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با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - data preprocessing

تعداد مقالات یافته شده: 22
ردیف عنوان نوع
1 Detection of moving objects using thermal imaging sensors for occupancy estimation
تشخیص اجسام متحرک با استفاده از سنسورهای تصویربرداری حرارتی برای تخمین اشغال-2022
Thermal imaging sensors have been increasingly integrated in a wide range of smart building and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable for applications that require non-intrusive monitoring with proper privacy protection. In this paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e., Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving objects. This type of sensors are designed to operate with a two-subpage chessboard reading pattern, which gives rise to blob displacements across two subpages when target objects are in motion. We have conducted systematic characterization of the sensor and demonstrated issues through experimental measurements and analysis. We have also proposed a subpage bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy estimation with moving objects. The performance of the proposed method is analyzed by training and testing classification algorithms using two datasets collected with objects of different moving speeds. Our performance results indicate that the proposed method could be used for occupancy estimation in various smart building and Internet of Things applications.
keywords: طبقه بندی | حسگر مادون قرمز | اینترنت اشیا | یادگیری ماشین | برآورد اشغال | ساختمان های هوشمند | Classification | Infrared array sensor | Internet of Things | Machine learning | Occupancy estimation | Smart buildings
مقاله انگلیسی
2 On the relevance of the metadata used in the semantic segmentation of indoor image spaces
ارتباط فراداده های مورد استفاده در تقسیم بندی معنایی فضاهای تصویر داخلی-2021
The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post- processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.
Keywords: Deep learning | U-net | Semantic segmentation | Metadata preprocessing | Fully convolutional network | Indoor scenes
مقاله انگلیسی
3 Implementation of a Vision-Based Worker Assistance System in Assembly: a Case Study
پیاده سازی سیستم کمک کارگری مبتنی بر دید در مونتاژ: مطالعه موردی-2021
The current introduction of Industry 4.0 is very challenging for industrial companies. On the one hand, there is an urge to implement concepts such as digital worker assistance systems or cyber-physical production systems, but besides theoretical work, there is very little research that shows examples of its practical implementation. Furthermore, there is currently a lack of a clear model of how sensor-based worker assistance systems for data acquisition and analytics can be designed and systematically implemented. In the present research, a model for a vision-based worker assistance system for assembly was developed based on an industrial case study regarding a manual assembly line. The proposed model consists of five integrated modules: data acquisition, data preprocessing, data storage, data analysis, and simulation. The data acquisition module was constructed in the assembly workstation of the production line by implementing a depth camera, which together with an algorithm developed in Python for preprocessing, tracks the activities of the operator and inserts the processing times into a SQL table of the data storage module. This module contains all the relevant information of the production system, from the shop floor to the Manufacturing Execution System, enabling vertical integration. The data analysis module, aimed at the streaming and predictive analytics, was deployed in the RStudio platform. Likewise, the simulation module was conceptualized to retrieve real-time data from the shop floor and to select the best strategy. To evaluate the model testing of the proposed system in real production was performed. The results of this use case provide useful information for academia as well as practitioners how to implement vision-based worker assistance systems.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 8th CIRP Global Web Conference – Flexible Mass Customisation
Keywords: industry 4.0 | data analytics | cyber-physical production system | computer vision | smart manufacturing | assembly ponsibility
مقاله انگلیسی
4 When Wireless Video Streaming Meets AI: A Deep Learning Approach
وقتی جریان ویدئو بی سیم با هوش مصنوعی روبرو می شود: رویکرد یادگیری عمیق-2020
Wireless multimedia big data contains valuable information on users’ behavior, content characteristics and network dynamics, which can drive system design and optimization. The fundamental issue is how to mine data intelligence and further incorporate them into wireless multimedia systems. Motivated by the success of deep learning, in this work we propose and present an integration of wireless multimedia systems and deep learning. We start with decomposing a wireless multimedia system into three components, including end-users, network environment, and servers, and present several potential topics to embrace deep learning techniques. After that, we present deep learning based QoS/QoE prediction and bitrate adjustment as two case-studies. In the former case, we present an end-to-end and unified framework that consists of three phases, including data preprocessing, representation learning, and prediction. It achieves significant performance improvement in comparison to the best baseline algorithm (88 percent vs. 80 percent). In the latter case, we present a deep reinforcement learning based framework for bitrate adjustment. Evaluating the performance with a real wireless dataset, we show that the perceived video QoE average bitrate, rebuffering time and bitrate variation can be improved significantly.
