Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
مروری بر تجمیع دستگاه های مدل سازی اطلاعات ساختمانی (BIM) و اینترنت اشیاء (IoT): وضعیت کنونی و روند آینده
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 56
تجمیع مدل سازی اطلاعات ساختمانی (BIM) با داده های زمان واقعی(بلادرنگ) دستگاه های اینترنت اشیاء (IoT)، نمونه قوی را برای بهبود ساخت وساز و بهره وری عملیاتی ارائه می دهد. اتصال جریان-های داده های زمان واقعی که بر گرفته از مجموعه هایی از شبکه های حسگرِ اینترنت اشیاء (که این جریان های داده ای، به سرعت در حال گسترش هستند) می باشند، با مدل های باکیفیت BIM، در کاربردهای متعددی قابل استفاده می باشد. با این حال، پژوهش در زمینه ی تجمیع BIM و IOT هنوز در مراحل اولیه ی خود قرار دارد و نیاز است تا وضعیت فعلی تجمیع دستگاه های BIM و IoT درک شود. این مقاله با هدف شناسایی زمینه های کاربردی نوظهور و شناسایی الگوهای طراحی رایج در رویکردی که مخالف با تجمیع دستگاه BIM-IoT می باشد، مرور جامعی در این زمینه انجام می دهد و به بررسی محدودیت های حاضر و پیش بینی مسیرهای تحقیقاتی آینده می پردازد. در این مقاله، در مجموع، 97 مقاله از 14 مجله مربوط به AEC و پایگاه داده های موجود در صنایع دیگر (در دهه گذشته)، مورد بررسی قرار گرفتند. چندین حوزه ی رایج در این زمینه تحت عناوین عملیات ساخت-وساز و نظارت، مدیریت ایمنی و بهداشت، لجستیک و مدیریت ساختمان، و مدیریت تسهیلات شناسایی شدند. نویسندگان، 5 روش تجمیع را همراه با ذکر توضیحات، نمونه ها و بحث های مربوط به آنها به طور خلاصه بیان کرده اند. این روش های تجمیع از ابزارهایی همچون واسط های برنامه نویسی BIM، پایگاه داده های رابطه ای، تبدیل داده های BIM به پایگاه داده های رابطه ای با استفاده از طرح داده های جدید، ایجاد زبان پرس وجوی جدید، فناوری های وب معنایی و رویکردهای ترکیبی، استفاده می کنند. براساس محدودیت های مشاهده شده، با تمرکز بر الگوهای معماری سرویس گرا (SOA) و راهبردهای مبتنی بر وب برای ادغام BIM و IoT، ایجاد استانداردهایی برای تجمیع و مدیریت اطلاعات، حل مسئله همکاری و محاسبات ابری، مسیرهای برجسته ای برای تحقیقات آینده پیشنهاد شده است.
کلمه های کلیدی: مدل سازی اطلاعات ساختمانی (BIM) | دستگاه اینترنت اشیاء (IoT) | حسگرها | ساختمان هوشمند | شهر هوشمند | محیط ساخته شده هوشمند | تجمیع.
|مقاله ترجمه شده|
Calibration of Portable Particulate Matter-Monitoring Device using Web Query and Machine Learning
کالیبراسیون دستگاه کنترل کننده ذرات قابل حمل با استفاده از پرس و جوی وب و یادگیری ماشین-2019
Background: Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringebased PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 mg/m3, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.
Keywords: Calibration | Machine learning | Monitoring and control | Particulate matter | Web query
Prediction of the apple scab using machine learning and simple weather stations
پیش بینی apple scabبا استفاده از یادگیری ماشین و ایستگاه های ساده آب و هوا-2019
Apple scab is an economically important pest for apple production. It is controlled by applying fungicides when conditions are ripe for the development of its spores. This occurs when leaves are wet for a long enough time at a given temperature. However, leaf wetness is not a sufficiently well-defined agro-meteorological variable. Moreover, the readings of leaf wetness sensors depend to a large extent on their location within the tree canopy. Here we show that virtual wetness sensors, which are based on the easily obtained meteorological parameters such as temperature, relative humidity and wind speed, can be used in place of physical sensors. To this end, we have first collected data for two growing seasons from two types of wetness sensors planted in four locations in the tree canopy. Then, for each sensor we have built a machine-learning model of leaf wetness using the aforementioned meteorological variables. These models were further used as virtual sensors. Finally, Mills models of apple scab infection were built using both real and virtual sensors and their results were compared. The comparison of apple scab models based on real sensors shows significant variability. In particular, the results of a model depend on the location of the sensor within the canopy. The models based on data obtained from virtual sensors, are similar to the models based on physical sensors. Both types of models generate results within the same range of variability. The outcome of the study shows that the control of apple scab can be based on machine learning models based on standard meteorological variables. These variables can be readily obtained using inexpensive meteorological stations equipped with basic sensors. These results open the way to a widespread application of precise control of apple scab and consequently significant reduction of the use of pesticides in apple production with benefits for environment, human health and economics of production.
