarios |
99 | ″ | ″ | simulations |
100 | ″ | ″ | size |
101 | ″ | ″ | size optimization |
102 | ″ | ″ | solid isotropic material |
103 | ″ | ″ | statistic results |
104 | ″ | ″ | stress |
105 | ″ | ″ | suggested method |
106 | ″ | ″ | system |
107 | ″ | ″ | systematic design method |
108 | ″ | ″ | topology |
109 | ″ | ″ | topology optimization |
110 | ″ | ″ | values |
111 | ″ | schema:name | Topology-based geometry optimization for a new compliant mechanism using improved adaptive neuro-fuzzy inference system and neural network algorithm |
112 | ″ | schema:pagination | 1-30 |
113 | ″ | schema:productId | N473beb5ded464a009249e0bd5228559c |
114 | ″ | ″ | Nfddec1198fa8423c870afdde73d9e45c |
115 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1143582277 |
116 | ″ | ″ | https://doi.org/10.1007/s00366-021-01552-y |
117 | ″ | schema:sdDatePublished | 2022-05-20T07:39 |
118 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
119 | ″ | schema:sdPublisher | N1fd6092bf8e74e1a8ac3f81252dc5151 |
120 | ″ | schema:url | https://doi.org/10.1007/s00366-021-01552-y |
121 | ″ | sgo:license | sg:explorer/license/ |
122 | ″ | sgo:sdDataset | articles |
123 | ″ | rdf:type | schema:ScholarlyArticle |
124 | N1fd6092bf8e74e1a8ac3f81252dc5151 | schema:name | Springer Nature - SN SciGraph project |
125 | ″ | rdf:type | schema:Organization |
126 | N3204c68c042a489b9255efa2825eb918 | rdf:first | Nfe2d0da3170b41379081206227c2639c |
127 | ″ | rdf:rest | Nbcf0dc992c36451cbfcacb4afef54bf3 |
128 | N473beb5ded464a009249e0bd5228559c | schema:name | doi |
129 | ″ | schema:value | 10.1007/s00366-021-01552-y |
130 | ″ | rdf:type | schema:PropertyValue |
131 | Naa0c2d1aa61f47fea9d21847dbaa8599 | rdf:first | sg:person.010652263274.60 |
132 | ″ | rdf:rest | Nec117538b7ff41c7b72979f0b6003d24 |
133 | Nbcf0dc992c36451cbfcacb4afef54bf3 | rdf:first | sg:person.011077663603.19 |
134 | ″ | rdf:rest | rdf:nil |
135 | Nec117538b7ff41c7b72979f0b6003d24 | rdf:first | sg:person.010236227624.21 |
136 | ″ | rdf:rest | N3204c68c042a489b9255efa2825eb918 |
137 | Nfddec1198fa8423c870afdde73d9e45c | schema:name | dimensions_id |
138 | ″ | schema:value | pub.1143582277 |
139 | ″ | rdf:type | schema:PropertyValue |
140 | Nfe2d0da3170b41379081206227c2639c | schema:affiliation | grid-institutes:grid.513012.4 |
141 | ″ | schema:familyName | Le |
142 | ″ | schema:givenName | Nam T. P. |
143 | ″ | rdf:type | schema:Person |
144 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
145 | ″ | schema:name | Information and Computing Sciences |
146 | ″ | rdf:type | schema:DefinedTerm |
147 | anzsrc-for:0801 | schema:inDefinedTermSet | anzsrc-for: |
148 | ″ | schema:name | Artificial Intelligence and Image Processing |
149 | ″ | rdf:type | schema:DefinedTerm |
150 | sg:journal.1041785 | schema:issn | 0177-0667 |
151 | ″ | ″ | 1435-5663 |
152 | ″ | schema:name | Engineering with Computers |
153 | ″ | schema:publisher | Springer Nature |
154 | ″ | rdf:type | schema:Periodical |
155 | sg:person.010236227624.21 | schema:affiliation | grid-institutes:grid.448730.c |
156 | ″ | schema:familyName | Chau |
157 | ″ | schema:givenName | Ngoc Le |
158 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010236227624.21 |
159 | ″ | rdf:type | schema:Person |
160 | sg:person.010652263274.60 | schema:affiliation | grid-institutes:grid.448730.c |
161 | ″ | schema:familyName | Dinh |
162 | ″ | schema:givenName | Van Bang |
163 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010652263274.60 |
164 | ″ | rdf:type | schema:Person |
165 | sg:person.011077663603.19 | schema:affiliation | grid-institutes:grid.444812.f |
166 | ″ | schema:familyName | Dao |
167 | ″ | schema:givenName | Thanh-Phong |
168 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011077663603.19 |
169 | ″ | rdf:type | schema:Person |
170 | sg:pub.10.1007/s00158-020-02786-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1134417052 |
