Real-time Input Data Research Articles

Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used.

Dynamic Modeling of Hydrogen Systems for Grid Operations: PEM Electrolyzer

While solar and wind as sources of energy have always had no cost, in the past the cost of the energy conversion technology was prohibitive. Today, due to improved efficiency in the technology and substantial increases in manufacturing volume the cost to generate electricity from the sun and wind and the cost to store this electricity in lithium-ion batteries (4 hours) has made these technologies more than competitive with traditional sources. As higher renewable energy penetrations occur the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues, so longer term energy storage (days, weeks, instead of hours) will be needed to increase the reliability of grid operation. A PEM electrolyzer can serve as a utility controllable load that can be available at all times and the stored hydrogen can be converted back into electricity directly through a PEM fuel cell. The hydrogen can also be mixed with natural gas to produce electricity through a gas turbine, used for transportation fuel and/or used as a chemical feedstock. The limit on energy production of a PEM fuel cell is a function of the volume of the tank. To understand the transient response of an electrolyzer under varying PV input, a thermal electrochemical dynamic model has been developed.We developed one-dimensional ("through-plane") and two-dimensional ("through-plane" and "in-plane") models. Firstly, a steady state model for a single cell was developed. The model considers mass, energy, momentum and current balance equations. Maxwell-Stefan diffusion equation is used to calculate the gas-phase species molar concentration in backing and catalyst layers. The electrochemical reactions are modeled using Butler-Volmer equation with varying surface molar concentrations of components. In addition, the thermal-electrochemical model considers the heat transfer through the backing layer, plate and the flow. Many of the kinetic parameters in the models were determined using parameter estimation with the data of single cell steady state polarization curves. These single cell steady state models were extended to a 510 kW electrolyzer stack model by adding cells in series and parallel to achieve a hydrogen flowrate of 90 Nm3/hr. To study the transient response of the electrolyzer stack a dynamic model was developed using a real-time PV input data. Results of the dynamic electrolyzer stack model under various changes with respect to the PV input will be presented. Monte-Carlo method was used to study the sensitivity of the electrolyzer design for given PV inputs. The available PV input data is scaled to meet the power requirement of a maximum of 510 kW and to meet the required hydrogen output, all while smoothing the PV output to the grid. The developed novel electrochemical dynamic model with varying PV input data shows the effectiveness of the electrolyzer in increasing the grid stability and flexibility.

Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.

Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

Lithium-ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium-ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real-time simulation of the multi-physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real-time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi-physics model of the cell is developed in COMSOL modelling software, and real-time data fusion process is implemented on dSPACE Microlabbox real-time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries.

Meteotsunami model forecast: can coastal hazard be quantified in real time?

A modeling study has been conducted to simulate the June 13, 2013 U.S. East Coast meteotsunami event as a test of the model forecast concept. A numerical simulation based on the MOST (Method of Splitting Tsunami) model was employed for the meteotsunami propagation forecast, while the weather radar reflection imagery was used to simulate real-time input data for the atmospheric pressure-induced tsunami generation. The model tsunami was generated by a moving pressure field during 2.87 h of forcing, and the resultant tsunami was then simulated for additional 5.68 h of propagation without any forcing for a total of 8.55 h of meteotsunami evolution from generation to coastal impact. Simulated time series were compared with the measurements from sea-level coastal gages and the Deep-ocean Assessment and Reporting for Tsunami (DART) data. The model is able to reproduce in general the recorded sea-level changes in the deep ocean and at the coast in terms of arrival times and amplitudes. The model was able to predict coastal tsunami impacts that occurred from one to two hours after the model data assimilation phase ended. Therefore, this approach shows promise for developing meteotsunami model forecast capability based on measurements and data assimilation in real time, at least for meteotsunamis generated by fast-moving weather systems visible on radar reflection imagery. All the data used in this study are already available in real time, the MOST model is already implemented as a seismically generated tsunami forecast model at the Tsunami Warning Centers (TWCs), which makes transition of potential meteotsunami forecast capability to warning operations straightforward.

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (QL) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.

Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.