Keywords: Apple scab | Machine learning | Random fores
Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests
گروه های یادگیری ماشینی بیماران براساس احتمال بهبود عملکرد زودهنگام بر اساس سنسورهای پوشیدنی ابزار تست شده به موقع قبل و بعد از عمل-2019
Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. Methods: Patients undergoing primary unilateral total knee arthroplasty (n ¼ 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. Results: Patients separated into 2 clusters (n ¼ 46 and n ¼ 22) with significantly different test completion times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P ¼ .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P ¼.07). Features with the largest effect size between groups were distinguished as important functional parameters. Conclusion: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters.
Keywords: total knee arthroplasty | wearable sensors | machine learning | functional testing | early recovery
Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگوی برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019
Data mining methods have been widely used for structural health monitoring (SHM) and damage identification for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals, using a through-substrate self-powered sensing technology, is presented within the context of artificial intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the developed learning framework for performance assessment of the monitored structures. Different levels of harvested energy were considered to evaluate the robustness of the SHM system with respect to such variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing binary signals, overcoming the notable issue of energy availability for smart damage identification platforms being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance data mining and interpretation techniques in the SHM domain, promoting the practical application of machine learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart infrastructure monitoring.
Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting
Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms
طبقه بندی ویژگی راه رفتن قطره پا به دلیل رادیکولوپاتی کمری با استفاده از الگوریتم های یادگیری ماشین-2019
Background: Recently, the study of walking gait has received significant attention due to the importance of identifying disorders relating to gait patterns. Characterisation and classification of different common gait disorders such as foot drop in an effective and accurate manner can lead to improved diagnosis, prognosis assessment, and treatment. However, currently visual inspection is the main clinical method to evaluate gait disorders, which is reliant on the subjectivity of the observer, leading to inaccuracies. Research question: This study examines if it is feasible to use commercial off-the-shelf Inertial measurement unit sensors and supervised learning methods to distinguish foot drop gait disorder from the normal walking gait pattern. Method: The gait data collected from 56 adults diagnosed with foot drop due to L5 lumbar radiculopathy (with MRI verified compressive pathology), and 30 adults with normal gait during multiple walking trials on a flat surface. Machine learning algorithms were applied to the inertial sensor data to investigate the feasibility of classifying foot drop disorder. Results: The best three performing results were 88.45%, 86.87% and 86.08% accuracy derived from the Random Forest, SVM, and Naive Bayes classifiers respectively. After applying the wrapper feature selection technique, the top performance was from the Random Forest classifier with an overall accuracy of 93.18%. Significance: It is demonstrated that the combination of inertial sensors and machine learning algorithms, provides a promising and feasible solution to differentiating L5 radiculopathy related foot drop from normal walking gait patterns. The implication of this finding is to provide an objective method to help clinical decision making.
Keywords: Foot drop | Inertial measurement unit | Machine learning | Gait classification
Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers
پتانسیل ها، گرایشات و چشم اندازها در روشهای لبه ای: مراکز داده ای مات، تکه ابر، لبه ای سیار و میکرو-2018
Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service providers data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.
keywords: Edge computing| Fog computing| Internet of Things
QoE-Driven Big Data Management in Pervasive Edge Computing Environment
مدیریت داده های بزرگ بر مبنای QoE در محدوده محاسباتی فراگیر لبه-2018
In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience (QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two aspects. First, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.
Key words: Quality-of-Experience (QoE); high-dimensional big data management; deep learning; pervasive edge computing
Big Data Architectures and the Internet of Things: A Systematic Mapping Study
معماری های داده های بزرگ و اینترنت اشیا: یک مطالعه نقشه ای سیستماتیک-2018
Big Data and IoT have made huge strides in detection technologies, resulting in "smart" devices consisting of sensors, and massive data processing. So far, there is no common strategy for designing Big Data architectures containing IoT, since they depend on the context of the problem to be solved. But in recent years, various architectures have been proposed that serve as examples for future research in this area. The aim of this article is to provide an overview of the architectures published so far, serving as a starting point for future research. The methodology used is that of systematic mapping
Keywords : Big Data; architecture; Internet of Things; IoT; systematic mapping