2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第9页

Causal Effects of Landing Parameters on Runway Occupancy Time using Causal Machine Learning Models 基于因果机器学习模型的着陆参数对跑道占用时间的因果影响

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308243

Zhi Jun Lim, S. Goh, Imen Dhief, S. Alam

{"title":"Causal Effects of Landing Parameters on Runway Occupancy Time using Causal Machine Learning Models","authors":"Zhi Jun Lim, S. Goh, Imen Dhief, S. Alam","doi":"10.1109/SSCI47803.2020.9308243","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308243","url":null,"abstract":"Limited runway capacity is a common problem faced by most airports worldwide. The two important factors that affect runway throughput are the wake-vortex separation and Runway Occupancy Time (ROT). Therefore, to improve runway throughput, Wake Turbulence Re-categorisation program (RECAT) was introduced to reduce the minimum separation distance required between successive aircraft on final approach. As a result, the constraining impact of ROT on runway throughput has now become significant. The objective of this paper is to identify data-driven intervention to reduce the ROT of landing aircraft. Specifically, we propose a data-driven approach to estimate the causal effect of landing parameters on ROT. We propose categorisation of each landing parameter into groups using Gaussian process models and employ Generalised Random Forest (GRF) to estimate the average treatment effect and the standard deviation of each landing parameters. Experimental results show that a few procedural changes to current landing procedure may reduce ROT. The results establish that slowing down the aircraft speed in the final approach phase leads to shorter ROT. In the final approach phase, ROTs of aircraft which are at least 10 knots slower than the average aircraft speed are on an average 2.63 seconds shorter. Furthermore, aircraft that are at least 10 knots faster than the average aircraft have on average 4 seconds longer ROTs. The second finding of this work is that flexible glide-slope angles should be introduced for the different aircraft types to achieve better ROT performance. Therefore, our findings also validate the industry need for Ground-Based Augmented System landing system which provides landing guidance with flexible glide-slopes.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115555995","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: 6

Detecting Subject-Weapon Visual Relationships 探测目标-武器视觉关系

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308574

Thomas Truong, S. Yanushkevich

引用次数: 3

Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients 混合模糊加权k近邻预测糖尿病患者再入院

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308286

Soha Bahanshal, Byung Kim

引用次数: 3

Hospital Readmission Prediction using Discriminative patterns 判别模式下的再入院预测

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308381

Sea Jung Im, Yue Xu, J. Watson, A. Bonner, H. Healy, W. Hoy

{"title":"Hospital Readmission Prediction using Discriminative patterns","authors":"Sea Jung Im, Yue Xu, J. Watson, A. Bonner, H. Healy, W. Hoy","doi":"10.1109/SSCI47803.2020.9308381","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308381","url":null,"abstract":"Avoidable hospital readmission is problematic as it increases the burden on healthcare systems, leads to a shortage of hospital beds and impacts on the costs of healthcare. Various machine learning algorithms have been applied to predict patient readmissions. However, existing literature has only focused on individual features of health conditions without consideration of associations between features. This paper proposes discriminative pattern-based features as a technique to improve readmission prediction. First, discriminative patterns that occur disproportionately between two classes: readmission and non-readmission, were generated based on hospital electronic health records. Second, the patterns were fed as features into a classification model for readmission prediction. Then, we have evaluated these discriminative pattern-based features in three datasets: diabetes, chronic kidney disease and all diseases. Experiments with each dataset showed that the features of chronic disease cohorts have fewer differences between the readmission and the non-readmission classes than the all-diseases cohort. Our proposed pattern-based model improved the prediction performance in terms of AUC (Area Under the receiver operating characteristic curve) by about 12% compared with the baseline models for the all-disease cohort, however, it showed little improvement for either diabetes or chronic kidney disease datasets.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377121","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: 2

Averaging Methods using Dynamic Time Warping for Time Series Classification 基于动态时间翘曲的时间序列分类平均方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308409

Shreyasi Datta, C. Karmakar, M. Palaniswami

引用次数: 4

Taxonomy and Survey of Interpretable Machine Learning Method 可解释机器学习方法的分类与综述

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308404

Saikat Das, Ph.D., Namita Agarwal, D. Venugopal, Frederick T. Sheldon, S. Shiva

引用次数: 6

AI-Powered Ransomware Detection Framework 人工智能驱动的勒索软件检测框架

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308387

Subash Poudyal, D. Dasgupta

引用次数: 7

An Effective Measure to Identify Meaningful Concepts in Engineering Design optimization 工程设计优化中识别有意义概念的有效方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308484

Felix Lanfermann, S. Schmitt, S. Menzel

{"title":"An Effective Measure to Identify Meaningful Concepts in Engineering Design optimization","authors":"Felix Lanfermann, S. Schmitt, S. Menzel","doi":"10.1109/SSCI47803.2020.9308484","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308484","url":null,"abstract":"Identifying similar solutions during an engineering design process and organizing the design data set into several concepts has substantial benefits. A concept is an abstract representation of design solutions that share comparable properties and behavior. Inspecting such concepts facilitates an increase of knowledge about the structure of the design problem. Concepts also allow for the selection of archetypal representatives which can be used as prototypes for further processing. Each prototype represents a different part of the design domain and can, for example, be effectively used to initialize the starting population of a design optimization leading to meaningful variations towards increased design performance. However, identifying meaningful concepts in a large engineering design data set and objectively quantifying the quality of the identified set of concepts is a challenging task. Existing measures to evaluate concepts of design solutions exhibit substantial drawbacks as they do not consider the simultaneous existence and interactions of multiple concepts on one design data set thoroughly. Therefore, we propose a new measure for objectively quantifying the quality of a set of concepts which explicitly takes the overlap and sizes of the concepts into account. We show the benefits of our measure by comparing it to state-of-the art measures in an automated optimization-based concept identification approach for a real-world inspired engineering design data set.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123000740","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: 6

Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks 基于递归神经网络的弱势道路使用者姿态动作识别

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308462

Viktor Kress, Steven Schreck, Stefan Zernetsch, Konrad Doll, B. Sick

{"title":"Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks","authors":"Viktor Kress, Steven Schreck, Stefan Zernetsch, Konrad Doll, B. Sick","doi":"10.1109/SSCI47803.2020.9308462","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308462","url":null,"abstract":"This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123792456","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: 3

A Change Detector for Prior Probabilities of Classes 一类先验概率的变化检测器

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308146

P. Gonçalves, Roberto S. M. Barros, S. Chartier

引用次数: 1