scholarly journals On the Impact of Variation on Self-Organizing Systems

2011 ◽  
Author(s):  
Adam Campbell ◽  
Cortney Riggs ◽  
Annie S. Wu
Keyword(s):  
The Impact ◽  
Self Organizing

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

The impact of inadequate customer collaboration on self-organizing Agile teams

2011 ◽  
Vol 53 (5) ◽  
pp. 521-534 ◽  
Author(s):  
Rashina Hoda ◽  
James Noble ◽  
Stuart Marshall
Keyword(s):  
The Impact ◽  
Agile Teams ◽  
Self Organizing

The impact of atmospheric circulation on wind energy resource using self-organizing maps

2021 ◽  
Author(s):  
Kostas Philippopoulos ◽  
Chris G. Tzanis
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Synoptic Climatology ◽  
Energy Potential ◽  
Self Organizing Maps ◽  
Wind Energy Potential ◽  
The Impact ◽  
Self Organizing

<p>The sensitivity of wind to the Earth’s energy budget and the changes it causes in the climate system has a significant impact on the wind energy sector. The scope of this work is to examine the association of atmospheric circulation with the wind speed distribution characteristics on different timescales over Greece. Emphasis is given to the effect of specific regimes on the wind speed distributions at different locations. The work is based on using synoptic climatology as a tool for providing information regarding wind variability. This approach allows a more detailed description of the effect of changes in large-scale atmospheric circulation on wind energy potential. The atmospheric classification methodology, upon the selection of relevant atmospheric variables and domains, includes a Principal Components Analysis for dimension reduction purposes and subsequently, the classification is performed using an artificial neural network and in particular self-organizing maps. In the resulting feature map, the neighboring nodes are inter-connected and each one is associated with the composites of the selected large-scale variables. Upon the assignment and the characterization of each day in one of the resulting patterns, a daily catalog is constructed and frequency analysis is performed. In the context of estimating wind energy potential variability for each atmospheric pattern, the fit of multiple probability functions to the surface wind speed frequency distributions is performed. The most suitable function is selected based on a set of difference and correlation statistical measures, along with the use of goodness-of-fit statistical tests. The study employs the ERA5 reanalysis dataset with a 0.25° spatial resolution from 1979/01/01 up to 2019/12/31 and the wind field data are extracted at the 10m and the 100m levels. The approach could be valuable to the wind energy industry and can provide the required scientific understanding for the optimal siting of Wind Energy Conversion Systems considering the atmospheric circulation and the electricity interconnection infrastructure in the region. Considering the emerging issue of energy safety, accurate wind energy production estimates can contribute towards the establishment of wind as the primary energy source and in meeting the increasing energy demand.</p>

A Self-Organizing Grouping Approach for Ship Traffic Scheduling in Restricted One-Way Waterway

2019 ◽  
Vol 53 (1) ◽  
pp. 83-96
Author(s):  
Xuri Xin ◽  
Kezhong Liu ◽  
Jinfen Zhang ◽  
Shuzhe Chen ◽  
Hongbo Wang ◽  
...  
Keyword(s):  
Delay Time ◽  
Model Simulation ◽  
Time Interval ◽  
Improvement Strategy ◽  
Long Distance ◽  
Ship Traffic ◽  
Proposed Model ◽  
Ship Scheduling ◽  
The Impact ◽  
Self Organizing

AbstractShip scheduling optimization is one of the most effective ways to eliminate the bottlenecks of waterway transportation, especially in restricted one-way waterways. In this study, a novel scheduling model called self-organizing grouping is proposed to minimize two types of delay time, which are the waiting time and the extra navigation time caused by speed reduction. The proposed model schedules ships in an iterative way based on the distributed scheduling mode. To alleviate the impact of local scheduling on the overall traffic efficiency, a grouping method is proposed, in which the ships are divided into different groups based on their arrival time interval. Moreover, the ships in the same group are scheduled to minimize the interferences among them by incorporating a grouping improvement strategy. The strategy is used to deal with the influence of ships with very small speed. Experiments are carried out by comparing the proposed model with the first-come-first-serve model and the ship self-organizing cooperation model. Simulation results show that the delay time is reduced by 25%‐30% and approximately by 5% compared with that from the two models, respectively. Such advantage also exists for different combinations of ship traffic parameters. In addition, long-distance sailing with limited speed can be avoided using the proposed method, which is beneficial to relieve waterway traffic congestion.

The impact of large-scale atmospheric patterns on Heatwaves in summer Northern China based on Self-Organizing Map

2020 ◽  
Author(s):  
Chen Shi ◽  
Wang Kaicun ◽  
Zhou Chunlüe
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Stepwise Regression ◽  
Northern China ◽  
Subtropical High ◽  
Self Organizing Map ◽  
Okhotsk Sea ◽  
Circulation Patterns ◽  
The Impact ◽  
Self Organizing

<p>Heatwave is affected by large-scale atmospheric circulation on temperature-related climates in the context of global warming. Recently Northern China have experienced an increase in heatwaves which is partly due to the atmospheric circulation. This study aims to address the influence clearly. Northern China heatwaves are computed on excess hot factor (EHF) and the five EHF indexes are studied afterwards to get a picture of heatwaves in summer Northern China. China circulation patterns are classified into nine typical circulation patterns on self-organizing map (SOM) which then can be described quantitatively by pattern factors: frequency, persistence and maximum persistence. Pearson correlation analysis and stepwise regression analysis are applied for exploring the impact. Results show the spatial pattern of the times of individual heatwave event (HWN) and the days of the longest heatwave duration (HWD) are high value everywhere in Northern China. The overall EHF indexes all rising in time series (P<0.05) and the regional heatwave occurrence have trends of 0.79 day per year (P<0.05). However, the factors of the patterns show inconspicuous tendency. Two patterns with significant correlations (P<0.05) are proved to be suggestive of Okhotsk Sea high and West Pacific Subtropical High. It declares that the Okhotsk Sea high favors Northern China heatwave occurrence rather than subtropical high: the warm center over Okhotsk Sea transfer heat upper and west, generating the high temperature and persist high pressure system, causing heatwave happening in summer Northern China. The two related atmospheric circulation patterns explain 38% of the heatwave occurrence based on stepwise regression model, the Okhotsk Sea high gets the coefficient of 0.443 and the subtropical high is -0.347.  </p>

scholarly journals On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Massimo Pacella ◽  
Antonio Grieco ◽  
Marzia Blaco
Keyword(s):  
Change Process ◽  
Good Practice ◽  
Text Clustering ◽  
Self Organizing Map ◽  
Use Of Self ◽  
Engineering Change ◽  
Engineering Changes ◽  
The Impact ◽  
Self Organizing

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

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