The setting values of thresholds for fault feature parameters are critical in all kinds of protection schemes.When the detected feature parameter value exceeds the setting value,the protection will trip.However,the se...The setting values of thresholds for fault feature parameters are critical in all kinds of protection schemes.When the detected feature parameter value exceeds the setting value,the protection will trip.However,the setting value based conventional protection schemes sometimes cannot satisfy the protection requirements of neutral ineffectively earthed power systems(NIEPS)due to wide variations in operating conditions and the complexities of fault cases.In this paper,a novel single phase grounding fault protection scheme without threshold setting is proposed.The fault detection is achieved based on operating states rather than setting values.A fuzzy c-means algorithm is used to divide the operating state of the protected feeder into non-fault states and fault states.The cluster center of each state is then obtained by classifying the historical feature samples of the protected feeder extracted under various operating conditions into their corresponding states in a constructed multi-dimensional fault feature space.The distances between the detected feature samples and the cluster centers of the non-fault and the fault states are calculated.If the distance to the fault state is shorter than that to the non-fault state,a fault is detected.Otherwise,the feeder is considered normal.A PSCAD/EMTDC simulator is used to simulate a 35 kV NIEPS under various operating conditions,non-linear loads,and complex fault cases.Results show that the proposed single phase grounding fault protection scheme without threshold setting can protect the system correctly under all kinds of faults.展开更多
Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that c...Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that combines the advan-tages of fuzzy C mean algorithm, thresholding and level set method. 3-class Fuzzy C mean thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. Parameters for performance evaluation are presented and segmentation results are compared with some other state-of-the-art segmentation methods. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of proposed method for skin cancer detection.展开更多
A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flot...A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.展开更多
In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same si...In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same size if the number of the data is big enough. But for some situations the data are not sufficient or not equal, the threshold used in FDA may have important influence on prediction results. This paper presents a study on the selection of the threshold. The eigen value of each exon/intron sequence is computed using the Z-curve method with 69 variables. The experiments results suggest that the size and the standard deviation of the data sets and the threshold are the three key elements to be taken into consideration to improve the prediction results.展开更多
A model （Bayesian oceanic front detection, BOFD） of sea surface temperature （SST） front detection in satel- lite-derived SST images based on a threshold interval is presented, to be used in different applications ...A model （Bayesian oceanic front detection, BOFD） of sea surface temperature （SST） front detection in satel- lite-derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradi- ent cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be deter- mined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.展开更多
In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the regi...In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the region. This method can retain the detail information of original image as far as possible, and do well in the stripe noise removal. The roughness of rough set was calculated respectively using directional diagram, Canny operator and Sobel operator. Comparing the three methods, the results indicate that the Canny operator keeps the more details of image, and directional diagram and Sobel operator have the better effects on denoising.展开更多
This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and s...This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.展开更多
开放集识别(Open Set Recognition,OSR)的主要目的是识别未标记数据中的新类样本,同时对已见类样本进行正确分类.现有的大多数识别方法对未标记数据的评估和伪标记信息的利用不足.本文提出一种基于主动学习的开放集图像识别方法(Open Se...开放集识别(Open Set Recognition,OSR)的主要目的是识别未标记数据中的新类样本,同时对已见类样本进行正确分类.现有的大多数识别方法对未标记数据的评估和伪标记信息的利用不足.本文提出一种基于主动学习的开放集图像识别方法(Open Set Image Recognition Method Based on Active Learning,AC-OSIR),充分利用未标记数据提升开放集识别性能.通过引入已见类别的语义知识,构建语义知识和图像特征的映射关系.对于未标记数据,利用阈值选择策略区分开放集样本和已见类样本,通过主动学习模型迭代地识别高置信度开放集样本和已见类样本,并将高置信度已见类样本添加到标记数据集中.本文在图像分类数据集CIFAR-10、TIN和LSUN,以及两个合成数据集的实验结果表明了基于主动学习的开放集图像识别方法的有效性.展开更多
基金supported in part by National Natural Science Foundation of China under Grant 61233008 and Grant 51277014.
文摘The setting values of thresholds for fault feature parameters are critical in all kinds of protection schemes.When the detected feature parameter value exceeds the setting value,the protection will trip.However,the setting value based conventional protection schemes sometimes cannot satisfy the protection requirements of neutral ineffectively earthed power systems(NIEPS)due to wide variations in operating conditions and the complexities of fault cases.In this paper,a novel single phase grounding fault protection scheme without threshold setting is proposed.The fault detection is achieved based on operating states rather than setting values.A fuzzy c-means algorithm is used to divide the operating state of the protected feeder into non-fault states and fault states.The cluster center of each state is then obtained by classifying the historical feature samples of the protected feeder extracted under various operating conditions into their corresponding states in a constructed multi-dimensional fault feature space.The distances between the detected feature samples and the cluster centers of the non-fault and the fault states are calculated.If the distance to the fault state is shorter than that to the non-fault state,a fault is detected.Otherwise,the feeder is considered normal.A PSCAD/EMTDC simulator is used to simulate a 35 kV NIEPS under various operating conditions,non-linear loads,and complex fault cases.Results show that the proposed single phase grounding fault protection scheme without threshold setting can protect the system correctly under all kinds of faults.
文摘Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that combines the advan-tages of fuzzy C mean algorithm, thresholding and level set method. 3-class Fuzzy C mean thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. Parameters for performance evaluation are presented and segmentation results are compared with some other state-of-the-art segmentation methods. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of proposed method for skin cancer detection.
文摘A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.
文摘In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same size if the number of the data is big enough. But for some situations the data are not sufficient or not equal, the threshold used in FDA may have important influence on prediction results. This paper presents a study on the selection of the threshold. The eigen value of each exon/intron sequence is computed using the Z-curve method with 69 variables. The experiments results suggest that the size and the standard deviation of the data sets and the threshold are the three key elements to be taken into consideration to improve the prediction results.
基金The National Key Technology R&D Program of China under contract No.2011BAH23B04the National High Technology Research and Development Program(863 Program)of China under contract No.2007AA092202
文摘A model （Bayesian oceanic front detection, BOFD） of sea surface temperature （SST） front detection in satel- lite-derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradi- ent cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be deter- mined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.
文摘In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the region. This method can retain the detail information of original image as far as possible, and do well in the stripe noise removal. The roughness of rough set was calculated respectively using directional diagram, Canny operator and Sobel operator. Comparing the three methods, the results indicate that the Canny operator keeps the more details of image, and directional diagram and Sobel operator have the better effects on denoising.
文摘This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.
文摘开放集识别(Open Set Recognition,OSR)的主要目的是识别未标记数据中的新类样本,同时对已见类样本进行正确分类.现有的大多数识别方法对未标记数据的评估和伪标记信息的利用不足.本文提出一种基于主动学习的开放集图像识别方法(Open Set Image Recognition Method Based on Active Learning,AC-OSIR),充分利用未标记数据提升开放集识别性能.通过引入已见类别的语义知识,构建语义知识和图像特征的映射关系.对于未标记数据,利用阈值选择策略区分开放集样本和已见类样本,通过主动学习模型迭代地识别高置信度开放集样本和已见类样本,并将高置信度已见类样本添加到标记数据集中.本文在图像分类数据集CIFAR-10、TIN和LSUN,以及两个合成数据集的实验结果表明了基于主动学习的开放集图像识别方法的有效性.
