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学者姓名：荣命哲
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Abstract ：
In this article, the magnetic energy harvester (MEH) based on the current transformer is an innovative method to provide a potential solution for the power supply of sensor networks. Due to the current fluctuation and nonlinearity of the core, the harvester may produce insufficient power at low primary currents, while the core saturates at high primary currents. Aiming at resolving this problem, a novel method is proposed to improve the performance of MEH. In this method, a capacitor and two switches are used to store energy and modulate the core saturation. The performed analyses demonstrate that the proposed method enhances the harvested power under different primary current and load conditions. This is especially more pronounced at low primary currents and loads. It is found that for a low primary current of 2 A(rms) at 50 Hz, the harvested power increases by 206%. Moreover, applying this method can also reduce the load voltage by controlling the alternating on-off of the switches, thereby protecting the harvester in case of high primary currents. The theoretical calculation, circuit simulation, and experimental results demonstrate the effectiveness of this method.
Keyword ：
Capacitor harvested power magnetic energy harvester (MEH) magnetic saturation modulation primary current
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| GB/T 7714 | Liu, Zhu , Zhao, Pengbo , Yang, Aijun et al. A Novel Method for Magnetic Energy Harvesting Based on Capacitive Energy Storage and Core Saturation Modulation [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 , 70 (3) : 2586-2595 . |
| MLA | Liu, Zhu et al. "A Novel Method for Magnetic Energy Harvesting Based on Capacitive Energy Storage and Core Saturation Modulation" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 70 . 3 (2023) : 2586-2595 . |
| APA | Liu, Zhu , Zhao, Pengbo , Yang, Aijun , Ye, Kai , Zhang, Renjie , Yuan, Huan et al. A Novel Method for Magnetic Energy Harvesting Based on Capacitive Energy Storage and Core Saturation Modulation . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 , 70 (3) , 2586-2595 . |
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Abstract ：
Semiconductor gas sensors are important candidates with small size, low-power consumption, and low-cost characteristics for gas sensing in the Internet of Things (IoT), but the nonlinear response, unstable baseline, and the variability of the responses toward temperature and humidity fluctuation harm its performance. Here, we proposed a new technique called virtual alternating current (ac) measurements to enable semiconductor gas sensors to achieve high linear response (R-2 > 0.99), stable baseline, and easily calibrated temperature and humidity fluctuation. The most outstanding advantage of our technique is to achieve excellent performance simply by measuring resistance. The universality and feasibility of our technique were verified by a range of commercial and homemade sensing elements and a range of analyte. We also successfully generalized our technique to non-Debye relaxation systems, which further expands the scope of semiconductor gas sensor types available in our technique. We believe that our technique will enable gas sensing nodes built with semiconductor gas sensors in the IoT to achieve markedly superior performance.
Keyword ：
Alternating current (ac) measurements gas sensor the Internet of Things (IoT)
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| GB/T 7714 | Wang, Dawei , Pan, Jianbing , Huang, Xianbo et al. Virtual Alternating Current Measurements Advance Semiconductor Gas Sensors' Performance in the Internet of Things [J]. | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (7) : 5502-5510 . |
| MLA | Wang, Dawei et al. "Virtual Alternating Current Measurements Advance Semiconductor Gas Sensors' Performance in the Internet of Things" . | IEEE INTERNET OF THINGS JOURNAL 9 . 7 (2022) : 5502-5510 . |
| APA | Wang, Dawei , Pan, Jianbing , Huang, Xianbo , Chu, Jifeng , Yuan, Huan , Yang, Aijun et al. Virtual Alternating Current Measurements Advance Semiconductor Gas Sensors' Performance in the Internet of Things . | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (7) , 5502-5510 . |
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Abstract ：
Here we develop a two-dimensional numerical model of wire and arc additive manufacturing (WAAM) to determine the relationship between process parameters and deposition geometry, and to reveal the influence mechanism of process parameters on deposition geometry. From the predictive results, a higher wire feed rate matched with a higher current could generate a larger and hotter droplet, and thus transfer more thermal and kinetic energy into melt pool, which results in a wider and lower deposited layer with deeper penetration. Moreover, a higher preheat temperature could enlarge melt pool volume and thus enhance heat and mass convection along both axial and radial directions, which gives rise to a wider and higher deposited layer with deeper penetration. These findings offer theoretical guidelines for the acquirement of acceptable deposition shape and optimal deposition quality through adjusting process parameters in fabricating WAAM components.
