Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

Empirical Orthogonal Function （EOF） analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas （here referring to Chinese marine territories） and adjacent waters from 1930 to 2002 （510 143 profiles）. A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that： 1） For the study area, the former four principal components can explain 95% of the overall variance, and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2） The model verifications based on the observed CTD data from the East China Sea （ECS）, South China Sea （SCS） and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level 〉95%, especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69℃, 0.52℃ and 1.18℃ respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3） Comparing the thermocline characteristics between the reconstructed and observed profiles, the results in the ECS show that the average absolute errors are 1.5m, 1.4 m and 0.17~C/m, and the average relative errors are 24.7%, 8.9% and 22.6% for the upper, lower thermocline boundaries and the gradient, respectively. Although the relative errors are obvious, the absolute error is small. In the SCS, the average absolute errors are 4.1 m, 27.7 m and 0.007℃/m, and the average relative errors are 16.1%, 16.8% and 9.5% for the upper, lower thermocline boundaries and the gradient, respectively. The average relative errors are all 〈20%. Although the average absolute error of the lower thermocline boundary is considerable, but contrast to the spatial scale of average depth of the lower thermocline boundary （165 m）, the average relative error is small （16.8%）. Therefore the model can be used to well estimate the thermocline.

Oversample Reconstruction Based on a Strong Inter-Diagonal Matrix for an Optical Microscanning Thermal Microscope Imaging System

Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscanning undersampling images from the real irregular undersampling images,and can then obtain a high spatial oversample resolution image. Simulations and experiments show that the proposed technique can reduce optical micro-scanning error and improve the system＇s spatial resolution. The algorithm is simple,fast and has low computational complexity. It can also be applied to other electro-optical imaging systems to improve their spatial resolution and has a widespread application prospect.

Data-driven sensor placement for efficient thermal field reconstruction

Complete temperature field estimation from limited local measurements is widely desired in many industrial and scientific applications of thermal engineering. Since the sensor configuration dominates the reconstruction performance, some progress has been made in designing sensor placement methods. But these approaches remain to be improved in terms of both accuracy and efficiency due to the lack of comprehensive schemes and efficient optimization algorithms. In this work, we develop a datadriven sensor placement framework for thermal field reconstruction. Specifically, we first tailor the low-dimensional model from the prior thermal maps to represent the high-dimensional temperature distribution states by virtue of proper orthogonal decomposition technique. Then, on such subspace, a recursive greedy algorithm with determinant maximization as the objective function is developed to optimize the sensor placement configuration. Furthermore, we find that the same sensor configuration can be yielded faster by the standard procedures of column-pivoted QR factorization, which allows concise software implementation with readily available function packages. When the sensor locations are determined, we advocate using the databased closed-form estimator to minimize the reconstruction error. Real-time thermal monitoring on the multi-core processor is employed as the case to demonstrate the effectiveness of the proposed methods for thermal field reconstruction. Extensive evaluations are conducted on simulation or experimental datasets of three processors with different architectures. The results show that our method achieves state-of-the-art reconstruction performance while possessing the lowest computational complexity when compared with the existing methods.