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As an important part of automotive shock absorber, the columnar parts in automotive shock absorber will inevitably have machining defects during the process, which will not only degrade the performance of the parts, but also degrade or even fail the performance of the final shock absorber after assembly. Yolov5, as a target detection algorithm, has received much attention due to its high accuracy and fast operation speed. However, the algorithm faces some challenges when applied in a practical industrial environment. In this paper, improvement measures are proposed to address the limitations of sample collection and the high speed of pipeline recognition in industrial environments. The network model is optimized and designed. Firstly, the ASPP module is replaced by the SPP module thus improving the viewability throughout the process providing recognition accuracy. Secondly, the Conv and C3 layers of Yolov5s are replaced by Transformer to obtain higher recognition accuracy. By improving and optimizing the above methods, we can better cope with the improvement of detection accuracy under small sample conditions. Experiments show that the method can significantly improve the detection accuracy and operation speed of Yolov5s under the hardware condition of lower computing power, which is more suitable for industrial scenario application scenarios. [ABSTRACT FROM AUTHOR]

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]

Few-shot semantic segmentation (FSS) is a challenging task that aims to segment new classes in query images with a few annotated support samples. One inherent challenge in FSS is the intra-class variation resulting from the limited availability of support samples and the diversity of query data. Current methods frequently employ prototype-support techniques to tackle this issue. However, it is important to note that a single support prototype shares limited commonalities with query features, and increases the difficulty of accurate segmentation. In this paper, we propose a lightweight and effective framework named Siamese Transformer (SiaT) with a mere 0.68M learnable parameters to enhance commonalities between prototypes and query features. The SiaT framework consists of two key modules: the Siamese Transformer Module (STM) and the Query Activation Module (QAM). The STM integrates two shared Transformer decoders for information propagation to generate two enhanced prototypes. One Transformer decoder facilitates the propagation of target-related information from the query to the original support prototype, while the other propagates foreground information from the support to the initial query prototype. The QAM utilizes the two enhanced prototypes from the STM as support information, engaging with query features through channel-wise allocation and concatenation, termed query activation. Moreover, SiaT showcases competitive performance on the widely used benchmarks PASCAL- 5 i and COCO- 20 i , which demonstrates its effectiveness in addressing the intra-class variation challenge within FSS tasks. [ABSTRACT FROM AUTHOR]

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time task fault-tolerant scheduling algorithm for a dynamic monitoring platform of distribution network operation under overload of distribution transformers. The proposed algorithm is based on wireless communication and mobile edge computing to address the challenges faced by distribution networks in handling the increasing load demand. For the considered system, we evaluate the system performance by analyzing the communication and computing latency, from which we then derive an analytical expression of system outage probability to facilitate the performance evaluation. We further optimize the system design by allocating computing resources for multiple mobile users, where a greedybased optimization scheme is proposed. The proposed algorithm is evaluated through simulations, and the results demonstrate its effectiveness in reducing task completion time, improving resource utilization, and enhancing system reliability. The findings of this study can provide a basis for the development of practical solutions for the dynamic monitoring of distribution networks. [ABSTRACT FROM AUTHOR]

Conventional phase‐shifting transformers (PST) have been utilized for power flow control in transmission networks for many years. Compared to the slow reacting PSTs, recently trending unified power flow controllers (UPFC) enable much faster power flow control. This paper introduces a decentralized power flow control scheme for power flow controllers (PFCs) without the need for communication between the PFCs. For each PFC, an influence area is defined. The line loading of the transmission lines in this influence area is monitored in real‐time operation and if there is an overloading of a line, a feedback control scheme is used to mitigate the overloading of the line. As the power flow control of UPFC is dependent of the network topology and the UPFC converter limit, additional control measures are introduced to circumvent potential critical scenarios. Supplementary control measures are also developed to increase the robustness of control scheme for the simultaneous operation of UPFC with slow reacting PST. Using RMS simulation in DIgSILENT PowerFactory, the developed control method is validated in the IEEE 68‐Bus NETS/NYPS test bench network. The method is also validated for UPFCs with overlapping influence areas and the combination of fast acting UPFCs and slow acting PST devices. [ABSTRACT FROM AUTHOR]

Predicting stock prices has long been the holy grail for providing guidance to investors. Extracting effective information from Limit Order Books (LOBs) is a key point in high-frequency trading based on stock-movement forecasting. LOBs offer many details, but at the same time, they are very noisy. This paper proposes a differential transformer neural network model, dubbed DTNN, to predict stock movement according to LOB data. The model utilizes a temporal attention-augmented bilinear layer (TABL) and a temporal convolutional network (TCN) to denoise the data. In addition, a prediction transformer module captures the dependency between time series. A differential layer is proposed and incorporated into the model to extract information from the messy and chaotic high-frequency LOB time series. This layer can identify the fine distinction between adjacent slices in the series. We evaluate the proposed model on several datasets. On the open LOB benchmark FI-2010, our model outperforms other comparative state-of-the-art methods in accuracy and F1 score. In the experiments using actual stock data, our model also shows great stock-movement forecasting capability and generalization performance. [ABSTRACT FROM AUTHOR]