Keyword ：
additive manufacturing arc plasma deposition geometry numerical analysis process parameter
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| GB/T 7714 | Fan, Shilong , Yang, Fei , Zhu, Xiaonan et al. Numerical analysis on the effect of process parameters on deposition geometry in wire arc additive manufacturing [J]. | PLASMA SCIENCE & TECHNOLOGY , 2022 , 24 (4) . |
| MLA | Fan, Shilong et al. "Numerical analysis on the effect of process parameters on deposition geometry in wire arc additive manufacturing" . | PLASMA SCIENCE & TECHNOLOGY 24 . 4 (2022) . |
| APA | Fan, Shilong , Yang, Fei , Zhu, Xiaonan , Diao, Zhaowei , Chen, Lin , Rong, Mingzhe . Numerical analysis on the effect of process parameters on deposition geometry in wire arc additive manufacturing . | PLASMA SCIENCE & TECHNOLOGY , 2022 , 24 (4) . |
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Abstract ：
The detection of copper particles in transformer oil based on laser-induced breakdown spectroscopy (LIBS) technology has been preliminarily studied, but its limit of detection (LOD) needs to be further strengthened. Therefore, spark-discharge assistant method was used to solve the problem, and its enhancement mechanism has been studied in the research. The study of the correlation between spark-discharge-assisted LIBS (SD-LIBS) spectral intensity and Cu concentration shows that spark discharge has a significant increase in plasma size and spectral intensity; for Cu atomic lines at 324.75 and 327.39 nm, the determination coefficient R-2 between their internal standard correction intensity and Cu concentration is promoted to 0.996 and 0.997, respectively; spark-discharge assistant method improves the LOD of LIBS from 0.775 to 0.112 mu g/g. Due to the great improvement in detection performance, the application prospect of LIBS as fine particles detection method in transformer oil has become practical.
Keyword ：
Copper particle enhancement laser-induced breakdown spectroscopy (LIBS) spark discharge
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| GB/T 7714 | Ye, Zhe , Ke, Wei , Wang, Wanting et al. Enhancement by Spark Discharge in LIBS Detection of Copper Particle Contamination in Oil-Immersed Transformer [J]. | IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION , 2022 , 29 (5) : 2034-2041 . |
| MLA | Ye, Zhe et al. "Enhancement by Spark Discharge in LIBS Detection of Copper Particle Contamination in Oil-Immersed Transformer" . | IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 29 . 5 (2022) : 2034-2041 . |
| APA | Ye, Zhe , Ke, Wei , Wang, Wanting , Yuan, Huan , Wang, Xiaonan , Wang, Xiaohua et al. Enhancement by Spark Discharge in LIBS Detection of Copper Particle Contamination in Oil-Immersed Transformer . | IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION , 2022 , 29 (5) , 2034-2041 . |
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Abstract ：
Modular hybrid dc circuit breakers (DCCBs) have been widely employed to protect multiterminal high-voltage dc (MT-HVdc) grids. However, breaker failures still pose a challenge to the protection of MT-HVdc grids, especially for a single module failure of breakers. To this end, a breaker failure detection method based on nonlinear parameter estimation is presented. This proposed method consists of three parts. The first is a signal preprocessing method that eliminates the effect of the RC circuit of the hybrid DCCB. The second is an offline optimal parameter acquisition method that has an improved numerical model of the metal-oxide varistor (MOV) and combines the Levenberg-Marquardt (LM) algorithm with the particle swarm optimization (PSO) algorithm. The third is an online breaker voltage error calculation that estimates the breaker voltage based on the improved numerical MOV model to detect the partial failure of the modular hybrid DCCB. The performance of the proposed algorithm is evaluated by both power system computer aided design (PSCAD)/electro-magnetic transient design and control (EMTDC) tests and field tests. The study results prove the effectiveness of the proposed method for the partial failure of the modular hybrid DCCB considering different faulty conditions and the aging of MOVs.
