Visualization of Relative Wind Profiles in Relation to Actual Weather Conditions of Ship Routes

2017 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo ◽  
Matthias P. Nowak
Keyword(s):  
Large Scale ◽  
Wind Profile ◽  
Weather Conditions ◽  
Data Sets ◽  
Navigation Data ◽  
External Data ◽  
Wind Profiles ◽  
Tools And Techniques ◽  
Ship Performance ◽  
Relative Wind

Ship performance and navigation data are collected by vessels that are equipped with various supervisory control and data acquisition systems (SCADA). Such information is collected as large-scale data sets, therefore various analysis tools and techniques are required to extract useful information from the same. The extracted information on ship performance and navigation conditions can be used to implement energy efficiency and emission control applications (i.e. weather routing type applications) on these vessels. Hence, this study proposes to develop data visualizing methods in order to extract ship performance and navigation information from the respective data sets in relation to weather conditions. The relative wind (i.e. apparent wind) profile (i.e. wind speed and direction) collected by onboard sensors and absolute weather conditions, which are extracted from external data sources by using position and time information a selected vessel (i.e. from the recorded ship routes), are considered. Hence, the relative wind profile of the vessel is compared with actual weather conditions to visualize ship performance and navigation parameters relationships, as the main contribution. It is believed that such relationships can be used to develop appropriate mathematical models to predict ship performance and navigation conditions under various weather conditions.

Visual Analytics in Ship Performance and Navigation Information for Sensor Specific Fault Detection

2017 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo
Keyword(s):  
Fault Detection ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Data Sets ◽  
Data Set ◽  
Navigation Data ◽  
Hidden Data ◽  
Ship Performance ◽  
Navigation Information ◽  
Erroneous Data

Ocean internet of things (IoT - onboard and onshore) collects big data sets of ship performance and navigation information under various data handling processes. That extract vessel performance and navigation information that are used for ship energy efficiency and emission control applications. However, the quality of ship performance and navigation data can play an important role in such applications, where sensor faults may introduce various erroneous data regions and that may degrade to the outcome. This study proposes visual analytics, where hidden data patterns, clusters, correlations and other useful information are visually from the respective data set extracted, to identify such erroneous data regions. The domain knowledge (i.e. ship performance and navigation conditions) has also been used to interpret such erroneous data regions and identify the respective sensors that relate to the same situations. Finally, a ship performance and navigation data set of a selected vessel is analyzed to identify erroneous data regions for three selected sensor fault situations (i.e. wind, log speed and draft sensors) under the proposed visual analytics. Hence, this approach can be categorized as a sensor specific fault detection methodology by considering the same results.

scholarly journals Rayleigh Damping in the Free Troposphere

2014 ◽  
Vol 71 (2) ◽  
pp. 553-565 ◽  
Author(s):  
David M. Romps
Keyword(s):  
Time Scale ◽  
Large Scale ◽  
Wind Profile ◽  
Large Eddy Simulations ◽  
Vertical Wavelength ◽  
Rayleigh Damping ◽  
Large Eddy ◽  
Convective Momentum Transport ◽  
Wind Profiles ◽  
Analytical Expressions

Abstract This paper explores whether cumulus drag (i.e., the damping of winds by convective momentum transport) can be described by an effective Rayleigh drag (i.e., the damping of winds on a constant time scale). Analytical expressions are derived for the damping time scale and descent speed of wind profiles as caused by unorganized convection. Unlike Rayleigh drag, which has a constant damping time scale and zero descent speed, the theory predicts a damping time scale and a descent speed that both depend on the vertical wavelength of the wind profile. These results predict that short wavelengths damp faster and descend faster than long wavelengths, and these predictions are confirmed using large-eddy simulations. Both theory and simulations predict that the convective damping of large-scale circulations occurs on a time scale of O(1–10) days for vertical wavelengths in the range of 2–10 km.

An Overview of Data Veracity Issues in Ship Performance and Navigation Monitoring

2018 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo
Keyword(s):  
Data Analysis ◽  
Anomaly Detection ◽  
Domain Knowledge ◽  
Study Data ◽  
Data Sets ◽  
Data Handling ◽  
Navigation Data ◽  
Industrial Iot ◽  
Ship Performance

