Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Factor and Cluster Analysis on the Competitiveness of Agri-food Processing Industry in China

This paper establishes 13 evaluation indicators for the competitiveness of agri-food processing industry,uses factor analysis to evaluate the competitiveness of agri-food processing industry in 31 provinces(cities,autonomous regions)of China,and does cluster analysis to divide these regions into several categories according to the difference in competitiveness,in order to understand the level of competitiveness of agri-food processing industry in China.

Analysis of Genetic Diversity of Processing Apple Varieties

Genetic diversity of 18 processing apple varieties and two fresh varieties were evaluated using 12 simple sequence repeats （SSR） primer pairs previously identified in Malus domestica Borkh. A total of 87 alleles in 10 loci were detected using 10 polymorphic SSR markers selected within the range of 5-14 alleles per locus. All the 20 varieties could be distinguished using two primer pairs and they were divided into four groups using cluster analysis. The genetic similarity （GS） of groups analyzed using cluster analysis varied from 0.14 to 0.83. High acid variety Avrolles separated from other varieties with GS less than 0.42. The second group contained Longfeng and Dolgo from Northeast of China, the inherited genes of Chinese crab apple. The five cider varieties with high tannin contents, namely, Dabinette, Frequin rouge, Kermerrien, M.Menard, and D.Coetligne were clustered into the third group. The fourth group was mainly composed of 12 juice and fresh varieties. Principal coordinate analysis （PCO） also divided all the varieties into four groups. Juice and fresh apple varieties, Longfeng and Dolgo were clustered together, respectively, using both the analyses. Both the analyses showed there was much difference between cider and juice varieties, cider and fresh varieties, as well as Chinese crab apple and western European crab apple, whereas juice varieties and fresh varieties had a similar genetic background. The genetic diversity and differentiation could be sufficiently reflected by combining the two analytical methods.

Relationship of public preferences and behavior in residential outdoor spaces using analytic hierarchy process and principal component analysis—a case study of Hangzhou City, China

This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.

Symmetry phases of asymmetric simple exclusion processes on two lanes with an intersection

This paper studies two-lane asymmetric simple exclusion processes(ASEPs)with an intersection.In the upstream segments of the intersection,one particle can move to the next site with rate 1 if the site is empty,and the other particle can move forward with rate p in the sites of downstream segments.The parameter p can represent the rate of slowing of motion,and the parameter is introduced to investigate spontaneous symmetry breaking(SSB)phenomenon.Extensive Monte Carlo simulations are carried out.It is shown that three symmetric phases exist and the SSB does not exist in the system.Simple mean field approach in which correlation of sites is ignored is firstly adopted to analyze the system,and the system is divided into four independent segments.It is found that the analytical results deviate from the simulation ones,especially when p is small.In addition,the inexsitence of SSB can only be explained qualitatively.Motivated by this,we carry out the cluster mean field analysis in which correlation of five sites is considered.It is shown that densities of the two upstream segments are equal,which demonstrates that the SSB does not exist.It is also shown that,as expected,the cluster mean field analysis performs much better than the simple mean field analysis.

Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

Application of Wavelet Analysis toInterference Elimination for Geochemical Hydrocarbon Exploration

Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.

Research and Implementation of the Enterprise Evaluation Based on a Fusion Clustering Model of AHP-FCM

Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.

Existence of spontaneous symmetry breaking in two-lane totally asymmetric simple exclusion processes with an intersection

We study two-lane totally asymmetric simple exclusion processes(TASEPs)with an intersection.Monte Carlo simulations show that only symmetric phases exist in the system.To verify the existence of asymmetric phases,we carry out a cluster mean-field analysis.Analytical results show that the densities of the two upstream segments of the intersection site are always equal,which indicates that the system is not in asymmetric phases.It demonstrates that the spontaneous symmetry breaking does not exist in the system.The density profiles and the boundaries of the symmetric phases are also investigated.We find that the cluster mean-field analysis shows better agreement with simulations than the simple mean-field analysis where the correlation of sites is ignored.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

基于图像处理的视觉采摘机器人作业控制研究

为了实现自动化的番茄分类采摘,基于视觉识别技术设计了识别系统。首先,基于HSV视觉体系中的H分量,采用聚类分析的方法,依据成熟度将番茄分为不熟,半熟和全熟3类,并计算3类番茄成熟度对应的H分量分布范围;其次,根据半熟和全熟番茄H分量的分布范围进行番茄图像分割,并利用形态学的方法得到图像中番茄区域的轮廓曲线;再次,采用椭圆拟合方法实现对番茄轮廓拟合,计算得到图像中番茄区域的中心坐标;最后,采用双目视觉系统实现图像中番茄区域中心坐标向实际空间坐标转化。对番茄轮廓曲线拟合精度和视觉定位精度进行测试,表明系统具有良好的可靠性。

