Engineering topics
Liu, Meng
Publications and source records attributed to Liu, Meng.
LSBGnet: an improved detection model for low-surface brightness galaxies
ABSTRACT The Chinese Space Station Telescope (CSST) is scheduled to launch soon, which is expected to provide a vast amount of image potentially containing low-surface brightness galaxies (LSBGs). However, detecting and characterizing LSBGs is known to be challenging due to their faint surface brightness, posing a significant hurdle for traditional detection methods. In this paper, we propose LSBGnet, a deep neural network specifically designed for automatic detection of LSBGs. We established LSBGnet-SDSS model using data set from the Sloan Digital Sky Survey (SDSS). The results demonstrate a significant improvement compared to our previous work, achieving a recall of 97.22 per cent and a precision of 97.27 per cent on the SDSS test set. Furthermore, we use the LSBGnet-SDSS model as a pre-training model, employing transfer learning to retrain the model with LSBGs from Dark Energy Survey (DES), and establish the LSBGnet-DES model. Remarkably, after retraining the model on a small DES sample, it achieves over 90 per cent precision and recall. To validate the model’s capabilities, we utilize the trained LSBGnet-DES model to detect LSBG candidates within a selected 5 sq. deg area in the DES footprint. Our analysis reveals the detection of 204 LSBG candidates, characterized by a mean surface brightness range of $23.5\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}\le \bar{\mu }_{\text{eff}}(g)\le 26.8\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}$ and a half-light radius range of 1.4 arcsec ≤ r1/2 ≤ 8.3 arcsec. Notably, 116 LSBG candidates exhibit a half-light radius ≥2.5 arcsec. These results affirm the remarkable performance of our model in detecting LSBGs, making it a promising tool for the upcoming CSST.
The comparison between effects of Taichi and conventional exercise on functional mobility and balance in healthy older adults: a systematic literature review and meta-analysis
Background Taichi is beneficial for functional mobility and balance in older adults. However, such benefits of Taichi when comparing to conventional exercise (CE) are not well understood due to large variance in study protocols and observations. Methods We reviewed publications in five databases. Eligible studies that examined the effects of Taichi on the outcomes of functional mobility and balance in healthy older adults as compared to CE were included. Subgroup analyses compared the effects of different types of CE (e.g., single and multiple-type exercise) and different intervention designs (e.g., Taichi types) on those outcomes (Registration number: CRD42022331956). Results Twelve studies consisting of 2,901 participants were included. Generally, compared to CE, Taichi induced greater improvements in the performance of Timed-Up-and-Go (SMD = −0.18, [−0.33 to −0.03], p = 0.040, I 2 = 59.57%), 50-foot walking (MD = −1.84 s, [−2.62 to −1.07], p < 0.001, I 2 = 0%), one-leg stance with eyes open (MD = 6.00s, [2.97 to 9.02], p < 0.001, I 2 = 83.19%), one-leg stance with eyes closed (MD = 1.65 s, [1.35 to 1.96], p < 0.001, I 2 = 36.2%), and functional reach (SMD = 0.7, [0.32 to 1.08], p < 0.001, I 2 = 86.79%) tests. Subgroup analyses revealed that Taichi with relatively short duration (<20 weeks), low total time (≤24 h), and/or using Yang-style, can induce significantly greater benefits for functional mobility and balance as compared to CE. Uniquely, Taichi only induced significantly greater improvements in Timed-Up-and-Go compared to single- (SMD = −0.40, [−0.55 to −0.24], p < 0.001, I 2 = 6.14%), but not multiple-type exercise. A significant difference between the effects of Taichi was observed on the performance of one-leg stance with eyes open when compared to CE without balance (MD = 3.63 s, [1.02 to 6.24], p = 0.006, I 2 = 74.93%) and CE with balance (MD = 13.90s, [10.32 to 17.48], p < 0.001, I 2 = 6.1%). No other significant difference was shown between the influences of different CE types on the observations. Conclusion Taichi can induce greater improvement in functional mobility and balance in older adults compared to CE in a more efficient fashion, especially compared to single-type CE. Future studies with more rigorous design are needed to confirm the observations here.
A physical method for downscaling land surface temperatures using surface energy balance theory
Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. Here, the DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.
The effects of 8-week complex training on lower-limb strength and power of Chinese elite female modern pentathlon athletes
Complex training (CT) is a combination training method that alternates between performing high-load resistance training (RT) and plyometric training within one single session. The study aimed to examine the effects of CT on lower-limb strength and power of elite female modern pentathlon athletes under the new modern pentathlon format and competition rules. Ten female participants (age: 23.55 ± 2.22 years, weight: 60.59 ± 3.87 kg, height: 169.44 ± 4.57 cm, and training experience: 6.90 ± 2.08 years) of the national modern pentathlon team completed 8 weeks of RT as followed by 8 weeks of CT, with 2 weeks of break. Then, the participants conducted 8 weeks of CT, which included RT combined with plyometric training (e.g., drop jump and continuous jump). All stages of training were designed by the linear strength training period theories, requiring participants to train twice for the first 4 weeks and three times for the second 4 weeks. The one-repetition maximum (1RM) of squat, isometric mid-thigh pull (IMTP), counter-movement jump (CMJ), squat jump (SJ), pre-stretch augmentation percentage (PSAP), and reaction strength index (RSI) were assessed before and after both RT and CT training. One-way repeated-measure ANOVA models revealed that the 1RM of squat was significantly improved ( p < 0.001) after RT as compared to pre-RT. No significant improvement in IMTP ( p = 0.055), CMJ ( p = 0.194), SJ ( p = 0.692), PSAP ( p = 0.087), and RSI ( p = 0.238) was not observed. After CT, 1RM of squat ( p < 0.001), IMTP ( p < 0.035), CMJ ( p < 0.001), SJ ( p < 0.008), RSI ( p < 0.006) were significant improved as compared to pre-RT, post-RT and pre-CT, while significant improvements in PSAP were observed as compared to pre-RT ( p = 0.003) and pre-CT ( p = 0.027), but not to post-RT ( p = 0.156). This pilot study showed the promise of CT following RT to improve lower-limb strength and power in elite female modern pentathlon athletes. The findings are worthwhile to be confirmed in future studies with larger sample size and randomized design.
Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms
Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.