SDG-based Model Validation in Chemical Process Simulation

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.

A CUSP CATASTROPHE, PRECURSORS PATTERN AND EVOLUTION PROCESS OF ROCKBUST OF COAL PILLAR UNDER A HARD ROCK SUBJECT TO ELASTIC SUPPORT

The rockburst of the poal pillar under a thick hard roof stratum is modelled as the instability failure problem of coal pillars under strata subject to elastic support. The instability mechanism of rockburst is studied by applying cusp catastrophic theory. The effects of the stiffness ratio of the system and loads imposed on the system on the rockburst are explicated.The factors affecting rockbursts are discussed. Based on them, the evolution process, the forewarning regularity arid forewarning sings of rockbursts are studied. It is indicated that the subsidence velocity of roof stratum, which increases quickly and tends to infinity, is the forewarning measurable signs of the rockbursts of coal pillar.

Fault diagnosis of AMT gear shifting process based on semi-quantitative SDG model

In order to diagnose gear shifting process in automated manual transmission（AMT）,a semi-quantitative signed directed graph（SDG）model is applied.Mathematical models are built by analysis of the power train dynamic and the gear shifting control process.The SDG model is built based on related priori knowledge.By calculating the fuzzy membership degree of each compatible passway and its possible fault source,we get the possibilities of failure for each possible fault source.We begin with the nodes with the maximum possibility of failure in order to find the failed part.The diagnosis example shows that it is feasible to use the semi-quantitative SDG model for fault diagnosis of the gear shifting process in AMT.

A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

Sign Language to Sentence Formation:A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages,whereas deaf people cannot do so.Therefore,the Sign Language(SL)is the communication medium of such people for their conversation and interaction with the society.The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs.The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively.The signs for singular words such as I,eat,drink,home are unalike the plural words as school,cars,players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately.Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration.The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment.Hence,there is still a demand for specific environment to automate such a communication solution to interact with such type of special people.This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words.Finally,these are converted into the respective words/sentences within a reasonable time.This provide a real time solution for the deaf people to communicate and interact with the society.

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

Continuous Arabic Sign Language Recognition in User Dependent Mode

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.

Mexican Sign Language Recognition Using Jacobi-Fourier Moments

The present work introduces a system for recognizing static signs in Mexican Sign Language (MSL) using Jacobi-Fourier Moments (JFMs) and Artificial Neural Networks (ANN). The original color images of static signs are cropped, segmented and converted to grayscale. Then to reduce computational costs 64 JFMs were calculated to represent each image. The JFMs are sorted to select a subset that improves recognition according to a metric proposed by us based on a ratio between dispersion measures. Using WEKA software to test a Multilayer-Perceptron with this subset of JFMs reached 95% of recognition rate.

科技论文中数学符号编排的标准化和规范化

为了解决科技论文编辑加工和作者写作中公认的难点和重点问题,实现数学符号编排的标准化和规范化,根据数学符号在相关国家标准中的使用规则,结合笔者在高等数学专业的学习和教学中积累的数学知识以及理工类科技期刊编辑加工经验,针对大量数学符号编排时的常见问题及同类问题的不同表现形式,分析导致问题出现的主要原因、弊端乃至危害,给出相应的对策,并提出提高含有大量数学符号的科技论文编辑加工和写作效率的通用的处理技巧,旨在抛砖引玉,便于科技论文编辑、作者等相关工作者结合自身科研、工作实际情况进行举一反三,事半功倍地有效避免问题,在提高科技论文学术质量的同时,轻松应对其中大量数学符号的编排。

