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Abstract In the field of RFID, the reading performance of tags is an important performance indicator for measuring tag. Related studies have shown that the tags’ geometrical distribution has an important influence on the tags’ reading performance. In order to optimize the tags’ geometrical distribution and improve the tags’ reading performance, this paper proposes a tag distribution optimization method based on multi‐level wavelet‐CNN (MWCNN) and extreme learning machine (ELM). First, this paper designs a tag distribution optimization system based on stereo‐vision. Second, the stereo‐cameras are used to capture the images of the tags. Aiming at the degradation phenomenon in the acquired images, MWCNN is used to recover the degraded tag images. On the basis of the image restoration, the template matching method is used to obtain the 3D coordinates of the tags. Then, ELM is used to model and predict the nonlinear relationship between 3D coordinates of the tags and the corresponding reading distance. The results show that the average prediction relative error is 0.56% and the time cost is 2.0 s. The average prediction relative error of ELM is smaller than GA‐BP and PSO‐BP. The time cost of ELM is smaller than the wavelet neural network.

Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.

Abstract Orthogonal frequency division multiplexing (OFDM) has been widely used in wireless communication systems due to its robustness against the frequency selectivity of fading channels. It provides optimal performance and it also meets the requirements of the next generation communication systems. Although OFDM has these advantages, its spectrum efficiency is usually degraded significantly due to the use of cyclic prefix (CP). In this paper, motivated to deal with this issue, the combined structure of Fast Walsh‐Hadamard Transform (FWHT) and Time Domain Equalizer (TDE) is proposed to use in CP‐free OFDM systems. The results validated by Monte Carlo simulations decipher the improved performances in terms of spectral efficiencies, bit error rate, peak to average power ratio and out of band emission for using this structure over frequency selective multipath fading channels in CP‐free OFDM systems. The acquired simulation results have illustrated that the proposed method provides 20% spectral efficiency without compromising the BER performance.

Abstract Recently, with the increased intermittent renewable energy penetration, many power grids have been incorporated into a large‐scale long‐distance UHV AC–DC hybrid system. In the actual grid, power disturbances, such as DC blocking faults and trip‐off of wind turbines, often occur, resulting in power shortages and large frequency fluctuations. However, the existing approaches to assess the system frequency security are not reliable. This paper proposes an analytic formula for the system frequency response based on a generic system frequency‐response (SFR) model, which can be applied to modern large‐scale power systems. First, a generic SFR model with a reasonable structure is designed according to the parameter determination strategy. Second, according to the transfer function in the model, the time‐domain analytic formula of the frequency response is obtained by realizing the inverse Laplace transform. Moreover, four main indexes are established to represent the characteristics of the frequency dynamic process in different periods, and these indexes are qualitatively and quantitatively analysed. Finally, the New England 10‐unit 39‐bus power system and East China Power Grid are considered to demonstrate the key features of the proposed method. The results show that the proposed analytic assessment method can be effectively adopted in several applications.

Abstract An extension of Radon transform by using a measure function capturing the user need is proposed. The new transform, called scale space Radon transform, is devoted to the case where the embedded shape in the image is not filiform. A case study is brought on a straight line and an ellipse where the SSRT behaviour in the scale space and in the presence of noise is deeply analyzed. In order to show the effectiveness of the proposed transform, the experiments have been carried out, first, on linear and elliptical structures generated synthetically subjected to strong altering conditions such blur and noise and then on structures images issued from real‐world applications such as road traffic, satellite imagery and weld X‐ray imaging. Comparisons in terms of detection accuracy and computational time with well‐known transforms and recent work dedicated to this purpose are conducted, where the proposed transform shows an outstanding performance in detecting the above‐mentioned structures and targeting accurately their spatial locations even in low‐quality images.

Abstract In hybrid bipolar DC transmission systems with different types of converters at each pole, the transient high‐frequency component of the voltage signal under a single‐pole grounding fault and an inter‐pole fault is significantly different for internal and external faults because of smooth‐wave reactors on both sides of the DC line. Based on these characteristics, a single‐ended electrical quantity protection scheme based on transient energy is proposed. First, the voltage fault component is extracted and then processed by using a wavelet packet transform to obtain the transient energy in each frequency band. Second, the protection criterion is determined based on the ratio between low‐frequency energy and the sum of high‐frequency energy. After the setting principle is given, the influence of the protection scheme under high transition resistance is analysed. The protection scheme is implemented in MATLAB and tested based on fault data obtained from a hybrid bipolar HVDC transmission model built in PSCAD under different operating conditions. The effectiveness of the proposed protection method is verified by simulation tests under different fault types at different fault distances. The proposed method can provide strong tolerance to high transient resistance, accurately identify internal/external faults and automatically identify fault poles.

