Non-intrusive load identification is a promising candidate in providing energy consumption information of individual appliances, which can improve energy-using habits and save energy. The load signature has a huge imp...
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ISBN:
(数字)9781728131375
ISBN:
(纸本)9781728131382
Non-intrusive load identification is a promising candidate in providing energy consumption information of individual appliances, which can improve energy-using habits and save energy. The load signature has a huge impact on the performance of non-intrusive load identification systems. In this paper, a new household appliance load identification model based on a novel load signature processing framework is proposed. Based on the proposed framework, the original vi trajectory of the household appliance is mapped to the binary image. The LeNet trained on the MNIST dataset is utilized to extract the deep features from the binary viimage. After that, the ReliefF algorithm is adopted to select the most important information from the deep features. The SVM is utilized to identify household appliances based on the obtained load signature. Finally, the experimental results based on the measured dataset and the PLAID dataset indicate that the proposed load signature processing framework is effective in improving the household appliance identification accuracy.
Color quantization is an important operation with many applications in computer graphics and imageprocessing and analysis. Clustering algorithms have been extensively applied to this problem. However, despite its pop...
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ISBN:
(纸本)9781538666500
Color quantization is an important operation with many applications in computer graphics and imageprocessing and analysis. Clustering algorithms have been extensively applied to this problem. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much attention in the colour quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we propose a novel color quantization method based on the k-means algorithm. The proposed method utilizes adaptive initialization, deterministic sub-sampling and efficient coreset construction to attain high speed and high quality quantization. Experiments on a set of benchmark images demonstrate the proposed method to be significantly faster than k-means while delivering nearly identical results.
Moisture marks (wet areas) are significant defects that may develop on the surfaces of subway structures as a result of water leakage through soil. The detection and assessment of moisture marks are predominantly cond...
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Moisture marks (wet areas) are significant defects that may develop on the surfaces of subway structures as a result of water leakage through soil. The detection and assessment of moisture marks are predominantly conducted on the basis of visual inspection (vi) methods, which are known to be costly, labor-intensive, and error-prone. The objective of this paper is to develop an integrated model based on imageprocessing techniques and artificial intelligence to automate consistent moisture marks detection and numerical representation of the distress in subway networks. The integrated model comprises sequential processors that automatically detect moisture marks on concrete surfaces, and artificial neural networks (ANNs) for moisture marks identification and quantification. First, red-green-blue (RGB) images are preprocessed by means of spatial domain filters to denoise the image and enhance the crucial clues associated with moisture marks. Second, a moisture detector is streamlined with a set of morphological algorithms to detect wet areas. Third, the area percentage and severity of moisture marks are measured using the ANN model in conjunction with cross-entropy optimization function. The integrated model was validated through 165 images. Regarding the moisture marks detection algorithm, the recall, precision, and accuracy attained were 93.2, 96.1, and 91.5%, respectively. The mean and standard deviation of error percentage in moisture marks region extraction were 12.2 and 7.9%, respectively. Also, the ANN model was able to satisfactorily quantify the moisture marks area with an average validity of 96%. The integrated model is a decision support tool, expected to assist infrastructure managers and civil engineers in their future plans and decision making. (c) 2017 American Society of Civil Engineers.
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the develo...
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ISBN:
(纸本)9781538675311
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the development of computer and telecommunications systems and networks determines the look of the present and future of medical technology and of medicine in general [8, 10]. At last years of one of the directions of development of cloud, computing technologies in high-tech-medicine is a processing the digital image: improvement of quality of image, recovering image, its recognition of separate elements. Recognition of pathological processes is one of the most important problems of processing the medical image. By now, a number of standards for medical imaging have been developed. By analogy with CAD/CAM systems (computer aided design and computer aided manufacturing) for technical applications, CAD (computer-aided diagnosis) systems are being developed for medical purposes. Some of them are already successfully operating, but to date these systems are only "assistants" of a diagnostician who takes decisions. CAD algorithms for medical imaging systems typically include image segmentation, the selection of some objects of interest ("masses"), their analysis, parametric description of the selected objects and their classification.
Ambient assisted living and intelligent transportation systems are becoming strongly coupled. There is the necessity of improving the quality of life by developing inclusive mobility solutions for impaired people. In ...
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Ambient assisted living and intelligent transportation systems are becoming strongly coupled. There is the necessity of improving the quality of life by developing inclusive mobility solutions for impaired people. In this paper, we focus on a monocular vision-based system to assist people during walking, jogging, and running in outdoor environments. The impaired user is guided along a path represented by a lane or line on a dedicated runway. We developed a set of imageprocessingalgorithms to extract lines/lanes to follow. The embedded system is based on a small camera and a board that is responsible for processing the images and communicating with the developed haptic device. The haptic device is formed by a set of two gloves equipped with vibration motors that drive the user to the right direction. The vibration sequences are generated according to a robotic-like controller, considering the user as a two wheel steering robot, where the rotational and translation velocity can be controlled. The results obtained show that the overall system is able to detect the right path and to provide the right stimuli to the user, by means of the gloves, up to a speed over 10 km/h.
The proceedings contain 147 papers. The special focus in this conference is on . The topics include: An indoor personnel positioning method based on spectrum;research on fault diagnosis based on test resource virtuali...
