scholarly journals Research on "Hot Zone" in Coal Mine Based on Multihop Uneven Clustering Algorithm for Vitual MIMO

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Qing Wu
Keyword(s):  
Coal Mine ◽  
Clustering Algorithm ◽  
Hot Zone

Monitoring of Flooding Water under Coal Mine Based on Improved Fuzzy Clustering

2014 ◽  
Vol 580-583 ◽  
pp. 2713-2716
Author(s):  
Meng Ya Nie ◽  
Meng Ran Zhou ◽  
Xue Qiang Fu
Keyword(s):  
Fuzzy Clustering ◽  
Coal Mine ◽  
Clustering Algorithm ◽  
Water Quality Monitoring ◽  
Underground Water ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Swarm Optimization ◽  
Mine Flooding ◽  
Monitoring Data Processing

Due to the complicated coal mine flooding water quality monitoring data, in order to extract the effective data from vast amounts of data, this article used the fuzzy clustering to analyze the monitoring data processing. This study makes a brief analysis of monitoring data structure, then analyzes the shortcomings of fuzzy C mean clustering algorithm (FCM),which also proposes fuzzy C-means algorithm based on particle swarm optimization (PSO-FCM). Finally samples of the underground water data are used to carry out the instance simulation. The experiment results showed that in dealing with data samples. The algorithm had some advantages and it was worthy of research.

Analysis on the Human Errors in Coal Mines Based on Ant Clustering Algorithm

2014 ◽  
Vol 484-485 ◽  
pp. 634-638
Author(s):  
Shuang Yue Liu ◽  
Zi Xin Fang
Keyword(s):  
Coal Mine ◽  
Human Error ◽  
Clustering Algorithm ◽  
Safety Management ◽  
Working Environment ◽  
Classification Model ◽  
Mine Safety ◽  
Mechanical Equipment ◽  
Physiological Factors ◽  
Human Errors

The human error mechanism in coal mine safety is analyzed specifically from safety psychological and physiological factors, workers quality, safety management, safety education, mechanical equipment, and working environment, and also a human error dominant factors classification model playing a great effect on the safety production of coal mine is established with the application of ant clustering algorithm. The experimental results show that management is the key in the human errors of coal mine.

The canary in the coal mine: Psychoanalysis and health

2007 ◽  
Author(s):  
Prudence Gourguechon
Keyword(s):  
Coal Mine

SEMI-CLASSICAL DESCRIPTION OF HOT ZONE DECAY

1987 ◽  
Vol 48 (C2) ◽  
pp. C2-211-C2-214
Author(s):  
J. N. DE ◽  
C. GREGOIRE
Keyword(s):  
Classical Description ◽  
Hot Zone

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Cebu Coal Mine Tragedies: Print Media Effects on Policy Legislation

2017 ◽  
Vol 2 (2) ◽  
pp. 83-91
Author(s):  
Nestor Godofredo Ramirez ◽  
◽  
Marjurie Lourince Zanoria ◽  
Andre Mikhail Obierez ◽  
◽  
...  
Keyword(s):  
Media Effects ◽  
Coal Mine ◽  
Print Media

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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