Abstract A novel asymmetric double image encryption technique is introduced to protect the algorithm from the interlopers by using hybrid chaotic structured phase masks in Arnold and Fresnel transforms. Mostly random phase masks are used routinely in many algorithms which are not strong against many attacks. So, taking in consideration replacing of random phase masks with the new hybrid chaotic structured phase masks takes place in the algorithm. With the assistance of Fresnel zone plates, radial Hilbert mask and logistic map functions, the chaotic structured phase mask is accumulated and hybrid chaotic structured phase mask provides extra security to system. Arnold transform helps in pixel shuffling, and with the pixel reorganization, it also helps in image reaping and bordering. The propagation distance of the Fresnel transform and the constraints of the Arnold transform serve as keys for encryption and decryption. To check the sturdiness of hybrid chaotic structured phase mask, simulations have been done which are carried out by the support of MATLAB R2018a (9.4.0.813654).

Abstract Oscillation method is the most effective approach to determine the inertia properties of rigid bodies. This paper proposes a new signal processing method to determine the moment of inertia (MoI) accurately in the presence of high frictional damping when using a torsion pendulum setup. A mathematical model of damped oscillation is established, and the analytic form of Fourier transformation of the damped torsional oscillation is derived. A formula for calculating the MoI in relation to the dominant frequency and damping ratio is also derived. An algorithm regarding periodic extension and normalization of torsional oscillation signal is proposed to calculate the dominant frequency and damping ratio of torsional oscillation in the frequency domain. The feasibility and accuracy of this algorithm are verified by both numerical simulations and measurement experiments. The experimental results show that the proposed method has good measurement repeatability, and the relative error of measurement is within 0.80%.

Abstract Self‐similarity, a prior of natural images, has attracted much attention. The attribute means that low‐rank group matrices can be constructed from similar image patches. For low‐rank approximation denoising methods based on singular value decomposition (SVD) the ability to accurately construct group matrices with noise and handle singular values are keys. Here, combining image priors, a two‐stage clustering method to adaptively construct group matrices is designed. The method is anti‐noise, that is, when noise levels are high, these matrices are more accurate than that constructed by other algorithms. Then, according to the significance of singular values and singular vectors, singular vectors of the low‐rank estimations are corrected so that the residual noise in the low‐rank estimations is further suppressed. For back projection , the authors use the original noise level and the residual image to adaptively determine projection parameters and new noise levels . So, authors' back projection can provide a good foundation for authors' two‐stage denoising methods, better remove noise and preserve image details. Experimental results show that compared with the existing state‐of‐the‐art denoising algorithms, the proposed algorithm achieves competitive denoising performances in terms of quantitative metrics and preserving details. Especially with the increase of noise, the competitiveness of authors' algorithms is gradually enhanced.

Abstract This study presents an earth fault distance determination algorithm for distribution networks using active travelling waves. Three triacs are used in parallel with a three‐phase breaker poles to overcome the mechanical inequality of the poles’ reclosing times, so that the three phases are simultaneously reclosed. As the proposed fault location technique is an active type with controllable reclosing instant, the arrival time of the reflected surge from the fault point should be stamped precisely. For this purpose, three different travelling‐wave detection algorithms are evaluated including the discrete wavelet transform, Hilbert transform, and signal derivative. The fault location performance is evaluated under different fault conditions such as fault distances, fault resistances, and busbar faults. Due to utilising the reclosing transients, the proposed fault location function successfully estimates the fault distance for different earthing concepts such as unearthed, compensated, and earthed networks. This study is accomplished via simulating a typical 20 kV distribution network by the ATP/EMTP program. The results ensure the superior performance of the proposed fault distance estimation algorithm for earth faults in distribution networks.

Abstract In this letter, a pyramid wavelet convolutional neural network for audio super resolution is presented. Since the audio signal is non‐stationary, previous convolutional neural network based approaches may fail in capturing the details, these method usually focus on the global approximation error and thus produce over smooth results. To cope with this issue, it is suggested to predict the wavelet coefficients of the audio signal, and reconstruct the signal from these coefficients stage by stage rather. The prediction errors of the wavelet coefficients are included to the loss function to force the model to capture the detail components. Experimental results show that the approach, training on the VCTK public dataset, achieves more appealing results than state‐of‐the‐art methods.