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JieHui JU,WeiZheng BAO,ZhongYou WANG,Ya WANG,WenJuan LI,WenJuan LI 보안공학연구지원센터 2014 International Journal of Grid and Distributed Comp Vol.7 No.5
In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.
Fenghu Li,Fan Mei,Shuishui Yin,Yanjun Du,Lili Hu,Wei Hong,Jiehui Li 대한부인종양학회 2024 Journal of Gynecologic Oncology Vol.35 No.1
Objective: To compare the efficacy and safety of neoadjuvant chemotherapy combined withconcurrent chemoradiotherapy (NACT+CCRT) vs. concurrent chemoradiotherapy (CCRT) inlocally advanced cer vical cancer (LACC) patients with large tumor masses. Methods: LACC patients with localized tumor diameter >4 cm, were randomly allocated in anunblinded 1:1 ratio to NACT+CCRT or CCRT groups. Patients in the NACT+CCRT group weregiven paclitaxel combined with cisplatin (TP) NACT ever y 3 weeks for 2 cycles, followed byCCRT, with the chemotherapy regimen the same as for NACT. CCRT group were given CCRTwith the same as for NACT. Results: From March 1, 2019, to June 30, 2021, 146 patients were included in the finalanalysis. Sixty-eight (93.2%) patients in the NACT+CCRT group and 66 (90.4%) patientsin the CCRT group completed the expected treatment course. The complete response (CR)rate in the NACT+CCRT group was significantly higher than in the CCRT group (87.7% vs. 67.6%, χ2=54.540, p=0.000). In the NACT+CCRT group, the 1- and 2-year overall sur vival(OS) rates were significantly higher than those in the CCRT group (96% vs. 89% and 89%vs. 79%, χ2=5.737, p=0.017). Additionally, the rate of recurrences and distant metastaseswas significantly lower in the NACT+CCRT group than in the CCRT group (4.11% vs. 7.35%,χ 2=4.059, p=0.021). Most treatment-related adverse events in both groups were grade 3. Conclusion: Compared to CCRT, NACT+CCRT might improve the treatment completionrate, increase CR rate and 1- and 2-year OS rates, and reduce distant metastases rate for LACCpatients with large tumor masses.
A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting
Cheng Yuxin,Le Haozhe,Li Chunquan,Huang Jiehui,Liu Peter X. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
It is still a challenging problem for most existing forecasting methods to obtain accurate and rapid prediction performance in short-term load forecasting because of the complexity and non-linearity of the electric load signals. To solve this problem, this paper proposes a hybrid forecasting model. In this hybrid forecasting model, an effi cient hybrid decomposition method is fi rst developed by a new combination mechanism between the ensemble empirical mode decomposition, approximate entropy, and empirical wavelet transform, enhancing the effi ciency and accuracy problems of traditional decomposition methods. Afterward, a new hybrid neural network called broad learning system-back propagation (BLS-BP) is established to predict multiple signal sequences from the proposed hybrid decomposition method. Specifi cally, in the proposed BLS-BP, a broad learning system can eff ectively reduce the computational cost, however, BP can eff ectively improve the prediction accuracy. Therefore, a reasonable combination of BLS and BP is established to obtain the compromise between computational cost and prediction accuracy. Finally, to improve the generalization ability of the model, a hybrid network based on the sliding window and cross-validation method is proposed, further improving the predictive accuracy. Owing to the novel and eff ective cooperation of the above three aspects, the proposed hybrid forecasting model has higher accuracy, faster effi ciency, and better robustness compared with other state-of-the-art algorithms. The experimental result demonstrates the above facts.