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A Multi-Layered Model for Heat Conduction Analysis of Thermoelectric Material Strip
No full textA multi-layered model for heat conduction analysis of a thermoelectric material strip (TEMs) with a Griffith crack under the electric flux and energy flux load has been developed. The materials parameters of the TEMs vary continuously in an arbitrary manner. To derive the solution, the TEMs is divided into several sub-layers with different material properties. The mixed boundary problem is reduced to a system of singular integral equations, which are solved numerically. The effect of strip width on the electric flux intensity factor and thermal flux intensity factor are studied
Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine
Get PDFThis paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 75% of the data has been used for development of model and 30% of the data is used for validation. The predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature
Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery
Get PDFTo improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image. Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones, and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image
Improved VGG Model for Road Traffic Sign Recognition
Get PDFRoad traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling operation and dropout operation after multiple convolutional layers, to catch the main features and save the training time. The paper proposes the method which adds dropout and Batch Normalization (BN) operations after each fully-connected layer, to further accelerate the model convergence, and then it can get better classification effect. It uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset in the experiment. The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning, and the spent time is also reduced greatly
Expression Preserved Face Privacy Protection Based on Multi-mode Discriminant Analysis
Get PDFMost visual privacy protection methods only hide the identity information of the face images, but the expression, behavior and some other information, which are of great significant in the live broadcast and other scenarios, are also destroyed by the privacy protection process. To this end, this paper introduces a method to remove the identity information while preserving the expression information by performing multi-mode discriminant analysis on the images normalized with AAM algorithm. The face images are decomposed into mutually orthogonal subspaces corresponding to face attributes such as gender, race and expression, each of which owns related characteristic parameters. Then, the expression parameter is preserves to keep the facial expression information while others parameters, including gender and race, are modified to protect face privacy. The experiments show that this method yields well performance on both data utility and privacy protection
Band Selection Method of Absorption Peak Perturbance for the FTIR/ATR Spectrum Analysis
Get PDFThe rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy (FTIR) and attenuated total reflection (ATR). By the subtracted spectra between glucose aqueous solution and de-ionized water, absorption peaks are calculated in fingerprint area. Based on these absorption peaks and multiple linear regression (MLR) model, discrete band selection method of absorption peaks disturbance model (APDM) was developed. 5 absorption peaks 1150 cm-1, 1103 cm-1, 1078 cm-1, 1034 cm-1, 991 cm-1 were found in fingerprint area. Used these absorption peaks to establish absorption peaks disturbance model, the optimal wavelength combinations are 1140 cm-1, 1096 cm-1, 1084 cm-1, 1030 cm-1, 993 cm-1, the corresponding C-RMSEP and C-RP are 1.164 mmol/L and 0.828 respectively. The results show that the optimal prediction effect of APDM was obviously better than the one of the Partial least squares (PLS) model, and the complexity of the optimal model is reduced greatly also. The results also provide a theoretical basis for design of small and portable human serum glucose spectrometer
Secure Video Streaming with Lightweight Cipher PRESENT in an SDN Testbed
Get PDFThe combination of traditional processors and Field Programmable Gate Arrays (FPGA) is shaping the future networking platform for intensive computation in resource-constrained networks and devices. These networks present two key challenges of security and resource limitations. Lightweight ciphers are suitable to provide data security in such constrained environments. Implementing the lightweight PRESENT encryption algorithm in a reconfigurable platform (FPGAs) can offer secure communication service and flexibility. This paper presents hardware acceleration of security primitives in SDN using NETFPGA-10G. We implement an efficient design of the PRESENT algorithm for faster, smaller and lower power consumption hardware circuit using Verilog. We evaluate the performance of the hardware and software implementations of PRESENT. Experimental results prove that the proposed hardware design is a viable option for use in resource constrained devices in future networks and their applications
A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering
Get PDFIn this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters, thunderstorm cloud clusters are quickly and accurately identified. The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images
Modeling and Displaying Dust Accumulation Process on Solar Panel and Impacts on Photoelectrical Conversion in a Virtual Reality Environment
Get PDFDust accumulation on the surface of solar photovoltaic (PV) panels can significantly decrease the amount of solar irradiance reaching the cells, and consequently, can negatively impact the amount of electricity generated. In the literature, there have been quite a few theoretical or empirical studies to model the impacts of dust on solar power generation, but little has been done to “observe” and model the process of how dust gets to solar cells in the first place and appreciate the major ambient factors that are related to dust accumulation. This paper attempts to bridge that gap by building such a photoelectrical conversion model that immerses users into a virtual reality (VR) environment made of solar panels, sun radiation, and dust particles. In specific, the photoelectrical conversion of solar panels is modeled by taking into account of four contributing factors: 1) relationship between solar angle and light incidence angle; 2) the impact of environment parameters on the effective irradiance, including temperature, wind speed, sun position, date, geographical location of solar panels; 3) statistics of accumulated dust, dust accumulation rates, and the effect of accumulated dust on the panel; and 4) solar panel cell equivalent circuit. The real-world environmental hourly data, over the course of 20 years, obtained from National Renewable Energy Laboratory (NREL), are fit into the VR model, and the dust accumulation effects on solar electricity production can be visualized. This work clearly demonstrates how latest VR technologies can play a central role in manipulating and displaying a large amount data from long-term, complex, incomplete, and irregular data sets
Photonic generation and detection of W-band OCDMA signals
Get PDFWe have designed and studied a W-band OCDMA radio-over-fiber system. To increase the number of channels, optical encoding and decoding are utilized. The theoretical analysis of generation, modulation and optical encoding of a signal is presented. Also, to confirm the theoretical analysis, the transmission of the encoded signal, decoding and its detection is simulated. Two optical codes (m-sequence and quaternary phase shift) with different code lengths are used and the results are presented. Simulation results for each set of codes are compared and the performance of each code is evaluated in the final bit error rate measurements. The quaternary phase shift codes show almost 20 dB of BER gain compared to m-sequence. In addition, the number of channels that are accommodated by the quaternary phase shift codes are more than four times the m-sequence code with the same length. The stability of the system is improved by using a low-frequency sinewave generator to modulate the signal to the W-band. Also, the capacity and reach of the system are improved while the cost and complexity are reduced by eliminating the necessity of using a high-frequency sinewave generator in the modulator. This system simultaneously guarantees increased capacity and reach for a radio-over-fiber network