An overview of data veracity issues in ship performance and navigation monitoring in relation to data sets collected from a selected vessel is presented in this study. Data veracity relates to the quality of ship performance and navigation parameters obtained by onboard IoT (internet of things). Industrial IoT can introduce various anomalies into measured ship performance and navigation parameters and that can degrade the outcome of the respective data analysis. Therefore, the identification and isolation process of such data anomalies can play an important role in the outcome of ship performance and navigation monitoring. In general, these data anomalies can be divided into sensor and data acquisition (DAQ) faults and system abnormal events. A considerable amount of domain knowledge is required to detect and classify such data anomalies, therefore data anomaly detection layers are proposed in this study for the same purpose. These data anomaly detection layers are divided into several levels: preliminary and advanced levels. The outcome of a preliminary anomaly detection layer with respect to ship performance and navigation data sets of a selected vessel is presented with the respective data handling challenges as the main contribution of this study.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yifan Feng ◽  
Ye Wang ◽  
Yangqin Xie ◽  
Shuwei Wu ◽  
Yuyang Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
Random Sampling ◽  
Tumour Size ◽  
Training Group ◽  
Data Sets ◽  
Prognostic Variables ◽  
Cancer Specific Survival ◽  
External Data ◽  
Seer Data

Abstract Background To explore the factors that affect the prognosis of overall survival (OS) and cancer-specific survival (CSS) of patients with stage IIIC1 cervical cancer and establish nomogram models to predict this prognosis. Methods Data from patients in the Surveil-lance, Epidemiology, and End Results (SEER) programme meeting the inclusion criteria were classified into a training group, and validation data were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan-Meier curves, OS and CSS of patients with stage IIIC1 cervical cancer in the training group were evaluated. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index, calibration plots, receiver operating characteristic (ROC) curves and decision-curve analysis (DCA) were calculated to validate the prediction models. Results The incidence of pelvic lymph node metastasis, a high-risk factor for the prognosis of cervical cancer, decreased slightly over time. Eight independent prognostic variables were identified for OS, including age, race, marriage status, histology, extension range, tumour size, radiotherapy and surgery, but only seven were identified for CSS, with marriage status excluded. Nomograms of OS and CSS were established based on the results. The C-indexes for the nomograms of OS and CSS were 0.687 and 0.692, respectively, using random sampling of SEER data sets and 0.701 and 0.735, respectively, using random sampling of external data sets. The AUCs for the nomogram of OS were 0.708 and 0.705 for the SEER data sets and 0.750 and 0.750 for the external data sets, respectively. In addition, AUCs of 0.707 and 0.709 were obtained for the nomogram of CSS when validated using SEER data sets, and 0.788 and 0.785 when validated using external data sets. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also indicated the value of the two models. Conclusions Eight independent prognostic variables were identified for OS. The same factors predicted CSS, with the exception of the marriage status. Both OS and CSS nomograms had good predictive and clinical application value after validation. Notably, tumour size had the largest contribution to the OS and CSS nomograms.

scholarly journals Performance of Strawberry Varieties Developed for Perennial Matted-Row Production in Annual Plasticulture in a Cold Climate Region

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1407
Author(s):  
Courtney A. Weber
Keyword(s):  
Large Scale ◽  
Fruit Size ◽  
Weather Conditions ◽  
Cold Climate ◽  
Annual Production ◽  
High Yield ◽  
Production Cycle ◽  
Late Season ◽  
Berry Weight ◽  
High Yields

Annual plasticulture production of strawberries promises superior weed control, fruit quality and yields. However, strawberry varieties adapted for perennial, matted-row production and local markets in cold climate regions have not been widely tested for adaptation to an annual production cycle. Productivity of seven short-day varieties developed for matted-row and/or annual production was examined in an annual plasticulture system in two consecutive trials in central NY (lat. 42.87° N, long. 76.99° W) harvested in 2013 and 2014. ‘Flavorfest’ demonstrated good performance in Trial 1 with high yield (390 g/plant) and large fruit size (13.9 g mean berry weight). ‘Jewel’ was shown to be well adapted to the annual plasticulture system with consistently high yields (330 and 390 g/plant) that equaled or surpassed other varieties and had moderate fruit size. ‘Chandler’ performed similarly to previous trials conducted in warmer regions with yield (340 g/plant) and fruit size (9.8 g mean berry weight) similar to ‘Jewel’. ‘Clancy’ yielded less but was consistent from year to year. The late season varieties Seneca and Ovation showed marked variability between years, possibly due to drastically different temperatures during flowering and fruit development in Trial 1 compared to Trial 2. High temperatures in Trial 1 likely caused higher early fruit yield, a compressed season and a precipitous decline in fruit size in the later season, thus reducing yield in the late season. Survival after a second dormant period was poor resulting in a small second harvest and reduced fruit size. Overall, the system demonstrated many of the expected benefits but may be more sensitive to weather conditions in the region. While many varieties developed for matted-row production may work well in an annual plasticulture system, not all varieties are equally adapted. Performance of each variety should be determined independently before large scale adoption by growers.

scholarly journals Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

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