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| 1 | Study of an athermal infrared dual band optical system design containing harmonic diffractive element显示文摘A harmonic diffractive element (HDE) is first successfully introduced to the athermal system of infrared dual band in this paper. In this system, there are only three lens and two materials, silicon and germanium. When the temperature ranges from -70℃ to 100℃ in the dual band, it can simultaneously accomplish the rectification of the longitudinal aberration in the big field of view, as well as the wave front aberration less than 1/4 wavelength. Modulation transfer function of dual band approaches or attains the diffraction limit. The calculation results show that the spectral properties of the HDE are between refractive and diffractive elements, so we can design a simple dual-band and athermal optical system by selecting the thickness and central wavelength of the HDE exactly. Compared with a conventional refractive optical system, this system not only reduces the demand for high technical levels, but also has a compact structure, few elements, a high transmittance better aberrations performances and athermal character. At the same time, the use of the HDE also offers a new element for the infrared optics design. | SUNQiang WANGZhaoqit LIFengyou LIUHongli LUZhenwu CHENBo MUGuoguang | 2003 | Chinese Science Bulletin2003，48，2： | 6 |
| 2 | 显示文摘 | YANGYu-ping ZHENGHai-fei SUNQiang(杨玉萍 郑海飞 孙樯) | 2006 | 自然科学进展2006，16，1： | 1 |
| 3 | Beam quality improvement by gain guiding effect in end-pumped Nd:YVO4 laser amplifiers显示文摘 | Xiang Zhen Wang Dan Pan Sunqiang | 2011 | Opt Express2011，19，21： | 1 |
| 4 | 显示文摘 | ZHANGXiao-long LIUYing SUNQiang(张晓龙 刘英 孙强) | 2012 | 光学学报2012，32，11： | 1 |
| 5 | Neural Machine Translation by Fusing Key Information of Text显示文摘When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that time.Although the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long sentences.On the other hand,the most important part in traditional translation tasks is the translation of key information.In the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be guaranteed.In order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on Transformer.The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder.After the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the decoder.With incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very reliable.In order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification set.The experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset.The experimental results show the advantages of the model proposed in this paper. | Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 6 | Coercivity of fractal aggregates formed by single-domain particles显示文摘Coercivityoffractalaggregatesformedbysingle-do-mainparticlesWangQian,SunQiang,LiJian(DepartmentofPhysics,SouthwestChinaNormal... | WangQian SunQiang LiJian | 1994 | 西南师范大学学报(自然科学版)1994，19，4： | 0 |
| 7 | Antireflective characteristics of hemispherical grid grating显示文摘In this paper, the optical characteristics of new type hemispherical grid subwavelength grating are studied by using multi-level column structure approximation and rigorous coupled-wave analysis. This kind of grating could be fabricated by chemical methods, thus simplifying the fab-rication technology of subwavelength gratings for visible light. By computer simulation and calculation, the hemi-spherical grid subwavelength gratings are proved to have antireflective characteristics. Two design schemes of this kind of grating are presented. In the first scheme, the grating could achieve a reflectivity as low as 3.4416×10?7, which can be adapted to 0.46―0.7 μm of visible waveband and ±12° incident angle field. In the second scheme, the grating can achieve a reflectivity as low as 3.112×10?4 and adapted to the whole visible waveband and ±23° incident angle field. The application field of the latter scheme is wider than that of the former. The results of this paper could provide reference for the applications of the hemispherical grid subwavelength gratings for the visible waveband. | RENZhibin JIANGHuilin LIUGuojun SUNQiang | 2005 | Chinese Science Bulletin2005，50，13： | 0 |
| 8 | A Semantic Supervision Method for Abstractive Summarization显示文摘In recent years,many text summarization models based on pretraining methods have achieved very good results.However,in these text summarization models,semantic deviations are easy to occur between the original input representation and the representation that passed multi-layer encoder,which may result in inconsistencies between the generated summary and the source text content.The Bidirectional Encoder Representations from Transformers(BERT)improves the performance of many tasks in Natural Language Processing(NLP).Although BERT has a strong capability to encode context,it lacks the fine-grained semantic representation.To solve these two problems,we proposed a semantic supervision method based on Capsule Network.Firstly,we extracted the fine-grained semantic representation of the input and encoded result in BERT by Capsule Network.Secondly,we used the fine-grained semantic representation of the input to supervise the fine-grained semantic representation of the encoded result.Then we evaluated our model on a popular Chinese social media dataset(LCSTS),and the result showed that our model achieved higher ROUGE scores(including R-1,R-2),and our model outperformed baseline systems.Finally,we conducted a comparative study on the stability of the model,and the experimental results showed that our model was more stable. | Sunqiang Hu Xiaoyu Li Yu Deng Yu Peng Bin Lin Shan Yang | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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