ALBERT

Description: Engineering design optimization in real life is a challenging global optimization problem, and many meta-heuristic algorithms have been proposed to obtain the global best solutions. An excellent meta-heuristic algorithm has two symmetric search capabilities: local search and global search. In this paper, an improved Butterfly Optimization Algorithm (BOA) is developed by embedding the cross-entropy (CE) method into the original BOA. Based on a co-evolution technique, this new method achieves a proper balance between exploration and exploitation to enhance its global search capability, and effectively avoid it falling into a local optimum. The performance of the proposed approach was evaluated on 19 well-known benchmark test functions and three classical engineering design problems. The results of the test functions show that the proposed algorithm can provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence rate. The results of the engineering problems prove that the new approach is applicable to challenging problems with constrained and unknown search spaces.

Description: The surface snowmelt on ice sheets in polar areas (ice sheets of Greenland and Antarctica) is not only an important sensitive factor of global climate change, but also a key factor that controls the global climate. Spaceborne earth observation provides an efficient means of measuring snowmelt dynamics. Based on an improved ice sheet snowmelt detection algorithm and several new proposed parameters for detecting change, polar ice sheet snowmelt dynamics were monitored and analyzed by using spaceborne microwave radiometer datasets from 1978 to 2014. Our results show that the change in intensity of Greenland and Antarctica snowmelt generally tended to increase and decrease, respectively. Moreover, we show that the de-trended snowmelt change in ice sheets of Greenland and Antarctica vary in anti-correlation patterns. Furthermore, analysis in Atlantic Multi-decadal Oscillation, North Atlantic Oscillation, and the Southern Annular Mode suggests that the Atlantic Ocean and atmosphere could be a possible link between the snowmelt variability of the ice sheets of Greenland and Antarctica.

Description: Microbubbles are considered a promising tool for noninvasive estimation of local blood pressure. It is reported that the subharmonic scattering amplitude of microbubbles decreases by 9 to 12 dB when immersed in the media under an ambient pressure variation from 0 to 180 mmHg. However, the pressure sensitivity still needs to be improved to satisfy clinical diagnostic requirements. Here, we investigated the effects of acoustic parameters on the pressure sensitivity of microbubbles through measuring the acoustic attenuation and scattering properties of commercially available SonoVue microbubbles. Our results showed that the first harmonic, subharmonic, and ultraharmonic amplitudes of microbubbles were reduced by 6.6 dB, 10.9 dB, and 9.3 dB at 0.225 mechanical index (MI), 4.6 dB, 19.8 dB, and 12.3 dB at 0.25 MI, and 18.5 dB, 17.6 dB, and 12.6 dB at 0.3 MI, respectively, when the ambient pressure increased from 0 to 180 mmHg. Our finding revealed that a moderate MI (0.25–0.4) exciting microbubbles could significantly improve their sensitivities to detect ambient pressure.

Description: State of charge (SOC) represents the amount of electricity stored and is calculated and used by battery management systems (BMSs). However, SOC cannot be observed directly, and SOC estimation is a challenging task due to the battery’s nonlinear characteristics when operating in complex conditions. In this paper, based on the new advanced deep learning techniques, a SOC estimation approach for Lithium-ion batteries using a recurrent neural network with gated recurrent unit (GRU-RNN) is introduced where observable variables such as voltage, current, and temperature are directly mapped to SOC estimation. The proposed technique requires no model or knowledge of the battery’s internal parameters and is able to estimate SOC at various temperatures by using a single set of self-learned network parameters. The proposed method is evaluated on two public datasets of vehicle drive cycles and another high rate pulse discharge condition dataset with mean absolute errors (MAEs) of 0.86%, 1.75%, and 1.05%. Experiment results show that the proposed method is accurate and robust.