Considering that current voltage transformer models of electrical multiple units (EMUs) are narrow-band models or transformer models, this paper introduces a wide-band model of EMU voltage transformers based on the vector fitting method, circuit synthesis theory and black-box model theory. The admittances of voltage transformers from 30 kHz to 5 MHz are measured by the vector network analyzer, the branch admittances in the pi-type equivalent circuit are calculated according to the equation of a two-port network equivalent circuit. Based on the vector matching method, the rational function formulas of branch admittances are obtained, and the formulas are converted into the circuit models by circuit synthesis theory. The pi-type equivalent circuit model is constructed in the simulation software, and so is the voltage transformer model in the range of 30 kHz–5 MHz. The frequency sweeping method is used to measure the transmission characteristics from direct current (DC)to 30 kHz. The pi-type model is modified according to transmission characteristics, whereby the wide-band model in DC-5 MHz is obtained. Fast pulse experiments are carried out on the voltage transformer, and the actual injected fast pulse voltage is used as the excitation source in the simulation model. The measurement and simulation results on the secondary side of the voltage transformer show that the wide-band model has a high accuracy. [ABSTRACT FROM AUTHOR]

As is well known, the magnetostrictive phenomenon of electrical steel sheet is the main source of electricity in equipment such as transformers. The magnetostrictive characteristic of the actual transformer core is more complicated than that of single-sheet steel. The magnetostriction phenomenon of the transformer core cannot be fully understood by studying only a single piece of electrical steel, so it is necessary to study the local magnetic characteristics of the transformer directly. In this paper, two-limb, one-phase transformer core with a multi-step-lap construction was assembled, a laminated magnetostrictive testing system based on triaxial strain gauges was built, and the local magnetic characteristics were studied using a self-developed B-H vector sensor. The magnetostrictive and magnetic properties in different local regions were measured and analyzed under several magnetization patterns, and the influence of DC bias on the magnetostrictive property of the corner, yoke, and limb of the core was investigated. The influence of the position of the clamp on the magnetostriction of the transformer core was also studied. The magnetostrictive strain of the single sheet and laminated core was compared and discussed. The results showed that the strain caused by the interaction between laminations in this area can be effectively reduced when clamping in the middle of the yoke. [ABSTRACT FROM AUTHOR]

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area. Dataset:  https://zenodo.org/record/7884270. Dataset License: CC-BY [ABSTRACT FROM AUTHOR]

Dense video caption is a task that aims to help computers analyze the content of a video by generating abstract captions for a sequence of video frames. However, most of the existing methods only use visual features in the video and ignore the audio features that are also essential for understanding the video. In this paper, we propose a fusion model that combines the Transformer framework to integrate both visual and audio features in the video for captioning. We use multi-head attention to deal with the variations in sequence lengths between the models involved in our approach. We also introduce a Common Pool to store the generated features and align them with the time steps, thus filtering the information and eliminating redundancy based on the confidence scores. Moreover, we use LSTM as a decoder to generate the description sentences, which reduces the memory size of the entire network. Experiments show that our method is competitive on the ActivityNet Captions dataset. [ABSTRACT FROM AUTHOR]

In this present paper, results for the temperature distribution in electromagnetic construction in the form of coil coupled with EI-type ferromagnetic core are presented. This construction is typical for small electric transformers (AC operation mode) or chokes—DC or AC operation mode. Investigation has been provided using 3D finite element method computer modeling at various load conditions to simulate power dissipation both in the coil and the core volumes. The results obtained were used to calculate overall thermal resistances toward ambient free air and the thermal resistance between the coil and the core. These results show the important role the thermal resistance between the coil and the core may play for both steady-state and transient device operation. [ABSTRACT FROM AUTHOR]

