A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

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 automated treatment planning strategy for highly noncoplanar radiotherapy arc trajectories

Radiation therapy is a technology-driven cancer treatment modality that has experienced significant advances over the last decades, due to multidisciplinary contributions that include engineering and computing. Recent technological developments allow the use of noncoplanar volumetric modulated arc therapy (VMAT), one of the most recent photon treatment techniques, in clinical practice. In this work, an automated noncoplanar arc trajectory optimization framework designed in two modular phases is presented. First, a noncoplanar beam angle optimization algorithm is used to obtain a set of noncoplanar irradiation directions. Then, anchored in these directions, an optimization strategy is proposed to compute an optimal arc trajectory. The computational experiments considered a pool of twelve difficult head-and-neck tumor cases. It was possible to observe that, for some of these cases, the optimized noncoplanar arc trajectories led to significant treatment planning quality improvements, when compared with coplanar VMAT treatment plans. Although these experiments were done in a research environment treatment planning software (matRad), the conclusions can be of interest for a clinical setting: automated procedures can simplify the current treatment workflow, produce high-quality treatment plans, making better use of human resources and allowing for unbiased comparisons between different treatment techniques.     

超高层劲性结构及大型机械设备立体协同施工技术

基于某超高层建筑群项目的主体施工阶段,对超高层劲性结构建筑群项目施工过程中可能遇到的问题,诸如大型机械立体协同、钢结构与钢筋连接节点优化、钢结构桁架层施工等重难点进行总结分析,并提出相应的解决方案以供探讨,可为同类型工程提供参考经验。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

变分自编码器模型综述

变分自编码器（VAE）作为深度隐空间生成模型的一种，近年来其表现性能取得了极大的成功，尤其是在图像生成方面。变分自编码器模型作为无监督式特征学习的重要工具之一，可以通过学习隐编码空间与数据生成空间的特征映射，进而在输出端重构生成输入数据。梳理了传统变分自编码器模型及其衍生变体模型的发展与研究现状，并就此做了总结和对比，最后分析了变分自编码器模型存在的问题与挑战，并就可能的发展趋势做了展望。     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

On the application of remote sensing towards the estimation of cultivated land lost to urbanization

In this research work, a 40-km² SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program’s Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.     

基于孪生支持向量回归机的转炉炼钢终点预测

为了提高转炉炼钢的终点命中率，建立了一种新的转炉终点预测模型，实现了对转炉终点碳质量分数和温度的准确预测。模型采用K最近邻孪生支持向量机(KNNWTSVR)算法，将权重矩阵引入到目标函数中，并利用鲸群优化算法进行求解，提高了传统算法的性能；然后基于某炼钢厂260 t转炉的实际生产数据，建立了转炉炼钢终点预测模型。结果表明，预测模型的终点碳质量分数(误差±0.005%)和温度(误差±15 ℃)的终点单命中率分别为94%和88%，双命中率达到84%。与其他两种现有的建模方法相比，本模型取得了最优的预测效果。该方法满足转炉炼钢实际生产的需求，也可适用于钢铁冶金其他领域的数学建模。     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.