Application for the Visually Impaired People With Voice Assistant

Visual perception, while an important means to navigate the world, can be replaced, as proven and demonstrated by studies on perception in animals, including humans. The system uses the tools and techniques of machine learning to create an application that would help the visually impaired to see thus giving them newfound hope for enjoying the richness and complexity of the world, all the while successfully navigate in it. The android application presented in this paper aims to enable people with visual impairment to live more independently. It uses a smartphone to capture real-time input data, with this approach the user could easily read menu cards in restaurants, hotel room number or even find their belongings. The system can recognize various objects at which the user is looking in its surrounding. The app uses a voice control feedback mechanism through which the user can perform various tasks with the help of his voice.

Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Design of Hardware Module for the Vehicle Condition Monitoring System Based on the Internet of Things

One problem in the development stage of driverless cars on public roads is how to monitor the condition of the vehicle that is affected by different weather and environment, including dry, snowy or heavy rain conditions, which are sometimes difficult to predict and require monitoring systems that should be real-time and accurate. For this reason, an internet of things-based car condition monitoring system is needed to be developed. The development covers three essential parts, which include the development of hardware and digital communication modules, web server development and data analysis and mobile application development for monitoring. The purpose of this study is to design a hardware module needed for car vehicle condition monitoring system; a prototype completed with the onboard diagnostic module and embedded system that will send data to the server in real time. The system was successfully designed and developed using ELM327 and ESP32 with SIM808 for communication module. The benefits of this research are as a tool for asset management of car vehicles both for personal and corporate needs. This study also supports the development of driverless cars that require real-time data input between the vehicle and the server.

Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone.

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

11 Reasons to Use Automated Metrology

<div class="section abstract"><div class="htmlview paragraph">Aerospace structures manufacturers find themselves frequently engaged in large-scale 3D metrology operations, conducting precision measurements over a volume expressed in meters or tens of meters. Such measurements are often done by metrologists or other measurement experts and may be done in a somewhat ad-hoc fashion, i.e., executed in the most appropriate method according to the lights of the individual conducting the measurement. This approach is certainly flexible but there are arguments for invoking a more rigorous process. Production processes, in particular, demand an automated process for all such “routine” measurements. Automated metrology offers a number of advantages including enabling data configuration management, de-skilling of operation, real time input data error checking, enforcement of standards, consistent process execution and automated data archiving. It also reduces training, setup time, data manipulation and analysis time and improves reporting.</div><div class="htmlview paragraph">This paper draws on experiences from a recent automated metrology project and examines some of the challenges and benefits of successful measurement automation.</div></div>

GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian Inference.

The probabilistic Bayesian inference of real-time input data is becoming more popular, and the importance of semisupervised learning is growing. We present a classification restricted Boltzmann machine (ClassRBM)-based hardware accelerator with on-chip semisupervised learning and Bayesian inference capability. ClassRBM is a specific type of Markov network that can perform classification tasks and reconstruct its input data. ClassRBM has several advantages in terms of hardware implementation compared to other backpropagation-based neural networks. However, its accuracy is relatively low compared to backpropagation-based learning. To improve the accuracy of ClassRBM, we propose the multi-neuron-per-class (multi-NPC) voting scheme. We also reveal that the contrastive divergence (CD) algorithm, which is commonly used to train RBM, shows poor performance in this multi-NPC ClassRBM. As an alternative, we propose an asymmetric contrastive divergence (ACD) training algorithm that improves the accuracy of multi-NPC ClassRBM. With the ACD learning algorithm, ClassRBM operates in the form of a combination of Markov Chain training and Bayesian inference. The experimental results on a field-programmable gate array (FPGA) board for a Modified National Institute of Standards and Technology data set confirm that the inference accuracy of the proposed ACD algorithm is 5.82% higher for a supervised learning case and 12.78% higher for a 1% labeled semisupervised learning case than the conventional CD algorithm. Also, the GeCo ver.2 hardware implemented on a Xilinx ZCU102 FPGA board was 349.04 times faster than the C simulation on CPU.

A Framework for Enhancing Real-time Social Media Data to Improve Disaster Management Process

Abstract. Social Media datasets are playing a vital role to provide information that can support decision making in nearly all domains of technology. It is due to the fact that social media is a quick and economical approach for data collection from public through methods like crowdsourcing. It is already proved by existing research that in case of any disaster (natural or man-made) the information extracted from Social Media sites is very critical to Disaster Management Systems for response and reconstruction. This study comprises of two components, the first part proposes a framework that provides updated and filtered real time input data for the disaster management system through social media and the second part consists of a designed web user API for a structured and defined real time data input process. This study contributes to the discipline of design science for the information systems domain. The aim of this study is to propose a framework that can filter and organize data from the unstructured social media sources through recognized methods and to bring this retrieved data to the same level as that of taken through a structured and predefined mechanism of a web API. Both components are designed to a level such that they can potentially collaborate and produce updated information for a disaster management system to carry out accurate and effective.