利益相关者视角下生前预嘱签署的困境与挑战

目的:分析在生前预嘱签署过程中各利益相关群体面临的困境与挑战,为优化生前预嘱的签署策略提供参考。方法:采用现象学研究方法,于2023年7月—11月对来自深圳市的生前预嘱(潜在)签署主体、家属、医护人员、社工、志愿者共33人进行半结构化访谈,采用Colaizzi 7步分析法进行资料分析。结果:共提炼出3个主题和6个子主题,分别为空间错位(医院之外、医院之内),时间错位(为时尚早、为时已晚),主体错位(家属“越位”、患方“让位”)。结论:生前预嘱签署在实践过程中,签署地点、签署时间、决策主体3个维度存在错位,导致生前预嘱在推广实施中遭受冷遇。提示未来需要搭建各利益相关群体的沟通平台,加强生命教育,在公众中建立生前预嘱的理念,从而推动生前预嘱签署的相关共识的达成。

基于认知心理学的电梯安全标志有效性评价

电梯安全标志是引导乘客规范乘梯的重要工具,但当前张贴状况和有效性不佳。为客观评价和引导提高电梯安全标志的有效性,基于认知心理学将电梯安全标志的认知行为过程分为5个阶段,进而建立包含5个二级指标和20个三级指标的评价体系,并基于国家标准和现有文献对各指标设置评分标准,以某市某商场为例进行电梯安全标志有效性评价,随后针对电梯安全标志使用现状提出相关建议。

基于居民需求层次分析的社区消防安全标志设计研究

目的社区消防安全标志在提高居民对火灾安全的认知和应对能力方面具有关键作用,从居民实际的需求角度出发,旨在设计一款易理解、实用性强、融入多感官的消防安全标志。方法参考现有的社区消防安全标志现状,通过调研对社区居民与消防安全标志的一系列互动行为进行拆解,对现存问题进行总结,利用AHP层次分析法提取出社区居民在消防安全标志中的重要需求因素,进而将其应用于产品实践中。结果内容简洁易懂、融入多感官设计、材质环保、图形稳定准确是社区消防安全标志的主要设计要素,以此进行设计研究。结论针对社区消防安全标志的使用问题进行讨论及研究,结合社区居民需求层次分析的方法,将分析结果运用于消防安全标志设计实践中,设计更符合社区住户需求的消防安全标志,以提高标志的实用性和社区居民对安全信息的接受度。

改进YOLOv5s算法在交通标志检测识别中的应用

针对复杂道路交通场景中交通标志检测识别精度低的问题,提出了一种改进YOLOv5s算法的目标检测识别方法。采用迭代自组织数据分析算法对TT100K数据集进行聚类分析选择更适合的先验框,新先验框能够更加全面地覆盖交通标志的尺寸,提高模型的检测精度;对特征图进行上采样操作,获得更大尺度的特征图后与主干网络特征图融合,得到特征信息更加丰富的新特征图用来小目标的检测识别,提高了小目标检测识别的精度;用真实框和先验框宽比和高比的差值替代真实框和先验框宽高比的差值对定位损失函数进行改进,解决了宽高比相同但实际尺寸不同时惩罚消失问题。实验结果表明改进算法与YOLOv5s算法相比,提高了9.55%的平均精度均值,对小目标的检测识别具有更好的效果。

流程化护理模式对胃肠镜检查术患者检查效率及睡眠质量的影响

目的:探讨流程化护理模式对接受胃肠镜检查术患者检查效率及睡眠质量的影响。方法:选取2022年6月至2023年6月福建省老年医院消化内二科收治的58例胃肠镜检查术患者作为研究对象,按照随机数字表法分为观察组和对照组,每组29例。对照组给予常规护理,观察组实施流程化护理,比较2组检查效率、睡眠质量以及生命体征变化情况。结果:观察组检查过程中麻醉药物使用剂量较对照组更少,检查所用时间较对照组更短(P<0.05)。检查结束后1个月,观察组匹兹堡睡眠质量指数量表(PSQI)评分较对照组更低(P<0.05)。检查结束,2组心率(HR)、呼吸频率(R)、收缩压(SBP)、舒张压(DBP)均有所升高,但观察组升高幅度明显更小,且HR、R、SBP、DBP均较对照组更低(P<0.05)。结论:流程化护理模式对胃肠镜检查术患者检查效率及睡眠质量提升改善具有显著作用,同时也能更好地维持患者生命体征的稳定,值得推荐和施行。