ISBN:
(纸本)9789811089435
The proceedings contain 147 papers. The special focus in this conference is on . The topics include: An indoor personnel positioning method based on spectrum;research on fault diagnosis based on test resource virtualization;solving airport gate assignment problem using an improved genetic algorithm with dynamic topology;sound detection and alarm system of unmanned aerial vehicle;Landscape pattern recognition on water quality protection in an urbanizing delta using remote sensing and GIS techniques;map building for a kind of robot’s structural environment;a study on hair density analysis for androgenic hair pattern of low-resolution leg images;an efficient algorithm based on resource regulatory network to predict potential safety hazards;The software quality prediction model based on DBN;Research on DOS attack effect evaluation technology;research and optimization of data sparsity in collaborative filtering algorithms;analyses on requirements of information and communication standard for global energy interconnection;a simplified prediction method of IoT service response time;real-time and distributed anomalies detection architecture and implementation with structured streaming;a distributed complex event processing system based on publish/subscribe;an event-driven multi-process collaborative interaction platform for internet of things;the impact of I-O structure on China’s macroeconomy;coordinated production and distribution scheduling in flowshop with limited waiting times;remove the confusion and speed up the construction of battlefield cyber warfare force;study on online learning intervention based on theory of constraints;LHR: Using LDA helps ranking;a robust image segmentation approach using fuzzy C-means clustering with local coefficient of variation.
Blind image steganalysis is the classification problem of determining whether an image contains any hidden data or not. This blind process doesn't need any prior information about the embedding algorithm which is ...
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Blind image steganalysis is the classification problem of determining whether an image contains any hidden data or not. This blind process doesn't need any prior information about the embedding algorithm which is used to hide data on the examined images. Recently, Convolutional Neural Network (CNN) is presented to deal with the blind image steganalysis classification problem. Most of the CNN-based image steganalysis approaches can't cope with low payloads. Improved Gaussian Convolutional Neural Network (IGNCNN) is presented with a transfer learning method in order to deal with stego-images with low payloads. IGNCNN contains a pre-processing layer which is consisted of a fixed coefficients (data-set independent) high pass filter (HPF). IGNCNN also is a fixed learning rate based-CNN. In this paper, a dynamic learning rate-based CNN approach is proposed, in order to highly minimize the detection error cost. Nevertheless, the proposed approach uses a dataset dependent-based Gaussian HPF instead, as a preprocessing layer, in order to well-choose a cutoff frequency depending on the training dataset. Experiments are performed on graphical processing units (GPUs) with the standard BOSSbase 1.01 dataset exposed to the S-UNIWARD and WOW image steganographic algorithms. Results show that the proposed approach outperforms computing approaches, GNCNN, improved GNCNN, SRM and SRM+EC, by an average increase of 7.4%, 5.3%, 4.1% and 2.8% respectively in terms of accuracy metric.
Recently more interest in the recognition algorithms based on human veins is observable. In the literature we can find results confirm that this trait provide huge accuracy level. This feature is used for instance in ...
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ISBN:
(数字)9783319999548
ISBN:
(纸本)9783319999548;9783319999531
Recently more interest in the recognition algorithms based on human veins is observable. In the literature we can find results confirm that this trait provide huge accuracy level. This feature is used for instance in cash machines. In the last years, more financial institutions took into consideration vein-based identification technology. Its popularity is connected with ease of use and analyzed trait uniqueness. A method to extract finger veins features with imageprocessingalgorithms is presented in this paper. In the preliminary stage of the research, the device to collect finger veins images was created. The second part of the work is implementation of the algorithm to process input images. The authors used soft computing algorithm that is artificial neural network to find specific structures on the image. The last stage of the work is connected with confirmation of the results obtained with artificial neural network.
Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision ...
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Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision systems. In this paper, a single image rain removal algorithm is proposed based on details preservation and background enhancement. We first decompose the rainy image into low-frequency part and high-frequency part by low pass smoothing filter. Then, edge detection is performed on the low-frequency part to extract a mask, which will be used to capture rain-free image details from the high-frequency part. Next, the rain-free image details are superimposed on the low-frequency part to obtain an image without rains but well preserved details. Finally, the dark channel prior method is utilized to further alleviate the blur due to raining. Experiments on both synthetic and real rainy images demonstrate the effectiveness and efficiency of the proposed method.
Analysis of changes in medical images is one of the most important areas of computer use, which ensures the progress of the diagnostic process at the moment. Along with the quality of visualization, automated diagnost...
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ISBN:
(纸本)9781728125961
Analysis of changes in medical images is one of the most important areas of computer use, which ensures the progress of the diagnostic process at the moment. Along with the quality of visualization, automated diagnostic methods are being developed, CADs are being increasingly used. Today, medical imaging is the most common diagnostic tool in modern medical practice. The most relevant areas for the implementation of medical image analysis in connection with the specificity of the objects under study are methods based on mathematical methods for processing input information, for example, on a fractal analysis (analysis of self-similarity of image parts), which can be realized by calculating the fractal dimension and Hurst index. The obtained data in world practice become the basis for creating new algorithms for qualitative analysis and imageprocessing for creating new algorithms.
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