工程师资格国际互认专项部分项目安排沟通会召开

<正>10月26日,工程师资格国际互认专项部分项目安排沟通会在中国科技会堂召开。中国科协学会学术部副部长朱雪芬出席会议并讲话,计划财务部预算管理处副处长郝茜、近30个全国学会的秘书长等相关负责人参加会议。朱雪芬在讲话中通报了近期工程教育专业认证等工作情况,重点介绍了中国科协将在2013年代表我国申请加入《华盛顿协议》的有关工作进展情况和全国学会参与工程教育专业认证工作的主要进展,并对2012年度工程师资格互认国际交流专项部分项目进行了部署安排。朱雪芬指出,当前正是我们全力以赴争取2013年加入《华盛顿协     

参与互认体系建设 加强国际双边合作

在国家认监委的带领下,作为中国的认可机构,中国合格评定国家认可委员会积极参与国际互认体系,加强国际合作。目前,中国认可国际合作组织,在国际互认体系中形成了"多边全面参与,局部引领,双边突出重点,对我有利,履行国际协议义务,服务国内认可发展"的工作模式。积极参与国际互认体系建设1998年1月22日,国际认可论坛     

浅议电能表OIML证书制度

本文介绍了电能表作为国家重点管理的计量器具推行OIML证书制度的必要性,并提出了国家法定计量技术机构通过电能表OIML国际互认实验室考核的具体措施。     

我国已认可合格评定机构6813家

<正>截至10月底,我国已累计认可各类合格评定机构6813家,认可的合格评定机构数量约占全球国际互认认可合格评定机构总量的八分之一,在世界上处于领先地位。这是记者从近日在京召开的第三届全国合格评定机构认可工作会议上获悉的。认可是一种证实能力、传递信任的评价方式。认可机构按照相关国际标准或国家标准,对认证机构、实验室、检查机构实施评审,证实其满足相关标准要求,确认其有能力开展相应的合格评定活动。中国合格评定国家认可委员会是我国唯一的合格评定国家     

我国注册监理工程师已愈10万人

我国推行工程监理制度20年来,建设监理事业从无到有,稳步发展。目前,我国工程监理行业已建立了一支有6100多家监理企业、52万从业人员的监理队伍,其中注册监理工程师已达到10万余人。     

注册公用设备工程师考试信息

人事部办公厅、建设部办公厅于2005年3月7日发出国人厅发[2005]20号文件，文件主要内容为：（a）注册公用设备工程师（暖通空调、动力、给排水三个专业）的资格考试分基础考试和专业考试。基础考试分两个半天进行，各为4小时，专业考试分专业知识和专业案例两部分内容，每部分均分两个半天进行，每个半天均为3小时。     

基于源头分类的长沙市生活垃圾治理框架设计

本文首先从长沙市生活垃圾处理现状、政策体系、垃圾回收体系和居民垃圾分类意识等方面介绍了长沙市垃圾分类的基本概况,简要分析了目前长沙市垃圾分类存在的问题,并从国外的垃圾分类处理模式中学习先进经验.最后,针对垃圾源头分类存在的问题提出了以党建、居委会、物业、居民为主体的社区垃圾分类治理措施,强调从垃圾产生的前端下手,围绕社区展开垃圾分类活动.     

我国自动化系统工程师资格认证（ASEA）工作全面启动

专业技术资格认证是国际上通行的职业准人制度。多年来，我国的职业资格认证工作主要是由国家人事部门和各个单位对企业技术人员进行“职称评定”，其评定标准及方法也只适用于我国国内，与国际是不交流和不互认的。随着国家和地区之间人才相互交流、合作日益频繁，我国各类专业技术人员在“专业知识”、“专业技能”等方面急需与国际接轨。因此改革我国现存“职称评定”制度，按照国际惯例实施“自动化系统工程师”的高质量培训和资格认证，并力争使他们的职业资格得到国际认可，是极其重要的事情。     

德国菜茵TUV注册供应商贡量工程师／经理培训课程

课程背景随着全球市场竞争加剧和全球化的趋势,中国企业面临着越来越严峻的挑战。如何提升供应商的产品质量、降低采购成本、更加及时且迅捷地交付和产品的同步开发已经成为企业亟待解决的问题。     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can train models with high accuracy. The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction (DR). The proposed deep learning framework utilizes the strength of selected models (VGG and Inception V3) by fusing the extracated features. To select the most discriminant features from a pool of features, an entropy concept is employed before the classification step. The deep learning models are fit for measuring the highlights as veins, liquid dribble, exudates, haemorrhages and miniaturized scale aneurysms into various classes. The model will ascertain the loads, which give the seriousness level of the patient’s eye. The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures.     

