A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Identification of digital technologies and digitalisation trends in the mining industry

Digitalisation in mining refers to the use of computerised or digital devices or systems and digitised data that are to reduce costs, improve business productivity, and transform mining practices. However, it remains increasingly difficult for mining companies to decide which digital technologies are most relevant to their needs and individual mines. This paper provides an overview of digital technologies currently relevant to mining companies as presented and discussed by mining journals, the media and insight reports of leading consultancy agencies. Relevant technologies were systematically identified using text-mining techniques, and network analyses established the relations between significant technologies. Results demonstrated that currently 107 different digital technologies are pursued in the mining sector. Also, an analysis of the actual implementation of digital technologies in 158 active surface and underground mines reveals a limited uptake of digital technologies in general and that the uptake increases with the run-of-mine production. Large-scale mining operations appear to select and apply digital technologies suitable to their needs, whereas operations with lower production rates do not implement the currently available digital technologies to the same extent. These minor producers may require other digital transformation solutions tailored to their capabilities and needs and applicable to their scale of operations.     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.     

Non-chaotic and chaotic propagation of stationary and dynamic images through MVKS turbulence

Anisoplanatic electromagnetic (EM) propagation across a turbulent atmosphere has been recently examined for an unmodulated carrier propagating over an image-bearing transparency through optical lensing, and for the embedded information inside a carrier recovered using heterodyning and digital demodulation. Carrier modulation yielded better recovery than simple lens-based imaging. A possible mitigation strategy is proposed whereby the image information is encrypted on an RF chaotic carrier, thereafter secondarily embedded onto an optical carrier. Results based on the modified von Karman (MVKS) and the Hufnagel-Valley (H-V) models showed that the signal/image recovery under turbulence is improved compared with non-chaotic propagation. The case of time-varying/dynamic images is also taken up; it is demonstrated via cross-correlation products that turbulence is mitigated by the use of chaotic carrier encryption. Overall, transmission via chaos offers mitigation against distortions due to turbulence along with the security feature inherent via the chaos keys which prevent signal recovery without key-matching.     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

Animations showing Lego manipulative tasks: Three potential moderators of effectiveness

Evidence suggests that transient visual information, such as animations, may be more challenging to learn than static visualizations. However, when a procedural-manipulative task is involved, our evolved embodied cognition seems to reverse this transitory challenge. Hence, for object manipulative tasks, instructional animations may be more suitable than statics. We investigated this argument further by comparing animations with statics using a Lego task shown to university students, by examining three potential moderators of effectiveness: (a) the environment of manipulation (virtual or physical), (b) the quality of visual information (focused or unfocused), and (c) the presence of hands (no hands or with hands). In Experiment 1 we found an advantage of animation over statics, and no differences among the environments. In Experiment 2, we again observed an animation advantage, a small advantage of focused static information compared to unfocused static information, and a positive effect of not showing the hands.     

《化学制药单元操控》工学结合课程的教学探索

《化学制药单元操控》是一门新开发的将情景教学、实验操作和仿真实训相结合的工学结合课程。其中,引入化工仿真教学这一新型的教学方式,即通过在非常逼真的操作环境中进行技能训练,使学生对实际生产装置有更加感性的了解和认识。本文对《化学制药单元操控》工学结合课程开发与实施应用过程进行分析和总结得到几点经验,同时分享对该门课程教学改革中的一些心得。     

基于强化学习的电力市场信息披露程度对市场成员交易行为影响研究

基于信息披露的主体框架，分析披露体系中具体的供需信息对于市场成员的引导作用。建立日前市场的双层竞价优化模型，采用强化学习的方法，通过市场中有无关键供求信息披露对于发电商报价预期的影响，推演模拟不同信息披露程度下发电商报价行为，然后从策略演化过程、策略偏离度以及策略收敛速度3个角度进行信息披露程度对市场成员交易行为影响的定量分析，验证市场供需信息的发布程度对于市场成员的引导作用，最后基于推演结果进行总结，并对我国信息披露的建设提出建议与思考。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。