scholarly journals A Real-Time Entity Monitoring based on States and Scenarios

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Mario José Diván ◽  
María Laura Sánchez-Reynoso
Keyword(s):  
Real Time ◽  
Decision Criteria ◽  
Real Time Processing ◽  
Discrete Simulation ◽  
Online Processing ◽  
Measurement Framework ◽  
New Concepts ◽  
Current Scenario ◽  
The Times ◽  
Occurrence Matrix

Scenario: The current markets require online processing and analysis of data as soon as they arrive to make decisions or implement actions as soon as possible. PAbMM is a real-time processing architecture specialized in measurement projects, where the processing is guided by measurement metadata derived from a measurement framework through the project definition. Objective: To extend the measurement framework incorporating scenarios and entity states as a way to online interpret the indicator’s decision criteria according to scenarios and entity states, approaching their conditional likelihoods. Methodology: An extension based on entity and context states is proposed to implement scenarios and entity states. A memory structure based on the occurrence matrix is defined to approach the associated conditional likelihoods while the data are processed. A new hierarchical complimentary schema is introduced to foster the project definition interoperability considering the new concepts. An extension of the cincamipd library was carried forward to support the complementary schema. An application case is shown as a proof-of-concept. Results: A discrete simulation is introduced for describing the times and sizes associated with the new schema when the volume of the projects to update grow-up. The results of the discrete simulation are very promising, only 0.308 seconds were necessary for updating 1000 active projects. Conclusions: The simulation provides an applicability reference to analyse its convenience according to the project requirements. This allows implementing scenarios and entity states to increase the suitability between indicators and decision criteria according to the current scenario and entity state under analysis.

A massive images classification method based on MapReduce parallel fuzzy C-means clustering

2021 ◽  
pp. 1-13
Author(s):  
Jinping Hu ◽  
Qian Cheng ◽  
Zhicheng Wen
Keyword(s):  
Real Time ◽  
Gray Level ◽  
Processing Efficiency ◽  
Single Node ◽  
Real Time Processing ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Low Performance ◽  
Speedup Ratio ◽  
Occurrence Matrix

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.

scholarly journals Grammatical gender in L2: A production or a real-time processing problem?

2012 ◽  
Vol 28 (2) ◽  
pp. 191-215 ◽  
Author(s):  
Theres Grüter ◽  
Casey Lew-Williams ◽  
Anne Fernald
Keyword(s):  
Real Time ◽  
Language Production ◽  
Native Speakers ◽  
Grammatical Gender ◽  
Lexical Representation ◽  
Real Time Processing ◽  
Online Processing ◽  
Gender Information ◽  
L2 Learners ◽  
Performance Problem

Mastery of grammatical gender is difficult to achieve in a second language (L2). This study investigates whether persistent difficulty with grammatical gender often observed in the speech of otherwise highly proficient L2 learners is best characterized as a production-specific performance problem, or as difficulty with the retrieval of gender information in real-time language use. In an experimental design that crossed production/comprehension and online/offline tasks, highly proficient L2 learners of Spanish performed at ceiling in offline comprehension, showed errors in elicited production, and exhibited weaker use of gender cues in online processing of familiar (though not novel) nouns than native speakers. These findings suggest that persistent difficulty with grammatical gender may not be limited to the realm of language production, but could affect both expressive and receptive use of language in real time. We propose that the observed differences in performance between native and non-native speakers lie at the level of lexical representation of grammatical gender and arise from fundamental differences in how infants and adults approach word learning.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

GPU Acceleration of Algorithm to Design Directional Volumetric Display for Real-time Processing

2019 ◽  
pp. 989
Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Real Time ◽  
Gpu Acceleration ◽  
Real Time Processing ◽  
Time Processing ◽  
Volumetric Display

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Limits on expectation-based processing: Use of grammatical aspect for co-reference in L2

2020 ◽  
pp. 1-25
Author(s):  
Theres Grüter ◽  
Hannah Rohde
Keyword(s):  
Real Time ◽  
Language Processing ◽  
Judgment Task ◽  
Linguistic Knowledge ◽  
Real Time Processing ◽  
Time Processing ◽  
Grammatical Aspect ◽  
Level Information ◽  
Native Speakers Of English ◽  
Reference Processing

Abstract This study examines the use of discourse-level information to create expectations about reference in real-time processing, testing whether patterns previously observed among native speakers of English generalize to nonnative speakers. Findings from a visual-world eye-tracking experiment show that native (L1; N = 53) but not nonnative (L2; N = 52) listeners’ proactive coreference expectations are modulated by grammatical aspect in transfer-of-possession events. Results from an offline judgment task show these L2 participants did not differ from L1 speakers in their interpretation of aspect marking on transfer-of-possession predicates in English, indicating it is not lack of linguistic knowledge but utilization of this knowledge in real-time processing that distinguishes the groups. English proficiency, although varying substantially within the L2 group, did not modulate L2 listeners’ use of grammatical aspect for reference processing. These findings contribute to the broader endeavor of delineating the role of prediction in human language processing in general, and in the processing of discourse-level information among L2 users in particular.

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