Liu Tao

Bio: Liu Tao is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Project management & Data management. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

A Research on Program Management

Abstract: Program management comes from project management,but project management can not on behalf of program management.This paper analyses and summarizes the knowledge of program and program management in detail,and then, advances the challenges and the issues needed to be resolved of program management.

Hybrid Strategy Improved Sparrow Search Algorithm in the Field of Intrusion Detection

Abstract: Aiming at the problem that Sparrow Search Algorithm(SSA) may fall into local optima and have slow convergence speed, a hybrid strategy improved sparrow search algorithm(HSISSA) is proposed in this paper, and it is applied to feature selection and model optimization of intrusion detection. First, a hybrid circle-piecewise map is proposed to initialize the population and improve the uniformity of the initial population distribution; second, merging the spiral search method in the vulture search algorithm and Levy’s flight formula to update the positions of the discoverer and scouter, respectively, to expand the population search range and enhance the search capability; and finally, the simplex method and pinhole imaging method are used to optimize the position of sparrows with poor fitness and optimal fitness, to avoid stagnation in the population search and fall into local optima. The performance of the algorithm was optimized using the aforementioned methods. The algorithm was tested on 10 classical benchmark functions and combined with Wilcoxon rank-sum test analysis to verify its effectiveness, which showed improvements in convergence speed and accuracy. Finally, it was applied to the feature selection and model optimization of intrusion detection. On average, 7.6 features and 10.1 features were retained on the CIC-IDS2017 and UNSW-NB15 datasets, respectively, and 99.5% and 96.01% accuracies were achieved. The number and accuracy of the optimized features were better than those of the original algorithm. For the DenseNet and random forest models, HSISSA achieved 99.34% and 97.22% accuracy after optimization, respectively, which improved the performance of the models. Thus, the algorithm showed a better performance than the other algorithms.

Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment

Abstract: Aiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA algorithm cascade MDS algorithm to process the DGA data is proposed. Subsequently, the TSSA-RF model is introduced to classify the DGA data. The DGA data is first mapped to a high-dimensional space. Next, the optimal feature subset is encoded using the SSA algorithm to reduce irrelevant and redundant features. In this study, the correlation between the optimal feature dimension and the transformer fault diagnosis accuracy is investigated. the expression of the optimal feature subset is obtained by decompiling the SSA operator. The pre-processed data are classified using the RF model, and the TSSA -RF model for classifying the DGA data is found with the highest accuracy through the comparison of different optimization algorithms. After the RF model is optimized using the TSSA algorithm, its accuracy increases by 7.89%, and the accuracy of the TSSA -RF model is obtained as 92.11%. The example results show that compared with the original data, the proposed data processing algorithm improves the diagnostic accuracy of transformer by 11.97 % in the RF model. Compared with multiple preprocessing methods, SSA-MDS has the highest accuracy. Compared with the original data, the accuracy of TSSA-RF model increases by 11.64 %.

Research on safety evaluation model for engineering multi-program group

Abstract: With the increasing complexity in civil engineering projects, the multi-program group has become a large-scale system problem. Three analysis models for safety evaluation are discussed in this paper: safety comprehensive analysis model, safety performance analysis model and safety contribution-rate model. Then the safety indexes of these models are deduced and build up respectively. Furthermore, the analysis of construction input-output is corresponding to the different safety indexes, which show that the safety evaluation for the construction process of multi-program group is very necessary for both security and economic benefit.

A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble

Abstract: Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.

Research on Safety Evaluation Model for Engineering Multi-program Group

Abstract: In the comprehensive safety evaluation for engineering multi-program group,there is a more complex evaluation requirement.This paper applies the theory of statistical methods,adopts single index and integrates modeling and dimensionless normalization technique to discuss the purpose,significance and function of safety assessment.A group of dimensionless indexes is constructed as safety index.Three analysis models for safety evaluation are discussed,including safety comprehensive analysis model,safety performance analysis model and safety contribution rate model.Then the safety indexes of these models are deduced and built up respectively.Furthermore,the input-output analysis is conducted for safety construction of engineering multi-program group,and an influencing analysis model for major accident loss is established.At last,an example is illustrated to verify the feasibility of relevant mathematical methods and models.