Proceedings of the 4th International Conference on Statistics: Theory and Applications最新文献

A Decade of Lessons Learned in Supporting a National Big Data Platform for Urban Research 支持国家城市研究大数据平台的十年经验

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.001

R. Sinnott

引用次数: 0

Leveraged Study Design for Identifying Dominant Causes of Variation 确定变异主要原因的杠杆研究设计

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.163

Mahsa Mahsa, S. Steiner, J. D. Mast

{"title":"Leveraged Study Design for Identifying Dominant Causes of Variation","authors":"Mahsa Mahsa, S. Steiner, J. D. Mast","doi":"10.11159/icsta22.163","DOIUrl":"https://doi.org/10.11159/icsta22.163","url":null,"abstract":"Extended Abstract Excessive variation in critical to quality process outputs is a common challenge in manufacturing industries. For variation reduction, most process quality improvement (variation reduction) frameworks follow Juran’s diagnostic and remedial journeys [1], that is, first using some methods to find the cause(s) of output variation (the diagnosis) and then, seeking a solution for eliminating the effect of the identified cause(s) (the remedy). Among all causes of variation, usually only a few have a big impact on the overall variability [2]. Shainin refers to them as the dominant cause(s) [3, 4]. Finding the dominant cause(s) requires a systematic strategy. The Shainin System TM [3, 5] is a coherent statistical stepwise variation reduction strategy with several problem-solving techniques. One of the techniques associated with the Shainin System TM that aims to help identifying the suspect dominant causes is group comparison , which exploits the concept of leveraging by comparing the extreme parts [5]. To do so, we select two groups of six or more (typically eight) parts, one group consisting of parts with large and the other with low quality characteristic values. Then, only for these selected parts, we measure as many suspect dominant cause input characteristic ’s as possible. If is a dominant cause, its values must be substantially different between the two groups. Shainin [3] suggests using the Tukey end-count test [6] to compare the values in the two groups. Although the investigation plan based on leveraging is an efficient way of gathering information in searching for a dominant cause using relatively small sample size, the Shainin analysis procedure is less than ideal.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Enabled Quality Improvement in SmartManufacturing Systems 机器学习助力智能制造系统质量提升

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.002

Jianjun Shi

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A Structural Learning Method for Graphical Models 图模型的结构学习方法

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.113

Benjamin Szili, Mu Niu, Tereza Neocleous

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Relative Belief and Combining Evidence 相对信念与综合证据

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.119

M. Evans

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Precision Agriculture: Herbicide Reduction with AI Models 精准农业:人工智能模型的除草剂减少

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.152

Renan Andrade, T. Ramires

{"title":"Precision Agriculture: Herbicide Reduction with AI Models","authors":"Renan Andrade, T. Ramires","doi":"10.11159/icsta22.152","DOIUrl":"https://doi.org/10.11159/icsta22.152","url":null,"abstract":"Extended Abstract Sugarcane cultivation has been concentrated in several countries due to its diversity of use, such as in fuel, sugar, as well as other areas. Among the 80 largest sugarcane producers, Brazil occupies the first place, representing 22% of world production in the 2020/2021 harvest. The modernization of agriculture, called agriculture 4.0, has allowed greater productivity, which are directly affected by the invasion of weeds. A survey presented by [1] shows that the invasion of Brachiaria decumbens and Panicum (weed varieties) were responsible for the loss of 40% of the sugarcane production. Integrated weed management, which includes constant mapping in a crop and the appropriate choice of control strategies, can be achieved through a better understanding of the structure and production system in relation to the behaviour of weeds in the field, as well as the optimization of its control. The adoption of the soil mapping method in the regular network allows producers, who use the localized application of fertilizers and herbicides, to make agribusiness more competitive and efficient in agricultural management and in increasing productivity [2]. In a study carried out by [3] it was observed that with the application of targeted herbicide (punctually) in beet, corn, wheat and others cultivars, it was possible to obtain a reduction from 6 to 81% in applications directed to weeds of broad-leaved and a 20 to 79% reduction in applications targeting narrow-leaf weeds. In this survey, we propose a supervised machine learning model, which was able to identify weed invasion in a sugarcane cultivar, using four colour spectra as input variables, being NIR, RE, R and G, which were obtained by a multispectral camera adapted to an unmanned aerial vehicle. The model used to predict weed infestation","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multifractal Analysis of MODIS Terra Satellite Time Series of Italian Urban Forests 意大利城市森林MODIS Terra卫星时间序列的多重分形分析