Geomagnetic storm forecasting service StormFocus: 5 years online

Forecasting geomagnetic storms is highly important for many space weather applications. In this study, we review performance of the geomagnetic storm forecasting service StormFocus during 2011–2016. The service was implemented in 2011 at SpaceWeather.Ru and predicts the expected strength of geomagnetic storms as measured by Dst index several hours ahead. The forecast is based on L1 solar wind and IMF measurements and is updated every hour. The solar maximum of cycle 24 is weak, so most of the statistics are on rather moderate storms. We verify quality of selection criteria, as well as reliability of real-time input data in comparison with the final values, available in archives. In real-time operation 87% of storms were correctly predicted while the reanalysis running on final OMNI data predicts successfully 97% of storms. Thus the main reasons for prediction errors are discrepancies between real-time and final data (Dst, solar wind and IMF) due to processing errors, specifics of datasets.

Smart Cameras Are Making Our Beaches Safer: A 5G-Envisioned Distributed Architecture for Safe, Connected Coastal Areas

With the explosive growth of smart city and Internet of Things (IoT) applications in recent years, a series of efforts has been undertaken to bring more intelligence to the smart cities and public transportation to solve essential problems like human surveillance for safety, traffic, and congestion management. In this article, we define a fifth-generation (5G)-envisioned architecture to enable cooperative sensing in the IoT for smart beaches and intelligent transportation systems and to develop a reference end-to-end implementation exploiting big data. Currently, many beaches are able to offer urban sophistication, but they lack a safety and connectivity infrastructure. Similarly, the dependence on road transport in our daily lives has grown massively, along with the problems arising from its use: permanent congestion, energy waste, and excessive carbon dioxide (CO2) emissions. We aim to address these safety and connectivity challenges and thus make a qualitative leap toward the future IoT and big-data application for smart cities and mobility. We present an architecture that raises the implementation of a platform to merge and integrate heterogeneous data sources into a common system and provide a set of advanced tools for the monitoring, simulation, and prediction to achieve a safer, less congested, and better connected beach. Our results will enable advanced surveillance at beaches along with traffic and travel management strategies based on reliable, real-time input data. The effectiveness of such new strategies, together with the proposed system, is assessed in field trials.

Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.

Design of LDPC Decoder Based On FPGA in Digital Image Watermarking Technology

LDPC code and digital image watermarking technology, which is an effective method of digital copyright protection and information security, has been widely used. But this is a multi-disciplinary, multi technology application scheme. In order to realize FPGA design of LDPC decoder in the application scheme, an effective implementation method of digital watermarking application system must be found. In this paper, MATLAB software and Qt development environment are combined to achieve the digital watermarking application software design. It could get real-time input data for the LDPC decoder. Then the hardware of the LDPC decoder is primarily implemented by FPGA in the digital image watermarking system. And the serial port is used to make the output data of the decoder back to computer for verification. Through the simulation results, the Modelsim time simulation diagram is given, and the watermark image compared with the original image is got. The results show that the resource usage of our system is few, and the decoding rate is fast. It has a certain practical value.

A NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data: A Case Study of Public Security Video Surveillance

With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach.

Automatic Classification on Bio Medical Prognosisof Invasive Breast Cancer

Breast Cancer one of the appalling diseases among the middle-aged women and it is a foremost threatening death possibility cancer in women throughout the world. Earlier prognosis and preclusion reduces the conceivability of death. The proposed system beseech various data mining techniques together with a real-time input data from a biosensor device to determine the disease development proportion. Surface acoustic waves (SAW) biosensor empowers a label-free, worthwhile and straight detection of HER-2/neu cancer biomarker. The output from the biosensor is fed into the proposed system as an input along with data collected from Winconsin dataset. The complete dataset are processed using data mining classification algorithms to predict the accuracy. The exactness of the proposed model is improved by ranking attributes by Ranker algorithm. The results of the proposed model are highly gifted with an accuracy of 79.25% with SVM classifier and an ROC area of 0.754 which is better than other existing systems. The results are used in designing the proper drug thereby improving the survivability of the patients.