The objective of this study is to extend the concept of analogous piezoelectric networks to vibration mitigation of multiple nonlinear resonances. First, the undamped linear part of the electrical ...network is designed so as to possess similar modal characteristics as those of the underlying linear mechanical structure. Then, nonlinear electrical components possessing the same mathematical form as that of the mechanical nonlinearities are added to the network. Because both modal and nonlinear analogies are enforced, the electrical network can be seen as an analogue twin of the mechanical structure. When the network is coupled to the structure via an array of piezoelectric elements, it is shown numerically and experimentally that such an analogue twin offers important benefits for vibration mitigation over a broad range of frequencies and excitation amplitudes.
Converting surplus renewable electricity into hydrogen by electrolyzers has been recognized as a promising scheme to reduce renewable energy spillage and to meet the increasing hydrogen demand. ...However, the scheme is challenged by the inherent spatiotemporal imbalance between renewable energy and hydrogen demand. Seasonal storages and interregional hydrogen supply chains (HSCs) are commonly employed in the literature to eliminate this imbalance, but long-distance hydrogen transportation can be costly. In this paper, we incorporated the electric network (EN) into the HSC for its ability to promptly and economically deliver energy at long distances. The uniform hierarchical time discretization method is utilized to achieve the unified operation of the HSC and the EN. On this basis, an integrated HSC-EN model is elaborated upon to investigate the optimal investment and operation of electrolyzers and storage. Finally, an industrial case in Sichuan province, China is analyzed to illustrate the benefits of incorporating the EN to reduce the investment cost and improve electrolyzers' utilization.
Detecting digital audio tampering is essential for ensuring judicial fairness and societal security. Traditional methods, primarily based on Electric Network Frequency (ENF), have been limited by ...their reliance on singular, static features, which overlook critical temporal dynamics inherent in ENF data. This results in suboptimal detection accuracy. Addressing these limitations, this paper introduces a novel transformer model, ENFformer, designed for the detection of digital audio tampering by leveraging both short and long-term temporal features of ENF data. Initially, the ENFformer model extracts traditional zero-order and first-order phase features using discrete Fourier transforms (DFT0 and DFT1), along with frequency features obtained through the Hilbert transform. These features are processed using a frame-based algorithm to develop their temporal counterparts. To enhance feature extraction, the model employs a two-layer one-dimensional Convolutional Long Short-Term Memory (ConvLSTM) network to assimilate short-term temporal features, followed by a Bidirectional Long Short-Term Memory (BiLSTM) network for long-term feature integration. A branch attention mechanism then synergizes these long-term features, which are further refined by a transformer module for accurate tampered audio identification. Our empirical evaluations on the Carioca and ENF-EDIT1 databases demonstrate that ENFformer achieves detection accuracies of 97.33% and 93.50% respectively, surpassing existing state-of-the-art methods. These results confirm the effectiveness of our approach, which significantly advances the field of digital audio tampering detection by incorporating a comprehensive analysis of temporal information in ENF features. The source code of this study is publicly available at https://github.com/CCNUZFW/ENFformer.
•Shallow ENF features extraction aids deep exploration, enhancing tampering detection.•ConvLSTM and BiLSTM networks extract deep ENF features, improved by branch attention.•The proposed ENFformer, incorporating transformers, outperforms current SOTA models.
Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to produce "green" ammonia compared with traditional coal-based technologies. As the key part of WtA, ...Power-to-Ammonia (PtA) has great potential to facilitate the usage of wind generation. This paper proposes a co-planning approach for regional wind resources-based ammonia industry and the electric network (EN). To this end, PtA is first modeled as a flexible power load of power systems with spatial and temporal constraints on hydrogen supply chains (HSC). Then a novel co-planning model of WtA and EN is established to optimize the WtA configuration and the EN expansion. An alternating direction method of multipliers (ADMM) based algorithm is introduced to effectively solve this model. Real data of Inner Mongolia Province in China is adopted to verify the effectiveness and significance of the proposed approach. It is shown that the siting and operation flexibility of PtA with HSC can reduce the expansion burden of EN. The co-planning of WtA and EN can significantly enhance wind power utilization and reduce total investment costs. Furthermore, feasibility analysis on WtA in comparison with coal-to-ammonia (CtA) and ultra-high voltage transmission (UHV) provides helpful guidelines for the realization of WtA.
Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network ...Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, scene diversity, motion interference, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we formulate and validate the physical mechanism for the ENF captured in events and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. To validate the effectiveness, we build the first Event-Video ENF Dataset (EV-ENFD) and its extension EV-ENFD+ with diverse scenarios, including static, dynamic, and extreme lighting scenes. Comprehensive experiments have been conducted on our proposed datasets, showcasing that our proposed E-ENF significantly outperforms the V-ENF in extracting accurate ENF traces, especially in challenging environments. The code and dataset are available at https://xlx-creater.github.io/Improved_E-ENF/ .
