基于人工神经网络的智能化架空线路应变快速解调方法北大核心

为了提高智能化光纤复合架空线路态势感知的实时性,将人工神经网络方法应用于光纤沿线应变解调,确定了神经网络的结构。编程实现了基于洛伦兹模型的最小二乘谱拟合方法和神经网络方法,采用不同信噪比和布里渊频移的布里渊谱训练神经网络,将它们应用于某光纤复合架空线路沿线光纤应变的测量,从不同角度比较了两种方法的计算结果。计算结果表明,神经网络方法能有效获得光纤沿线的布里渊频移进而获得应变,具有与谱拟合方法相似的准确性,但应变解调时间仅约为谱拟合方法的1/20000。研究结果为提高智能光纤复合架空线路态势感知的实时性提供了参考。     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

Effects of Electromagnetic Waves with LTE and 5G Bandwidth on the Skin Pigmentation In Vitro

With the rapid growth of wireless communication devices, the influences of electromagnetic fields (EMF) on human health are gathering increasing attention. Since the skin is the largest organ of the body and is located at the outermost layer, it is considered a major target for the health effects of EMF. Skin pigmentation represents one of the most frequent symptoms caused by various non-ionizing radiations, including ultraviolet radiation, blue light, infrared, and extremely low frequency (ELF). Here, we investigated the effects of EMFs with long-term evolution (LTE, 1.762 GHz) and 5G (28 GHz) bandwidth on skin pigmentation in vitro. Murine and Human melanoma cells (B16F10 and MNT-1) were exposed to either LTE or 5G for 4 h per day, which is considered the upper bound of average smartphone use time. It was shown that neither LTE nor 5G exposure induced significant effects on cell viability or pigmentation. The dendrites of MNT-1 were neither lengthened nor regressed after EMF exposure. Skin pigmentation effects of EMFs were further examined in the human keratinocyte cell line (MNT-1-HaCaT) co-culture system, which confirmed the absence of significant hyper-pigmentation effects of LTE and 5G EMFs. Lastly, MelanoDerm™, a 3D pigmented human epidermis model, was irradiated with LTE (1.762 GHz) or 5G (28 GHz), and image analysis and special staining were performed. No changes in the brightness of MelanoDerm™ tissues were observed in LTE- or 5G-exposed tissues, except for only minimal changes in the size of melanocytes. Collectively, these results imply that exposure to LTE and 5G EMFs may not affect melanin synthesis or skin pigmentation under normal smartphone use condition.     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.     

新工科背景下电子通信专业“人工智能技术基础”课程改革实践

人工智能技术已经成为新一轮科技革命的重要驱动力量，这要求电子、通信等非计算机专业的人才培养引入针对性强的人工智能课程教学。本文针对新工科背景下电子、通信专业“人工智能技术基础”课程存在的问题进行课程改革，从授课和实验两个环节入手，分别从教学内容、教学方法和考核办法三个方面提出了具体的改革举措，并在“人工智能技术基础”课程中进行了实践，激发了学生的学习热情，拓展了学生的专业视野，提高了学生的实践创新能力，取得了良好的教学效果。为了进一步培养电子、通信学科交叉型AI人才，该课程选拔优秀学生开展丰富的人工智能课外实践活动，进一步提升学生的实践创新能力。     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

Corrosion behavior of under‐, peak‐, and over‐aged 6063 alloy: A comparative study

The mechanical property of age‐hardenable Al‐alloys is governed by the state of ageing, which determines the microstructure and consequently, their corrosion behavior which is a vital aspect for a number of applications. This article presents a comparative assessment of corrosion behavior of under‐, peak‐ and over‐aged Al‐Mg‐Si alloy. Corrosion characteristics have been determined via immersion tests in 0.1 M ortho‐phosphoric acid solution and intergranular corrosion (IGC) tests. Corroded surfaces are examined by field emission scanning electron micrographs‐energy dispersive spectroscopy and 3D optical profilometer. The obtained results reveal that the corrosion rate at a specific immersion time as well as the depth of IGC increases in the order for under‐, peak‐, and over‐aged states. Irrespective of the state of ageing, corrosion loss increases linearly but the rate of corrosion decreases rapidly with increasing immersion time. The dominant mode of corrosion in under‐aged alloy is identified as localized pitting, while peak‐aged is highly susceptible to IGC in contrast extensive pitting corrosion is observed for over‐aged alloy. The observed differences in corrosion behavior are explained considering characteristics of precipitates. Formation of β (Mg₂Si) in case of over‐aged alloy and presence of inclusions like AlFeMnSi particles are found to accelerate pitting corrosion.