| 1. |
- Ahmed, Abdelsalam, et al.
(författare)
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Fire-retardant, self-extinguishing triboelectric nanogenerators
- 2019
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Ingår i: Nano Energy. - : Elsevier BV. - 2211-2855. ; 59, s. 336-345
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Tidskriftsartikel (refereegranskat)abstract
- The development of highly sensitive sensors and power generators that could function efficiently in extreme temperatures and contact with fire can be lifesaving but challenging to accomplish. Herein, we report, for the first time, a fire-retardant and self-extinguishing triboelectric nanogenerator (FRTENG), which can be utilized as a motion sensor and/or power generator in occupations such as oil drilling, firefighting or working in extreme temperature environments with flammable and combustible materials. The device takes advantage of the excellent thermal properties of carbon derived from resorcinol-formaldehyde aerogel whose electrical, mechanical and triboelectric properties have been improved via the introduction of Polyacrylonitrile nanofibers and graphene oxide nanosheets. This FRTENG is not flammable even after 90 s of trying, whereas conventional triboelectric materials were entirely consumed by fire under the same conditions. The developed device shows exceptional charge transfer characteristics, leading to a potential difference up to 80 V and a current density up to 25 mu A/m(2). When integrated into firefighter's shoes, the FRTENG is able to discern the movements of a firefighter in hazardous situations, while providing the high thermal stability missing in conventional TENGs. The fire-retardant and self-extinguishing characteristics offered by the FRTENG makes it a path-breaking device for lifesaving wearable applications.
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| 2. |
- Alexander, Naomi E., et al.
(författare)
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IMAGINE project : A low cost, high performance, monolithic passive mm-wave imager front-end
- 2012
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 0277-786X .- 1996-756X. - 9780819492852
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Konferensbidrag (refereegranskat)abstract
- The FP7 Research for SME project IMAGINE - a low cost, high performance monolithic passive mm-wave imager front-end is described in this paper. The main innovation areas for the project are: i) the development of a 94 GHz radiometer chipset and matching circuits suitable for monolithic integration. The chipset consists of a W-band low noise amplifier, fabricated using the commercially available OMMIC D007IH GaAs mHEMT process, and a zero bias resonant interband tunneling diode, fabricated using a patented epi-layer structure that is lattice matched to the same D007IH process; ii) the development of a 94 GHz antenna adapted for low cost manufacturing methods with performance suitable for real-time imaging; iii) the development of a low cost liquid crystal polymer PCB build-up technology with performance suitable for the integration and assembly of a 94 GHz radiometer module; iv) the assembly of technology demonstrator modules. The results achieved in these areas are presented.
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| 3. |
- Nopchinda, Dhecha, 1991, et al.
(författare)
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Dual Polarization Coherent Optical Spectrally Efficient Frequency Division Multiplexing
- 2015
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Ingår i: IEEE Photonics Technology Letters. - 1041-1135 .- 1941-0174. ; 28:1, s. 83-86
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Tidskriftsartikel (refereegranskat)abstract
- A new optical spectrally efficient frequency division multiplexing technique, utilizing coherent detection and polarization division multiplexing, is proposed and demonstrated. The proposed system uses non-orthogonal and overlapping subcarriers to provide a significant reduction in both the electrical and optical bandwidth of up to 33%, relative to dual polarization orthogonal frequency division multiplexing (DP-OFDM), with an implementation penalty
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| 4. |
- Du, J., et al.
(författare)
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A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 118
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Tidskriftsartikel (refereegranskat)abstract
- Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM.
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