Aiming to minimize the total electricity usage in realistic operation of urban rail transit (URT) trains, this article establishes the coupled model for the train-line-electric network-timetable ...(TLET) comprehensive system and proposes a multitrain cooperative eco-driving method to achieve the system energy optimal. The dynamic power flow within the dc railway system with multiple trains is analyzed by modeling the traction power supply system (TPSS), and an improved power flow calculation (PFC) method is proposed to calculate the dynamic power flow distribution. To achieve the total substation energy minimization, a space-time-speed (STS) 3-D network is presented to transform continuous train motion process into discrete states on space-speed plane, and a multiple individuals dynamic programming (MIDP) algorithm is thoroughly developed to cooperatively optimize the multitrain trajectories along the time horizon, with which the optimal speed profiles and timetables of each train can be obtained simultaneously. Moreover, a computationally efficient heuristic algorithm together with a train-to-train (T2T) communication policy is developed as a comparative study. Numerical experiment with field data from Guangzhou Metro Line 8 is implemented, to illustrate the energy-saving performance of the proposed methods.
ENF Based Robust Media Time-Stamping Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
IEEE signal processing letters,
2022, Volume:
29
Journal Article
Peer reviewed
Electric Network Frequency (ENF) continuously fluctuates around a nominal value (50/60 Hz) due to a persistent imbalance between supplied and demanded power. In certain circumstances, ENF gets ...intrinsically embedded into audio and video recordings and can be extracted from these recordings. Consequently, ENF can be used in a number of media forensic applications, such as verifying the time of recording of the media. In this work, a robust media time-stamping approach is proposed for media whose ENF content is relatively contaminated. It essentially entails two procedures: first, detecting all useful, i.e., considerably accurate, samples of an estimated ENF signal, and then applying an adapted normalized cross-correlation process that is designed for exploiting just the selected ENF portions based on a binary mask of the identified accurate samples. Experimental results show that the proposed approach provides significantly increased performance.
•A carbonized melamine skeletons @ knitted Ni/NCNTs structure is proposed.•Efficient and tunable microwave absorption performance is achieved by ALD of MoS2.•Minimum RL of −96.13 dB is achieved at ...2.62 mm with a 3.97 % filling load.•Coverage of X and Ku band is achieved at 2.5 and 1.9 mm with a 6.68 % filling load.
The construction of three-dimensional (3D) electric network structures is playing a crucial role in improving the electromagnetic energy attenuation ability in the design and synthesis of efficient and tunable electromagnetic waves absorbers.Herein, an extraordinary carbonized melamine skeletons (CMS) @ knitted Ni/N-doped carbon nanotubes (NCNTs) structure (labeled CK), in which the hollow CMS wrapped with 3D electric network layer is constructed via the intertwining of in-situ grown NCNTs, has been rationally designed and successfully fabricated through the anchoring of nickel hydroxide and pyrolysis processes. Meanwhile, the efficient and tunable microwave absorption performance of the CMS @ knitted Ni/NCNTs @ MoS2 (CKM) is achieved by introducing MoS2 into the 3D electric network layer via atomic layer deposition. The optimal microwave absorption performance can be observed in involved absorbers with a filling ratio of only 3.97 %, whose minimum reflection loss reaches up to −96.13 dB at a thickness of 2.62 mm and maximum effective absorption bandwidth is 8.2 GHz at 3.2 mm. By manipulating the loads of MoS2, the coverage of X-band and Ku-band can be implemented with a 6.68 % filling load at an absorber thickness of 2.5 and 1.9 mm, respectively. The construction of the unique microstructure and regulation of MoS2 loads are demonstrated to be feasible pathways for the production of efficient and tunable electromagnetic waves absorbers in future.
Abstract
After the Industrial Revolution, with the continuous improvement of people’s living standards and the great changes in science and technology, the traditional energy has been unable to meet ...people’s new needs of production and life and the concept of sustainable development. As a hot topic in recent years, new energy can become one of the new ways to promote the continuation of resources. The traces of the rapid development of new energy can be found in various fields related to people’s lives. Electric power information system is one of the aspects. It is well known that human beings can not live or work without electricity. Therefore, it is very important to apply new energy technology in electric power information system. This study studies from the overview of new energy technology, the overview of power information intelligent system and the application of new energy technology in power information intelligent system.
Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to ...tampering; and landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time-location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence-classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace; if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image's capturing location.
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