LNG应急调峰站工艺与总图结合设计在某站中的实际应用

[目的]解决现有LNG(液化天然气)应急调峰气源设施无法满足未来燃气供应突增需求的问题。[方法]采用综合设计方法,着重于LNG应急调峰站的工艺设计和总图规划。通过详细分析两者之间的协同作用,探讨了如何在设计过程中实现工艺与总图的有机结合,以提高设施的整体效能和适应性。[结果]将工艺设计与总图规划相结合,能够显著提升LNG应急调峰站的运行效率和安全性。这种集成设计方法不仅优化了空间布局,还促进了操作流程的流畅性,确保了在紧急情况下快速响应的能力。[结论]将工艺设计与总图规划有效结合,可以构建高效、安全且具有高度灵活性的LNG调峰系统,从而保障城市在冬季等高需求期的燃气稳定供应。

基于复杂网络理论的符号有向图(SDG)化工故障诊断

化工过程系统的大型化和复杂性,仅通过常规方式来描述故障机理越来越受到限制。本文以流程图建模法构建的符号有向图(signed directed graph,SDG)故障模型为基础,将化工过程系统抽象为网络拓扑结构,通过对网络模型的统计特征描述,判断网络的复杂性、小世界性和无标度性,进而以复杂网络中心性理论定量计算网络中各个节点的重要性,分析比较各指标来确定网络中的核心节点,并通过Capocci算法对网络进行社团结构的定量划分,最后以网络中的核心节点确定化工过程中易引起安全事故的关键变量,并用社团划分的结果绘制出化工故障诊断模型的关键路径,确定重点监测部位。案例应用结果表明:该方法可行,为化工过程系统中故障节点和监测提供了新的解决思路,丰富了化工过程故障诊断和预防控制的相关理论。

证候自然过程中自身内在的演变规律

The syndromes have theirself spontaneous process from production,development to end,and the compositing symptoms and signs of syndrome is from no to be,from light to heavy,from simple to multiple.The different essential forms of the syndrome are composed with the different symptoms and signs.And the different essential forms of the syndrome and their sub-assembly of compositing symptoms and signs are the reflection of objective rule existing within the internal evolving changes of spontaneous process of syndromes.

烟气条件下疏散指示能见度实验及图像分析

开展了烟气条件下疏散指示能见度的初步研究。首先通过实验统计分析了观察者给出的烟气条件下疏散指示能见度等级变化规律,重点分析了疏散指示的亮度对能见度的作用,发现亮度是影响能见度的主要因素。这区别于无烟条件下疏散指示能见度的主要影响因素:亮度、大小和颜色。然后对实验过程中采集到的图像进行分析,获得了疏散指示亮度信息的图像特征——对比度的变化,发现烟气条件下疏散指示图像的对比度变化信息较好地符合了观察者对疏散指示能见度等级评价的变化规律,且在烟气浓度变化缓慢的条件下可以细致评价疏散指示性能,故采用对比度信息来衡量烟气条件下疏散指示的安全性有更大的安全余量。结果表明,数字图像分析是量化烟气条件下疏散指示能见度的一种有效方法。

产品多意象造型进化设计

为解决消费者对产品造型的多意象需求,在分析产品造型进化设计整体流程的基础上,提出产品多意象造型进化设计的模型、实现方法和关键技术。确定了多意象目标需求和产品样本造型参数,建立了意象造型神经网络评价系统,利用非支配排序遗传算法建立了多意象造型进化设计系统。该算法的非劣分类、拥挤度计算和修剪运算等技术保证了多意象造型进化设计的多样性和高水平性。以汽车侧轮廓设计为对象进行实例研究,结果表明该方法可有效应用于产品多意象造型进化设计。