scholarly journals The Effects of Munsell Neutral Grey Backgrounds on the Colour of Chrysoprase and the Application of AP Clustering to Chrysoprase Colour Grading

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1092
Author(s):  
Yuansheng Jiang ◽  
Ying Guo ◽  
Yufei Zhou ◽  
Xiang Li ◽  
Simin Liu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Fluorescent Lamp ◽  
Colour Difference ◽  
Green Colour ◽  
Difference Formula ◽  
First Time ◽  
Ap Clustering ◽  
Scientific Classification

Chrysoprase is a popular gemstone with consumers because of its charming apple green colour but a scientific classification of its colour has not yet been achieved. In this research, we determined the most effective background of the Munsell Chart for chrysoprase colour grading under a 6504 K fluorescent lamp and applied an affinity propagation (AP) clustering algorithm to the colour grading of coloured gems for the first time. Forty gem-quality chrysoprase samples from Australia were studied using a UV-VIS spectrophotometer and Munsell neutral grey backgrounds. The results determined the effects of a Munsell neutral grey background on the observed colour. It was found that the Munsell N9.5 background was the most effective for colour grading in this case. The observed chrysoprase colours were classified into five groups: Fancy Light, Fancy, Fancy Intense, Fancy Deep and Fancy Dark. The feasibility of the colour grading scheme was verified using the colour difference formula DE2000.

scholarly journals K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

2021 ◽  
Vol 24 (1) ◽  
pp. 464
Author(s):  
Novendri Isra Asriny ◽  
Muhammad Muhajir ◽  
Devi Andrian
Keyword(s):  
Clustering Algorithm ◽  
Optimal Number ◽  
Affinity Propagation ◽  
The Internet ◽  
Affinity Propagation Clustering ◽  
Part Time ◽  
Second Period ◽  
Ap Clustering ◽  
Cluster Points

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.

scholarly journals Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihan Liu ◽  
Yi Jia ◽  
Xiaolu Zhu
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Urban Traffic ◽  
Affinity Propagation ◽  
Superior Performance ◽  
Trajectory Data ◽  
Car Sharing ◽  
Gps Trajectory Data ◽  
Ap Clustering

Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation

2016 ◽  
Vol 20 (7) ◽  
pp. 1035-1043 ◽  
Author(s):  
Qi Lei ◽  
◽  
Jun Liu ◽  
Min Wu ◽  
Jie Wang ◽  
...  
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Color Space ◽  
Image Data ◽  
Affinity Propagation ◽  
Image Feature ◽  
Image Clustering ◽  
Active Constraint ◽  
Pairwise Constraints ◽  
Ap Clustering

Image clustering is an effective way to discover and analyze large quantities of image data. The HSV color space is particularly advantageous in image feature extraction because of its relatively prominent feature vector. The objective of this study is to develop an image clustering method using the active-constraint semi-supervised affinity propagation (ACSSAP) algorithm. The algorithm adds supervision to the affinity propagation (AP) clustering algorithm with pairwise constraints and uses active learning to guide the AP clustering algorithm. Active learning of pairwise constraints leads to an adjustment of the similarity matrix in AP at each iteration. In the experiments, the advantage of HSV space is analyzed and the ACSSAP algorithm is evaluated for data sets of different sizes in comparison with other algorithms. The result demonstrates that the ACSSAP has better performance.

scholarly journals Hearing aid classification method based on improved AP algorithm

2018 ◽  
Vol 246 ◽  
pp. 03037
Author(s):  
Xiaomei Chen ◽  
Meina Ren ◽  
Zhong Bo
Keyword(s):  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Hearing Aid ◽  
Recognition Performance ◽  
Data Sets ◽  
Subjective Score ◽  
Speech Segments ◽  
Ap Clustering ◽  
Shape Data

