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Yuqin Ding,Mengsu Zeng,Shengxiang Rao,Caizhong Chen,Caixia Fu,Jianjun Zhou 대한영상의학회 2016 Korean Journal of Radiology Vol.17 No.6
Objective: To compare the diagnostic accuracy of intravoxel incoherent motion (IVIM)-derived parameters and apparent diffusion coefficient (ADC) in distinguishing between renal cell carcinoma (RCC) and fat poor angiomyolipoma (AML). Materials and Methods: Eighty-three patients with pathologically confirmed renal tumors were included in the study. All patients underwent renal 1.5T MRI, including IVIM protocol with 8 b values (0–800 s/mm2). The ADC, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated. One-way ANOVA was used for comparing ADC and IVIM-derived parameters among clear cell RCC (ccRCC), non-ccRCC and fat poor AML. The diagnostic performance of these parameters was evaluated by using receiver operating characteristic (ROC) analysis. Results: The ADC were significantly greater in ccRCCs than that of non-ccRCCs and fat poor AMLs (each p < 0.010, respectively). The D and D* among the three groups were significantly different (all p < 0.050). The f of non-ccRCCs were less than that of ccRCCs and fat poor AMLs (each p < 0.050, respectively). In ROC analysis, ADC and D showed similar area under the ROC curve (AUC) values (AUC = 0.955 and 0.964, respectively, p = 0.589) in distinguishing between ccRCCs and fat poor AMLs. The combination of D > 0.97 x 10-3 mm2/s, D* < 28.03 x 10-3 mm2/s, and f < 13.61% maximized the diagnostic sensitivity for distinguishing non-ccRCCs from fat poor AMLs. The final estimates of AUC (95% confidence interval), sensitivity, specificity, positive predictive value, negative predictive value and accuracy for the entire cohort were 0.875 (0.719–0.962), 100% (23/23), 75% (9/12), 88.5% (23/26), 100% (9/9), and 91.4% (32/35), respectively. Conclusion: The ADC and D showed similar diagnostic accuracy in distinguishing between ccRCCs and fat poor AMLs. The IVIM-derived parameters were better than ADC in discriminating non-ccRCCs from fat poor AMLs.
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Dezhou Xu,Chunhua Zheng,Yunduan Cui,Shengxiang Fu,Namwook Kim,Suk Won Cha 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.10 No.1
Hybrid vehicles (HVs) that equip at least two different energy sources have been proven to be one of effective and promising solutions to mitigate the issues of energy crisis and environmental pollution. For HVs, one of the core supervisory control problems is the power distribution among multiple power sources, and for this problem, energy management strategies (EMSs) have been studied to save energy and extend the service life of HVs. In recent years, with the rapid development of artificial intelligence and computer technologies, learning algorithms have been gradually applied to the EMS field and shortly become a novel research hotspot. Although there are some brief reviews on the learning-based (LB) EMSs for HVs in recent years, a state-of-the-art and thorough review related to the applications of learning algorithms in HV EMSs still lacks. In this paper, learning algorithms applied in HV EMSs are categorized and reviewed in terms of the reinforcement learning algorithms and deep reinforcement learning algorithms. Apart from presenting the recent progress of learning algorithms applied in HV EMSs, advantages and disadvantages of different learning algorithms and LB EMSs are also discussed. Finally, a brief outlook related to the further applications of learning algorithms in HV EMSs, such as the integration towards autonomous driving and intelligent transportation system, is presented.
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Dongfang Zhang,Yunduan Cui,Yao Xiao,Shengxiang Fu,Suk Won Cha,Namwook Kim,Hongyan Mao,Chunhua Zheng 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.11 No.1
With the rapid development of artificial intelligence, deep reinforcement learning (DRL)-based energy management strategies (EMSs) have become an important research direction for hybrid electric vehicles recently, which still face some problems such as fragile convergence characteristics, slower convergence speed, and unsatisfactory optimization effects. In this research, a novel DRL algorithm, i.e. an improved soft actor-critic (ISAC) algorithm is applied to the EMS of a fuel cell hybrid vehicle (FCHV), in which the priority experience replay (PER) and emphasizing recent experience (ERE) methods are adopted to improve the convergence performance of the algorithm and to enhance the FCHV fuel economy. In addition, the fuel cell durability is also considered in the proposed EMS based on a nonlinear fuel cell degradation model while considering the fuel economy. Results indicate that the FCHV fuel consumption of the proposed EMS is decreased by 7.87%, 2.79%, and 2.44% compared to that of the deep deterministic policy gradient (DDPG)-based, the twin delayed deep deterministic policy gradient (TD3)-based, and the SAC-based EMSs respectively while the fuel consumption gap to the dynamic programming-based EMS is narrowed to 2.37% by the proposed EMS. Moreover, the proposed EMS presents the best training performance considering both the convergence speed and stability, and the convergence speed of the proposed EMS is increased by an average of 47.89% compared to that of the other DRL-based EMSs. Furthermore, the fuel cell durability is improved by more than 95% using the proposed EMS compared to that of the EMS without considering the fuel cell degradation.
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