Integrated multi-factory production and distribution scheduling applying vehicle routing approach

This paper introduces a new integrated multi-factory production and distribution scheduling problem in supply chain management. This supply chain consists of a number of factories joined together in a network configuration. The factories produce intermediate or finished products and supply them to other factories or to end customers that are distributed in various geographical zones. The problem consists of finding a production schedule together with a vehicle routing solution simultaneously to minimise the sum of tardiness cost and transportation cost. A mixed-integer programming model is developed to tackle the small-sized problems using CPLEX, optimally. Due to the NP-hardness, to deal with medium- and large-sized instances, this paper develops a novel Improved Imperialist Competitive Algorithm (IICA) employing a local search based on simulated annealing algorithm. Performance of the proposed IICA is compared with the optimal solution and also with four variants of population-based metaheuristics: Imperialist Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimisation (PSO), and Improved PSO. Based on the computational results, it is statistically shown that quality of the IICA’s solutions is the same as optimal ones solving small problems. It also outperforms other algorithms in finding near-optimal solutions dealing with medium and large instances in a reasonably short running time.     

Modeling and analysis for multilayered complex communication network of satellite navigation system

This study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchical architecture. Firstly, a method called snapshots was proposed to describe the dynamic topology for CCN‐SNS; secondly, another method was put forward to model the different nodes of the CCN‐SNS; thirdly, the different links between every two different nodes were modeled. Therefore, based on the OPNET tools, a simulation for the CCN‐SNS, which contains the models that proposed earlier used to analyze the navigation accuracy and network transmission performance, was performed.     

改进的卷积神经网络单幅图像超分辨率重建

针对经典的基于卷积神经网络的单幅图像超分辨率重建方法网络较浅、提取的特征少、重建图像模糊等问题，提出了一种改进的卷积神经网络的单幅图像超分辨率重建方法，设计了由密集残差网络和反卷积网络组成的新型深度卷积神经网络结构。原始低分辨率图像输入网络，利用密集残差学习网络获取更丰富的有效特征并加快特征梯度流动，其次通过反卷积层将图像特征上采样到目标图像大小，再利用密集残差学习高维特征，最后融合不同卷积核提取的特征得到最终的重建图像。在Set5和Set14数据集上进行了实验，并和Bicubic、K-SVD、SelfEx、SRCNN等经典重建方法进行了对比，重建出的图像在整体清晰度和边缘锐度方面更好，另外峰值信噪比（PSNR）平均分别提高了2.69?dB、1.68?dB、0.74?dB和0.61?dB。实验结果表明，该方法能够获取更丰富的细节信息，得到更好的视觉效果，达到了图像超分辨率的增强任务。     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

Convolutional neural network: Deep learning-based classification of building quality problems

The rapid development of the construction industry in China has introduced unprecedented quality-related problems in the country’s building industry. In response to this issue, the government has established various complaint channels to report quality problems. Therefore, building quality complaints (BQCs) need to be classified and solved by respective agencies or departments rapidly for avoiding adverse impact on the safety, health, and well-being of people. However, the current process of classifying BQCs is labor intensive, time consuming, and error prone. An automatic complaint classification is required to improve the effectiveness and efficiency of complaint handling, but studies on this issue are limited. Prevailing text classification research in construction has focused on utilizing conventional shallow machine learning. By contrast, this study explores a novel convolutional neural network (CNN)-based approach that incorporates a deep-learning method to automatically classify the short texts contained within BQCs. The presented approach enables capturing the semantic features in BQC texts and automatic classification of the BQCs into predefined categories. After the model optimization, tests are conducted to examine the practical application of the text classification approach compared with Bayes-based and support vector machine classifiers. Results indicate that the developed CNN-based approach performs well in the Chinese BQC classification with limited manual intervention and few complicated feature engineering.     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

面向任务口语理解研究现状综述

口语理解是对话系统重要的功能模块，语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来，联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法，介绍两个任务由独立建模到联合建模的方法，重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法，并总结了目前存在的问题以及未来的发展趋势。     

Study on the long-term behaviour of glass fibre in the tensile stress field

Glass fibre is a random network structure composed of [SiO₄] tetrahedra. The structure contains a large number of defects, which act as crack initiation points. Under tensile stress, cracks undergo crack initiation, stable propagation, failure propagation, and fracture, and the stress that begins after unstable propagation is called the critical fracture stress. When the stress is less than the critical value, the crack is subject to the force of chemical bonds during the crack propagation process, and crack arrest occurs. When the stress is greater than the critical value, the glass fibre will undergo destructive fracture. In this paper, long-term tensile tests were carried out on glass fibre and a glass fibre composite under different constant tensile stress conditions. The fracture times of the glass fibre and glass fibre composite under different tensile stresses were obtained, the critical fracture stress of glass fibre was inferred, and the fracture mechanism was explained.     

Microwave irradiated Carrageenan-Guar gum micro-porous IPN: a novel material for isotropic tissue scaffolding

In this study, a novel micro porous honeycomb structured Crg-GG-IPN material was incepted to be applicable as scaffold and accomplished. The hydrophilicity was confirmed by FT-IR and OCA. Amorphous nature and micro-rough surface were confirmed by XRD and AFM. Void fraction was 0.61?±?0.04. Void space, hemocompatibility and platelet adhesion were captured by SEM. Degradability of the material was confirmed by in-vitro degradation study. Incision method using mice model was a clear evidence for cell attachment and non-toxicity and was confirmed from hematology and histopathology. Thus, it appears that Crg-GG scaffolds can be useful as wound healing material for clinical applications.