Effect of Atmosphere on Volatile Emission Characteristic in Oxy-Fuel Combustion

A new type of power supply which was called oxy-fuel combustion power plant was introduced to reduce greenhouse gasses emission. In this paper the volatile emission characteristic of pulverized coal is studied under air atmosphere and oxy-fuel atmosphere. Combustion experiments of Datong bituminous coal were carried out in a wire mesh reactor at heating rates of 1 K/s, 10 K/s and 1000 K/s respectively under air and O2/CO2 atmosphere conditions in order to investigate the volatile emission characteristic. The concentrations of volatile (mainly CO and CH4) emission were on-line measured by infrared gas analyzer. It was indicated that the concentrations of CO and CH4 in O2/CO2 atmosphere were higher than those in air. The direct oxidation of carbon and gasification reaction between carbon and CO2 are the main causes of the increased amount of CO. The higher concentration of CO2 also results in the increased amount of CH4 in O2/CO2 conditions.

基于美学评价的图像管理系统设计与实现

随着计算机视觉和计算机美学的兴起,图像管理系统也不再满足于普通存取图像的基本功能,如百度云、时光像册等软件的图像管理模块里加入了许多智能化功能,以满足用户的使用习惯。但它们存在美学功能单一、数据存储在公有云上等缺点。因此,这一基于美学评价的图像管理系统,将针对上述问题,以Python为主要编程语言,以深度学习框架Caffe为主、以OpenCV为辅,实现图像分类、人脸识别、艺术风格迁移、美学评价、相似图片搜索、图像融合等美学功能。并采用Django-RestFramework为后端框架,提供WebAPI,并以Json格式的数据与前端进行通信,其中前端的框架采用AngularJS。本系统采用上述技术手段,搭建一个能够实现各种美学智能功能的后端服务器,这一服务器负责提供规范的Json格式数据,前端网页利用这些Json数据,给用户呈现出美学计算结果。

Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.

Composing Like an Ancient Chinese Poet:Learn to Generate Rhythmic Chinese Poetry

Automatic generation of Chinese classical poetry is still a challenging problem in artificial intelligence.Re-cently,Encoder-Decoder models have provided a few viable methods for poetry generation.However,by reviewing the pri-or methods,two major issues still need to be settled:1)most of them are one-stage generation methods without further polishing;2)they rarely take into consideration the restrictions of poetry,such as tone and rhyme.Intuitively,some an-cient Chinese poets tended first to write a coarse poem underlying aesthetics and then deliberated its semantics;while oth-ers first create a semantic poem and then refine its aesthetics.On this basis,in order to better imitate the human creation procedure of poems,we propose a two-stage method(i.e.,restricted polishing generation method)of which each stage fo-cuses on the different aspects of poems(i.e.,semantics and aesthetics),which can produce a higher quality of generated poems.In this way,the two-stage method develops into two symmetrical generation methods,the aesthetics-to-semantics method and the semantics-to-aesthetics method.In particular,we design a sampling method and a gate to formulate the tone and rhyme restrictions,which can further improve the rhythm of the generated poems.Experimental results demon-strate the superiority of our proposed two-stage method in both automatic evaluation metrics and human evaluation met-rics compared with baselines,especially in yielding consistent improvements in tone and rhyme.

Leveraging proficiency and preference for online Karaoke recommendation

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these platforms.In the meantime,the system automatically evaluates user proficiency according to their singing behavior.Recommending approximate songs to users can initialize singers5 participation and improve users,loyalty to these platforms.However,this is not an easy task due to the unique characteristics of these platforms.First,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still open.Second,the sparsity of the user-song interaction behavior may greatly impact the recommendation task.To solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing data.Specifically,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are leveraged.Then we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating matrix.Finally,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

Improving the performance of SSVEP-BCI contaminated by physiological noise via adversarial training

Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer communication.In practical application scenarios,SSVEP-BCI systems are easily interfered by physiological noises such as electromyography(EMG)and electrooculography(EOG).The performance of traditional SSVEP recognition methods will degrade in such a noisy environment,which limits their real-world applications.To alleviate the interference of noise,existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises.In this study,we utilize adversarial training(AT)and neural networks(NNs)to construct a robust recognition method for SSVEP contaminated by physiological noise.During model training,we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them.In this way,we strengthen the robustness of the model to potential noises,such as physiological noises.In this study,we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet.The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG.We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario.Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.

Illuminating Recommendation by Understanding the Explicit Item Relations

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

超越直觉正义?——有关正义的实证研究评述

刑事司法与民意的关系问题无论在我国还是在西方国家都有探讨。刑事司法活动可能无法忽视或者回避社会大众的正义需求。社会大众对正义的感受与评价,也直接影响其对法律制度和程序的遵从。国外在此方面的研究,尤其是'直觉正义'和'程序正义'的两位代表人物——罗宾逊和泰勒的实证研究,可能为我国对此问题的探讨提供一些借鉴。事实上,国内近年来社科法学的发展,也在借鉴西方理论框架的基础上产生了一些实证研究。本文试图就罗宾逊和泰勒两位重要学者的研究进行梳理,并结合国内在此领域内已产生的一些实证研究。在此基础上,本文也将讨论究竟这些理论点和相关实证发现,对于我国刑事司法领域的研究和实践有哪些启示。