- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Joint Deep Learning and Clustering Algorithm for Liquid Particle Detection of Pharmaceutical Injection
摘要： At present, the detection of pharmaceutical injection products is a quite important step in the pharmaceutical manufacturing, as it has the direct related to the quality of medical product quality. Aiming at the difficulty that liquid particle has a smaller pixel point in the high resolution image of detection of pharmaceutical liquid particle, hence consider combined with deep neural network and clustering algorithm for detection and localization of little particle, and a processing method combining single frame images with multi-frame images was proposed to identifying liquid particle. Firstly, the single-frame image is detected by using Faster-RCNN deep neural network, and it can obtain the detection result of the 8-frame sequence image. Then hierarchical clustering and K-means clustering algorithm are used for clustering to obtain the same target motion area. In this way, liquid particle can be more accurately identified and the accuracy of detection can be greatly improved. The experimental results show that the accuracy of detection and recognition of foreign substances in liquid medicine is improved by more than 10% on average.
关键词： Liquid particle detection,Injection detection,K-means clustering,Hierarchical clustering,Faster-RCNN
更新于2025-09-23 15:22:29
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[IEEE 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Hurgada, Egypt (2019.11.17-2019.11.20)] 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Co-Planar Waveguide Resonator to Mediate Coupling between Superconducting Quantum Bits
摘要： Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
关键词： security,multi-keyword search,Cloud computing,ranked search,hierarchical clustering,ciphertext search
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - Bangkok, Thailand (2018.12.16-2018.12.19)] 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) - Distributed-based Hierarchical Clustering System for Large-scale Semiconductor Wafers
摘要： In this paper, we propose a Distributed-based Hierarchical Clustering System for Large-Scale Semiconductor Wafers (DHCSSW). By applying the big-data clustering algorithm, the proposed system makes it feasible to cluster large-scale wafers with up to 320,000 wafers. To verify the performance of our approach, we used simulated wafer maps. The experimental results show that our system outperformed in processing large-scale wafers, suggesting that currently used hierarchical clustering is insufficient in analyzing large-scale wafer maps. In addition, some failure patterns, which the existing approach is not able to detect, can be found with the DHCSSW. We anticipate that the DHCSSW will contribute to identifying failure patterns in semiconductor wafers.
关键词： Distributed computing system,Big data analytics,Semiconductor wafer map,Hierarchical clustering
更新于2025-09-19 17:15:36
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A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion
摘要： Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.
关键词： minimum redundancy maximum relevance criterion,hierarchical clustering,photovoltaic,regional power output prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Optical fiber methane sensor using refractometry
摘要： Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
关键词： security,ciphertext search,hierarchical clustering,multi-keyword search,ranked search,Cloud computing
更新于2025-09-16 10:30:52
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Laser-Doppler-Dehnungssensor / Laser-Doppler strain gauge
摘要： Topic Detection and Tracking is a popular topic clustering method in the big data age, which aims at automatic recognition of new topics and continuous tracking of known topics in news information flow. Traditional Topic Detection and Tracking mainly studies short text. With the rapid development of digital devices and communication techniques, the news is going to be longer and richer. So nowadays traditional Topic Detection and Tracking is faced with three problems, first, long news text usually contains multiple traditional clustering algorithm cannot accurately identify them. Second, traditional clustering mostly uses multi-dimensional computation based on word bag, but the time-consuming of this multi-dimensional computation increases exponentially with the increase of the length and number of articles. Third, long-text news contains more information. How to show the continuity and relevance of long-text news in a better way is very important and meaningful. Therefore, an improved clustering algorithm based on single-pass is presented in this paper, which can solve the above problems primly. Experiments show that, compared with K-means clustering algorithm, agglomerative hierarchical clustering algorithm, Density-Based Spatial Clustering of Applications with Noise and hierarchical clustering on the constructed concept graph, the accuracy of this algorithm is improved by about 20% to 30%, the recall rate is increased by 10% to 20%, and the algorithm time is reduced by more than 40%. With the increase of the number of articles, the time-consuming curve of the improved single-pass clustering algorithm approximates a linear function. For each additional article, the time required for the algorithm is only 0.1-0.5 times that of other algorithms. Besides, by adding timelines and extracting topics in the theme during presentation, the algorithm can effectively mine the continuity and relevance information of news topics and track the changes of news topics.
关键词： Text clustering,Big data,Hierarchical clustering,Topic detection
更新于2025-09-11 14:15:04
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Hyperspectral Tissue Image Segmentation using Semi-Supervised NMF and Hierarchical Clustering
摘要： Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease-states can be directly assessed by analyzing the mid-IR spectra of different cell-types (e.g. epithelial cells) and sub-cellular components (e.g. nuclei), provided we can accurately classify the pixels belonging to these components. The challenge is to extract information from hundreds of noisy mid-IR bands at each pixel, where each band is not very informative in itself, making annotations of unstained tissue HSI images particularly tricky. Because the tissue structure is not necessarily identical between the two sections, only a few regions in unstained HSI image can be annotated with high confidence, even when serial (or adjacent) H&E stained section is used as a visual guide. In order to completely use both labeled and unlabeled pixels in training images, we have developed an HSI pixel classification method that uses semi-supervised learning for both spectral dimension reduction and hierarchical pixel clustering. Compared to supervised classifiers, the proposed method was able to account for the vast differences in spectra of sub-cellular components of the same cell-type and achieve an F1-score of 71.18% on two-fold cross-validation across 20 tissue images. To generate further interest in this promising modality we have released our source code and also showed that disease classification is straightforward after HSI image segmentation.
关键词： microspectroscopy,semi-supervised learning,hierarchical clustering,Hyperspectral imaging,non-negative matrix factorization
更新于2025-09-09 09:28:46