An Improved Repetitive Control for Circulating Current Restraining in MMC-MTDC

The modular multilevel converter (MMC) is widely used in many important application fields such as high voltage DC transmission system. And the multi-terminal architecture of it attracts many attentions. However, the circulating current of MMC is an inherent problem which is mainly caused by the voltage mismatch between arms and DC bus. In this paper, an advanced repetitive control method is proposed. This method is based on the even-harmonic characteristic of the circulating current and the potential feature of repetitive control that it has an internal integration part. The pole diagram of the closed loop transform function of the proposed control system proves the stability of the proposed method. And according to the simulation results of a three-terminal MMC-MTDC model in PSCAD/EMTDC, the improved repetitive control presents better circulation repression ability and superior anti-interference capability by comparing with traditional PI control method. Additionally, the simulation results also indicate that the proposed repetitive controller can restrain the fluctuation of SM voltage more effectively than PI control

Supervised clustering of high dimensional data using regularized mixture modeling

Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high dimensional molecular manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects. We proposed a novel supervised clustering algorithm using penalized mixture regression model, called CSMR, to deal with the challenges in studying the heterogeneous relationships between high dimensional molecular features to a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical manifestations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease, and could be of special relevance in the growing field of personalized medicine

Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning

Stable imaging in adverse environments (e.g., total darkness) makes thermal infrared (TIR) cameras a prevalent option for night scene perception. However, the low contrast and lack of chromaticity of TIR images are detrimental to human interpretation and subsequent deployment of RGB-based vision algorithms. Therefore, it makes sense to colorize the nighttime TIR images by translating them into the corresponding daytime color images (NTIR2DC). Despite the impressive progress made in the NTIR2DC task, how to improve the translation performance of small object classes is under-explored. To address this problem, we propose a generative adversarial network incorporating feedback-based object appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module and corresponding appearance consistency loss are proposed to reduce the context dependence of object translation. As a representative example of small objects in nighttime street scenes, we illustrate how to enhance the realism of traffic light by designing a traffic light appearance loss. To further improve the appearance learning of small objects, we devise a dual feedback learning strategy to selectively adjust the learning frequency of different samples. In addition, we provide pixel-level annotation for a subset of the Brno dataset, which can facilitate the research of NTIR image understanding under multiple weather conditions. Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task.Comment: 14 pages, 14 figures. arXiv admin note: text overlap with arXiv:2208.0296

Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address this problem, we have developed a novel algorithm, which we call Relative Entropy Tomographic RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation is introduced for high-fidelity temperature image retrieval from jointly reconstructed two-line absorbance distributions. We have carried out numerical simulations and proof-of-concept experiments to validate the proposed algorithm. Compared with the well-established Simultaneous Algebraic Reconstruction Technique (SART), the RETRO algorithm significantly improves the quality of the tomographic temperature images, exhibiting excellent robustness against TDLAS tomographic measurement noise. RETRO offers great potential for industrial field applications of TDLAS tomography, where it is common for measurements to be performed in very harsh environments.Comment: Preprint submitted to IEEE Transactions on Instrumentation and Measuremen

Applications of Generalized Cascade Scattering Matrix on the Microwave Circuits and Antenna Arrays

The ideal lossless symmetrical reciprocal network (ILSRN) is constructed and introduced to resolve the complex interconnections of two arbitrary microwave networks. By inserting the ILSRNs, the complex interconnections can be converted into the standard one-by-one case without changing the characteristics of the previous microwave networks. Based on the algorithm of the generalized cascade scattering matrix, a useful derivation on the excitation coefficients of antenna arrays is firstly proposed with consideration of the coupling effects. And then, the proposed techniques are applied on the microwave circuits and antenna arrays. Firstly, an improved magic-T is optimized, fabricated, and measured. Compared with the existing results, the prototype has a wider bandwidth, lower insertion loss, better return loss, isolation, and imbalances. Secondly, two typical linear waveguide slotted arrays are designed. Both the radiation patterns and scattering parameters at the input ports agree well with the desired goals. Finally, the feeding network of a two-element microstrip antenna array is optimized to decrease the mismatch at the input port, and a good impedance matching is successfully achieved