K-Medoids聚类算法的计算机信息处理技术研究

当前计算机信息处理技术在大规模数据集上存在计算效率低下、对噪声和异常值敏感等问题。为了解决这些问题,本文提出了一种改进的K-medoids聚类算法。该方法通过优化初始中心点的选择和更新策略,提高了算法的收敛速度和稳定性,并引入基于密度的聚类评价指标,提高了对噪声数据的鲁棒性。通过在真实和人工数据集上的实验验证,证明了本方法在提高聚类效果和处理大规模数据方面的有效性。

基于聚类分析的Dupal异常指标遴选及其在印度洋的应用

Dupal异常是一种广泛分布于南半球的大尺度地幔地球化学不均一现象,其成因机制和分布范围一直是地球科学研究的热点。由于Dupal异常概念提出时间较早,仅基于少量岩石样品,其概念存在模糊性和不确定性,目前仍缺乏统一的异常判别指标,影响了对异常分布范围和成因的认识,进而制约了对全球Dupal异常的进一步研究。本文选取印度洋洋底(包括洋中脊轴部和离轴区域)玄武岩的铅同位素数据,结合西南印度洋中脊和东南印度洋中脊的已知地球化学边界,采用K-means++聚类分析方法突出大尺度地球化学特征,初步遴选Δ7/4>2.5,Δ8/4>35作为Dupal异常的铅同位素判别标准。基于这一标准,识别出西北印度洋中脊欧文断裂带为Dupal异常边界,边界以南洋中脊区域显示较明显的异常。就其分布而言,整个印度洋洋中脊的Dupal异常分布于安德鲁-贝恩断裂带、澳大利亚-南极错乱带以及欧文断裂带所围限的区域;离轴区域的凯尔盖朗海底高原、Broken海脊、东经90度海岭、圣诞群岛、莫桑比克海底高原、厄加勒斯海底高原等也具有明显的Dupal异常;整体上印度洋Dupal异常呈现东西分带特征。基于印度洋洋底玄武岩数据识别的Dupal异常指标将为全球Dupal异常的研究提供参考。印度洋Dupal异常分布范围的厘定将为揭示其异常成因提供关键基础。

十里香夏季花香型红茶加工工艺优化及其品质分析

为优化十里香夏季花香型红茶的加工工艺,提升其品质,该研究采用感官审评、化学品质分析、聚类分析和偏最小二乘判别分析(partial least squares discrimination analysis,PLS⁃DA)对不同工艺加工的茶样品质进行综合评价。结果表明,16个工艺加工的十里香夏季花香型红茶的感官品质得分为88.3~93.8,其中有8个产品的得分超过90,整体表现较优;水浸出物、茶多酚、游离氨基酸、茶黄素、茶红素、茶褐素、咖啡碱含量以及儿茶素总量范围分别为30.39%~43.38%、9.69%~22.68%、1.55%~2.96%、0.06%~0.66%、0.86%~6.71%、0.29%~8.93%、5.18%~5.96%、5.79%~12.72%;系统聚类以欧式平方距离为15时可将16个茶样聚为3大类;PLS⁃DA可通过品质成分得分图、拟合曲线、载荷图及其变异权重参数预测型将不同茶样进行区分。十里香夏季花香型红茶的最优加工工艺为萎凋时间12 h、发酵时间2 h、足火温度80℃。

香附饮片与4种醋制香附饮片的快速鉴别

目的建立香附饮片与4种醋制香附饮片的傅里叶变换红外光谱图,快速鉴别香附饮片与4种醋制香附饮片;建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,并测定α-香附酮、香附烯酮、5-羟甲基糠醛(5-HMF)、对香豆酸、阿魏酸、木犀草素的含量。方法建立25批香附样品的红外光谱图,利用OMNIC 9.2软件分析其平均红外光谱间的差异,采用Spectrum for Window 3.02软件标定共有峰,计算相对峰高;采用《中药色谱指纹图谱相似度评价系统(2012版)》建立香附饮片与4种醋制香附饮片的高效液相指纹图谱,进行相似度评价,确定共有峰个数;采用SIMCA 14.1软件进行聚类分析、主成分分析、正交偏最小二乘法-判别分析,判断红外光谱图结果与高效液相指纹图谱结果是否相互验证;测定α-香附酮、香附烯酮、5-HMF、对香豆酸、阿魏酸、木犀草素的含量。结果25批香附样品的红外光谱图的相关系数为0.9347~0.9829,共标定16个共有峰,1500~1300 cm^(-1)波段处差异明显。正态分布分析结果显示,2928、1649、995 cm^(-1)波段处可分别将醋煮品、醋炙品、醋蒸品与饮片区分;高效液相指纹图谱结果显示25批样品共有21个共有峰,相似度均大于0.9。指认出3号峰为5-HMF,7号峰为对香豆酸,8号峰为阿魏酸,14号峰为木犀草素,19号峰为香附烯酮,21号峰为α-香附酮;红外光谱图与高效液相指纹图谱聚类分析结果、主成分分析结果与正交偏最小二乘法-判别分析结果一致,均可聚为五类,即能够相互验证;5-HMF的含量经炮制后均有增加。对香豆酸、阿魏酸、木犀草素在饮片中含量最低,经炮制后均有增加。香附烯酮经醋炙、醋煮、醋蒸、醋煮蒸后均有减少,在香附饮片中含量最高。α-香附酮经醋炙后含量略有增加,经醋煮、醋蒸、醋煮蒸后均减少。结论香附饮片与4种醋制香附饮片的红外光谱图之间存在明显差异,能够快速鉴别香附饮片与4种醋制香附饮片;所建立的高效液相指纹图谱与含量测定方法操作简单、准确。