Description: A detailed description of the flooding process is crucial to analyze the complex hydrodynamic behaviors and enhance the survivability of the damaged ship. In this paper, through establishing three typical damage scenarios with various locations, the commercial software CD Adapco STAR-CCM+ based on the Reynolds-Averaged Navier-Stokes (RANS) solver is applied to simulate the flooding process involving multiple compartments. The basic computational fluid dynamics (CFD) models and specific simulation settings are elaborated. The volume of fluid (VOF) method combined with the user defined field function is developed to distribute the initial free surface. The captured flooding process indicates that the air compression due to the restricted ventilation decreases the flooding amount. The obtained flooding time can provide necessary data to support for appropriate rescue management and evacuation options.

Description: In this paper, a new recycled aggregate concrete (RAC) was produced with composite coarse aggregate and fine recycled aggregate. The composite coarse aggregate was mixed into continuous gradation by large particle natural aggregate with small particle recycled aggregate. To explore the time-dependent developments of the compressive strength and splitting tensile strength of this new RAC, 320 groups of cubic specimens were tested at different curing ages from 3 days to 360 days to measure the compressive and splitting tensile strengths. The amount of large particle natural aggregate varied from zero to 70% in mass of the total coarse aggregate. The water/cement ratio was taken as 0.60, 0.49, 0.41 and 0.36 to represent four strength grades of the RAC at about C20, C30, C40 and C50. Based on the tested results, the curves of the compressive and tensile strengths of the RAC that changed with curing age are plotted, which clearly exhibit that the amount of large particle natural aggregate had a rational range in different strength grades of the RAC which had better aging strength. When the RAC was no larger than C30 with a water/cement ratio of 0.60 and 0.49, the amount of large particle natural aggregate should be no more than 30%; when the RAC was no less than C40 with a water/cement ratio of 0.41 and 0.36, the amount of large particle natural aggregate should be no less than 50%. Along with the general prediction of the strength development of all the tested RAC, the optimal predictive formulas are proposed for the strength development of RAC with a rational amount of natural aggregate. Meanwhile, the strength developments of RAC with a rational amount of natural aggregate are assessed by the time-dependent models proposed by the ACI Committee 209 and CEB-FIP MC 2010.

Description: Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

Description: The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

Description: To understand characteristics of on-line oxidation-reduction potential (ORP) in a subsurface wastewater infiltration system (SWIS) under different intermittent influent conditions, ORP among five matrix depths at wet-dry ratios (Rwds) of 2:1, 1:1 and 1:2 with a hydraulic load of 0.10 m3·(m2·d)−1 were monitored. Results showed that the optimal Rwd for the SWIS was 1:1. In that case, ORP at 40 and 65 cm depths changed significantly, by 529 mV and 261 mV, respectively, from the inflow period to the dry period, which was conducive to the recovery of the oxidation environment. It was concluded that ORP varied nonlinearly in strongly aerobic and hypoxic environment. Wastewater was fed into the SWIS at 80 cm and dissolved oxygen diffused at the initial period of one cycle. As a consequence, ORP at 65 cm increased with water content increasing. However, ORP at 40 and 95 cm displayed inverse trends. Moreover, results showed that ORP decreased with Fe2+ and Mn2+ increasing under aerobic conditions (p 〈 0.05) because Fe2+ and Mn2+ moved with wastewater flow. Effluent met reuse requirements and no clogging was found in the SWIS during the operation.

Description: With the rapid development of China’s economy, the demand for water resources continues to sharply increase, which has gradually contributed to serious environmental problems. The Chinese government has proposed establishing a natural resource balance sheet, which is expected to solve this problem by assessing the value of water resources. The main purpose of this study was to assess the value of water resources in Wuhan from 2013 to 2017. Based on a fuzzy mathematical evaluation model, 15 indicators were chosen considering the three main aspects of resources, society, and the environment to construct a water resource evaluation indicator system. In addition, the analytic hierarchy process (AHP) and entropy weight methods were combined to determine the index weight. Based on this, we calculated the value of water resources in Wuhan from 2013 to 2017. The results demonstrated that the values of water resources in Wuhan from 2013 to 2017 were US$2.910 billion, US$5.006 billion, US$9.223 billion, US$14.167 billion, and US$7.189 billion, respectively. Therefore, this paper provides a scientific foundation for the rational establishment of water prices, the assessment of local natural resource assets, and the preparation of natural resource balance sheets.