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Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy
Full text linkThe algorithm based on combination learning usually is superior to a single classification algorithm on the task of protein secondary structure prediction. However, the assignment of the weight of the base classifier usually lacks decision-making evidence. In this paper, we propose a protein secondary structure prediction method with dynamic self-adaptation combination strategy based on entropy, where the weights are assigned according to the entropy of posterior probabilities outputted by base classifiers. The higher entropy value means a lower weight for the base classifier. The final structure prediction is decided by the weighted combination of posterior probabilities. Extensive experiments on CB513 dataset demonstrates that the proposed method outperforms the existing methods, which can effectively improve the prediction performance
Instance Retrieval Using Region of Interest Based CNN Features
Full text linkRecently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that the visual matching can be implemented at image region-level to effectively identify target objects from cluttered backgrounds. Moreover, we test the performance of the proposed RoI-based CNN feature, when it is extracted from different convolutional layers or fully-connected layers. Also, we compare the performance of RoI-based CNN feature with those of the state-of-the-art CNN features on two instance retrieval benchmarks. Experimental results show that the proposed RoI-based CNN feature provides superior performance than the state-of-the-art CNN features for in-stance retrieval
Radiation Cross Calibration Based on GF-1 Side Swing Angle
Full text linkRadiation cross-calibration is an effective method to check and verify the accuracy and stability of sensor measurements. Satellites with high radiation accuracy are used to calibrate satellites with low radiation accuracy. In order to ensure the reliability of the radiation cross-calibration method, we propose to obtain the gain and offset of the GaoFen-1 satellite by linear regression after the radiation cross-calibration of the satellite with low precision and compare with the official coefficient. Finally, we get the relationship between the error in radiation cross-calibration results and side swing angle. The linear correction coefficients of each band are: 0.618, 0.625, 0.512 and 0.474. The results show that after the method is corrected by the linear correction coefficient, the error caused by the side swing angle during the cross-calibration of the orbital radiation is reduced. The accuracy of radiation cross-calibration is improved, the frequency of calibration is improved and the requirements of remote sensing applications in the new era are adapted
A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks
Full text linkForest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index can be calculated
Review of Access Control Model
No full textAccess control is one of the core problems in data management system. In this paper, the system requirements were described in three aspects: the traditional access control model, the access control model in the Internet era and the access control model in the cloud computing environment. Meanwhile, the corresponding major models were listed and their characteristics and problems were analyzed. Finally, the development trend of the corresponding model was proposed
Code-Based Preservation Mechanism of Electronic Record in Electronic Record Center of Cloud Storage
No full textWith the rapid development of E-commerce and E-government, there are so many electronic records have been produced. The increasing number of electronic records brings about storage difficulties, the traditional electronic records center is difficult to cope with the current fast growth requirements of electronic records storage and management. Therefore, it is imperative to use cloud storage technology to build electronic record centers. However, electronic records also have weaknesses in the cloud storage environment, and one of them is that once electronic record owners or managers lose physical control of them, the electronic records are more likely to be tampered with and destroyed. So, the paper builds a reliable electronic records preservation system based on coding theory. It can effectively guarantee the reliability of record storage when the electronic record is damaged, and the original electronic record can be restored by redundant coding, thus ensuring the reliable storage of electronic records
Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem
