scholarly journals Study on the Vibration Characteristics of the Telescope T80 in the Javalambre Astrophysical Observatory (JAO) Aimed at Detecting Invalid Images

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6523
Author(s):  
Fernando Arranz Martínez ◽  
Raúl Martín Ferrer ◽  
Guillermo Palacios-Navarro ◽  
Pedro Ramos Lorente
Keyword(s):  
Data Processing ◽  
Vibration Characteristics ◽  
Alarm System ◽  
Low Frequencies ◽  
The Real ◽  
Vibration Signature ◽  
Preliminary Study ◽  
Logistical Problem ◽  
Large Telescopes ◽  
Right Ascension

The location of large telescopes, generally far from the data processing centers, represents a logistical problem for the supervision of the capture of images. In this work, we carried out a preliminary study of the vibration signature of the T80 telescope at the Javalambre Astrophysical Observatory (JAO). The study analyzed the process of calculating the displacement that occurs because of the vibration in each of the frequencies in the range of interest. We analyzed the problems associated with very low frequencies by means of simulation, finding the most critical vibrations below 20 Hz, since they are the ones that generate greater displacements. The work also relates previous studies based on simulation with the real measurements of the vibration of the telescope taken remotely when it is subjected to different positioning movements (right ascension and/or declination) or when it performs movement actions such as those related to filter trays or mirror cover. The obtained results allow us to design a remote alarm system to detect invalid images (taken with excess vibration).

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

A Processing Method for Combined Fatigue Accelerated Test Data

2017 ◽  
Author(s):  
Songwang Zheng ◽  
Cao Chen ◽  
Lei Han ◽  
Xiaoyong Zhang ◽  
Xiaojun Yan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Turbine Blades ◽  
Processing Method ◽  
Test Time ◽  
Accelerated Test ◽  
The Real ◽  
Data Processing Method ◽  
Stress Levels ◽  
Vibration Stress

To carry out combined low and high cycle fatigue (CCF) test on turbine blades in a bench environment, it is imperative to simulate the vibration loads of turbine blades in the field. Due to the low vibration stress of turbine blades in the working state, the test time will be very long if the test vibration stress is equal to the real vibration stress in working state. Therefore, an accelerated test will be used when the test life reach the target value (typically 107). During the accelerated test, each blade is tested at two or more times than the real vibration stress. That means some specimens are tested under two vibration stress levels. In this case, a reasonable data processing method becomes very important. For this reason, a data processing method for the CCF accelerated test is proposed in this paper. These test data are iterated on the basis of S-N curve. Finally, ten real turbine blades are tested in a bench environment, one of them is tested under two vibration stress levels. The test data is processed using the method proposed above to obtain the unaccelerated life data.

Vibration Characteristics of Metamaterial Beams With Periodic Local Resonances

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
M. Nouh ◽  
O. Aldraihem ◽  
A. Baz
Keyword(s):  
Band Gap ◽  
Low Frequency ◽  
Element Model ◽  
Small Mass ◽  
Vibration Characteristics ◽  
Structural Vibrations ◽  
Low Frequencies ◽  
Theoretical Predictions ◽  
Gap Characteristics ◽  
Unique Band

Vibration characteristics of metamaterial beams manufactured of assemblies of periodic cells with built-in local resonances are presented. Each cell consists of a base structure provided with cavities filled by a viscoelastic membrane that supports a small mass to form a source of local resonance. This class of metamaterial structures exhibits unique band gap behavior extending to very low-frequency ranges. A finite element model (FEM) is developed to predict the modal, frequency response, and band gap characteristics of different configurations of the metamaterial beams. The model is exercised to demonstrate the band gap and mechanical filtering capabilities of this class of metamaterial beams. The predictions of the FEM are validated experimentally when the beams are subjected to excitations ranging between 10 and 5000 Hz. It is observed that there is excellent agreement between the theoretical predictions and the experimental results for plain beams, beams with cavities, and beams with cavities provided with local resonant sources. The obtained results emphasize the potential of the metamaterial beams for providing significant vibration attenuation and exhibiting band gaps extending to low frequencies. Such characteristics indicate that metamaterial beams are more effective in attenuating and filtering low-frequency structural vibrations than plain periodic beams of similar size and weight.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

scholarly journals Text Relationship dalam Jejaring Sosial di Kalangan Dewasa Muda (Studi Awal dengan Pendekatan Fenomenologis)

