Tars from two Mongolian coals (Tavan Tolgoi and Baganuur) produced by simple distillation have been characterized using size exclusion chromatography (SEC) with elution in both 1-methyl-2-pyrrolidinone (NMP) and a mix...Tars from two Mongolian coals (Tavan Tolgoi and Baganuur) produced by simple distillation have been characterized using size exclusion chromatography (SEC) with elution in both 1-methyl-2-pyrrolidinone (NMP) and a mixed solvent (NMP and chloroform), UV-fluorescence in chloroform and NMP, gas chromatography (GC), mass spectrometry (GC-MS, probe-MS and LD-MS with thin layer chromatography) and infra-red spectroscopy. The SEC chromatograms using NMP and the solvent mixture NMP: chloroform indicates that similar conclusions can be drawn from using either eluent. The synchronous UV-fluorescence spectra were shifted to longer wavelengths in chloroform solution than in NMP and chloroform may be the better solvent for these tars prepared without extensive secondary thermal treatment. Infra-red spectra indicated differences between the two coal tars that reflected their different ranks, with more oxygenate groups in the lower rank Baganuur coal. Mass spectrometry (GC-MS and probe-MS) of both coal tars confirmed the presence of aliphatic components as well as aromatics and the relatively extensive alkylation of aromatics. Molecular mass ranges indicated for Baganuur tar by SEC compared well with the mass range by LD-MS although the LD-MS extended to higher mass values. The high mass fractions of the tars were revealed by fractionation by thin layer chromatography with the relevant sections of the developed plates inserted directly into the mass spectrometer;laser desorption was directly from the surface of the plate. LD-MS of the unfractionated samples failed to detect the high mass components because of mass discrimination effects. The high mass components were carried over in the distillation by mass transfer of vapours into the condenser.展开更多
Optical Character Recognition(OCR)algorithm is a technology that converts text images from paper documents into a digital format using electronic devices such as scanners and digital cameras.This process transforms th...Optical Character Recognition(OCR)algorithm is a technology that converts text images from paper documents into a digital format using electronic devices such as scanners and digital cameras.This process transforms the captured text images into editable and searchable versions using text recognition technology.As advancements in deep learning,Al models have increasingly become pivotal in applications requiring operation on mobile devices without network connectivity,including small underwater devices,high-altitude environments,and license plate recognition systems in front-end cameras.Despite the maturity of general OCR models,there is a notable scarcity of OCR algorithms that are compatible with embedded single-chip microcomputers.These models,capable of functioning autonomously at the front-end without network support,are particularly crucial for remote applications.However,virtually no models for single-chip systems currently support the recognition of the Mongolian language.This study focuses on the development of an OCR system designed for single-chip microcomputers operating without network connectivity.The system is engineered to perform character recognition for Mongolian,English,and Chinese scripts,thereby expanding the utility of front-end single-chip devices.Specifically,the research introduces a novel approach to the recognition of modern Mongolian characters,broadening the scope of OCR system in linguistically diverse contexts.展开更多
文摘Tars from two Mongolian coals (Tavan Tolgoi and Baganuur) produced by simple distillation have been characterized using size exclusion chromatography (SEC) with elution in both 1-methyl-2-pyrrolidinone (NMP) and a mixed solvent (NMP and chloroform), UV-fluorescence in chloroform and NMP, gas chromatography (GC), mass spectrometry (GC-MS, probe-MS and LD-MS with thin layer chromatography) and infra-red spectroscopy. The SEC chromatograms using NMP and the solvent mixture NMP: chloroform indicates that similar conclusions can be drawn from using either eluent. The synchronous UV-fluorescence spectra were shifted to longer wavelengths in chloroform solution than in NMP and chloroform may be the better solvent for these tars prepared without extensive secondary thermal treatment. Infra-red spectra indicated differences between the two coal tars that reflected their different ranks, with more oxygenate groups in the lower rank Baganuur coal. Mass spectrometry (GC-MS and probe-MS) of both coal tars confirmed the presence of aliphatic components as well as aromatics and the relatively extensive alkylation of aromatics. Molecular mass ranges indicated for Baganuur tar by SEC compared well with the mass range by LD-MS although the LD-MS extended to higher mass values. The high mass fractions of the tars were revealed by fractionation by thin layer chromatography with the relevant sections of the developed plates inserted directly into the mass spectrometer;laser desorption was directly from the surface of the plate. LD-MS of the unfractionated samples failed to detect the high mass components because of mass discrimination effects. The high mass components were carried over in the distillation by mass transfer of vapours into the condenser.
基金supported by the School of Information Technology of the Mongolian University of Science and Technology,as well as the central guidance and local science and technology development fund projects(transfer and transformation projects of scientific and technological achievements),project No:226Z1707GResearch and development project of 3D hub size measuring machine.
文摘Optical Character Recognition(OCR)algorithm is a technology that converts text images from paper documents into a digital format using electronic devices such as scanners and digital cameras.This process transforms the captured text images into editable and searchable versions using text recognition technology.As advancements in deep learning,Al models have increasingly become pivotal in applications requiring operation on mobile devices without network connectivity,including small underwater devices,high-altitude environments,and license plate recognition systems in front-end cameras.Despite the maturity of general OCR models,there is a notable scarcity of OCR algorithms that are compatible with embedded single-chip microcomputers.These models,capable of functioning autonomously at the front-end without network support,are particularly crucial for remote applications.However,virtually no models for single-chip systems currently support the recognition of the Mongolian language.This study focuses on the development of an OCR system designed for single-chip microcomputers operating without network connectivity.The system is engineered to perform character recognition for Mongolian,English,and Chinese scripts,thereby expanding the utility of front-end single-chip devices.Specifically,the research introduces a novel approach to the recognition of modern Mongolian characters,broadening the scope of OCR system in linguistically diverse contexts.