No full textCoupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X0 , Y0) and release time (T0), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed. The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index. The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events. Based on the optimized monitoring well position and sampling frequency, the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach. The case study results show that the following parameters were obtained: 1) the optimal monitoring well position (D) is at (445, 200); and 2) the optimal monitoring frequency (Δt) is 7, providing that the monitoring events is set as 5 times. Employing the optimized monitoring well position and frequency, the mean errors of inverse modeling results in source parameters (M, X0, Y0, T0) were 9.20%, 0.25%, 0.0061%, and 0.33%, respectively. The optimized monitoring well position and sampling frequency can effectively safeguard the inverse modeling results in identifying the contamination source parameters. It was also learnt that the improved Metropolis-Hastings algorithm (a Markov chain Monte Carlo method) can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization, which significantly improved the accuracy and numerical stability of the inverse modeling results
CHT/CFD Analysis of Thermal Sensitivity of a Transonic Film-Cooled Guide Vane
No full textThermal parameters are important variables that have great influence on life time of turbine vanes. Therefore, accurate prediction of the thermal parameters is essential. In this study, a numerical approach for conjugate heat transfer (CHT) and computational fluid dynamics (CFD) is used to investigate thermal sensitivity of a transonic guide vane which is fully film-cooled by 199 film holes. Thermal barrier coating (TBC), i.e., the typical TBC and a new one as the candidate TBC, and turbulence intensity (Tu), i.e., Tu=3.3%, 10% and 20%, are two variables used for the present study. At first the external surface temperatures of the vane material are compared. Next, the TBC surface temperatures are considered. Results show the major role of the lower thermal conductivity of TBC which results in the lower and more uniform temperature on the external surface of the vane substrate. Finally, the thermal sensitivity is presented in terms of the percentage reduction of the external surface temperatures of the vane material and the structural temperatures of the vane material at midspan, including the variations of average and maximum vane temperatures. Results show that TBC and Tu have significant effects on the external surface and structural temperatures of the vane substrate. The lower thermal conductivity of TBC leads to the higher difference between the thermal conductivity of the vane substrate and TBC, the reduction of heat transfer and the more uniform temperature within the vane structure. The results also show more effective protection for the average vane temperature from the two TBCs at higher Tus. However, Tu does not significantly affect the reduction of the maximum vane temperature even though the new TBC, which has the very low thermal conductivity, is used
IoT Based Approach in a Power System Network for Optimizing Distributed Generation Parameters
No full textThe objective of this paper is to provide a robust Virtual Power Plant (VPP) network collaborated with Internet of Things (IoT) which uses a conceptual model to integrate each device in the grid. Based on the functionality all the devices which are purely distributed within the grid are networked initially from residential units to substations and up to service data and demand centres. To ensure the trapping of the available power and the efficient transfer of Distributed Generation (DG) power to the grid Distribution Active Control (DAC) strategy is used. Synchronized optimization of DG parameter which includes DG size, location and type are adopted using Dispatch strategy. The case studies are optimized by rescheduling the generation and with load curtailment. Maximized Customer Benefit (MCB) is taken as an objective function and a straight forward solution is given by heuristic search techniques. This method was vindicated in a practical Indian Utility system. This control proposes better performances, ensures reliability and efficiency even under parameter variations along with disturbances which is justified using IEEE 118 bus system and real time Indian utility 63 bus system. Results reveal that the proposed technique proves advantages of low computational intricacy
Novel Membranes Regenerated from Blends of Cellulose/Gluten Using Ethylenediamine/Potassium Thiocyanate Solvent System
No full textCurrent industrial methods for dissolution of cellulose in making regenerated cellulose products are relatively expensive, toxic and dangerous and have environmental problems coming with the hazard chemical wastes. To solve these problems, a novel ethylenediamine and potassium thiocyanate (ED/KSCN) solvent system was developed, that is economical, ecofriendly, and highly efficient. The ED/KSCN solvent system was proven to be a suitable solvent for fabricating cellulose (blended with other polymers) membranes. In this study, gluten was used to develop nonporous membranes with cellulose. The method of casting these membranes provided better ones than the former researchers’ techniques. These composite membranes’ physical and mechanical properties were studied by analysis of morphology, viscosity, crystallinity, thermal behaviors, tensile properties and water absorption of membranes. Results showed that membranes are nonporous, uniform, strong, flexible, ecofriendly and renewable. Mechanical and physical properties were influenced by the ratio of cellulose/gluten. By blending 40% gluten, the tensile strength of cellulose membrane dropped to 15.89 MPa from 35.11 MPa. However, its elongation at break increased from 35.3% to 57.02% accordingly