مقاله انگلیسی
5 A Summary of the Research on AIS Track Clustering Methods
خلاصه ای از تحقیقات در مورد روش های خوشه بندی آهنگ AIS-2020
There are a lot of water traffic characteristics in the track data of ship Automatic Identification System (AIS) [1]. Lots of effective and potential information can be obtained by using clustering methods to analyze and study the historical track data of AIS. Based on the relevant literature at home and abroad, firstly, this paper briefly introduces the AIS track data and sums up the general steps of AIS track clustering, and then classifies and summarizes the common algorithms of AIS track clustering and their advantages and disadvantages. Finally, the future development trend and main problems of the study are prospected from the perspectives of data preprocessing, the influence of environmental factors and the application in the military field. This paper has certain reference significance to the related research.
Keywords: AIS track | Clustering methods | Abnormal target detection | Maritime intelligent supervision
مقاله انگلیسی
6 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
7 Classifying longevity profiles through longitudinal data mining
طبقه بندی پروفایل های طول عمر از طریق داده کاوی طولی-2019
Populational studies of human ageing often generate longitudinal datasets with high dimensionality. In order to discover knowledge in such datasets, the traditional knowledge discovery in database task needs to be adapted. In this article, we present a full knowledge discovery process that was performed on a lon- gitudinal dataset, mentioning the singularities of this process. We investigated the English Longitudinal Study of Ageing’s (ELSA’s) database, employing both semi-supervised and supervised learning techniques to determine and describe the profiles of individuals annotated with the class labels “short-lived”and “long-lived”who participated in the study. We report on the data preprocessing, the clustering task of finding the best sets of representatives of the profiles of each class, and the use of supervised learning to describe these profiles and perform a longitudinal classification on the dataset to investigate how consis- tently the unlabelled records would fit into the classes. The results show that several aspects are used to discriminate the individuals between the longevity profiles. Those aspects include economic, social and health-related attributes. The findings have pointed towards a need to further investigate the relation- ships between the different aspects, especially those related to physical health and wellbeing, and how they affect the lifespan of an individual. Furthermore, our methodology and the adopted procedures can be applied to any other data mining applications for longitudinal studies of ageing.
Keywords: Machine learning | Longitudinal data | Cluster analysis | Ageing studies
مقاله انگلیسی
8 A new machine learning technique for an accurate diagnosis of coronary artery disease
یک روش جدید یادگیری ماشین برای تشخیص دقیق بیماری عروق کرونر-2019
Background and objective: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients. Methods: We first tested ten traditional machine learning algorithms, and then the three-best perform- ing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features. Results: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field. Conclusions: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
Keywords: Coronary artery disease (CAD) | Machine learning | Normalization | Genetic algorithm | Particle swarm optimization | Feature selection | Classification
مقاله انگلیسی
9 Learning and predicting operation strategies by sequence mining and deep learning
یادگیری و پیش بینی استراتژی های عملیاتی توسط دنباله سازی و یادگیری عمیق-2019
The operators of chemical technologies are frequently faced with the problem of determining optimal interventions. Our aim is to develop data-driven models by exploring the consequential relationships in the alarm and event-log database of industrial systems. Our motivation is twofold: (1) to facilitate the work of the operators by predicting future events and (2) analyse how consequent the event series is. The core idea is that machine learning algorithms can learn sequences of events by exploring connected events in databases. First, frequent sequence mining applications are utilised to determine how the event sequences evolve during the operation. Second, a sequence-to-sequence deep learning model is proposed for their prediction. The long short-term memory unit-based model (LSTM) is capable of evaluating rare operation situations and their consequential events. The performance of this methodology is presented with regard to the analysis of the alarm and event-log database of an industrial delayed coker unit
Keywords: Alarm management | Data mining | Data preprocessing | Deep learning | LSTM
مقاله انگلیسی
10 Unsupervised extraction of patterns and trends within highway systems condition attributes data
استخراج بدون نظارت الگوها و روندها در داده های ویژگی های شرایط سیستم های بزرگراه -2019
Highway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.
Keywords: Highway | Composite health | Future outlook | Data abstraction | Cluster analysis | Normalization | Entropy | Time series
مقاله انگلیسی
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