We used model-free methods to explore the brain's functional properties adopting a partitioning procedure based on cross-clustering. We selected fuzzyc-means (FcM) and Neural Gas (NG) algorithms to find spatial p...
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ISBN:
(纸本)9783030208059;9783030208042
We used model-free methods to explore the brain's functional properties adopting a partitioning procedure based on cross-clustering. We selected fuzzyc-means (FcM) and Neural Gas (NG) algorithms to find spatial patterns with temporal features and temporal patterns with spatial features. We applied these algorithms to a shared fMRI repository of face recognition tasks. We matched the classes found and our results of functional connectivity analysis with partitioning of BOLD signal signatures. We compared the outcomes using the just acquired model-based knowledge as likely ground truth, confirming the role of Fusiform Brain Regions. In general, partitioning results show a better spatial clustering than temporal clustering for both algorithms. In the case of temporal clustering, FcM outperforms Neural Gas. The relevance of brain sub-regions related to face recognition were correctly distinguished by the algorithms and the results are in agreement with the current neuroscientific literature.
In this paper, we propose two fuzzyclustering algorithms in the differential privacy scheme based on the fuzzy c-means algorithm. Up to the author's knowledge, these are the first algorithms of their kind which p...
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ISBN:
(纸本)9781538628362
In this paper, we propose two fuzzyclustering algorithms in the differential privacy scheme based on the fuzzy c-means algorithm. Up to the author's knowledge, these are the first algorithms of their kind which provide privacy for fuzzy data. Moreover, these two algorithms are experimentally compared with the original fuzzyalgorithm and shown to be good approximation for the original fuzzy c-means algorithm.
Security is the main issue for real time systems, specially for financial and banking systems. Some of the customers who pay much attention to confidentiality and security on their network activities and transactions ...
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ISBN:
(纸本)9783319986784;9783319986777
Security is the main issue for real time systems, specially for financial and banking systems. Some of the customers who pay much attention to confidentiality and security on their network activities and transactions prefer to use the most secure channels, and for the others speed and the ease of services are more important. An optimized method should be a solution, but both strategies follow one common idea that any anomaly, abnormality, and intrusion should be handled in advance, as the reputation of each organization is based on trust. This paper proposes a new method with the aim of considering any anomaly in advance, in addition to partitioning strategy. The BFPM method makes use of the well-known fuzzyc-meansclustering algorithm to evaluate whether packets or transactions are risky or not, and in what extent they will be risky in the near future. The proposed method aims to provide a flexible search space to cover prevention and prediction techniques at the same time.
In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the incre...
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ISBN:
(纸本)9781728126586
In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzyc-means (FcM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-china Sea offshore wind power ScADA database.
convergence to local minima point is one of the major disadvantages of conventional fuzzyc-means (FcM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overco...
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ISBN:
(纸本)9781509010479
convergence to local minima point is one of the major disadvantages of conventional fuzzyc-means (FcM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overcome this, a modified genetic (MfGA) algorithm is proposed to improve the performance of FcM. The optimized class levels derived from the MfGA are employed as initial input to FcM for finding global optimal solutions in a large search space. An extensive performance comparison of the proposed MfGA inspired conventional FcM and GA based FcM on two multilevel color images establishes the superiority of the proposed approach.
In this paper, we propose a new method for construction of distance functions and metrics, by applying aggregation operators on some given distance functions and metrics. For some types and examples of aggregation ope...
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In this paper, we propose a new method for construction of distance functions and metrics, by applying aggregation operators on some given distance functions and metrics. For some types and examples of aggregation operators, we analyze which properties of the given distance functions and metrics are preserved by such construction. We also present one possible application of the distance functions constructed in such way in image segmentation by fuzzy c-means algorithm. Other similar applications in image processing are also possible.
The aim of the study was to evaluate the potential of the thermogram in diagnosing rheumatoid arthritis and to compare the implementation of k-meansalgorithm and fuzzycmeansalgorithm using a computer aided diagnos...
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The aim of the study was to evaluate the potential of the thermogram in diagnosing rheumatoid arthritis and to compare the implementation of k-meansalgorithm and fuzzycmeansalgorithm using a computer aided diagnostic tool for classification of rheumatoid arthritis (RA) and normal based on the feature extracted from the segmented thermal image. The skin surface temperature measurement, thermal image segmentation based on k-means and fuzzy c-means algorithm and the feature extraction were used in this study. The average skin surface temperature measured at the second meta carpo-phalangeal (McP) and McP3 in RA patients (35.40 +/- 0.6 A degrees c and 35.52 +/- 0.7 A degrees c, respectively) were significantly higher (p < 0.01) than those measured in normal subjects (33.66 +/- 0.2 A degrees c and 33.74 +/- 0.2 A degrees c, respectively). The mean difference in temperature between RA patients and healthy controls in the McP2 and McP3 region was found to be 1.74 and 1.78 A degrees c respectively. The receiver operating characteristics (ROc) curve depicted a sensitivity of 86.6% and specificity of 79% achieved in the McP region of the hand thermal image. Thermal image segmentation using the k-meansalgorithm provided better segmentation results compared to the fuzzy c-means algorithm in diagnosing the disease. Therefore, the computer aided diagnostic based thermography method could be used as a validated quantification method for interpreting and evaluating arthritis.
This paper presents a novel fuzzyc-means (FcM) clustering simultaneously incorporating local and global information (FLGIcM) method to unsupervised change detection (cD) from remotely sensed images. A new factor incl...
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This paper presents a novel fuzzyc-means (FcM) clustering simultaneously incorporating local and global information (FLGIcM) method to unsupervised change detection (cD) from remotely sensed images. A new factor including three local, global and edge parameters is added into the conventional FcM to enhance the insensitivity to noise and preserve detailed features. The spatial attraction between the central pixel and its neighborhood pixels is incorporated as a local parameter to utilize spatial information. A global parameter designed based on the estimated mean values of changed and unchanged pixels is introduced into the new factor to enhance its robustness and ability of separating changed from unchanged pixels. In addition, an edge parameter is also added to remain accurate edges and change details. Two experiments were carried out on Landsat images to test the performance of FLGIcM. Experimental results indicate that FLGIcM always achieves high accuracy and overperforms some state-of-the-art cD methods. Therefore, the proposed FLGIc provides an effective unsupervised cD method.
Fault diagnosis for turnouts is crucial to the safety of railways. Existing studies on fault diagnosis depend on human experiences to select reference curves and require fault type information beforehand. Therefore, w...
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Fault diagnosis for turnouts is crucial to the safety of railways. Existing studies on fault diagnosis depend on human experiences to select reference curves and require fault type information beforehand. Therefore, we proposed a turnout fault diagnosis method, named similarity function and fuzzyc-means based two-stage algorithm to detect faults and identify fault types in real time. First, the reference curve is selected from current curves representing turnout actions by K-meansalgorithm;then, a similarity function called Frechet distance is used to distinguish normal and abnormal curves. Second, an improved fuzzy c-means algorithm is employed to cluster curves automatically. To be more specific, it can double-confirm the normal curves recognized in the first step as well as divide the abnormal curves into different types. Furthermore, possible causes for each fault type are inferred according to their curves. Our approach integrates fault detection and fault classification into one model and would better help the diagnosis of turnouts. The analysis results based on the similarity function and fuzzyc-means based two-stage algorithmalgorithm indicate that the analyzed turnout fault types can be diagnosed automatically with high accuracy. Furthermore, since the proposed similarity function and fuzzy c-means algorithm does not need to know fault types in advance, it is applicable in identifying new fault types.
cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architect...
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cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzyclustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzyclustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.
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