Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

基于层次分析法(AHP)优化葛根乙醇提取物制备工艺及其抗氧化活性检测

基于层次分析法(AHP)计算综合评分,在单因素试验基础上采用正交试验设计,优化回流法制备葛根乙醇提取物的工艺,并测定葛根乙醇提取物的抗氧化能力。结果表明:葛根乙醇提取物的最佳制备工艺条件为乙醇体积分数50%、料液比1:15(g/mL)、提取时间45min、提取次数2次,在此条件下综合评分平均值为19.33。抗氧化试验测得葛根乙醇提取物对DPPH和羟基自由基清除率最大分别为55.56%和78.22%,具有一定的抗氧化活性。

基于AHP的高校过程性考核实施效果满意度调查与分析

高校教学改革背景下,过程性考核与结果性考核加以结合的形式逐步取代单一的结果性考核。然而,过程性考核方案设计、应用等方面的合理性、有效性仍然存在一定的改善空间。因此文章使用AHP层次分析法以调查分析高校本科生对于校内过程性考核实施效果的满意度情况。采用层次分析法(AHP)构建满意度评价指标体系,选取安徽大学本科生作为研究样本,使用问卷调查法收集数据,并对收集到的数据进行满意度得分计算。结果显示,学习态度改善、提升就业能力、掌握专业知识、提升交流能力方面过程性考核实施效果满意度得分较低。结合满意度计算结果,文章提出基于考核方式、考核主题、考核方案、教学模式四个角度的四点过程性考核设计建议。

双循环背景下石化企业供应链韧性评价研究——基于AHP-BP方法

随着环境不确定性的提高,中国石化企业供应链稳定性需求日渐攀升,供应链韧性评价已经成为判断石化企业风险应对能力的重要手段。基于双循环背景,通过构建石化企业供应链韧性评估指标体系,利用层次分析法和BP神经网络,对石化企业供应链韧性强度进行评估,确定了供应链韧性水平。结果表明,各石化企业的供应链韧性强度存在较大差异,供应链韧性整体水平偏低。在研究结果的基础上,对韧性供应链锻造提出了切实可行的建议。

中国新能源汽车海外市场开拓的影响因素——基于ISM-AHP模型分析

把握新能源汽车在海外市场开拓过程中的影响因素,对于更好地实现我国新能源汽车的战略目标极为重要。影响新能源汽车海外市场的开拓因素归纳为新能源汽车产品生产、东道国市场条件、新能源汽车产品营销、新能源汽车技术支持、我国政策条件五个层面共22个子要素,并构建我国新能源汽车海外市场开拓影响因素的解释结构模型(ISM),运用层次分析法(AHP)确定各直接影响因素的相对重要性。基于此,提出要注重产品研发、优化出口策略、提升用户黏度、优化政策体系,以提升我国新能源汽车的发展动力,夯实发展基础,并更好地应对替代产品威胁。

基于D-S证据理论改进AHP-熵权的流域洪涝灾害评估研究

考虑致灾因子危险性、孕灾环境敏感性以及承灾体易损性,选取指标构建小清河流域洪涝灾害风险评估指标体系,提出一种基于D-S证据理论的改进AHP-熵权法计算指标权重,求取洪涝灾害风险指数,运用自然断点分级法确定洪涝灾害风险等级,分析小清河流域洪涝灾害风险空间分布情况。结果表明:小清河流域洪涝灾害风险总体上表现出南低北高的趋势,其中高风险区和较高风险区分别占流域面积的8.7%和14.3%,主要分布在小清河干流以及主要支流两岸。所得评估结果同“利奇马”台风发生期间实际洪灾风险分布情况一致,对比证明基于D-S证据理论的改进AHP-熵权法优于AHP和熵权法,可为小清河流域防洪减灾决策提供依据。

基于ISM-AHP-Entropy航空公司风险因素研究

针对航空公司风险因素研究,引入ISM-AHP-Entropy综合评价方法对风险因素进行评价。构建了风险因素指标体系,利用解析结构模型(ISM)将因素指标分为表层和深层致因,构建结构模型;根据问卷结果,用AHP-Entropy求出综合权重,结果表明人因和管理因素对风险影响最为显著;进一步运用AHP-Entropy综合法分别计算表层和深层致因权重并分析,结果表明表层致因中机组资源管理水平、签派员应急处置能力影响最为显著,深层致因中安全规章方针的制定情况、应急管理培训与实施情况影响最为显著;从多个现实角度出发,结合CBTA理念,为航空公司降低风险、提高安全意识与水平提出多条建议,可供航空公司参考采纳。

