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1

Research on characteristics of embedded resonator sensor based on PtS2

Li Xin, Li Xieyuan, Zhang Haitao, Chen Shuang, Liu Shurong, Li Yang

Optica Applicata

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2023

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Vol. 53, nr 2

213--226

EN

An embedded microring resonator model using PtS2 as the core layer was designed and optimized for sensing. The inner layer is made of PtS2, and SiO2 and Si3N4 are used as cladding. The overall structure is Si3N4-SiO2-PtS2-SiO2-Si3N4. Field strength distribution of longitudinal section of single straight waveguide and the longitudinal section of coupling part of straight and annular waveguides are simulated according to the coupled-mode theory. The transfer matrix method is used to analyze characteristics between the length of the U-shaped feedback waveguide and the circumference of microring and the change of attenuation factor and coupling coefficient on the output spectrum. The simulation results showed that the embedded microring resonator with PtS2 as the core presents excellent optical properties. The resonance depth is more than –50 dB, and the sensitivity can reach 1806.61 dB/RIU. When the resonance wavelength is 1550.86 nm and the self-coupling coefficient is 0.9849. The corresponding detection limit is about 1.66056×10–7 dB/RIU, and the quality factor is 2.8848×10–5 under the measurement system with a signal-to-noise ratio of 30 dB. Compared with the traditional single microring structure, the proposed microring presents a higher free spectral range and more suitable for the fabrication of high-sensitivity, low-detection limit, and large-measurement range sensors.

2

Charging and Discharging Strategies for Clustered Regional Energy Storage System

Li Yang, Janik Przemysław, Pfeiffer Klaus, Schwarz Harald

Power Electronics and Drives

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2022

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Vol. 7 (42)

56--67

EN

With the massive expansion of decentralised renewable energy in electricity grid networks, the power supply system has been changed from centralised to decentralised one and from directional to bi-directional one. However, due to the regional energy structure difference in the power imbalance between electricity generation and consumption is becoming more and more serious. A grid-scale energy storage system (ESS) can be one solution to balance the local difference. In this paper, two charging/discharging strategies for the grid-scale ESS were proposed to decide when and with how much power to charge/discharge the ESS. In order to realise the two strategies, this paper focuses on the application of fuzzy logic control system. The proposed strategies aim to reduce the peak power generation, consumption and the grid fluctuation. In particular, this paper analysis the ratio between energy-capacity and rated power of ESS. The performance of the proposed strategies is evaluated from two aspects, the normalised power of ESS itself and the influence on the power grid. Simulation studies were carried out on the rule-based control systems with different energy-to-power (e2p) ratios, and the results show that the proposed charging strategy with combination of extreme situation of power imbalance and the rest capacity of ESS provides a smooth load curve for the regional power grid system while the external power exchange is reduced effectively.

3

Proposal of a regional grid cluster model for analysis of electrical power network performance

Li Yang, Janik Przemysław, Schwartz Harald, Pfeiffer Klaus

Archives of Electrical Engineering

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2022

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Vol. 71, nr 3

601--613

EN

This paper provides a method for simplified description of a regional power grid model aimed to deliver a grid reduction, and improve grid performance observability. The derived power grid model can be used to analyze the regional allocation of the decentralized energy generation and consumption. The expansion of wind and solar generation in the power system affects the residual load. The power balance between electricity consumption and generation was calculated and analyzed based on the temporal and spatial scales. The proposed grid clustering method is a useful approach for performance analysis in systems with a growing share of renewable generation.

4

New approach to spindle thermal extension measuring based on machine vision for the vertical maching centre

Su Dongxu, Cai Xin, Li Yang, Zhao Wanhuan, Zhang Huijie

Metrology and Measurement Systems

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2021

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Vol. 28, nr 2

357--370

EN

When machine tool spindles are running at a high rotation speed, thermal deformation will be introduced due to the generation of large amounts of heat, and machining accuracy will be influenced as a result, which is a generalized issue in numerous industries. In this paper, a new approach based on machine vision is presented for measurements of spindle thermal error. The measuring system is composed of a Complementary Metal-Oxide-Semiconductor (CMOS) camera, a backlight source and a PC. Images are captured at different rotation angles during end milling process. Meanwhile, the Canny edge detection and Gaussian sub-pixel fitting methods are applied to obtain the bottom edge of the end mill which is then used to calculate the lowest point coordinate of the tool. Finally, thermal extension of the spindle is obtained according to the change of the lowest point at different time steps of the machining process. This method is validated through comparison with experimental results from capacitive displacement sensors. Moreover, spindle thermal extension during the processing can be precisely measured and used for compensation in order to improve machining accuracy through the proposed method.

5

A deep attention network via high-resolution representation for liver and liver tumor segmentation

Li Yang, Zou Beiji, Liu Qing

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1518--1532

EN

The segmentation of liver and liver tumor is an essential step for computer-aided liver disease diagnosis, treatment and prognosis. Although deep convolutional neural networks have contributed to liver and tumor segmentation, their architectures can not maintain spatial details and long-range context information. Besides, the fixed receptive fields of these networks limit the segmentation performance of livers and tumors with variant sizes and shapes. To address above problems, we propose a deep attention neural network which contains high-resolution branch and multi-scale features aggregation for cascaded liver and tumor segmentation from CT images. To be specific, the high-resolution branch can maintain the resolution of the input image and thus preserves the spatial details. The multi-scale features exchange and fusion enable the receptive fields of the network to adapt to liver and tumor with variant shapes and sizes. The appended attention module evaluates the similarities between every two pixels to model the long-range dependence and context information so that the network can segment liver and tumor areas located in distant regions. Experimental results on the LiTS and the 3D-IRCADb datasets demonstrate that our method can generate satisfying performance.