171 | ″ | ″ | https://doi.org/10.1007/s00158-020-02786-y |
172 | ″ | rdf:type | schema:CreativeWork |
173 | sg:pub.10.1007/s00366-019-00810-4 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1117649990 |
174 | ″ | ″ | https://doi.org/10.1007/s00366-019-00810-4 |
175 | ″ | rdf:type | schema:CreativeWork |
176 | sg:pub.10.1007/s00366-019-00814-0 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1120405742 |
177 | ″ | ″ | https://doi.org/10.1007/s00366-019-00814-0 |
178 | ″ | rdf:type | schema:CreativeWork |
179 | sg:pub.10.1007/s00366-019-00822-0 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1118045038 |
180 | ″ | ″ | https://doi.org/10.1007/s00366-019-00822-0 |
181 | ″ | rdf:type | schema:CreativeWork |
182 | sg:pub.10.1007/s00366-019-00849-3 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1120475015 |
183 | ″ | ″ | https://doi.org/10.1007/s00366-019-00849-3 |
184 | ″ | rdf:type | schema:CreativeWork |
185 | sg:pub.10.1007/s00366-020-00963-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1124769071 |
186 | ″ | ″ | https://doi.org/10.1007/s00366-020-00963-7 |
187 | ″ | rdf:type | schema:CreativeWork |
188 | sg:pub.10.1007/s00366-020-00977-1 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1124867481 |
189 | ″ | ″ | https://doi.org/10.1007/s00366-020-00977-1 |
190 | ″ | rdf:type | schema:CreativeWork |
191 | sg:pub.10.1007/s00500-015-1883-2 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1041419020 |
192 | ″ | ″ | https://doi.org/10.1007/s00500-015-1883-2 |
193 | ″ | rdf:type | schema:CreativeWork |
194 | sg:pub.10.1007/s00521-016-2267-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1002517126 |
195 | ″ | ″ | https://doi.org/10.1007/s00521-016-2267-y |
196 | ″ | rdf:type | schema:CreativeWork |
197 | sg:pub.10.1007/s00521-016-2404-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1019347449 |
198 | ″ | ″ | https://doi.org/10.1007/s00521-016-2404-7 |
199 | ″ | rdf:type | schema:CreativeWork |
200 | sg:pub.10.1007/s10462-017-9610-2 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1100163009 |
201 | ″ | ″ | https://doi.org/10.1007/s10462-017-9610-2 |
202 | ″ | rdf:type | schema:CreativeWork |
203 | sg:pub.10.1007/s10846-017-0671-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1092059332 |
204 | ″ | ″ | https://doi.org/10.1007/s10846-017-0671-x |
205 | ″ | rdf:type | schema:CreativeWork |
206 | sg:pub.10.1007/s11081-019-09469-8 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1121377058 |
207 | ″ | ″ | https://doi.org/10.1007/s11081-019-09469-8 |
208 | ″ | rdf:type | schema:CreativeWork |
209 | sg:pub.10.1007/s12541-018-0213-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1110203574 |
210 | ″ | ″ | https://doi.org/10.1007/s12541-018-0213-x |
211 | ″ | rdf:type | schema:CreativeWork |
212 | sg:pub.10.1007/s13369-018-3445-2 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1105876020 |
213 | ″ | ″ | https://doi.org/10.1007/s13369-018-3445-2 |
214 | ″ | rdf:type | schema:CreativeWork |
215 | grid-institutes:grid.444812.f | schema:alternateName | Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam |
216 | ″ | schema:name | Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam |
217 | ″ | ″ | Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam |
218 | ″ | rdf:type | schema:Organization |
219 | grid-institutes:grid.448730.c | schema:alternateName | Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam |
220 | ″ | schema:name | Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam |
221 | ″ | rdf:type | schema:Organization |
222 | grid-institutes:grid.513012.4 | schema:alternateName | Institute of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, Vietnam |
223 | ″ | schema:name | Institute of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, Vietnam |
224 | ″ | rdf:type | schema:Organization |