- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Adaptive Solar Power Forecasting based on Machine Learning Methods
摘要： Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.
关键词： machine learning,k-means,BPNN,adaptive solar power forecasting,LARS
更新于2025-09-23 15:23:52
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[IEEE 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) - PUNE, India (2017.8.17-2017.8.18)] 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) - Analysis of Shape Based Image Retrieval Using Different Wavelets Transforms
摘要： The shape of an object is one of a defining factor in image processing. Here we will be focusing on image retrieval techniques relevant to shape features. The similarity within these shape features are calculated using wavelets transforms, principal components using Singular Value Decomposition(SVD) and clustering via K-Means.
关键词： M-band Wavelets,Singular Valve Decomposition,Shape features,K-Means
更新于2025-09-23 15:23:52
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[IEEE 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA) - Beijing, China (2018.8.16-2018.8.16)] 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA) - Improved Monocular ORB-SLAM2 Inspired By The Optical Flow With Better Accuracy
摘要： ORB-SLAM2 is currently the best open source SLAM system with high positioning accuracy and map reusability. However, when using a monocular camera in a dynamic environment, the accuracy will be disturbed by the moving objects. Besides, even though there are no moving objects in the frame, there is space for further improvement in accuracy. This article improves the feature point selection based on monocular ORB-SLAM2 system, by creatively using the idea comes from optical flow and then using the K-Means algorithm to classify the matched feature point pairs. The existing open source datasets are used for evaluating the improvement. Under the pre-requirement that the improved system should ensure the real-time performance, the positioning accuracy of the improved system has been significantly improved.
关键词： Accuracy,Feature Point Classification,Optical Flow,K-Means
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Cyberworlds (CW) - Singapore, Singapore (2018.10.3-2018.10.5)] 2018 International Conference on Cyberworlds (CW) - Towards Automatic Optical Inspection of Soldering Defects
摘要： This paper proposes a method for automatic image-based classification of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Machine learning-based approaches are frequently used for image-based inspection. However, a main challenge is to manually create sufficiently large labeled training databases to allow for high accuracy of defect detection. Creating such large training databases is time-consuming, expensive, and often unfeasible in industrial production settings. In order to address this problem, an active learning framework is proposed which starts with only a small labeled subset of training data. The labeled dataset is then enlarged step-by-step by combining K-means clustering with active user input to provide representative samples for the training of an SVM classifier. Evaluations on two databases with insufficient and shifting solder joints samples have shown that the proposed method achieved high accuracy while requiring only minimal user input. The results also demonstrated that the proposed method outperforms random and representative sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the uncertainty sampling method by ~ 0.5%.
关键词： Classification of solder joint defects,active learning,Automatic Optical Inspection (AOI),SVM classifier,K-means
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Houston, TX (2018.5.14-2018.5.17)] 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Multi-core cable fault diagnosis using cluster time-frequency domain reflectometry
摘要： Guaranteeing the integrity and functionality of the control and instrumentation (C&I) cable system is essential in ensuring safe nuclear power plant (NPP) operation. When a fault occurs in a multi-core cable, it not only affects the signals of faulty lines but in fact, disturbs the rest as well due to crosstalk and noise interference. Therefore, this results in C&I signal errors in NPP operation and further leads to a rise in concern regarding the NPP operation. Thus, it is necessary for diagnostic technologies of multi-core C&I cables to classify the faulty line and detect the fault to assure the safety and reliability of NPP operation. We propose a diagnostic method that detects the fault location and faulty line in multi-core C&I cable using a clustering algorithm based on TFDR results. The faulty line detection clustering algorithm uses TFDR cross-correlation and phase synchrony results as input feature data altogether which can detect the faulty line and identify the fault point successfully. The proposed clustering algorithm is verified by experiments with two possible fault scenarios in NPP operation.
