- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Ultra-low Noise Widely-Tunable Semiconductor Lasers Fully Integrated on Silicon
摘要： This paper presents a new robust EM algorithm for the ?nite mixture learning procedures. The proposed Spatial-EM algorithm utilizes median-based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. It is robust to outliers and initial values. Compared with many robust mixture learning methods, the Spatial-EM has the advantages of simplicity in implementation and statistical ef?ciency. We apply Spatial-EM to supervised and unsupervised learning scenarios. More speci?cally, robust clustering and outlier detection methods based on Spatial-EM have been proposed. We apply the outlier detection to taxonomic research on ?sh species novelty discovery. Two real datasets are used for clustering analysis. Compared with the regular EM and many other existing methods such as K-median, X-EM and SVM, our method demonstrates superior performance and high robustness.
关键词： ?nite mixture,spatial rank,robustness,EM algorithm,outlier detection,Clustering
更新于2025-09-19 17:13:59
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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 The output prediction of single power plants is no longer applicable to large-scale power dispatching.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 PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Macao, Macao (2019.12.1-2019.12.4)] 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Photovoltaic Power Generation Prediction Using Data Clustering and Parameter Optimization
摘要： With the rapid development of the photovoltaic industry, photovoltaic power forecasting has become an urgent problem to be solved. In this paper, a method for predicting photovoltaic power based on data clustering and parameter optimization is proposed. The proposed method can be implemented as follows: firstly, the meteorological feature to be collected is determined by analyzing the physical model of the photovoltaic cell and the collected numerical weather information is divided into a set of categories by K-means. Then, the BP neural network is adopted and trained for individual categories, and an adaptive parameter optimization method is proposed to prevent model from local optimum. In the end, the proposed method is compared with other models to verify its effectiveness.
关键词： Photovoltaic Power Prediction,BP Neural Network,Data Clustering,Parameter Optimization
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - A Fast Point Cloud Segmentation Algorithm Based on Region Growth
摘要： Point cloud segmentation is a key prerequisite for object classification recognition. We propose a fast region growing algorithm by using the neighborhood search, filter sampling, Euclidean clustering and region growth. Segmentation experiment on point cloud data in indoor environment demonstrated that segmentation accuracy and efficiency were improved by the proposed algorithm.
关键词： regional growth,Euclidean clustering,point cloud segmentation
更新于2025-09-16 10:30:52
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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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Automated nanoplasmonic analysis of spherical nucleic acids clusters in single cells
摘要： Spherical nucleic acids (SNAs) have been extensively used in the field of biosensing, drug delivery and theranostics. Precise engineering of SNAs and their clinical application require better understanding of their cellular internalization process. We demonstrate a colorimetry-based algorithm (CBA) that can analyze the aggregation states of SNAs clusters based on the changes of plasmonic colors of SNAs. The dark field microscopy (DFM) images of cytoplasmic region of single cells are imported as raw data. All the image spots are analyzed in the interference reduction process, and the clustering states of target image spots are assigned based on the distribution of coordinates of all the pixels in the CIE map. This method provides faster analysis on clustering states of extracellular and intracellular SNAs with good accuracy. Moreover, the clustering states of SNAs in 20 single cells (generally more than 1000) can be efficiently distinguished within 200 seconds. Therefore, our method provides an automatic, quantitative, objective and repeatable way to analyze SNAs aggregations, and shows good application potential in robust and quantitative nanoplasmonic analysis in single cells.
关键词： Single-cell analysis,Plasmonic analysis,Single-particle analysis,Spherical nucleic acids,Clustering states
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) - Bari, Italy (2019.10.6-2019.10.9)] 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) - Human Following of Mobile Robot With a Low-cost Laser Scanner
摘要： Human-following robots can bring a lot of convenience to our lives. The function of human-following can be decomposed into three processes: human detection, tracking and following. Considering cost and adaptability. In this paper, a low-cost laser scanner is used to achieve it. And an improved method of threshold-based clustering is proposed, which can effectively solve the problem of excessive clustering when it is far away from the laser scanner. Three types of features are extracted to recognize the legs from other objects by the random forest classifier. The position in the middle of the legs is used to indicate the position of the target person, which will be tracked by the robot using a Kalman filter algorithm and nearest neighbor algorithm. After inputting the coordinates of the followed target into the controller, the linear velocity and angular velocity of the following can be obtained. Experiments show that the robot can follow the target person stably.
关键词： Adaptive clustering,low-cost laser scanner,Human following
更新于2025-09-12 10:27:22
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Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool
摘要： Selective laser melting has become one of the most current new technologies used to produce complex components in comparison to conventional manufacturing technologies. Especially, existing selective laser melting machine tools are not equipped with analytics tools that evaluate sensor data. This paper describes an approach to analyze and visualize offline data from different sources based on machine learning algorithms. Data from three sensors were utilized to identify clusters. They illustrate the normal operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that enables those machine tools for predictive maintenance solutions.
关键词： clustering,machine learning,Selective laser melting,sensor data,predictive maintenance
更新于2025-09-12 10:27:22
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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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A theoretical investigation of erbium‐doped fiber laser as a function of ion‐clustering, temperature, pumping wavelength, and configuration
摘要： The lasing performance of an erbium-doped fiber laser (EDFL) is affected by several factors such as doping concentrations, erbium-ion clustering, pumping wavelength, pumping configuration, and temperature. In this article, the performance of an EDFL has been investigated as a function of erbium-ion clustering, pumping wavelength, pumping configuration, and temperature. In our investigation, we have found that power degradation, which happens due to ion-clustering, at around 1530 nm is less affected for 980 nm pumping than for 1480 nm pumping. Moreover, a wider tuning range is achieved for 980 nm pumping than 1480 nm pumping. Our simulation results also show that the forward pumping configuration is suitable for a wider tuning range. To our best knowledge, pumping wavelength and configuration effects on an EDFL performance have not been investigated yet. We have also investigated the temperature effects on an EDFL performance and found little influence on lasing performance, at least within 30°C to 80°C.
关键词： doping concentration,pumping wavelength,temperature,pumping configuration,erbium-ion clustering,erbium-doped fiber laser (EDFL)
更新于2025-09-11 14:15:04