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.123

L. Telesca, N. Abate, F. Faridani, Carmen Fattore, M. Lovallo, R. Lasaponara

{"title":"Multifractal Analysis of MODIS Terra Satellite Time Series of Italian Urban Forests","authors":"L. Telesca, N. Abate, F. Faridani, Carmen Fattore, M. Lovallo, R. Lasaponara","doi":"10.11159/icsta22.123","DOIUrl":"https://doi.org/10.11159/icsta22.123","url":null,"abstract":"Urban forests can improve the environmental quality of urban areas increasing their sustainability and contributing to reduce the effects of natural and anthropogenic hazards, like climate change, hydrogeological hazards, heat waves, acoustic and atmospheric pollution. Therefore, identifying any disturbance, which could affect vegetation, represents an important task within the framework of urban forest monitoring. Among the causes of plant diseases and loss of biodiversity, pathogenic bacteria have been documented as severely impacting vegetation status, as in the case of Toumeyella parvicornis, an alien species prevalent from southern Canada to northern Mexico, that has been detected for the first time in Europe at the end of 2014, in Campania (Italy) on Pinus pinea, in the urban area of Naples [1], and now spreading in Southern Italy, where it could have devastating effects. It is well known that remote sensing is an effective means for monitoring the status of forests, thanks to the availability of advanced sensors that make possible to capture in advance trends of vegetation degradation [2]. In particular, remote sensing could be used to detect pre-visual stages of the plant infection, thus preventing the epidemic spread by infected but asymptomatic trees. In work we study six forests located in different of Castel Castel","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130138187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longitudinal Beta GEE Modelling for Analysing Global and Regional Prevalence of Anaemia in Women 用于分析全球和区域妇女贫血流行的纵向Beta GEE模型

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.131

E. Ibrahimi, Jona Shkurti, Aldiona Kërri, Thao Mai Thi Phuong

{"title":"Longitudinal Beta GEE Modelling for Analysing Global and Regional Prevalence of Anaemia in Women","authors":"E. Ibrahimi, Jona Shkurti, Aldiona Kërri, Thao Mai Thi Phuong","doi":"10.11159/icsta22.131","DOIUrl":"https://doi.org/10.11159/icsta22.131","url":null,"abstract":"- In this study, we use a beta regression approach to model the worldwide longitudinal prevalence of anaemia in pregnant and non-pregnant women. The estimates of anaemia prevalence from 1990 to 2016 are extracted for each country from the WHO Data Repository. Since the data for the subjects (i.e., countries) are clustered within sampling units, and the measurements within the same country are correlated, a beta-distributed Generalized Estimating Equation (GEE) model allowing for a population-averaged interpretation of the regression coefficients is fitted. The analysis is implemented in the SAS GLIMMIX procedure. Regardless, parameter coefficients in the GEE are estimated invariably; even if the covariance structure is miss-specified, a careful selection of the working correlation structure is performed to improve the efficiency of the estimates. Pregnancy and WHO regions had significant effects on the prevalence of anaemia. The significant interaction between pregnancy and time suggested that the decline in prevalence over time was larger in non-pregnant women than in pregnant women.","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement Learning Based Optimal Adversarial Pathway Estimation Using Remotely Sensed Spectral-Terrain Data and Human Value Assessment 基于遥感光谱地形数据和人类价值评估的强化学习最优对抗路径估计

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.112

Josef Affourtit, Nicholas V. Scott

{"title":"Reinforcement Learning Based Optimal Adversarial Pathway Estimation Using Remotely Sensed Spectral-Terrain Data and Human Value Assessment","authors":"Josef Affourtit, Nicholas V. Scott","doi":"10.11159/icsta22.112","DOIUrl":"https://doi.org/10.11159/icsta22.112","url":null,"abstract":"Extended Abstract Geo-intelligence organizations are often faced with the need to determine optimal pathways that adversaries may take based on various types of information including remotely sensed imagery and human geo-intelligence. The mobile enemy problem, where the objective is to predict the pathway that a mobile enemy may take, is considered here as a way to develop a statistical/signal processing formulism to assist leadership in making better decisions about how to estimate the whereabouts of an adversary. A two-tier processing pipeline utilizing feature extraction and reinforcement learning-based optimal pathway estimation was created to demonstrate how human/machine learning teaming can be exploited to address a geo-intelligence problem. The information used in the processor development consists of an open-source hyperspectral imagery (HSI) data set [1]. A strip map of terrain HSI was divided into 32 x 32 pixel image chips where principal component analysis [2] was used to reduce the dimension and decrease the noise of the hyperspectral signatures. Spectral dictionary endmembers [3] were estimated from the denoised HSI data using the unsupervised learning algorithms of k-means clustering [4] and automatic target generator processing [3]. This substage was necessary in order to perform image chip value estimation. In this evaluation stage, five different algorithms were used to calculate different value fields. Each technique used a feature extraction method designating the relative value of each image chip comprising the complete HSI scene. The first algorithm used for HSI image chip value estimation consisted of abundance estimation via nonnegative constrained least squares matched filtration [3] along with a","PeriodicalId":325859,"journal":{"name":"Proceedings of the 4th International Conference on Statistics: Theory and Applications","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115730282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Comparison Of Two Mean Vectors Under Differential Privacy For High-Dimensional Data 高维数据差分隐私下两种均值向量的比较

Proceedings of the 4th International Conference on Statistics: Theory and Applications Pub Date : 2022-08-01 DOI: 10.11159/icsta22.167

Caizhu Huang, Di Wang, Yan Hu, N. Sartori

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