Based on the large medical data to evaluate the performance of the hearing aid is a promising way. Achieving the classification of the hearing aid is the foundation. In this paper an improved semisupervised AP clustering algorithm based on density path is proposed. The PESQ score is taken as the substitution of subjective score for the speech segments, which is also taken as a semi-supervised basis to improve classification accuracy. The Euclidean distance similarity is improved based on the density path, making it suitable for complex shape data sets. Through experimental verification, compared with the traditional AP algorithm, the improved algorithm shows obvious advantages in terms of hearing aid classification accuracy and recognition performance.

scholarly journals Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule

2021 ◽  
Vol 13 (16) ◽  
pp. 8957
Author(s):  
Yajun Zhang ◽  
Jie Deng ◽  
Kangkang Zhu ◽  
Yongqiang Tao ◽  
Xiaolin Liu ◽  
...  
Keyword(s):  
Capital Investment ◽  
Clustering Algorithm ◽  
Empirical Investigation ◽  
Affinity Propagation ◽  
Guizhou Province ◽  
Electric Bus ◽  
The Government ◽  
Charging Stations ◽  
Ap Clustering

With the escalating contradiction between the growing demand for electric buses and limited supporting resources of cities to deploy electric charging infrastructure, it is a great challenge for decision-makers to synthetically plan the location and decide on the expansion sequence of electric charging stations. In light of the location decisions of electric charging stations having long-term impacts on the deployment of electric buses and the layout of city traffic networks, a comprehensive framework for planning the locations and deciding on the expansion of electric bus charging stations should be developed simultaneously. In practice, construction or renovation of a new charging station is limited by various factors, such as land resources, capital investment, and power grid load. Thus, it is necessary to develop an evaluation structure that combines these factors to provide integrated decision support for the location of bus charging stations. Under this background, this paper develops a gridded affinity propagation (AP) clustering algorithm that combines the superiorities of the AP clustering algorithm and the map gridding rule to find the optimal candidate locations for electric bus charging stations by considering multiple impacting factors such as land cost, traffic conditions, and so on. Based on the location results of the candidate stations, the expansion sequence of these candidate stations is proposed. In particular, a sequential expansion rule for planning the charging stations is proposed that considers the development trends of the charging demand. To verify the performance of the gridded AP clustering and the effectiveness of the proposed sequential expansion rule, an empirical investigation of Guiyang City, the capital of Guizhou province in China, is conducted. The results of the empirical investigation demonstrate that the proposed framework that helps find optimal locations for electric bus charging stations and the expansion sequence of these locations are decided with less capital investment pressure. This research shows that the combination of gridded AP clustering and the proposed sequential expansion rule can systematically solve the problem of finding the optimal locations and deciding on the best expansion sequence for electric bus charging stations, which denotes that the proposed structure is pretty pragmatic and would benefit the government for long-term investment in electric bus station deployment.

scholarly journals An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
XiuLi Zhao ◽  
WeiXiang Xu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Similar Degree ◽  
Clustering Method ◽  
Data Set ◽  
Affinity Propagation Clustering ◽  
Initial Cluster ◽  
Data Density ◽  
Data Points ◽  
Ap Clustering

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

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.

MEDIATEXT IS A TOOL THAT DETERMINES THE QUALITY OF INFORMATION

2020 ◽  
Vol 6 (3) ◽  
pp. 158-164
Author(s):  
Navruza Yakhyayeva ◽  
Keyword(s):  
Functional Properties ◽  
Linguistic Features ◽  
Text Linguistics ◽  
Quality Of Information ◽  
The Creation ◽  
Linguistic Units ◽  
Scientific Classification

The quality and content of information in the article media text is based on scientific classification of linguistic features. The study of functional styles of speech, the identification of their linguistic signs, the discovery of the functional properties of linguistic units and their separation on the basis of linguistic facts is one of thetasks that modern linguistics is waiting for a solution. Text Linguistics, which deals with the creation, modeling of its structure and the study of the process of such activity, is of interest to journalists today as a science.

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