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The differential transformer is an important component in the front-end electronics of high-precision capacitive position sensing circuits, which are widely employed in space inertial sensors and electrostatic accelerometers. The position sensing offset, one of the space inertial sensors' most critical error sources in the performance range, is dominated by the differential transformer asymmetry and requires a high-precision evaluation. This paper proposes a method to assess differential transformers' asymmetry and realize a prototype circuit to test a transformer sample. The results show that the asymmetry measurement precision can achieve 0.6 ppm for the transformer with an asymmetry level of about −278.2 ppm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel, degradation-sensitive, adaptive SST controller for cascode GaN-FETs. Unlike in traditional transformers, a semiconductor switch's degradation and failure can compromise its robustness and integrity. It is vital to continuously monitor a switch's health condition to adapt it to mission-critical applications. The current state-of-the-art degradation monitoring methods for power electronics systems are computationally intensive, have limited capacity to accurately identify the severity of degradation, and can be challenging to implement in real time. These methods primarily focus on conducting accelerated life testing (ALT) of individual switches and are not typically implemented for online monitoring. The proposed controller uses accelerated life testing (ALT)-based switch degradation mapping for degradation severity assessment. This controller intelligently derates the SST to (1) ensure robust operation over the SST's lifetime and (2) achieve the optimal degradation-sensitive function. Additionally, a fast behavioral switch loss model for cascode GaN-FETs is used. This proposed fast model estimates the loss accurately without proprietary switch parasitic information. Finally, the proposed method is experimentally validated using a 5 kW cascode GaN-FET-based SST platform. [ABSTRACT FROM AUTHOR]

A reactive matching (RM) power amplifier (PA) can achieve higher efficiency than a distributed amplifier (DA). In this paper, RM PAs that cover 18–40 GHz are proposed by using one‐order and two‐order synthesised transformer networks (STNs) with GaN on Si technology. In this band, the PAs can provide ≥30 dBm and ≥31.9 dBm output power, with power‐added efficiency (PAE) of ≥17% and ≥15% in the continuous mode, respectively. Further, STNs are analysed using the transmission poles method (TPM) instead of the resonant frequency method (RFM). Thus, the limitations of output power and bandwidth are comprehensively derived. In the one‐order STN, the impedance transformation ratio (T1N) has a maximum value (TMAX_1N), limiting the output power. A two‐order STN is proposed, and the impedance transformation ratio maximum (TMAX_2N) can be improved to (TMAX_1N)2. The result of this paper will be helpful for wideband high‐power amplifier applications. [ABSTRACT FROM AUTHOR]

The current paper investigated an improved threephase Magnetic Flux Leakage Transformer (MFLT) comprising a magnetic circuit of the core-type, intended for supplying electrical energy to the power circuits of microwave ovens. It is widely used to stabilize the electric current in the supply of electrotechnical equipment, in particular for the case of the high voltage power supply. In this paper, a novel contribution to a new iron core structure instead of shell-type, which often exists in electrotechnical devices, is presented. Thereafter, this new model provides a stabilized state after a drastic change in the load or the voltage of the supply network. An approach is used to model the electric circuit of the Core-Type Transformer (CTT). The duality of the electric circuit and the magnetic circuit can generate a model that considers the design and construction characteristics of the transformer. Thus, detailed circuit analysis using a reluctance network method is given, thereby its performance is evaluated for an 800W/2450MHZ magnetron. At last, a newly developed method describes an approach for modelling the nonlinear inductances by an analytic expression under the MATLAB-SIMULINK® code. It deduced by an analytic fitting of the nonlinear B-H curve. As a result, the waveforms simulations are consistent with the experiments in the case of the conventional power supply used in a microwave oven. Detailed design and implementation procedures are presented in the following sections, including the design of the new power supply. [ABSTRACT FROM AUTHOR]

In this paper, a single synchronous reference-frame based control method is proposed to improve the performance of AC-DC interlink converters feeding DC loads under unbalanced AC grid conditions. Unbalanced grid voltages cause undesirable double frequency (2!0) oscillations on the DC link voltage in AC-DC converters. In the medium/high power applications, low switching frequency and the oscillatory power of the input filter considerably degrade the functionality of the existing methods for removing 2!0 ripples from DC link voltage. In this paper, an analytical equation for the terminal active power is derived considering the input filter inductance. Accordingly, suitable current references for the control are proposed to eliminate the undesirable 2!0 ripples from DClink voltage considering low switching frequency. It is shown that the filter inductance adds a non-linear term to the active power equation, which complicates the current reference calculation. Also, a real-time recursive method is proposed to solve the equations and find the current references. To evaluate the performance of the proposed method, different grid unbalanced conditions, including asymmetrical short circuit faults, are applied to a test system in the PSCAD/EMTDC environment. Furthermore, the functionality of the proposed method is compared with that of the existing method for unbalanced conditions. [ABSTRACT FROM AUTHOR]

