A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM

A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection

For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the efciency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. Using 22 attributes that highly related to the target, the performance of the proposed method achieves a 99.86% detection rate and 0.14% false alarm rate on the KDDTrain+dataset, a 77.46% detection rate on the KDDTest+dataset, which is better than many classifers. Besides, the interpretable model can help us demonstrate the complex and overlapped pattern of intrusions and analyze the pattern of various intrusions

On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection

Deep Residual Networks (ResNets) are prone to overfitting in problems with uncertainty, such as intrusion detection problems. To alleviate this problem, we proposed a method that combines the Adaptive Neuro-fuzzy Inference System (ANFIS) and the ResNet algorithm. This method can make use of the advantages of both the ANFIS and ResNet, and alleviate the overfitting problem of ResNet. Compared with the original ResNet algorithm, the proposed method provides overlapped intervals of continuous attributes and fuzzy rules to ResNet, improving the fuzziness of ResNet. To evaluate the performance of the proposed method, the proposed method is realized and evaluated on the benchmark NSL-KDD dataset. Also, the performance of the proposed method is compared with the original ResNet algorithm and other deep learning-based and ANFIS-based methods. The experimental results demonstrate that the proposed method is better than that of the original ResNet and other existing methods on various metrics, reaching a 98.88% detection rate and 1.11% false alarm rate on the KDDTrain+ datase

CONSTRUCTAL ENTRANSY DISSIPATION MINIMIZATION FOR "VOLUME-POINT" HEAT CONDUCTION BASED ON TRIANGULAR ELEMENT

By taking equivalent thermal resistance, which reflects the average heat conduction effect and is defined based on entransy dissipation, as optimization objective, the "volume to point" constructal problem based on triangular element of how to discharge the heat generated in a fixed volume to a heat sink on the border through relatively high conductive link is re-analyzed and re-optimized in this paper. The constructal shape of the control volume with the best average heat conduction effect is deduced. For the same parameters, the constructs based on minimization of entransy dissipation and the constructs based on minimization of maximum temperature difference are compared, and the results show that the constructs based on entransy dissipation can decrease the mean temperature difference better than the constructs based on minimization of maximum temperature difference. But with the increase of the number of order, the mean temperature difference does not always decrease, and there exists some fluctuations. Because the idea of entransy describes heat transfer ability more suitably, the optimization results of this paper can be put to engineering applica tion of electronic cooling

The area-point constructal optimization for discrete variable cross-section conducting path

According to constructal area-point heat conduction model, the thermal current in the high conductive link increases only at conjunction points. If the optimum number of the lower order constructs which formed higher order constructs is finite, the number of the conjunction points is finite and the thermal current in the high conductive link increases discretely. The cross-section of the high conductive link should be adapted with the change of the thermal current through it. For minimizing the thermal resistance, the more thermal current flows into the high conducting path, the wider the cross-section of the high conducting path should be. A new method based on discrete variable cross-section conducting path is introduced in this paper. Both the case of the elemental area with constant cross-section conducting path and the case of the elemental area with variable cross-section conducting path are discussed. The results show that the minimum of maximum thermal resistance which is obtained through assembling can be obtained by changing the cross-section conducting path based on constructal theory and in each assembly, the optimized minimum thermal resistance based on variable cross-section conducting path element is smaller than that based on constant cross-section conducting path element. When the optimum number of the lower order constructs (ni [greater-or-equal, slanted] 4) which formed higher order constructs is fixed, at the same construct, the constructal optimal method based on discrete variable cross-section conducting path can reduce the thermal resistance further.Constructal theory Area-point conduction Generalized thermodynamic optimization

Online observation for IGBT module loss and spatial temperature with aging tracking : a data-driven method

The loss and heat flux of IGBT module change with package degradation, resulting in the variation of case temperature and its distribution. Inspired by the aging mechanism of module, this study performs an online observation for IGBT’s time-varying profile by collecting temperature of multiple points in the bottom case, which achieves a spatial temperature reconstruction beyond junction temperature with aging track capability. Moreover, the loss can also be obtained by the thermal performance at steady state. A deep neural network (DNN) based data-driven model is established by collecting data in varied degradation level and operating condition, which favors the online observation by its low computation cost. A dedicated optical fiber positioning is proposed to facilitate temperature acquisition, and the process of data-driven modeling is illustrated in detail. Results show that the spatial temperature inside the module and the power loss can be accurately reconstructed within 5% error, and the module degradation status are well tracked

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM