In order to improve the long-term prediction accuracy of feed grain demand, a dynamic forecast model of long-term feed grain demand is realized with joint multivariate regression model, of which the correlation betwee...
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In order to improve the long-term prediction accuracy of feed grain demand, a dynamic forecast model of long-term feed grain demand is realized with joint multivariate regression model, of which the correlation between the feed grain demand and its influence factors is analyzed firstly;then the change trend of various factors that affect the feed grain demand is predicted by using ARIMA model. The simulation results show that the accuracy of proposed combined dynamic forecastingmodel is obviously higher than that of the grey system model. Thus, it indicates that the proposed algorithm is effective.
Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classifi...
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Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classification results. So we put forward a feature selection approach, IIRCT, based on interclass and intraclass relative contributions of terms in the paper. In our proposed algorithm, three critical factors, which are term frequency and the interclass relative contribution and the intraclass relative contribution of terms, are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20 NewsGroup and SougouCS corpora show that IIRCT algorithm achieves better performance than DF, t-Test, and CMFS algorithms.
A method is proposed to determine lifetime of luminescent emissions based on the phase shift measurement employing the digitalized Lissajous representation: this diagramhas been typically used with analogical algorith...
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A method is proposed to determine lifetime of luminescent emissions based on the phase shift measurement employing the digitalized Lissajous representation: this diagramhas been typically used with analogical algorithms, whereas the proposed-method is performed in digital domain, showing an improved accuracy and repeatability. The procedure is studied and tested with two different oxygen sensors that show different sensitivities and signal levels in order to confirm the no influence of the signals intensity on the calibration process. The computational cost of the proposed method is low, which makes it possible to monitor in real time luminescence sensors based on reversible quenching with a potential low cost system based on a digital signal processor (DSP).
Performance evaluation of cloud computing systems studies the relationships among system configuration, system load, and performance indicators. However, such evaluation is not feasible by dint of measurement methods ...
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Performance evaluation of cloud computing systems studies the relationships among system configuration, system load, and performance indicators. However, such evaluation is not feasible by dint of measurement methods or simulation methods, due to the properties of cloud computing, such as large scale, diversity, and dynamics. To overcome those challenges, we present a novel Dynamic Scalable Stochastic PetriNet (DSSPN) to model and analyze the performance of cloud computing systems. DSSPN can not only clearly depict system dynamic behaviors in an intuitive and efficient way but also easily discover performance deficiencies and bottlenecks of systems. In this study, we further elaborate some properties of DSSPN. In addition, we improve fair scheduling taking into consideration job diversity and resource heterogeneity. To validate the improved algorithm and the applicability of DSSPN, we conduct extensive experiments through Stochastic Petri Net Package (SPNP). The performance results show that the improved algorithm is better than fair scheduling in some key performance indicators, such as average throughput, response time, and average completion time.
Arid region characterizes more than 30% of the Earth's total land surface area and the area is still increasing due to the trends of desertification, yet the extent to which it modulates the global C balance has b...
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Arid region characterizes more than 30% of the Earth's total land surface area and the area is still increasing due to the trends of desertification, yet the extent to which it modulates the global C balance has been inadequately studied. As an emerging technology, IoT monitoring can combine researchers, instruments, and field sites and generate archival data for a better understanding of soil abiotic CO2 uptake in arid region. Images' similarity analyses based on IoT monitoring can help ecologists to find sites where the abiotic uptake can temporally dominate and how the negative soil respiration fluxes were produced, while IoT monitoring with a set of intelligent video recognition algorithms enables ecologists to revisit these sites and the experiments details through the videos. Therefore, IoT monitoring of geospatial images, videos, and associated optimization and control algorithms should be a research priority towards expanding insights for soil abiotic CO2 uptake and a better understanding of how the uptake happens in arid region. Nevertheless, there are still considerable uncertainties and difficulties in determining the overall perspective of IoT monitoring for insights into the missing CO2 sink.
Decision-making algorithm, as the key technology for uncertain data fusion, is the core to obtain reasonable multisensor information fusion results. DS evidence theory is a typical and widely applicable decision-makin...
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Decision-making algorithm, as the key technology for uncertain data fusion, is the core to obtain reasonable multisensor information fusion results. DS evidence theory is a typical and widely applicable decision-making method. However, DS evidence theory makes decisions without considering the sensors' difference, which may lead to illogical results. In this paper, we present a novel decision-making algorithm for uncertain fusion based on grey relation and DS evidence theory. The proposed algorithm comprehensively takes consideration of sensor's credibility and evidence's overall discriminability, which can solve the uncertainty problems caused by inconsistence of sensors themselves and complexity of monitoring environment and simultaneously ensure the validity and accuracy of fusion results. The innovative decision-making algorithm firstly obtains the sensor's credibility through the introduction of grey relation theory and then defines two impact factors as sensor's credibility and evidence's overall discriminability according to the focal element analyses and evidence's distance analysis, respectively;after that, it uses the impact factors to modify the evidences and finally gets more reasonable and effective results through DS combination rule. Simulation results and analyses demonstrate that the proposed algorithm can overcome the trouble caused by large evidence conflict and one-vote veto, which indicates that it can improve the ability of target judgment and enhance precision of uncertain data fusion. Thus the novel decision-making method has a certain application value.
Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent late...
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Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent lateral control algorithmfor vehicles at various speeds, formulating a strategy, introducing the Gauss cloud model and the cloud reasoning algorithm, and proposing a cloud control algorithm for calculating intelligent vehicle lateral offsets. A real vehicle test is applied to explain the implementation of the algorithm. Empirical results show that if the Gauss cloud model and the cloud reasoning algorithm are applied to calculate the lateral control offset and the vehicles drive at different speeds within a direction control area of +/- 7 degrees, a stable control effect is achieved.
Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on f...
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Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%-35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.
We tackle a fundamental security problem in underwater acoustic networks (UANs). The S-box in the existing block encryption algorithm is more energy consuming and unsuitable for resources-constrained UANs. In this pap...
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We tackle a fundamental security problem in underwater acoustic networks (UANs). The S-box in the existing block encryption algorithm is more energy consuming and unsuitable for resources-constrained UANs. In this paper, instead of S-box, we present a lightweight, 8-round iteration block cipher algorithm for UANs communication based on chaotic theory and increase the key space by changing the number of iteration round. We further propose secure network architecture of UANs. By analysis, our algorithmcan resist brute-force searches and adversarial attacks. Simulation results show that, compared with traditional AES-128 and PRESENT algorithms, our cryptographic algorithm can make a good trade-off between security and overhead, has better energy efficiency, and applies to UANs.
We propose a bulk restoration scheme for software defined networking- (SDN-) based transport network. To enhance the network survivability and improve the throughput, we allow disrupted flows to be recovered synchrono...
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We propose a bulk restoration scheme for software defined networking- (SDN-) based transport network. To enhance the network survivability and improve the throughput, we allow disrupted flows to be recovered synchronously in dynamic order. In addition backup paths are scheduled globally by applying the principles of load balance. We model the bulk restoration problem using a mixed integer linear programming (MILP) formulation. Then, a heuristic algorithm is devised. The proposed algorithm is verified by simulation and the results are analyzed comparing with sequential restoration schemes.
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