167 research outputs found
ANALISIS PENGGUNAAN APLIKASI HANPING CHINESE DICTIONARY LITE TERHADAP PENGUASAAN KOSAKATA BAHASA MANDARIN PADA MAHASISWA KELAS A ANGKATAN 2019 PENDIDIKAN BAHASA MANDARIN FKIP UNTAN
No full textVocabulary plays a vital role in every language. At present, many educational media are developed as auxiliary tools for language learning, such as Hanping Chinese Dictionary Lite. Hanping Chinese Dictionary Lite is an electronic dictionary that can help learners learn Chinese vocabulary. This study aims to understand the efficiency of students using the Hanping Chinese Dictionary Lite and students' evaluation of the Hanping Chinese Dictionary Lite. This study uses three research methods: test method, questionnaire survey method, and interview method. The results of the test and questionnaire survey show that the use of Hanping Chinese Dictionary Lite can effectively improve students' Chinese vocabulary. Because after using the dictionary software, the student's mastery of Chinese language has improved, that is, the result of the test is 60% and the result of the questionnaire is 53.50%. In the learning process, the author adopts six steps: showing the mobile phone dictionary to the students, conducting a pre-test, implementing the mobile phone dictionary, conducting a post-test, conducting a questionnaire survey, and conducting interviews. The final results of the interviews show that the evaluation of the dictionary software by the students is very good. It can be said that the students are satisfied with the use of the Hanping Chinese Dictionary Lite and think it is worth using the software
Establishment of Rape Leaf Moisture Content Spectral Character Models Based on RSR-PCA Method
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RETRACTED: Two emissive-magnetic composite platforms for Hg(II) sensing and removal: The combination of magnetic core, silica molecular sieve and rhodamine chemosensors
No full textDevelopment of a riding-type fully automatic transplanter for vegetable plug seedlings
No full textAim of study: The aim of this study was to develop a riding-type fully automatic vegetable seedling transplanter enabling continuous transplanting work on 2 rows simultaneously with plug seedlings fed automatically.
Material and methods: In design, the transplanter consists of a 4-wheel drive system, a seedling pick-up mechanism, a seedling feeding mechanism, a rotation discharging mechanism, a 2-row planting mechanisms, and a multi-source control unit. The 4-wheel drive system is a riding type well adapting to slops. The seedling pick-up mechanism could extract several seedlings at a time from the tray cells conveyed by the feeding mechanism, and then transfer them to the rotation discharging mechanism where they would be released into the 2-row planting mechanisms. The multi-source control unit was constructed to carry out the flexible automation of seedling transplanting. Being the first prototype, the performance tests under actual production conditions were conducted on a vegetable base.
Main results: The testing results showed that the developed fully automatic transplanter could well grasp seedlings from the trays, transfer them, discharge them, and plant them into the ground. The success ratio in picking up seedlings and the qualified percent in planting seedlings were all up to 90%, and the coefficient of variation of plant spacing was less than 5% at the working speed of 60 plants row-1 min-1.
Research highlights: The overall planting effects could well meet the requirements of agronomy cultivation, and the quality of automatic transplanting was satisfactor
Inspection of Lettuce Water Stress Based on Multi-sensor Information Fusion Technology
No full textInternational audienceCharacteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce’s water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models’ universality. By the extraction of lettuce’s multi-sensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4 characteristic wavelengths, characteristic model of multi-spectral image and CWSI were established. These multi-sensor characteristics were fused by using the BP artificial neural network. Based on the fused multi-sensor characteristics, the lettuce moisture evaluation model was established. The results showed that the correlation coefficient of multi-spectral images model, spectral characteristics model and information fusion model were in turn increased, the correlation coefficients were respectively 0.8042, 0.8547 and 0.9337. It was feasible to diagnose lettuce water content by using multi-sensor information fusion of reflectance spectroscopy, multi-spectral images and canopy temperature. The correct rate and robustness of the discriminating model from multi-sensor information fusion were better than those of the model from the single-sensor information
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