This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a ...
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ISBN:
(纸本)9781728155463
This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a novel mechanism referred to as SortAlgo-Metrics analysis to identify cloud-based servers. We deployed four cloud-based servers to run four sorting algorithms to allow features extraction that are used for analysis. Consequently, the model has been validated by comparing training data and the testing data with 96% probability. Therefore, with more complex properties pulled out from the cloud-based servers and advanced statistical model, SortAlgo-Metrics mechanism could generate a higher degree of basis numbers for ICMetrics technology entropy key generation for cloud-based server authentication, and other complex systems.
Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages ove...
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ISBN:
(纸本)9781728109107
Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages over other multilevel topologies when considering high number of levels. This paper presents an alternative modulation technique for a modified modular multilevel converter (M-MMC) topology for low-voltage applications. By using level-shifted carrier (LSC) modulation and the proposed sorting algorithm, the capacitance requirement for this topology can be reduced. Finally, the proposed modulation scheme and the reduction of capacitance are verified by simulation results using PLECS.
Toll Vehicle Classification is an important task. Indeed, it has many uses in traffic management and toll collection systems. In this paper, Vinci Autoroutes group Networks (the biggest French Highways concession) are...
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ISBN:
(纸本)9781728105604
Toll Vehicle Classification is an important task. Indeed, it has many uses in traffic management and toll collection systems. In this paper, Vinci Autoroutes group Networks (the biggest French Highways concession) are considered, where every year, millions of vehicles are classified in real-time. Then, a small decrease in classification performance can have serious economic losses. Therefore, the accuracy and the time complexity become critical for the toll collection system. The current classification algorithm uses the scene features' to detect vehicles classes. However, it requires a large labeled datasets, and has a limitations when multiple vehicles are in the scene. Herein, we propose a novel context-aware vehicle classification method that takes profit from the semantic spatial relationship of the objects. The experiments show that our method is performing as accurately as the existing model with significantly lower labeled datasets (74 times smaller). Moreover,the obtained accuracy of the proposed method is 99.97% compared to 99.79% achieved by the current method when using the same training set.
Fault diagnosis and fault-tolerant control are significant for the reliability of an MMC system, which usually has a large number of switching devices and high potential of operation failure. This paper proposes a sim...
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ISBN:
(纸本)9781728157047
Fault diagnosis and fault-tolerant control are significant for the reliability of an MMC system, which usually has a large number of switching devices and high potential of operation failure. This paper proposes a simple fault diagnosis method for sub-module open-circuit faults and a new fault-tolerant control method. The proposed methods combine a novel sequential indirect MPC structure with sorting algorithm. Different from the well-known solutions, we detect and locate open-circuit faults in any sub-module efficiently with the maximum voltage output of the capacitor voltage sorting result, which is based on the physical characteristics of circuits when capacitor voltage rises. Thereafter, a new fault-tolerant control solution based on MPC is implemented through modifying circuit structure and control algorithm. The proposed methods are easy to implement and add little computational complexity to the control algorithm. Finally, the MATLAB/Simulink verification results confirm the effectiveness of the proposed solutions.
Unlike conventional converters, modular multilevel converter (MMC) has a higher switching frequency - which has direct implication on important parameters like converter loss and reliability - mainly due to increased ...
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ISBN:
(纸本)9781728145624
Unlike conventional converters, modular multilevel converter (MMC) has a higher switching frequency - which has direct implication on important parameters like converter loss and reliability - mainly due to increased number of switching components. However, conventional switching techniques, where submodule sorting is just based on capacitor voltage balancing, are not able to achieve switching frequency reduction objective. A novel modulation algorithm for modular multilevel converters (MMCs) is proposed in this paper to reduce the switching frequency of MMC operation by defining a constrained multiobjective optimization model. The optimized switching algorithm incorporates all control objectives required for the proper operation of MMC and adds new constraints to limit the number of submodule switching events at each time step. Variation of severity of the constraints leads to a desired level of controllability in MMC switching algorithm to trade-off between capacitor voltage regulation and switching frequency reduction. Finally, performance of the proposed algorithm is validated against a seven-level back-to-back MMC-HVDC system under various operating conditions.
This paper presents an approach to manage metadata (target class labels) for the recorded primary Doppler radar data. This information is necessary for further research and development of target classification algorit...