Humaniora ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 518
Author(s):  
Esther Widhi Andangsari
Keyword(s):  
Social Networking ◽  
Young Adulthood ◽  
Qualitative Method ◽  
Internet Access ◽  
Interaction Pattern ◽  
Online Interaction ◽  
The Real ◽  
Face To Face ◽  
Preliminary Study

This study is a preliminary study about social networking and text relationship among young adulthood. The purpose of this study is to get information or description about text relationship through social networking. Method of this study is qualitative method with phenomenology approach. The phenomenon of using social networking to build relationship with others is growing popular especially among young adulthood. Observing this phenomenon accurately, there is a changing in interaction pattern. It was a physically interaction or face to face interaction. But as growing popularity of technology or internet access, today interaction can do through online and without face to face interaction. Surprisingly, this online interaction and without face to face interaction is very popular at the present. From this preliminary study, the findings are social networking become a media to share emotion, opinion openly among people. Text relationship through social networking also need emotional setting which is substituted electronically and it is virtual emotional and not the real emotional. Social networking still give a chance to people to gather face to face, not only virtual gathering. 

Localizing the Source of Type II Emission Around a CME with the Sun Radio Interferometer Space Experiment (SunRISE) and MHD Simulations

2021 ◽  
Author(s):  
Alexander Hegedus ◽  
Ward Manchester ◽  
Justin Kasper ◽  
Joseph Lazio ◽  
Andrew Romero-Wolf
Keyword(s):  
Data Processing ◽  
Low Frequency ◽  
Solar Energetic Particle ◽  
Radio Interferometer ◽  
Valuable Insight ◽  
Type Ii ◽  
Plasma Parameters ◽  
Low Frequencies ◽  
The Earth ◽  
Type Ii Bursts

<p>The Earth’s Ionosphere limits radio measurements on its surface, blocking out any radiation below 10 MHz. Valuable insight into many astrophysical processes could be gained by having a radio interferometer in space to image the low frequency window, which has never been achieved. One application for such a system is observing type II bursts that track solar energetic particle acceleration occurring at Coronal Mass Ejection (CME)-driven shocks. This is one of the primary science targets for SunRISE, a 6 CubeSat interferometer to circle the Earth in a GEO graveyard orbit. SunRISE is a NASA Heliophysics Mission of Opportunity that began Phase B (Formulation) in June 2020, and plans to launch for a 12-month mission in mid-2023. In this work we present an update to the data processing and science analysis pipeline for SunRISE and evaluate its performance in localizing type II bursts around a simulated CME.</p><p>To create realistic virtual type II input data, we employ a 2-temperature MHD simulation of the May 13th 2005 CME event, and superimpose realistic radio emission models on the CME-driven shock front, and propagate the signal through the simulated array. Data cuts based on different plasma parameter thresholds (e.g. de Hoffman-Teller velocity and angle between shock normal and the upstream magnetic field) are tested to get the best match to the true recorded emission.  This model type II emission is then fed to the SunRISE data processing pipeline to ensure that the array can localize the emission. We include realistic thermal noise dominated by the galactic background at these low frequencies, as well as new sources of phase noise from positional uncertainty of each spacecraft. We test simulated trajectories of SunRISE and image what the array recovers, comparing it to the virtual input, finding that SunRISE can resolve the source of type II emission to within its prescribed goal of 1/3 the CME width. This shows that SunRISE will significantly advance the scientific community’s understanding of type II burst generation, and consequently, acceleration of solar energetic particles at CMEs.  This unique combination of SunRISE observations and MHD recreations of space weather events will allow an unprecedented look into the plasma parameters important for these processes. </p>

The Deluge

Mirror Worlds ◽  
1991 ◽  
Author(s):  
David Gelernter
Keyword(s):  
Data Processing ◽  
Real World ◽  
Data Gathering ◽  
Middle Region ◽  
Raw Data ◽  
The Real ◽  
External Data ◽  
The World ◽  
Initial Question ◽  
The Subject

