Clustering and application of grain temperature statistical parameters based on the DBSCAN algorithm

Cui, Hongwei; Wu, Wenfu; Zhang, Zhongjie; Han, Feng; Liu, Zhe

doi:10.1016/j.jspr.2021.101819

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…It is worth noting that the clustering algorithm also has a wide range of applications in precision agriculture, 25 and food storage 26 . Because clustering allows objects with similar features to be grouped meaningfully, it is an excellent application to classify grain temperature data and then extract the feature information of each category 27 …”

Section: Methodsmentioning

confidence: 99%

“…It is worth noting that the clustering algorithm also has a wide range of applications in precision agriculture, 25 and food storage. 26 Because clustering allows objects with similar features to be grouped meaningfully, it is an excellent application to classify grain temperature data and then extract the feature information of each category. 27 Since the theoretical optimal number of categories usually varies for different grain data, 28 the clustering algorithm determines category numbers automatically, such as MeanShift and DBSCAN, rather than specifying the number in advance through hyperparameters as in the k-Means algorithm.…”

Section: Region Divisionmentioning

confidence: 99%

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Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

2023

J Sci Food Agric

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BACKGROUNDPost‐harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three‐dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA‐based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile.RESULTSThe experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA‐based visualization method for grain pile temperatures has a shorter rendering time and better visual effects.CONCLUSIONThis research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Section: Region Divisionmentioning

confidence: 99%

Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

2023

J Sci Food Agric

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“…Algoritma Density Based Spatial Clustering Algorithm with Noise (DBSCAN) digunakan untuk mengelompokkan parameter statiska suhu gabah selama penyimpanan normal sekitar satu tahun dari 27 gudang gabah di China, dimana hasil analisis parameter menggunakan algoritma DBSCAN menunjukkan bahwa perbedaaan termperatur butir antara lapisan yang berdekatan dan rasio agregasi temperatur butir empat lapis dapat digunakan untuk mendeteksi kekosongan gudang [9]. Algoritma DBSCAN untuk indexing dan cosine similarity untuk proses pencarian cluster yang relevan pada aplikasi Case-based Reasoning (CBR) yang telah banyak diterapkan dalam sistem pakar medis.…”

Section: Implementasi Algoritma Dbscan Dalam Mengelompokan Data Pasie...unclassified

Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK)

Anggara¹,

Rahman²

2022

algoritme

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Penyakit ginjal kronis(PGK) adalah penyakit ginjal yang ditandai dengan kerusakan struktural atau fungsional pada ginjal yang berlangsung lebih dari 3 bulan. PGK ditandai dengan satu atau lebih tanda kerusakan ginjal, yaitu albuminuria, sedimen urin abnormal, elektrolit, histologi, struktur ginjal, atau riwayat transplantasi ginjal, dengan penurunan laju filtrasi glomerulus. Pada penelitian ini dilakukan implementasi algoritma DBSCAN untuk mengelompokan data pasien tediagnosis PGK. Basis data yang digunakan adalah Chronic Kidney Disease yang berjumlah 400 data medis dengan 24 atribut atau fitur. Hasil penelitian dievaluasi dengan mencatat banyak jumlah cluster dan noise yang didapatkan menggunakan metric euclidean. Hasil terbaik ada pada skenario kedua dengan nilai epsilon sebesar 3,5, dan Min sample=5 yang menghasilkan jumlah cluster sebanyak 2 cluster dengan nilai Silhouette sebesar 0.158.

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“…, p n , where p = p 1 , q = p n , if p i is directly density-reachable from p i−1 , then q is density-reachable from p. In terms of p and q, if there is a core point o, and p and q are density-reachable from o, then p is density-connected to q. Given D, Eps, and MinPts, the specific algorithm steps of DBSCAN are presented as follows [37][38][39]:…”

Section: Dbscan Clusteringmentioning

confidence: 99%

Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser

Liang

Zhao

et al. 2021

Remote Sensing

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The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods.

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Clustering and application of grain temperature statistical parameters based on the DBSCAN algorithm

Cited by 10 publications

References 18 publications

Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK)

Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser

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