Efficient and robust CNN-LSTM prediction of flame temperature aided light field online tomography

Light field tomography,an optical combustion diagnostic technology,has recently attracted extensive attention due to its easy implementation and non-intrusion.However,the conventional iterative methods are high data throughput,low efficiency and time-consuming,and the existing machine learning models use the radiation spectrum information of the flame to realize the parameter field measurement at the current time.It is still an offline measurement and cannot realize the online prediction of the instantaneous structure of the actual turbulent combustion field.In this work,a novel online prediction model of flame temperature instantaneous structure based on deep convolutional neural network and long short-term memory(CNN-LSTM)is proposed.The method uses the characteristics of local perception,shared weight,and pooling of CNN to extract the threedimensional(3D)features of flame temperature and outgoing radiation images.Moreover,the LSTM is used to comprehensively utilize the ten historical time series information of high dynamic combustion flame to accurately predict 3D temperature at three future moments.A chaotic time-series dataset based on the flame radiation forward model is built to train and validate the performance of the proposed CNN-LSTM model.It is proven that the CNN-LSTM prediction model can successfully learn the evolution pattern of combustion flame and make accurate predictions.

卖方私人信息对消费者在线短租入住意向的影响研究

以往的研究指出分享住房市场的在线评分趋于同质性,无法有效影响消费者信任。在线短租房东私人信息(面孔照片)的使用推动了本文研究的深入,本文建构了信任倾向和面孔可信度对消费者信任及其入住意向的影响机制模型,运用情境实验法证明了信任倾向和面孔可信度通过影响消费者信任,进而影响其入住意向,且房间价格在信任和入住意向之间起负向调节作用。本文的研究结果为住房分享市场中卖方私人信息(面孔照片)的使用提供了理论指导,在消费者自身信任倾向一定的情况下,消费者会更多地利用外界信息(如卖方的私人信息面孔照片)来做出决策。

Java环境下网上审批流程的设计及实现

针对某油田租赁企业的业务实际,开发了基于工作流控制的油田租赁企业网上审批流程系统,该系统采用MVC模式的Struts框架,将数字签名技术和工作流技术应用其中,实现了油田租赁业务相关单据、报表等文件的网上流转和审批,大大提高了企业的工作效率。

地理信息资源共享服务平台设计

构建地理信息资源共享服务平台,推动服务保障网络化,是提高数据保障能力的重要手段和未来服务保障的发展趋势。本文立足于地理信息数据保障实际需求,探讨了地理信息资源共享服务平台的基本架构和主要功能,可以为相关系统建设提供参考,满足各行业在建设中对地理信息资源的需求。

DVD在线租赁的优化模型

对2005年全国大学生数学建模B题，在满足会员需求、兼顾利润的原则下进行了建模。对问题1）利用参数法，建立了以最少购买量为目标的概率模型；对问题2）引入了0-1变量，建立了以会员总满意度为目标的0．1线性规划模型；对问题3）建立了以会员的总满意度和网站的总利润的双目标规划，并将其转化为单目标规划。最后通过Lingo编程求解，取得了满意的结果；并进行了结果分析，验证了模型的可行性及高效性。

Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.

Long-term Visual Tracking: Review and Experimental Comparison

As a fundamental task in computer vision,visual object tracking has received much attention in recent years.Most studies focus on short-term visual tracking which addresses shorter videos and always-visible targets.However,long-term visual tracking is much closer to practical applications with more complicated challenges.There exists a longer duration such as minute-level or even hour-level in the long-term tracking task,and the task also needs to handle more frequent target disappearance and reappearance.In this paper,we provide a thorough review of long-term tracking,summarizing long-term tracking algorithms from two perspectives:framework architectures and utilization of intermediate tracking results.Then we provide a detailed description of existing benchmarks and corresponding evaluation protocols.Furthermore,we conduct extensive experiments and analyse the performance of trackers on six benchmarks:VOTLT2018,VOTLT2019(2020/2021),OxUvA,LaSOT,TLP and the long-term subset of VTUAV-V.Finally,we discuss the future prospects from multiple perspectives,including algorithm design and benchmark construction.To our knowledge,this is the first comprehensive survey for long-term visual object tracking.The relevant content is available at https://github.com/wangdongdut/Long-term-Visual-Tracking.

A vision system based on CNN-LSTM for robotic citrus sorting Author links open overlay panel

Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and efficiency.In this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential data.The fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic grasping.High detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames later.The average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time performance.The results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.