we’ve installed the foundation piles and are ready to start building Mirror worlds. In this chapter we discuss (so to speak) the basement, in the next chapter we get to the attic, and the chapter after that fills in the middle region and glues the whole thing together. The basement we are about to describe is filled with lots of a certain kind of ensemble program. This kind of program, called a Trellis, makes the connection between external data and internal mirror-reality. The Trellis is, accordingly, a key player in the Mirror world cast. It’s also a good example of ensemble programming in general, and, I’ll argue, a highly significant gadget in itself. The hulking problem with which the Trellis does battle on the Mirror world’s behalf is a problem that the real world, too, will be confronting directly and in person very soon. Floods of data are pounding down all around us in torrents. How will we cope? what will we do with all this stuff? when the encroaching electronification of the world pushes the downpour rate higher by a thousand or a million times or more, what will we do then? Concretely: I’m talking about realtime data processing. The subject in this chapter is fresh data straight from the sensor. we’d like to analyze this fresh data in “realtime”—to achieve some understanding of data values as they emerge. Raw data pours into a Mirror world and gets refined by a data distillery in the basement. The processed, refined, one-hundredpercent pure stuff gets stored upstairs in the attic, where it ferments slowly into history. (In the next chapter we move upstairs.) Trellis programs are the topic here: how they are put together, how they work. But there’s an initial question that’s too important to ignore. we need to take a brief trip outside into the deluge, to establish what this stuff is and where it’s coming from. Data-gathering instruments are generally electronic. They are sensors in the field, dedicated to the non-stop, automatic gathering of measurements; or they are full-blown infomachines, waiting for people to sit down, log on and enter data by hand.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

scholarly journals T218. A REAL ENVIRONMENT BASED VIRTUAL REALITY PSYCHOSOCIAL TREATMENT MAY HELP PATIENTS WITH SCHIZOPHRENIA TO INCREASE SOCIAL FUNCTIONING: A PRELIMINARY STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S315-S316 ◽  
Author(s):  
Emine Ilgın Hoşgelen ◽  
Faik Kartelli ◽  
Markus Berger ◽  
Simay Erinç ◽  
Deniz Yerlikaya ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Social Skills ◽  
Treatment Program ◽  
Psychosocial Functioning ◽  
Psychiatric Symptoms ◽  
Psychosocial Treatment ◽  
The Real ◽  
Real Environment ◽  
Significant Difference ◽  
Preliminary Study

Abstract Background Emerging new technologies may lead to the discovery of new treatment techniques in psychiatric disorders. Virtual Reality (VR) is being one of the newly developed techniques that has also taken its place in literature very recently. VR is a new technology for treatment of psychiatric symptoms. This is a pilot study that aims to determine the behavioral and symptomatic response of patients to a real recorded VR environment. In this study, a virtual reality laboratory has been established and a psychosocial treatment program through virtual reality has been developed for patients with schizophrenia. The aim of this study is to investigate the effect of VR psychosocial treatment program on psychosocial functioning in schizophrenia. Methods Data were collected from the patients who applied to Dokuz Eylül University School of Medicine, Schizophrenia and Psychosis Outpatient Clinic. Seven schizophrenia patients who met schizophrenia according to DSM-V diagnostic criteria were included into the study. The level of psychosocial functioning was assessed using the Personal and Social Performance Scale (PSP), the positive and negative symptom severity was evaluated using the Positive and Negative Syndrome Scale (PANSS) and social skills were assessed by using the Social Skills Checklist (SSC). The VR psychosocial treatment program included 10 sessions and was carried on for five weeks as twice a week. Each session had different real virtual environment applications including social interaction components such as in a café to buy a beverage, a bazaar or market to do shopping, taking a bus, tram, and/or ferry, etc. Results PSP scores were statistically different after and before virtual reality assessment (p=0,018). SSC scores were trend to be significance after the VR application (p=0,062). After five weeks, patients’ the number of going outside home, the places they go and the activities they do have been increased compared to the numbers at the beginning but did not differ in statistically significance. None of the patients reported motion sickness due to exposure to real environment during or after immersive process of VR. There was no significant difference regarding PANSS scores after the VR psychosocial treatment. In this study real environment VR sessions did not trigger positive symptoms of schizophrenia patients. Discussion In this preliminary study, we found that the real environment VR psychosocial application is eligible for schizophrenia patients to improve their social skills and daily activities. This study helped patients to experience the real environment without being there and encouraged them to be “really” in there. Soon, cognitive remediation programs and psychosocial functioning therapies may be conducted via VR and may help the patients to cope with their symptoms and daily life difficulties.

Preliminary study on data processing algorithms for SOFIS

2001 ◽  
Author(s):  
Nobuyuki Uemura ◽  
Tatsuya Yokota ◽  
Hideaki Nakajima ◽  
Takafumi Sugita ◽  
Yasuhiro Sasano ◽  
...  
Keyword(s):  
Data Processing ◽  
Preliminary Study ◽  
Processing Algorithms ◽  
Data Processing Algorithms
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