一种调度遥控操作双路径安全校核机制的探索

为解决调度端远方操作路径单一情况下可能出现操作错误的问题,文章探索提出一种对远方操作进行双路径安全确认的构建方案,在常规遥控发起路径之外建立独立的操作许可路径,在操作业务通信的出口处部署操作许可安全网关、操作许可工作站,通过操作发起路径的操作员、监护人和操作许可路径的许可员相结合的多重校验,强化对遥控操作的合法性认证,可以有效避免操作人、操作员站、操作网络环境的不可靠因素带来的操作安全风险,提高电网操作的安全性。     

Viscosity reduction of isotonic solutions of the photosensitizer TPCS2a by cyclodextrin complexation

Meso-tetraphenyl chlorin disulphonate (TPCS_2a) is a photosensitizer (PS) particularly developed and patented for use in the technology of photochemical internalization (PCI) against cancer. TPCS_2a is known to aggregate in aqueous media even at low concentrations (≥0.1?µM) and to form a high-viscosity network at clinically relevant concentrations (mM). The aim of this work was to evaluate the effect of two hydroxypropylated cyclodextrin derivatives of beta and gamma type, respectively i.e. HPβCD and HPγCD, on the aggregation and solubilization of TPCS_2a in isotonic solutions. Samples containing micromolar concentrations of TPCS_2a were studied spectrophotometrically, while samples containing a clinical relevant concentration (10?mM?=?9?mg/ml) of TPCS_2a were evaluated by dynamic viscosity measurements. HPβCD was determined to be a more suitable solubilizer of TPCS_2a than HPγCD in aqueous media both in the absence and presence of salt. The complexation stoichiometry between TPCS_2a/HPβCD at micromolar to millimolar concentrations of TPCS_2a was determined to be 1:3 and 1:2 in the absence and presence of isotonic NaCl, respectively. The network of TPCS_2a (10?mM) was broken down in the presence of 3% w/v (=?20?mM) HPβCD, i.e. a 1:2 molar ratio between TPCS_2a and the cyclodextrin. Formation of the inclusion complex resulted in low viscosity samples both in water and in the presence of isotonic NaCl or phosphate buffered saline (PBS) at 25?°C and 37?°C.     

Value stream mapping approach and analytical network process to identify and prioritize production system’s Mudas (case study: natural fibre clothing manufacturing company)

The current research has been done to identify and prioritize the Mudas of the production line of a natural fibre clothing manufacturing company; in this way, value stream mapping has been used to identify the Mudas, then analytical hierarchy method has been used to screen the Mudas. In the first step, necessary information to design value stream of production process has been obtained by observation, measurement and interview; afterward, according to the obtained information and by forming a focus group, Mudas in the production line have been identified and prioritized according to seven categories of Mudas. In order to prioritizing the Mudas by the usage of analytical hierarchy method, the Super Decision software has been applied. Then, according to the obtained priorities, suggested map of the future value system has been drawn and some suggestions have been presented to eliminate Mudas and move towards the lean manufacturing.     

Sustainability assessment of Iranian petrochemical companies in stock exchange: A data envelopment analysis-based approach

The assessment of efficiency is always of particular importance according to different indicators from different perspectives. There are various techniques for evaluating petrochemical companies, among which the data envelopment analysis technique is one of the best techniques that can be used to calculate the relative efficiency of a set of decision-making units with network structures. In the present paper, seven petrochemical companies listed in the Iranian stock exchange were analysed. These companies were evaluated in terms of financial performance and sustainable development, and their relative efficiency was calculated during 2015–2016. According to the obtained results, only Marun Petrochemical Co. was found to be efficient in all areas and years. The results also showed that four companies were efficient in financial terms over the period under study. In the general conclusion regarding the companies' performance, Marun was ranked first, Jam was ranked second, and Zagros was ranked third.     

基于卷积神经网络的葡萄叶片氮含量识别方法

为实现对葡萄叶片氮素含量快速、便捷的识别，在卷积神经网络VGG-16网络结构基础上，将数据增广后的图像按不同梯度划分进行模型训练，通过十折交叉验证法探究最佳的训练集与验证集分配比例，并构建4个不同深度的网络模型进行训练对比，采用全局平均池化代替全连接层约简网络参数量。训练结果表明，氮含量梯度设为0.70%、0.35%和0.175%时，室内简单背景识别准确率分别为85.9%、76.2%和71.1%；晴天室外复杂背景下识别准确率分别为44.6%、35.0%和30.4%。研究结果表明利用VGG-16建立的网络学习模型对葡萄叶片氮含量识别提供了一种新的便捷方法，对农业信息化和智能化技术应用具有一定促进作用。     

Densely connected convolutional network block based autoencoder for panorama map compression

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.     

神经网络全局势函数在多相催化中的应用

当今的多相催化研究需要新的技术和方法从原子尺度上表征活性中心结构和反应中间体。本文作者课题组近期开发了理论模拟新技术来探索催化剂活性位点结构，即基于神经网络势函数的大规模原子模拟（LASP）软件中实现的全局神经网络势函数计算方法。本文介绍了该方法可以显著降低催化体系的计算代价，而维持与密度泛函理论同一级别的计算精度，从而解决多相催化中的许多复杂问题。本文对神经网络势函数方法的实现细节和目前已实现的应用场景进行了详细介绍。神经网络势函数可以用来预测材料晶体结构，理解高压氢化条件下TiO₂表面的结构演化和确定三元氧化物ZnCrO晶相中合成气制甲醇活性位点。最后文章分析了神经网络势函数的局限性和今后可能的三个研究方向，即材料性质预测、多元素体系神经网络势函数构造和化学反应拟合。     

环形供热管网水力工况设计方法研究

为提高环形供热管网水力工况运行稳定性,提出了基于模糊PID控制的环形供热管网水力工况控制方法。构建环形供热管网水力工况控制的约束参量模型,在此基础上采用回归模型逼近方法构建其测试指标集,结合自适应融合反馈跟踪方法进行环形供热管网水力工况信息的融合处理,并从中提取分布特征量;采用模糊PID控制方法调节提取结果的输出稳定性,实现环形供热管网水力工况优化设计和自适应控制。仿真结果表明,采用该方法进行环形供热管网水力工况控制的稳定性较好,自适应控制能力较强,实现了环形供热管网水力工况优化调节。     

某带钢连续热浸镀机组卧式连续退火炉水冷炉辊的开裂原因

采用宏观和微观形貌观察、化学成分分析、显微组织观察、硬度测试等方法,对某带钢连续热浸镀机组卧式连续退火炉水冷炉辊的开裂原因进行了分析。结果表明:水冷炉辊辊套成分中不含铌元素,镍元素含量偏低,导致组织中形成网状富铬碳化物析出相;辊套内腔结垢、水冷不畅使水冷炉辊产生局部过热,导致工作侧辊套基体中粒状合金碳化物的析出和网状富铬碳化物的长大,使工作侧辊套的脆性增加;随着结垢的沉积,水冷炉辊出现轴承转动不畅,甚至堵转现象;在热应力、扭力作用下,裂纹在网状富铬碳化物析出相中形成并沿网状碳化物逐渐向基体内扩展,最终导致水冷炉辊的开裂。