AHP-改进物元法在整体钢平台顶升模架体系安全风险评估中的应用

针对整体钢平台顶升模架体系施工安全性的灰色特征,经实地调研及专家评审,建立了整体钢平台顶升模架体系的安全风险评估模型。首先,考虑人员、设备、环境、管理及技术5个方面,选取19个指标构建安全风险评估指标体系;其次,采用层次分析法计算各指标权重,基于改进物元法确定各指标的风险等级,然后将两者向量相乘,以最大隶属度原则确定整体钢平台顶升模架体系的安全风险等级;最后,将所建立的基于层次分析法(Analytic Hierarchy Process, AHP)-改进物元法的安全风险评估模型应用到某超高层工程实例中,并将评估结果与实际施工情况对比分析。研究表明,该评估模型的计算结果合理且准确可靠,可为超高层整体钢平台顶升模架体系的安全管理提供参考。

基于AHP方法的应急物流配送网络优化研究

针对传统物流配送方式效率低,配送网络复杂的问题,基于AHP法和聚类分析法对应急物流配送网络进行优化调整,重新规划配送中心选址,最短配送路线采用层次分析法设计。利用SPSS软件进行聚类分析,从而优化特殊时期下应急物资的配送路线,优化后的配送网络力求能用最快的速度响应,从而保障应急物流配送的时效性。

Online pharmaceutical process analysis of Chinese medicine using a miniature mass spectrometer: Extraction of active ingredients as an example

The automation of traditional Chinese medicine(TCM)pharmaceuticals has driven the development of process analysis from offline to online.Most of common online process analytical technologies are based on spectroscopy,making the identification and quantification of specific ingredients still a challenge.Herein,we developed a quality control(QC)system for monitoring TCM pharmaceuticals based on paper spray ionization miniature mass spectrometry(mini-MS).It enabled real-time online qualitative and quantitative detection of target ingredients in herbal extracts using mini-MS without chromatographic separation for the first time.Dynamic changes of alkaloids in Aconiti Lateralis Radix Praeparata(Fuzi)during decoction were used as examples,and the scientific principle of Fuzi compatibility was also investigated.Finally,the system was verified to work stably at the hourly level for pilot-scale extraction.This mini-MS based online analytical system is expected to be further developed for QC applications in a wider range of pharmaceutical processes.

基于Delphi-AHP的轻武器训练安全风险因素分析

轻武器是军队数量最多、使用频率最高,也是训练中出现安全事故较多的一类装备。为解决轻武器训练安全风险因素缺乏量化分析的问题,采用“Delphi构建指标-AHP确定权重-事故分析对比验证”的方法,首次构建了轻武器训练安全评价指标体系,系统分析风险因素。结果表明:基于Delphi-AHP确定的指标权重与事故分析结果较为一致,研究方法科学合理;轻武器训练风险性高、影响因素多,需重点考虑人员和管理因素,关注受训者技能水平、受训者安全意识、组织指挥、风险评估及隐患排查等因素,组训时需考虑不同种类轻武器风险因素差异。

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

Determination AHP Analysis of the Virtual Stage-Gate Process in the Global Scale Automotive Design

In this study, it is aimed to determine the ranking importance levels of the stages to be taken into consideration for new product development on a global scale in the automotive design process. New product design activity and stage-gate process differences between local automotive firms (serial production factory and stage-gate department in Turkey) and global automotive companies (serial production factory and stage-gate department in Turkey) are examined comparatively in the research area. In the automotive industry, which has been developing for a century, the question of how the local company products operating in the last sixty years have not been able to spread globally or how to develop global products is the background question of the research. For this purpose, one on one interviews were held with the managers of 3 national and 3 international automotive companies, who worked in the same region and who had previously designed a new vehicle, with design and product development departments.?According to?the data obtained by the AHP (Analytic Hierarchy Process) in the automotive design process, the importance of the criteria that should be taken into account for global product development has revealed. According to the results of the study, it was found that design validation stages were the most important globalization criterion in automotive design process as a new study area. In the comprehensive survey of the study, no other publication has been encountered to measure or evaluate the stages in the automotive design and new product development process in other sectors, including the vehicle industry. As in every industry sector, in the automotive industry, with the new product companies provide market development or competitive advantage. The new product is the life channel of a company and in the realization of this new vehicle;the disciplines of the automotive industry are formed by a hundred years of experience.

