scholarly journals Dynamic Visualization Of Sensor Measurements: Context Based Approach

2015 ◽  
Vol 34 (3) ◽  
pp. 117-128 ◽  
Author(s):  
Radim Stampach ◽  
Petr Kubicek ◽  
Lukas Herman
Keyword(s):  
Real Time ◽  
Interactive Visualization ◽  
Meteorological Conditions ◽  
Sensor Data ◽  
Dynamic Visualization ◽  
Research Project ◽  
Experimental Application ◽  
Time Period ◽  
Long Time ◽  
Interactive Map

Abstract An amount of data measured with sensors is increasing year to year. Every sensor has a location and sensor data are mostly measured for long time period, so visualization of location and regular updating of visualized value is necessary. Various characteristics (e.g. meteorological conditions) can be automatically read at frequent intervals and those readings can be aggregated into the interactive map visualization. This map must be not only legible but also understandable also for readers that are experts in their specialisation, however, not in cartography. This paper presents possibilities of using and implementation of adaptive cartography and visual seeking principles for interactive visualization and analysis of sensor based data measured in real time. Our solution is described on experimental application for precise farming that we developed during research project Agrisensor.

scholarly journals Internet page content analysis of north European Sea ports

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Olli-Pekka Hilmola ◽  
Andres Tolli ◽  
Ain Kiisler
Keyword(s):  
Content Analysis ◽  
Environmental Issues ◽  
Basic Design ◽  
Time Period ◽  
Long Time ◽  
Sea Ports ◽  
Selection Of

Abstract This study analyses 98 Internet pages of sea ports located in Sweden, Finland and Estonia during years 2017–2019. Aim of the study is to find, how website basic design is completed (colours and languages), how slogans, environmental issues, statistics and hinterland transports are reported. Based on the analysis, it appears as rather common that sea ports follow conservative selection of colours in their websites, where blue and white are clearly most popular. Typically, English and Swedish are as the most common used language, followed by Finnish, Russian and Estonian. In some rare cases, websites are offered in Chinese or German. Larger sea ports do have clear “slogans”, where smaller ones are just having lengthy justification for their existence. Environmental issues are increasing concern among sea ports, and these are mostly mentioned in details within Swedish actors. Providing statistics varies among companies, and in some sea ports these are provided from very long time period, where in others from just previous years or then only from last year (or even at all). It is common for companies to report that they have sustainable hinterland access, railway available.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Examining the contribution of near real-time data for rapid seismic loss assessment of structures

2021 ◽  
pp. 147592172199621
Author(s):  
Enrico Tubaldi ◽  
Ekin Ozer ◽  
John Douglas ◽  
Pierre Gehl
Keyword(s):  
Real Time ◽  
Damage Assessment ◽  
Free Field ◽  
Seismic Source ◽  
Seismic Damage ◽  
Added Value ◽  
Sensor Data ◽  
Sources Of Information ◽  
Bridge Model ◽  
Seismic Damage Assessment

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

Gonad morphology and gametogenesis in the deep-sea jellyfish Atolla wyvillei and Periphylla periphylla (Scyphozoa: Coronatae) collected from Cape Hatteras and the Gulf of Mexico

2009 ◽  
Vol 90 (6) ◽  
pp. 1095-1104 ◽  
Author(s):  
Cathy H. Lucas ◽  
Adam J. Reed
Keyword(s):  
Gulf Of Mexico ◽  
Gonad Development ◽  
Bell Diameter ◽  
Western Atlantic ◽  
Gonad Morphology ◽  
Time Period ◽  
Cape Hatteras ◽  
Literature Evidence ◽  
Long Time ◽  
North Western

Observations on gonad morphology and the structure of ovaries and testes of the coronate scyphozoans Atolla wyvillei and Periphylla periphylla are described based on samples collected from the Gulf of Mexico and Cape Hatteras (north-western Atlantic). In A. wyvillei, gonads of distinguishable sex were observed in medusae as small as 17 mm bell diameter (BD). Spermatogenesis occurred within follicles (average 366 × 254 μm) that were evenly distributed throughout the gonad. Oocytes in different stages of development were observed in all the females with gonads. Oocytes arise from the gastrodermis and migrate into the mesoglea to develop from early-mid to late vitellogenic oocytes characterized by a large nucleus and granular (organic-rich) cytoplasm. The largest oocytes measured were 543 μm and 263 μm from the Gulf of Mexico and Cape Hatteras respectively. Possible reasons for this difference are discussed. In P. periphylla gonads were also initially observed in medusae 17 mm BD, although not all larger medusae had obvious gonads. Unlike A. wyvillei sperm follicles were arranged in long convoluted rows normally only one follicle thick. The organization of ooytes in female P. periphylla was very similar to A. wyvillei, although the gonads were small and the number of oocytes present in each gonad very low (<22). The largest oocyte measured was 777 μm in a 53 mm BD medusa. Although medusae were collected from one time period only (September) in this study, our findings appear to be in agreement with literature evidence indicating that coronate jellyfish produce few eggs continuously over a long time period. Aspects of gonad development and gametogenesis are discussed with respect to potential differences in site productivity and species identification.

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

Experimental Application of a Damage Localisation Technique Based on Smoothed Proper Orthogonal Modes

2007 ◽  
Vol 347 ◽  
pp. 121-126 ◽  
Author(s):  
U. Galvanetto ◽  
L. Monopoli ◽  
Cecilia Surace ◽  
Alessandra Tassotti
Keyword(s):  
Damage Detection ◽  
Proper Orthogonal Decomposition ◽  
Real Time ◽  
Orthogonal Decomposition ◽  
Steel Beams ◽  
Acceleration Response ◽  
Experimental Application ◽  
Lab Experiments ◽  
Proper Orthogonal Modes ◽  
Proper Orthogonal

The paper presents an experimental application of the Proper Orthogonal Decomposition (POD) to damage detection in steel beams. A damaged beam has been excited with a sinusoidal force, the acceleration response at points regularly spaced along the structure has been recorded and the relevant Proper Orthogonal Modes calculated. In this way it is possible to locate damage by comparing the measured dominant Proper Orthogonal Mode with a smoothed version of it which does not exhibit apparent peaks in correspondence with the damage. One of the principal advantages of the proposed damage detection technique is that it does not require vibration measurements to be performed on the undamaged structure. Moreover the ‘optimality’ of the proper orthogonal modes only requires the use of a few (one-two) of them which can be computed in real time during lab experiments or while the structure is functioning in the field.

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