Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

Physics-informed few-shot learning for wind pressure prediction of low-rise buildings

This research proposes a physics-informed few-shot learning model to predict the wind pressures on full-scale specimens based on scaled wind tunnel experiments. Existing machine learning approaches in the wind engineering domain are incapable of accurately extrapolating the prediction from scaled data to full-scale data. The model presented in this research, on the other hand, is capable of extrapolating prediction from large-scale or small-scale models to full-scale measurements. The proposed ML model combines a few-shot learning model with the existing physical knowledges in the design standards related to the zonal information. This physical information helps in clustering the few-shot learning model and improves prediction performance. Using the proposed techniques, the scaling issue observed in wind tunnel tests can be partially resolved. A low mean-squared error, mean absolute error, and a high coefficient of determination were observed when predicting the mean and standard deviation wind pressure coefficients of the full-scale dataset. In addition, the benefit of incorporating physical knowledge is verified by comparing the results with a baseline few-shot learning model. This method is the first of its type as it is the first time to extrapolate in wind performance prediction by combining prior physical knowledge with a few-shot learning model in the field of wind engineering. With the benefit of the few-shot learning model, only a low-resolution of the measuring tap configuration is required, and the reliance on physical wind tunnel experiments can be reduced. The physics-informed few-shot learning model is an efficient, robust, and accurate alternate solution to predicting wind pressures on full-scale structures based on various modeled scale experiments.     

Tailoring the secondary phases and mechanical properties of ODS steel by heat treatment

The oxide dispersion strengthened (ODS) steel with the nominal composition of Fe–14Cr–2W–0.3Ti–0.2V–0.07Ta–0.3Y₂O₃ (wt%) was fabricated by mechanical alloying and hot isostatic pressing (HIP). In order to optimize the relative volume fraction of secondary phases, the as-HIPed ODS steel was annealed at 800 °C, 1000 °C, 1200 °C for 5 h, respectively. The microstructures and different secondary phases of the as-HIPed and annealed ODS samples were identified by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The tensile properties of all the ODS steels at room temperature were also investigated. The results indicate that annealing is an effective way to control the microstructure and the integral secondary phases. The annealing process promotes the dissolution of M₂₃C₆ particles, thus promoting the precipitation of TiC. No obvious coarsening of Y₂Ti₂O₇ nanoparticles can be observed during annealing. The tensile results indicate that the annealed ODS sample with the optimized secondary phases and high density possesses the best mechanical properties.     

基于DFA的长江上游径流演化标度特性研究

针对河川径流时序所具有的非线性与非平稳性特征,运用消除趋势波动分析法（Detrended Fluctuation Analysis,DFA）对长江上游控制性水文站-宜昌站1878～2001年的月均流量时序的演化特征进行了研究,以揭示该流域河川径流的长程相关性及其内在规律性。结果表明：长江上游河川径流时序波动具有长程相关性,且其标度指数具有显著的区域性,即在短时间尺度上具有强烈的正持续性,在长时间尺度上具有强烈的负持续性,且其正持续性至多可持续19个月,显示了长江上游流域产汇流系统演化具有复杂的物理机制     

Certificate sharing system for secure certificate distribution in mobile environment

As mobile and Internet technologies evolve, mobile services (e.g., Internet banking, social commerce) continuously expand and diversify. In order to use these mobile services, it is essential that security services, especially distribution certificates (e.g., bank certificates), relevant to mobile devices be provided. Some approaches to providing distribution certificates between a user's mobile device and a personal computer (PC) have been proposed. However, the existing approaches do not guarantee that the certificate in the mobile devices same with the issued one from the PC, causing constraints on mobile services such as mobile phone banking and mobile commerce (M-commerce).In this paper, we propose a novel approach that shares certificates securely without modification of the existing standard certificate format between a smartphone and a PC. We also implemented the certificate sharing system (CSS) in a virtual private network (VPN). The CSS provides strong end-to-end data security for the certificate with a key size of 192-bits which is able to guarantee an expiration date of three years. It also provides strong data security on physical devices with the use of device ID. The certificate that is shared between devices is available only through the CSS's authorization process. In addition, the CSS provides a flexible and extensible system for sharing certificates in enterprise environments. The CSS module of a PC was implemented by way of a standard web language, and the CSS module of a smartphone was developed with the assistance of mobile applications with a small size of 1210KB.     

Scour evaluation for foundation of a cable-stayed bridge based on ambient vibration measurements of superstructure

This study develops a foundation scour evaluation method merely using the ambient vibration measurements taken from the superstructure of a cable-stayed bridge. Various modal frequencies of girder and those of the local pier are first identified for Kao-Ping-Hsi Bridge. The finite element model of this bridge is then constructed to perform the modal analysis with original design parameters. Combining the above results, this work further determines the best boundary support conditions for the model to fit the identified modal frequencies of bridge girder. According to this globally best fitted model, the optimal soil stiffness is first decided by fitting the critical bridge frequencies with a known deposit height at the pylon. Subsequently, the scour depth at a pier can be estimated by varying the depth of its supporting soil to fit the two sensitive frequencies of local pier modes. Finally, a direct measurement scheme is carried out to verify the estimated scour depth.     

Dislocation climb in Mo5SiB2 during high-temperature deformation

During high-temperature compression tests on intermetallic Mo₅SiB₂, the dislocation microstructures vary with increasing temperature and strain rate. At 1400 °C, an increasing tendency exists for slip planes to be of an unexpected type (e.g., {143) and {523)) as a function of the decreasing strain rate and increasing strain that originates from a dislocation climb. As the temperature increases to 1600 °C, the internal strain rate of 6.07 × 10^− 3 s^− 1 from the dislocation climb at 4% strain exceeds the applied value of 1.67 × 10^− 3 s^− 1, and thus, the climb mainly controls the plastic strain, as evidenced by a strength that is lower than that at 1200 °C under the same conditions.