Expert elicitations of smallholder agroforestry practices in Seychelles:A SWOT-AHP analysis

Agroforestry can leverage the co-benefits of climate change adaptation and mitigation while conserving biodiversity and restoring degraded and deforested lands.The preference of relevant stakeholders regarding agroforestry practices enhances sustainable land management through strategic decision-making in Seychelles and other island states.A suitable approach for assessing stakeholders'preferences of agroforestry is the implementation of the strengths,weaknesses,opportunities,and threats(SWOT)approach in combination with the analytic hierarchy process(AHP)method.The entry point of this study is an extensive literature review process,during which 28 SWOT factors were identified.These SWOT factors were deliberated on during a half-day workshop with agricultural experts who agreed on 20 SWOT factors that reflect the local realities of the Seychelles through a consensus approach.Using the SWOT-AHP approach,focus group discussions were conducted to examine the perceptions of researchers and extension workers about the adoption of agroforestry in Seychelles.The results indicated that the positive aspects of smallholder agroforestry outweigh the negative aspects.For example,increased agricultural production,control runoff and soil erosion receive the highest scores among the strength factors perceived by researchers and extension workers,respectively.The willingness of international organizations to fund agroforestry-related projects and the existence of native tree species on farmlands have the highest scores among the opportunity factors.The lack of education,information,and communication between the government and farmers,and the small land size and crop competition have the highest scores among the weakness factors.Lastly,change in government policies on land use has the highest score among the threat factors by researchers,whereas the most significant threat is climate change and variability for the extension workers.The provision for a 30-year land lease agreement in the National Agroforestry Policy of Seychelles is viewed by both groups as an incentive that could potentially drive the adoption and acceptability of agroforestry.Furthermore,better coordination of various efforts to promote agroforestry and more substantial extension services for farmers,especially the role of technologies for optimal production on small plots of land,can enhance climate resilience in Seychelles and other small island developing states.

Analysis of Characteristic and Causes of a Strong Cooling Process in China in the Early Winter of 2020/2021

The characteristics and causes of a drop in temperature during a cold wave process in the early winter of 2020/2021 were analyzed.The results show that the air temperature at 700-600 hPa over China was firstly and mostly influenced by the cold wave process,and then the cold air gradually extended to the lower layer,causing the most severe cooling in North China and its nearby areas.During the cold wave,the longitude of the upper-level jet over the Chinese mainland was larger;the Ural blocking high and the East Asian trough were stronger,so that the geopotential height gradient between the two was also significantly larger;the meridional air flow was abnormally strong,which was conducive to the southward transport of cold air from the middle and high latitudes.Results of the diagnostic analysis further show that the outbreak of the cold wave and the negative temperature tendency anomaly in the key area were mainly caused by the meridional temperature horizontal advection anomaly,while the temperature rise accompanied by abnormal air subsidence compensated for the abnormal decrease in temperature,which was conducive to the gradual rise of temperature in the key area.

多指标结合AHP-CRITIC优化酸枣叶拌蒸酸枣仁炮制工艺

目的:采用正交设计结合层次分析法-客观赋权法(AHP-CRITIC)优化酸枣叶拌蒸酸枣仁的炮制工艺。方法:采用单因素试验优化辅料酸枣叶的加入倍量和蒸制时间,以酸枣叶的加入倍量和蒸制时间为因素,以斯皮诺素、6‴-阿魏酰斯皮诺素、酸枣仁皂苷A、白桦脂酸含量为评价指标进行综合加权评分,结合AHP-CRITIC优化酸枣仁的拌蒸工艺。结果:最佳拌蒸工艺为酸枣叶加入量是酸枣仁的4倍,1层酸枣叶1层酸枣仁交替平铺,上锅蒸制3 h。结论:多指标结合AHP-CRITIC优选酸枣仁拌蒸工艺合理可行,可为酸枣叶拌蒸酸枣仁炮制工艺的生产实践提供参考。

基于BN-AHP的UUV系统效能评估方法

提出了一种结合层次分析法(Analytic Hierarchy Process,AHP)和贝叶斯网络(Bayesian Networks,BN)的新型效能评估方法。首先,梳理提炼了UUV(Unmanned underwater vehicle,UUV)效能评估的五个维度;其次,利用AHP方法为每个维度建立了指标体系,并将其作为贝叶斯网络的节点输入,从而将不确定性因素纳入评估过程;随后,通过贝叶斯推理得出UUV效能评估值;最后,通过计算机仿真与传统AHP法,信息熵和人工神经网络法进行对比,实验结果表明,所提的方法相较于传统AHP方法和信息熵方法的评估结果更具收敛性和集中度,相较于人工神经网络方法可以在数据缺乏的情况下完成评估,同时在效能影响因子分析和方案优选方面更具区分度。