Keyword ：
Breaker failure detection Circuit breakers Circuit faults Fault currents Insulated gate bipolar transistors Integrated circuit modeling metal-oxide varistor (MOV) modular hybrid dc circuit breaker (DCCB) multiterminal high-voltage dc (MT-HVdc) grids Numerical models Switching circuits voltage error calculation
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| GB/T 7714 | Gao, Jie , Yuan, Huan , Yang, Aijun et al. A Breaker Failure Detection Method Based on Nonlinear Parameter Estimation for Modular Hybrid DC Circuit Breakers [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Gao, Jie et al. "A Breaker Failure Detection Method Based on Nonlinear Parameter Estimation for Modular Hybrid DC Circuit Breakers" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Gao, Jie , Yuan, Huan , Yang, Aijun , Rong, Mingzhe , Wang, Xiaohua . A Breaker Failure Detection Method Based on Nonlinear Parameter Estimation for Modular Hybrid DC Circuit Breakers . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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Abstract ：
This article proposes a lightweight network called multiscale convolutional neural network with attention (MCNA), which combines a multiscale deep convolutional network with a self-attention mechanism. MCNA identifies ambient gases through signals of semiconductor gas sensor arrays, despite poor selectivity and drift problems. Notably, MCNA extracts temporal features of each signal and relevance among different signals more effectively than deep convolutional networks. MCNA requires much fewer parameters and computation costs than previous deep learning networks, but it still achieves the same high gas identification accuracy; this is crucial for gas sensing embedded systems. When the operating conditions of the gas sensor array change, it also exhibits better generalization ability and identification accuracy. We also discuss the effects of different MCNA architecture parameters and compare MCNA and other baseline approaches.
Keyword ：
Convolution Convolutional neural network (CNN) Feature extraction Gas detectors gas identification lightweight network Machine learning multiscale learning strategy self-attention mechanism Sensor arrays Temperature sensors Transformers
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| GB/T 7714 | Pan, Jianbin , Yang, Aijun , Wang, Dawei et al. Lightweight Neural Network for Gas Identification Based on Semiconductor Sensor [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Pan, Jianbin et al. "Lightweight Neural Network for Gas Identification Based on Semiconductor Sensor" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Pan, Jianbin , Yang, Aijun , Wang, Dawei , Chu, Jifeng , Lei, Fangfei , Wang, Xiaohua et al. Lightweight Neural Network for Gas Identification Based on Semiconductor Sensor . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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Abstract ：
In this paper, we study the conducted interference to an electronic current transformer introduced in the process of bus-charging currents which are caused by switching a gas insulated switchgear (GIS) disconnector. To cope with these issues, the EMTP-ATP and Matlab/Simulink software are used to carry out equivalent modeling simulations and experimental research, respectively. More specifically, the very fast transient current generated by disconnector switching (DS) is used as the input source of the equivalent simulation model of the Rogowski coil, and the characteristics of conducted interference waveforms of the Rogowski coil, the active integrator and filter outputs under single and multiple breakdowns are analyzed step by step. Moreover, several anti-interference methods are proposed to improve the resistance to the high-voltage and high-frequency conducted interference for the Rogowski coil, such as reducing the Rogowski cut-off frequency, increasing the transient voltage suppressor (TVS), active filter, and Cy capacitor. Besides, the study also reveals that the residual charge of the integral capacitor will discharge with a time constant tau = 1 s after arc quenching with the first-order discharge circuit, which is composed of the feedback resistance and the integral capacitor C. Lastly, the experimental results demonstrate the correctness of the modeling method proposed in this paper and the effectiveness of anti-interference measures.
Keyword ：
active integrator electronic current transformer (ECT) gas-insulated switchgear (GIS) Rogowski coil very fast transient current (VFTC)
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| GB/T 7714 | Bai, Shijun , Yue, Fanding , Zeng, Lincui et al. A Study to Resist Conduced Interference from GIS Bus-Charging Currents Switching for Electronic Current Transformer [J]. | ELECTRONICS , 2021 , 10 (8) . |
| MLA | Bai, Shijun et al. "A Study to Resist Conduced Interference from GIS Bus-Charging Currents Switching for Electronic Current Transformer" . | ELECTRONICS 10 . 8 (2021) . |
| APA | Bai, Shijun , Yue, Fanding , Zeng, Lincui , Li, Yi , Wang, Chuanchuan , Wang, Xiaohua et al. A Study to Resist Conduced Interference from GIS Bus-Charging Currents Switching for Electronic Current Transformer . | ELECTRONICS , 2021 , 10 (8) . |
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Abstract ：
Cold atmospheric plasma, a promising technology for cancer therapy, can simultaneously induce electrochemistry, which has been widely demonstrated to be effective against cancer. Herein, the anticancer effects of direct current plasma jets and electrochemical circuit are comparatively studied with similar topological structures and average currents. The cell inactivation patterns formed by plasma and electrochemical treatment (EChT) are disparate, and the inactivation is mainly caused by the emission of charged species, helium flow, in situ OH radical production, or local alkalization. The culture medium treated by EChT exerted no sustained anticancer effect, whereas the medium treated by plasma could maintain a strong effect due to the production of more reactive species, suggesting that the electrochemistry does not dominate the plasma inactivation of cancer cells.