关键词： fault diagnosis,reflectometry,control and instrumentation cable,K-means clustering,crosstalk,time-frequency analysis
更新于2025-09-23 15:23:52
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Hybrid technique for the detection of suspicious lesions in digital mammograms
摘要： This paper presents an efficient system for the detection of suspicious lesions in mammograms. The proposed detection system consists of three steps. In the first step, an efficient pre-processing technique is developed using Top-Hat morphological filter and NL means filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM), gradient magnitude (GM), and intensity contrast (IC). Finally, computed threshold is used to extract the suspicious lesions in mammograms. The Free Response Operating Characteristics (FROC) curve is used to assess the performance of the proposed system. Proposed system achieved the sensitivity of 93.8% at the rate of 0.51 false positives per image.
关键词： breast cancer,segmentation,computer-aided diagnosis,fuzzy C-means,mammograms
更新于2025-09-23 15:22:29
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[IEEE 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) - Mexico City (2018.9.5-2018.9.7)] 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) - Data Mining for the Analysis of Eye Tracking Records
摘要： It is proposed the implementation of a methodology for the analysis and classification of large volumes of records. It is studied and evaluated the application of DM as a tool to analysis qualitatively and quantitatively the register obtained by an eye movement tracking device, eye-tracking, when bring under to people with different levels of orthographic knowledge (OK: High, Medium and Low), in the face of two tasks; (i) detection of spelling error and (ii) in the detection of a simple character, in the brief exposure (1500 milliseconds) of words without and with misspelling. It used some analytical procedure series of DM such as: the search for response patterns; the creation of secondary variables; the use of classification of trees and grouping the data (k-means). New models were created as of the distance between the position of the spelling error and the position of the gaze of the participants. Differences in the visual attention were found between the participants; in the same way, it was observed that the misspelling influences the performance of the task (ii), diverting visual attention to spelling error, in the participants with High OK. It is concluded that the DM helps to find the particularities of eye movements from large volumes of data that generates eye-tracking, which cannot be analysed with simple procedures.
关键词： k-means,Data Mining,Orthographic Knowledge,Eye Tracking,Eye Movements
更新于2025-09-23 15:22:29
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Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images
摘要： The presence of mixed pixels in remote sensing images is the major issue for accurate classification. In this paper, we have focused on two aspects of mixed pixel problem: firstly, to identify mixed pixels from an image and secondly to label them to their appropriate class. In phase I, extraction of mixed pixels has been performed from the RSI images-based super-pixel algorithm and RGB model by using fuzzy C-means (FCM). In phase II, the extracted mixed pixel from phase I has been decomposed to the appropriate class. This new proposed technique is the amalgamation of PSO-FCM (particle swarm optimization-fuzzy C-means) for clustering of mixed pixels and ANN-BPO (artificial neural network-biogeography-based particle swarm optimization) for the classification purpose. Experimental results reveal that the proposed method has improved the accuracy as compared to the existing techniques and succeeds in better classification of the remote sensing images.
关键词： Fuzzy C-means,BBO,Remote sensing images,Pure pixels,Mixed pixels,PSO,Neural network
更新于2025-09-23 15:22:29
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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 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Developing Modified Fuzzy C-Means Clustering Algorithm for Image Segmentation
摘要： Effective algorithm for segmenting image is important for images analysis and computer vision. Fuzzy c-means (FCM) is the mostly used methodology in image clustering. However, the results of the standard and the modified version FCM are not always satisfactory. This paper introduces a modification on spatial FCM considering the weighted fuzzy effect of neighboring pixels on the center of the cluster. So, the objective function in FCM algorithm is modified to minimize the intensity inhomogeneities by implicating the spatial information and the modified membership weighting. The advantages of the new FCM algorithm are: (a) produces homogeneous regions, (b) handles noisy spots, and (c) relatively less sensitive to noise. Experimental results on real images show that the algorithm is effective, efficient, and is relatively independent of the type of noise. Especially, it can process non-noisy and noisy images without knowing the type of the noise.
关键词： image processing,images segmentation,fuzzy c-means,image clustering
更新于2025-09-23 15:22:29