不同炮制方法对大皂角和猪牙皂中油类成分的影响

目的考察炒制、酥制、炒炭对大皂角和猪牙皂中油类成分的影响。方法水蒸气蒸馏法提取挥发油,索氏提取法提取脂肪油,测定提取率。GC-MS法分析油类成分种类和相对含量,进行聚类分析。结果炮制后,2种药材中脂肪油提取率升高,挥发油提取率(除酥制外)降低;大皂角中油类成分提取率高于猪牙皂;有毒烯烃苯类成分相对含量降低。结论大皂角和猪牙皂中油类成分种类和相对含量存在明显差异,前者药用品质更优,其减缓燥烈之性的炮制机理可能是降低有毒烯烃苯类成分相对含量。

基于柱前衍生GC-MS法比较不同工艺制备黄精的氨基酸含量

建立了柱前衍生GC-MS法测定黄精中15种氨基酸含量的方法。测定经不同工艺制备的黄精鲜品、干品及炮制品样品中氨基酸的含量,并运用多元数据统计对这3种不同黄精样品中的氨基酸进行综合分析。结果表明:三种黄精制品中的15种氨基酸在33min内有良好的分离效果,在线性范围内线性关系良好(r:0.9983〜0.9999),平均加样回收率为95.66%〜105.55%(n=6)。说明该方法能准确测定黄精中多种氨基酸的含量,且黄精鲜品氨基酸含量明显高于干品及炮制品。聚类及正交偏最小二乘法-判别分析结果表明,黄精鲜品、干品和炮制品可以明显聚为不同三类,说明上述三种黄精制品的氨基酸含量有明显差异性,并筛选出赖氨酸、组氨酸、甘氨酸和络氨酸作为区分黄精鲜品、干品和炮制品的标志物。

融合结构重参数化变换的气体泄漏红外检测

针对常规工业气体泄漏检测装置需泄漏扩散到一定范围并与传感器接触时才能响应的不足,提出一种融合结构重参数化变换的红外非接触式检测网络模型GRNet。GRNet模型采用Mosaic-Gamma变换的预处理方法增加泄漏样本数量并提高图像对比度以增强模型的鲁棒性;通过K-means聚类分析出适用于气体泄漏红外检测的候选框以预置模型参数;优化定位损失函数以提高模型对泄漏区域的定位准确性;采用改进后的轻量化网络RepVGG模块重构特征提取网络增强模型的特征提取能力,以实现轻量化并提高检测精度。实验结果表明,GRNet模型对氨气泄漏的平均检测精度达到94.90%,单张图像平均检测时间达到3.40 ms。采用伪色彩映射实现泄漏浓度的视觉感知效果,采用PyQt5将GRNet模型进行封装实现气体泄漏红外检测系统界面的可视化并在Jetson Nano B01嵌入式实验平台部署该模型,验证了实际工程应用的可行性和有效性,为开发气体泄漏非接触探测装置以保障涉气企业的安全生产和稳定运行提供一种有效的检测算法。

面向电力营销的多源日志安全数据挖掘方法

针对当前电力营销业务系统内部电力营销数据分散、缺乏对电力营销数据统一管理,在多源电力营销数据库中应用了Apache Lucene的Elasticsearch分布式搜索引擎。通过采用主控芯片型号为XC7Z035FFGH676-2的Cortex-A9处理器,提高了电力营销多源电力营销安全数据信息的挖掘和计算能力;通过自组织映射神经网络与模糊聚类算法的聚类分析方法,提高了电力营销数据异常检测能力;利用自组织映射神经网络与模糊聚类算法减少能源数据消耗,提高了数据挖掘能力。所提方法的聚类分析时间最短为104 s,为下一步研究奠定了基础。

一种基于EGK-均值聚类和PCA的海流发电机叶片冲击故障检测方法

海流发电机受海流流速变化、随机湍流的影响呈现多变工况,想要准确检测出叶片冲击故障是一项巨大挑战。基于此,提出一种基于包络几何特征聚类(EGK-均值聚类)的方法用于划分变工况下产生的定子电流信号,在不同子工况下建立故障检测模型。首先,根据样本矩阵构建包络几何特征矩阵,利用几何特征矩阵确定初始聚类中心进行聚类;然后,基于各工况下的包络数据分别进行主元分析以降低数据维度;最后,T^(2)和SPE统计量被计算用于实时冲击故障检测。一台海流发电机样机及配套循环水槽被搭建验证本文所提方法。实验结果表明本文所提方法在变工况下针对海流发电机叶片冲击故障有着显著的识别能力和检测精度。