scholarly journals Average Temperature Forecasting Based on Deli Serdang Station Using Long Short-Term Memory Model

2021 ◽  
Vol 1 (1) ◽  
pp. 7-10
Author(s):  
Ilham Junaedi ◽  
Endah Paramita ◽  
Nora Valencia Sinaga ◽  
Sri Wahyuni ◽  
Syahrul Humaidi
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Temperature Data ◽  
Climate Data ◽  
Average Temperature ◽  
Single Station ◽  
Temperature Forecasting ◽  
Resource Organization ◽  
Typical Temperature

An understanding of designs and gage of typical temperature joined of parameter climate and climate data for better water resource organization and orchestrating amid a bowl is uncommonly imperative. Examine climate designs utilizing ordinary and neighborhood every year typical temperatures, compare and make discernments. amid this consider, we'll analyze adjacent and conventional typical temperature data in 96031 Station backed recognition station input. the preeminent objective of this considers to appear the execution of the conventional temperature in an exceedingly single station and to predict the ordinary temperature data utilizing the Long memory Illustrate approach. bolstered the comes about of standard informatics of exploring temperature with adjacent temperature relationship, we got the appear of preparing bend, remaining plot, and thus the diffuse plot is showed up utilizing these codes. the decent execution of 96031 Station had a Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.

scholarly journals TIME SERIES FORECASTING FOR AVERAGE TEMPERATURE IN 96041 STATION USING LONG SHORT-TERM MEMORY MODEL

2021 ◽  
Vol 5 (1) ◽  
pp. 33-36
Author(s):  
Nancy Lusiana Damanik ◽  
◽  
Elida Pane ◽  
Kartika Dewi ◽  
Efrianses F. H. Sinaga ◽  
...  
Keyword(s):  
Long Memory ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Climate Information ◽  
Short Term ◽  
Average Temperature ◽  
Squared Error ◽  
Single Station ◽  
Long Short Term Memory

An understanding of patterns and gauge of normal temperature joined of parameter climate and climate information for way better water asset administration and arranging during a bowl is exceptionally vital. Investigate climate patterns utilizing typical and neighborhood annually normal temperatures, compare and make perceptions. during this consider, we'll analyze nearby and ordinary normal temperature information in 96041 Station supported perception station in place. the foremost objective of this considers to seem the execution of the traditional temperature in an exceedingly single station and to foresee the conventional temperature information utilizing the Long memory Demonstrate approach. supported the results of ordinary informatics of investigating temperature with nearby temperature relationship, we got the show of preparing bend, remaining plot, and therefore the diffuse plot is appeared utilizing these codes. the nice execution of 96041 Station had an Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1178
Author(s):  
Bo Sun ◽  
Bo Tan ◽  
Wenbo Wang ◽  
Elena Simona Lohan
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Time Estimation ◽  
Satellite System ◽  
Base Station ◽  
Rectangular Array ◽  
Single Station ◽  
5G Network ◽  
Autonomous Machines ◽  
3D Positioning

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation

2021 ◽  
Vol 13 (1) ◽  
pp. 427
Author(s):  
Magdalena Rykała ◽  
Łukasz Rykała
Keyword(s):  
Weather Conditions ◽  
Neural Model ◽  
Motor Vehicles ◽  
Bulk Materials ◽  
The Sustainable Development ◽  
Average Temperature ◽  
Transport Services ◽  
Vehicle Operation ◽  
The Impact

The article describes the issues of transport of bulk materials. The knowledge of this process has a key impact on the rational planning of transport tasks. It is necessary to have knowledge about the transport services market and the competition that exists in it. In order to achieve a competitive advantage on the market, enterprises should analyze data on the implementation of transport tasks on an ongoing basis. It is also important that the costs incurred from the conducted activity are minimized, while increasing the quality of services and taking into account the sustainable development of the enterprise. The study analyzes data from a few selected motor vehicles in the period of 3 years of operation, coming from an enterprise specializing in the transport of bulk materials. Moreover, a global sensitivity analysis was performed based on a neural model describing the impact of the analyzed factors on the company’s profit. The results show that the most important factors influencing the company’s profit are the fuel consumption of individual vehicles, the driver (driving style) and the month (average temperature, weather conditions).

scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 948
Author(s):  
Carlos Eduardo Maffini Santos ◽  
Carlos Alexandre Gouvea da Silva ◽  
Carlos Marcelo Pedroso
Keyword(s):  
Video Streaming ◽  
Short Term Memory ◽  
Transmission Rate ◽  
Video Quality ◽  
Video On Demand ◽  
Live Streaming ◽  
Network Congestion ◽  
Congested Networks ◽  
Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

A Pilot Study: Application of Hemoglobin and Cortisol Levels, and A Memory Test to Evaluate the Quality of Life of Breast Cancer Patients on Chemotherapy

2013 ◽  
Vol 28 (4) ◽  
pp. 348-356 ◽  
Author(s):  
Wings TY Loo ◽  
Michael CW Yip ◽  
Louis WC Chow ◽  
Qing Liu ◽  
Elizabeth LY Ng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Cancer Patients ◽  
Blood Cells ◽  
Healthy Subjects ◽  
Short Term Memory ◽  
White Blood Cells ◽  
Breast Cancer Patients ◽  
Cortisol Levels

Background Short-term memory (STM) decline in breast cancer patients resulting from chemotherapy was evaluated by means of blood biomarkers, a questionnaire, and a computerized STM test. Methods This study was conducted from January 2013 to June 2013, recruiting 90 subjects: 30 breast cancer patients beginning the 3rd of 4th cycles of docetaxel and cyclophosphamide chemotherapy, 30 recovered patients (who completed 4 cycles of docetaxel for a minimum of 6 months), and 30 healthy subjects (disease-free females). The levels of hemoglobin, red and white blood cells, and cortisol in serum, and a computerized STM test were analyzed to estimate the effects of chemotherapy on STM. A questionnaire was given to all subjects to assess quality of life. Results Statistically significant differences were observed for the blood parameters (hemoglobin, red and white blood cells, and cortisol levels) between healthy and on-treatment subjects (respectively 13.47±0.96 g/dL vs 5.37±0.38 g/dL, 4.58±0.41 1012/L vs 2.07±0.13 1012/L, and 6.15±1.03 109/L vs 0.86±0.41 109/L). Scores of the STM test were significantly lower for patients compared to healthy subjects. As indicated by the results of the questionnaire, breast cancer patients had a higher tendency to forget than healthy controls (X2=3.15; p<0.0001) and recovered subjects (X2=3.15; p<0.0001). Conclusion We found depleted levels of hemoglobin, red and white blood cells as a result of chemotherapy, and elevated levels of stress correlated with poor performances in the computerized STM test. A higher cortisol level might be an important precursor of STM deterioration. Monitoring cortisol would be beneficial for evaluating the quality of life of breast cancer patients on chemotherapy.

scholarly journals Complete resource pooling of a load-balancing policy for a network of battery swapping stations

2021 ◽  
Author(s):  
Fiona Sloothaak ◽  
James Cruise ◽  
Seva Shneer ◽  
Maria Vlasiou ◽  
Bert Zwart
Keyword(s):  
Carbon Emission ◽  
Queueing Network ◽  
Service Levels ◽  
Resource Pooling ◽  
Transportation Sector ◽  
Single Station ◽  
Optimal Resource ◽  
Promising Solution ◽  
Reduce Carbon Emission

AbstractTo reduce carbon emission in the transportation sector, there is currently a steady move taking place to an electrified transportation system. This brings about various issues for which a promising solution involves the construction and operation of a battery swapping infrastructure rather than in-vehicle charging of batteries. In this paper, we study a closed Markovian queueing network that allows for spare batteries under a dynamic arrival policy. We propose a provisioning rule for the capacity levels and show that these lead to near-optimal resource utilization, while guaranteeing good quality-of-service levels for electric vehicle users. Key in the derivations is to prove a state-space collapse result, which in turn implies that performance levels are as good as if there would have been a single station with an aggregated number of resources, thus achieving complete resource pooling.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

&lt;p&gt;Here we present the progress of the first work package (WP1) of the project &amp;#8220;Assessing centennial wind speed variability from a historical weather data rescue project in Sweden&amp;#8221; (WINDGUST), funded by FORMAS &amp;#8211; A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the &amp;#8220;stilling&amp;#8221; phenomenon and the recent &amp;#8220;recovery&amp;#8221; since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).&lt;/p&gt;&lt;p&gt;The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.&lt;/p&gt;&lt;p&gt;During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the &amp;#8220;Guidelines on Best Practices for Climate Data Rescue&amp;#8221; of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.&lt;/p&gt;

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