2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第2页

Supporting the DevOps Feedback Loop using Unsupervised Machine Learning 使用无监督机器学习支持DevOps反馈循环

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778283

Iris Figalist, A. Biesdorf, Christoph Brand, Sebastian Feld, Marie Kiermeier

{"title":"Supporting the DevOps Feedback Loop using Unsupervised Machine Learning","authors":"Iris Figalist, A. Biesdorf, Christoph Brand, Sebastian Feld, Marie Kiermeier","doi":"10.1109/INISTA.2019.8778283","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778283","url":null,"abstract":"Nowadays, software systems and applications need to adapt rapidly to changing requirements and evolve in an agile manner. This creates the need for independent deployments, e.g. as part of DevOps. Due to the flexibility and fast release cycles comprised by DevOps, continuous monitoring and the generation of feedback is crucial to a system's quality, especially if it is continuously developed. For this purpose, we propose a feedback system that combines operations data with development data in order to trace anomalies occurring in production back to their root cause by defining patterns, detecting anomalous behavior, and generating feedback that is transferred back into the development process. To this end, we utilize two different unsupervised machine learning techniques, the k-means clustering and the archetypal analysis, to describe the data set and use the results as a basis to characterize the behavior of new data points as either normal or anomalous. The feedback system was tested and evaluated using real data produced by an application that is currently developed within a large, industrial company and serves as a link to support the loop of continuous planning, development, deployment, monitoring, and feedback.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868688","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

Trajectory Tracking for the Magnetic Ball Levitation System via Fuzzy PID Control Based on CS Algorithm 基于CS算法的磁球悬浮系统模糊PID轨迹跟踪

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778271

B. Ataşlar-Ayyıldız, O. Karahan

引用次数: 5

Anomaly Detection in Aluminium Production with Unsupervised Machine Learning Classifiers 基于无监督机器学习分类器的铝生产异常检测

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778419

Nikolaos Kolokas, T. Vafeiadis, D. Ioannidis, D. Tzovaras

引用次数: 2

Modeling and Performance Analysis of the IEEE 802.11 MAC for VANETs under Channel Fading and Capture Effect 信道衰落和捕获效应下vanet的IEEE 802.11 MAC建模与性能分析

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778288

A. Shah, H. Ilhan, U. Tureli

引用次数: 6

Modelling Negotiation Strategies 谈判策略建模

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778284

M. Koit

引用次数: 0

Finger-Print Image Super-Resolution via Gradient Operator based Clustered Coupled Sparse Dictionaries 基于梯度算子的聚类耦合稀疏字典指纹图像超分辨率研究

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778289

F. Yeganli, Kuldeep Singh

{"title":"Finger-Print Image Super-Resolution via Gradient Operator based Clustered Coupled Sparse Dictionaries","authors":"F. Yeganli, Kuldeep Singh","doi":"10.1109/INISTA.2019.8778289","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778289","url":null,"abstract":"In this paper, a novel approach is employed for fingerprint image super-resolution based on sparse representation over a set of coupled low and high-resolution dictionary pairs. The primary step of fingerprint super-resolution involves learning a pair of coupled low- and high-resolution sub-dictionaries for each cluster of patches sampled from training set of fingerprint images. The clusters are formulated based on patch sharpness and the dominant phase angle via the magnitude and phase of the gradient operator for each image patch. In the reconstruction stage, for the low-resolution patch the most appropriate dictionary pair is selected, and the sparse coding coefficients are calculated with respect to the low-resolution dictionary. The equality assumption of the sparse representation of the low and high-resolution patches is the link between the low and high-resolution features space. For the reconstruction of high resolution patch, the sparse coefficients calculated for low-resolution patch are directly multiplied with corresponding high-resolution dictionary. The conducted experiments over fingerprint images show that the algorithm is competitive with the state-of-art super-resolution algorithms.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019447","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

Recommending Points of Interest in LBSNs Using Deep Learning Techniques 使用深度学习技术推荐lbsn中的兴趣点

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778310

Giannis Christoforidis, Pavlos Kefalas, A. Papadopoulos, Y. Manolopoulos

引用次数: 4

Performance Analysis of SVM, ANN and KNN Methods for Acoustic Road-Type Classification SVM、ANN和KNN方法在声学道路类型分类中的性能分析

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778247

Daghan Dogan, S. Bogosyan

{"title":"Performance Analysis of SVM, ANN and KNN Methods for Acoustic Road-Type Classification","authors":"Daghan Dogan, S. Bogosyan","doi":"10.1109/INISTA.2019.8778247","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778247","url":null,"abstract":"In the study, a low-cost acoustic system which classifies different roads using acoustic signal processing tool is proposed (group1 road types: asphalt, gravel, stony and snowy road; group2 road types: asphalt data with car pass noise, asphalt data with rain noise, asphalt data with tire squeal noise). Thus it is aimed to estimate road/tire friction forces using slip ratio/friction curve in the active safety systems of the automobiles. Because friction forces cannot be measured directly and it can be only observed or estimated. In the study, acoustic data features which are linear predictive coding (LPC), power spectrum coefficients (PSC) and mel-frequency cepstrum coefficients (MFCC) are used for the acoustic signal processing methods with minimum variance and maximum distance principle. The features are extracted using time windows 0.1 second as the best representative window of signal properties. The classification process is also executed by support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN) algorithms and compared to different road types. The most important difference of this study from our previous studies is that it compares performances of these three classification methods for different feature vectors obtained from different road conditions and indicates that the KNN is better method than SVM and ANN methods for the acoustic road type classification. According to the results, the KNN method classifies group1 road data with %90 accuracy rate and group2 road data with % 100 accuracy rate.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125183148","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

A social robot empowered with a new programming language and its performance in a laboratory 一个拥有新编程语言的社交机器人及其在实验室中的表现

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778239

Biel Piero E. Alvarado Vasquez, F. Matía

{"title":"A social robot empowered with a new programming language and its performance in a laboratory","authors":"Biel Piero E. Alvarado Vasquez, F. Matía","doi":"10.1109/INISTA.2019.8778239","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778239","url":null,"abstract":"For the last years, social robots have been used to serve as tour-guide robots in museums, laboratories, trade fairs, etc. So they are intended to perform tasks which will be affected by the interaction with people. In order to perform these tasks, a task planner must be developed in order to integrate the several modules that the robot contains in its software architecture. In this paper a new programming language was developed in order to command the actions of the robot over the place where the social robot is going to perform its actions. Lexical, syntactical and semantical analyzers were developed based on a simple language syntax with the possibility to create events. These events can come from different sensors installed like the RFID, cameras, etc. The testbed is Doris, an interactive robot developed in the Centre of Automation and Robotics. The results show that the new language can merge all the modules from Doris, executing the actions (trajectories, speeches and actions based on events) specified in the program provided by the developer without making any changes in the lower layers of the framework.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126984306","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: 4

Deep Convolutional Neural Network for Robust Facial Emotion Recognition 鲁棒面部情绪识别的深度卷积神经网络

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778282

Andrinandrasana David Rasamoelina, F. Adjailia, P. Sinčák

引用次数: 5