The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data...The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.展开更多
A microcalorimetric study on molecular recognition of p-sulfonatocalix[4]arene derivatives at selfassembled interface in comparison with in bulk water was performed,inspired by the dramatic change in physicochemical c...A microcalorimetric study on molecular recognition of p-sulfonatocalix[4]arene derivatives at selfassembled interface in comparison with in bulk water was performed,inspired by the dramatic change in physicochemical characteristics from bulk water to interface.A total of six cationic molecules were screened as model vips,including ammonium（NH_4~＋）,guanidinium（Gdm~＋）.N,N＇-dimethyl-1,4-diazabicyclo[2.2.2]octane（DMDABCO^（2＋））,tropylium（Tpm~＋）,N-methyl pyridinium（N-mPY＊） and methyl viologen（MV^（2＋））.The complexation with NH_4~＋.Gdm~＋ and DMDABCO2＊ is pronouncedly enhanced when the recognition process moved from bulk water to interface,whereas the complexation stabilities with Tpm~＋,N-mPY~＋ and MV2＊ increase slightly or even decrease to some extent.A more interesting phenomenon arises from the NH_4~＋/Gdm~＋ pair that the thermodynamic origin at interface differs definitely from each other although with similar association constants.The results were discussed in terms of differential driving forces,electrostatic,hydrogen bond as well as π-stacking interactions,originating from the unique physicochemical features of interfaces,mainly the polarity and dielectric constant.展开更多
Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of inte...Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.展开更多
基金This project is supported by Provincial Youth Science Foundation of Shanxi China (No.20011020)National Natural Science Foundation of China (No.59975064).
文摘The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.
基金supported by NSFC(Nos.21322207 and 21672112)the Fundamental Research Funds for the Central Universities and Program of Tianjin Young Talents
文摘A microcalorimetric study on molecular recognition of p-sulfonatocalix[4]arene derivatives at selfassembled interface in comparison with in bulk water was performed,inspired by the dramatic change in physicochemical characteristics from bulk water to interface.A total of six cationic molecules were screened as model vips,including ammonium（NH_4~＋）,guanidinium（Gdm~＋）.N,N＇-dimethyl-1,4-diazabicyclo[2.2.2]octane（DMDABCO^（2＋））,tropylium（Tpm~＋）,N-methyl pyridinium（N-mPY＊） and methyl viologen（MV^（2＋））.The complexation with NH_4~＋.Gdm~＋ and DMDABCO2＊ is pronouncedly enhanced when the recognition process moved from bulk water to interface,whereas the complexation stabilities with Tpm~＋,N-mPY~＋ and MV2＊ increase slightly or even decrease to some extent.A more interesting phenomenon arises from the NH_4~＋/Gdm~＋ pair that the thermodynamic origin at interface differs definitely from each other although with similar association constants.The results were discussed in terms of differential driving forces,electrostatic,hydrogen bond as well as π-stacking interactions,originating from the unique physicochemical features of interfaces,mainly the polarity and dielectric constant.
基金funded by the National Natural Science Foundation of China(52004201,52274143,52204153)China Postdoctoral Science Foundation(2021T140551).
文摘Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.