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ISBN:
(数字)9781728161556
ISBN:
(纸本)9781728161563
This paper presents an approach to manage metadata (target class labels) for the recorded primary Doppler radar data. This information is necessary for further research and development of target classification algorithms. For this purpose, a labelling methodology and an application radar data analysis and target labelling was developed. The application includes radar records file processing, Doppler filtering, tag creation, data visualizationand tag database. For the better context of analysed data, an interface to Geographic Information Software (GIS) program is included as well. GIS program allows overlaying radar Plan Position Indicator (PPI) data with map data, airplane transponder tracks, drone flight path log and other support data. Finally, the application notes and observations are presented.
The massive technological expansions in modern healthcare solutions have substantially influenced the development of several unfolding research fields. One such interesting area involves the therapeutic prognosis of p...
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ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
The massive technological expansions in modern healthcare solutions have substantially influenced the development of several unfolding research fields. One such interesting area involves the therapeutic prognosis of pathological conditions in patients by using knowledge engineering techniques. These techniques have inevitably assisted in the growth of healthcare systems. In this paper, we provide a comprehensive classification framework for identification of pathological disorders in children. We consider a real dataset to study the accuracy in prediction of different classification algorithms for the identification of seven different pathological conditions. We then present an experimental analysis corresponding to the performance of each algorithm. This framework can be used for the early detection of diseases and for suggesting appropriate precautionary measures. Further a more stratified understanding of the demographics of a patient's pathological conditions can be determined and appropriate treatment can be recommended.
X-ray-based security inspection systems are widely used in public places, such as airports, train stations and other critical locations with a large-scale of crowds. However, security checks manually are not efficient...
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ISBN:
(数字)9781728176475
ISBN:
(纸本)9781728176482
X-ray-based security inspection systems are widely used in public places, such as airports, train stations and other critical locations with a large-scale of crowds. However, security checks manually are not efficient enough. Current X-ray-based automatic detection methods, including deep learning and conventional ways, usually face low accuracy, poor universality, and other weakness. In this paper, we proposed an X-ray-based dangerous goods detection scheme named SSD-X, an improved SSD (Single Shot Multi-Box Detector) object detection algorithm, and built X-DOG: an intelligent X-ray-based dangerous goods detection and automatic alarm system with SSD-X. First, in consideration of the position uncertainty and the overlap of objects, multiple data enhancement is utilized to effectively improve the accuracy and ameliorate the overfitting phenomenon. To solve the problem of unbalanced positive and negative samples in detection algorithms, focal loss is adopted to the confidence loss function so as to accelerate the rate of convergence. What's more, soft-NMS is added to enhance performance of detection of dense objects. We built a portable detecting system X-DOG implemented with SSD-X, which can run on mobile platform. Real time detecting and automatic alarming functions are implemented in X-DOG. Compared with the baseline algorithm, SSD-X shows the better performance in our experiments.
Ability to automatically identify objects of interest and to automatically classify their signals belongs to essential functionalities of electronic intelligence systems. Objects identification results and signals cla...
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ISBN:
(数字)9781728161556
ISBN:
(纸本)9781728161563
Ability to automatically identify objects of interest and to automatically classify their signals belongs to essential functionalities of electronic intelligence systems. Objects identification results and signals classification results are conditioned by accurate measurement and technical analysis of objects signal parameters. Classification algorithm based on Feedforward Neural Network, and classification algorithms based on Support-Vector Machine, with three types of Kernel Function, are tested and compared in this paper as the first stage of objects identification in electronic intelligence systems.
This research is intended for the creation of an automatic cardiovascular disease detection system with pre-marked SPECT (Single photon emission computed tomography)data for training the program and for subsequently s...
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ISBN:
(数字)9781728123394
ISBN:
(纸本)9781728123400
This research is intended for the creation of an automatic cardiovascular disease detection system with pre-marked SPECT (Single photon emission computed tomography)data for training the program and for subsequently supplying it with unclassified SPECT data for classification as an indication of probable heart disease or not. We used a well-known and widely used classification algorithm named Support Vector Machines (SVM) for the purposes of this classification. In other terms, you instruct the machine; the system learns from the inputs you give. When the training is over, you can provide the system with undefined SPECT data and it is immediately identified by the system Clearly, the accuracy of this classification would depend on the precision of the pictures of the training system, the support vector machine parameters, the sophistication and many more variables that are discussed later. The project is of considerable significance in today's world, particularly if businesses offer a million-dollarreward to anyone capable of building automated systems detecting nearly every Star Wars (such as transcoder) disease.
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