Keyword ：
cancer therapy electrochemistry plasma jet reactive species
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| GB/T 7714 | Liu, Dingxin , Wang, Zifeng , Chen, Zeyu et al. Comparison of the anticancer effects between helium plasma jets and electrochemical treatment (EChT) [J]. | PLASMA PROCESSES AND POLYMERS , 2021 , 18 (11) . |
| MLA | Liu, Dingxin et al. "Comparison of the anticancer effects between helium plasma jets and electrochemical treatment (EChT)" . | PLASMA PROCESSES AND POLYMERS 18 . 11 (2021) . |
| APA | Liu, Dingxin , Wang, Zifeng , Chen, Zeyu , Xu, Dehui , Chen, Min , Chen, Jinkun et al. Comparison of the anticancer effects between helium plasma jets and electrochemical treatment (EChT) . | PLASMA PROCESSES AND POLYMERS , 2021 , 18 (11) . |
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Abstract ：
Recent years have witnessed the prominent advancements of deep learning (DL) in the arsenal of prognostics and health management. However, the prognostic uncertainty problem extensively existed in industrial devices is not addressed by most DL approaches. This article formulates a novel Bayesian Deep Learning (BDL) framework to characterize the prognostic uncertainties. A distinguished advantage of the framework is its capacity of capturing the comprehensive effects of two critical uncertainties: 1) epistemic uncertainty, accounting for the uncertainty in the model, and 2) aleatoric uncertainty, representing the impact of random disturbance, such as measurement errors. The former arises from the variability of the model weights, and the latter is characterized by selected lifetime distributions. We integrate both uncertainties by defining BDL as priors of lifetime parameters. A sequential Bayesian boosting algorithm is executed to improve the estimation accuracy and compress the credible intervals. The superior prediction performance of our framework is validated by a real-world dataset collected from hydraulic mechanisms of circuit breakers.
Keyword ：
Aleatoric uncertainty Bayesian neural network Bayes methods epistemic uncertainty Machine learning Modeling Prediction algorithms Probabilistic logic Prognostics and health management remaining useful life Uncertainty
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| GB/T 7714 | Li, Gaoyang , Yang, Li , Lee, Chi-Guhn et al. A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (9) : 8829-8841 . |
| MLA | Li, Gaoyang et al. "A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68 . 9 (2021) : 8829-8841 . |
| APA | Li, Gaoyang , Yang, Li , Lee, Chi-Guhn , Wang, Xiaohua , Rong, Mingzhe . A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (9) , 8829-8841 . |
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Abstract ：
Tellurene has a phosphorene-like puckered configuration and exhibits great air stability. This characteristic makes it highly sensitive to some gases, and therefore, it has a huge potential in gas sensing applications. In this paper, the sensing performance of tellurene-based gas sensors toward SF6 decomposition products, including H2S, SO2, SO2F2, and SOF2, was systematically investigated. Three types of tellurene-based gas sensors were fabricated: single tellurene flake with ohmic contact (RCTF sensor), single tellurene flake with nonohmic contact (DCTF sensor), and drop-cast tellurene flakes with heat function (HTF sensor). The RCTF sensor exhibited good sensitivity and selectivity toward hydrogen sulfide but slow recovery speed like most two-dimensional materials. The DCTF sensor exhibited a significant improvement in sensitivity, selectivity, and recovery speed compared with the RCTF sensor. Finally, the HTF sensor demonstrated the fastest recovery speed of all tellurene-based sensors. This work showed that the nonohmic contact and heat pulse are easy and efficient methods to enhance the performance of tellurene-based gas sensors toward hydrogen sulfide, which supports the application potential of tellurene in gas sensors.
Keyword ：
gas sensor heat pulse P-type contact SF6 decomposition products tellurene
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| GB/T 7714 | Wang, Dawei , Pan, Jianbin , Lan, Tiansong et al. Tellurene Nanoflake-Based Gas Sensors for the Detection of Decomposition Products of SF6 [J]. | ACS APPLIED NANO MATERIALS , 2020 , 3 (8) : 7587-7594 . |
| MLA | Wang, Dawei et al. "Tellurene Nanoflake-Based Gas Sensors for the Detection of Decomposition Products of SF6" . | ACS APPLIED NANO MATERIALS 3 . 8 (2020) : 7587-7594 . |
| APA | Wang, Dawei , Pan, Jianbin , Lan, Tiansong , Chu, Jifeng , Fan, Chengyu , Yuan, Huan et al. Tellurene Nanoflake-Based Gas Sensors for the Detection of Decomposition Products of SF6 . | ACS APPLIED NANO MATERIALS , 2020 , 3 (8) , 7587-7594 . |
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