培养有中国特色的工业工程师

分析了我国企业与国外企业的不同及差距，指出在我国工业企业推行工业工程的重要意义，提出了我国应努力培养高素质的工业工程师。     

未来设计

度过了不平凡的2020年,我们迎来了一个伟大转折的大时代。随着抗击新冠疫情和脱贫攻坚的胜利,国家进入了社会主义现代化强国建设的新征程,《湖南包装》的任务也因此变得更为广阔和不同。新的目标是面向中华民族伟大复兴的第二个百年,见证和总结设计实践,推动设计学术研究。借由着设计的无处不在,和设计的革命与创造,这本曾经专注于设计实践与设计相关学术研究的刊物,将把视野投向更广阔的领域。     

一个未来中国街区的诞生

街道是我们……大家的？ 在世界所有的城市，都存在同样的问题： 街道是为谁，又是为什么事物而存在？ 街道属于谁？ 谁在决定街道？控制街道？监视街道？ 我们该如何缓解街道的速度、各种交通、以及居民、路人和店主的需求？ 我们该怎样使街道变得明了、流畅、友好？ 街道被多少广告、交易、自由表达以及艺术占领才不算太多？……     

需求驱动型供应链物流仿生优化设计框架

为提高供应链运作效率,运用仿生学理论和方法,仿生生物基因表达调控模式对需求驱动型供应链物流进行了优化设计。分析了基因表达调控机制,建立了基因表达调控的“功能-行为-结构”模式,识别了需求驱动型供应链物流与基因表达调控过程的相似性,提出了包括需求驱动--响应机制、设施位置、信息传输过程和物流路径在内的需求驱动型供应链物流仿生设计框架。研究表明,仿生设计能够从整体上优化供应链物流的运作过程,提高运作效率。     

基于深度学习的图像分类算法框架研究

目的 提高图像分类精度是实现自动化生产的基础,提出一种更加准确的图像分类方法,使自动化包装和生产更加高效.方法 基于ResNeSt特征图组的思想,通过引入通道域和空间域注意力机制,并将自适应卷积核思想和Gem池化引入空间域注意力模块,从而使网络在空间域注意力机制中能够对不同图片使用不同的感受野使其关注更重要的部分,提出一种具有通道域和空间域注意力机制,且具有很好移植性的图像分类网络模型结构.结果 文中方法提高了图像分类准确度,在ImageNet数据集上,top-1准确度为81.39％.结论 文中提出的ResNeSkt算法框架优于目前的主流图像分类方法,同时网络整体结构具有很好的移植性,可以作为图像检测、语义分割等其他图像研究领域的主干网络.     

Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and data can be learned and not limit to hand-crafted features. On the other hand, hyperparameters are commonly set manually, which may take a long time and leave the risk of non-optimal hyperparameters for classification. An effective tool for tuning optimal hyperparameters of deep CNN is Bayesian optimization. However, due to the complexity of the CNN, the network can be regarded as a black-box model where the information stored within it is hard to interpret. Hence, Explainable Artificial Intelligence (XAI) techniques are applied to overcome this issue by interpreting the decisions of the CNNs in such wise the physicians can trust. To play an essential role in real-time medical diagnosis, CNN-based models need to be accurate and interpretable, while the uncertainty must be handled. Therefore, a novel method comprising of three phases is proposed to classify these life-threatening diseases. At first, hyperparameter tuning is performed using Bayesian optimization for two state-of-the-art deep CNNs, and then Darknet53 and InceptionV3 features are extracted from these fine-tunned models. Secondly, XAI techniques are used to interpret which part of the images CNN takes for feature extraction. At last, the features are fused, and uncertainties are handled by selecting entropy-based features. The experimental results show that the proposed method outperforms existing methods by achieving an accuracy of 97% based on a Bayesian optimized Support Vector Machine classifier.

     

未来的设计学府

现代的设计学院不仅是传授设计知识平台，也是交流项目和职业实习的积极推动者，它们实际上经常是学生和制造者的桥梁，分布全球的大学。文化机构和知名的研究机构，创造性地培育了新一代设计师。它们的重任就是传播设计的力量，还有就是发展个人设计风险，第136期的《ddn》杂志中展示了一系列世界顶级设计学府中活跃的景象。[编者按]     

A Subjective Design Framework for Conceptual Design of Polymeric Processes with Multiple Parameters

Polymer processes are in general difficult to model, especially because of coupling between process conditions, polymeric behaviors and geometries. Arising from high thermal gradients, non-Newtonian viscous behaviors and non-linear pressure effects, polymer processing is not well described mathematically. A conceptual design methodology is proposed formally as a useful tool for treating polymeric processes with multiple performance parameters on a structured design platform. Without resorting to engineering models, this approach deals with the initial selection of process conditions within a three-stage framework: (1) qualitative design; (2) process modeling; and (3) quantitative design. Based on subjective reasoning, this procedure makes it possible to account for one’s prior experience, and incorporate it into the process development. The notion of conceptual robutstness is introduced to ensure deesign quality in the early-stage process development. A case study of compression molding is illustrated in a step-by-step manner.