One of the important research directions in pulse power technology is to increase power density and operational stability of compact high-power pulse transformers. In this paper, the research object is a high-power pulse transformer using combined insulation, and the operating characteristics are obtained using simulation and experiment methods. The experimental results show that the maximum output voltage of the transformer increases by 30% in the combined insulation. At the same time, the combined insulation transformers are safer and have better insulation holding capacity. Moreover, multiple breakdowns have less effect on the subsequent breakdown voltage of the combined insulation transformer. The average subsequent breakdown voltage after 30 breakdowns of combination insulated transformers is still higher than the first breakdown voltage of non-combined insulation transformers. In addition, the energy storage density of combined insulation transformers is ∼40% higher than that of non-combined insulation transformers because of the higher permittivity of Midel 7131 than transformer oil. [ABSTRACT FROM AUTHOR]

HFCT method is a common detection technology for partial discharge (PD) in transformer. However, it suffers a lot from electromagnetic interference in field applications because conventional detection band of it is relatively low, usually in HF band. In this paper, a HFCT, which frequency response includes the whole of the VHF band and extends up to 500 MHz in the UHF band, is adopted to improve the anti-interference capacity of HFCT method. And we make a full investigation on the detection characteristics of HFCT method in UHF band based on an actual 220 kV transformer with complete internal structures. Four typical defects model is established as PD sources, additionally, conventional methods are also adopted in the experiment to make comparison with HFCT method in UHF band. The experiment results indicate that HFCT method can detect PD effectively in UHF band, and the detection sensitivity of it is no less than that of conventional methods. The results confirm the effectiveness of HFCT method in UHF band, therefore, the problem of low anti-interference capacity of conventional HFCT method that detects PD in HF band can be overcome. It offers a new effective and practical solution for transformer field monitoring of PD, especially for the transformers to which internal antenna UHF method can't be applied due to the lack of dielectric window. [ABSTRACT FROM AUTHOR]

When the lighting conditions are poor and the environmental light is weak, the image captured by the imaging device often has lower brightness and is accompanied by a lot of noise. The paper designs a progressive self‐calibration network model (PSCNet) for recovering high‐quality low‐light‐enhanced images. First, shallow features in low‐light images can be better focused and extracted with the help of attention mechanism. Next, the feature mapping is passed to the encoder and decoder modules, where the transformer and encoder‐decoder jump connection structures can be better combined with the semantic information of the context to learn rich deep feature information. Finally, the self‐calibration module can adaptively cascade the features decoded by the decoder and input them into the residual attention module quickly and accurately. Meanwhile, the LBP features of the image are also fused into the feature information of the residual attention module to enhance the detailed texture information of the image. Qualitative analysis and quantitative comparison of a large number of experimental results show that this method outperforms existing methods. [ABSTRACT FROM AUTHOR]

An indirect procedure for real-time monitoring the neutral conductor condition in three-phase distribution networks, based on watching over the growth of a novel parameter (∆τ), has been described in this paper. The parameter ∆τ has been defined as the relationship between the neutral-displacement power and Buchholz's apparent power measured at the fundamental frequency in the loads of the distribution networks for any condition of the neutral conductor and in its nominal conditions. The effectiveness of this procedure has been compared with other traditional indirect procedures, such as the surveillance of the RMS values of the line-to-neutral load voltages or their zero-sequence component. The practical application on a real distribution network reveals that the growth of the parameter ∆τ in the early stages of the breaking process of the neutral conductor follows a straight line whose equation is known for each length and section of that conductor, regardless of the loads and the voltage regulation of the transformer of the distribution network. This characteristic of the ∆τ parameter shows that the proposed procedure is suitable for monitoring neutral conductor deterioration and can be used for preventive maintenance of distribution networks. [ABSTRACT FROM AUTHOR]

The single pole-to-ground (SPG) fault is one of critical failures which will have a serious impact on the stable operation of the multi-terminal DC distribution network based on the modular multilevel converter (MMC). It is very significant to analyze fault characteristics for detecting faults and protection design. This paper established the DC SPG fault model, which showed that in the presence of a reactor, the short-circuit current was reduced from 2.3 kA to 1 kA at 6 ms after the fault. Then, a novel SPG fault protection strategy was proposed, which detected the current derivative in connection transformer grounding branch. When the value increases past the threshold of current derivative, small resistance was switched on to increase fault current. Thus, the reliability of differential protection was enhanced. Compared with the traditional protection method, the proposed method does not need communication, and improved the speed of protection. Finally, the simulation model was established in PSCAD/EMTDC. The model included three converter stations: T1, T2 and T3. Among them, T1 outputs power, and T2 and T3 receive power. The results of RTDS showed that the DC circuit breaker operated within 3 ms, the three-port circuit breaker worked within 50 ms, which proves that the proposed strategy was effective. At this time, the system switched from the T1–T2–T3 three-terminal networking operation mode to the T1–T2 two-terminal hand-in-hand operation mode. Since the T3 terminal no longer received power, the transmission power of the T1 terminal decreased, and the received power of the T2 terminal remained unchanged. [ABSTRACT FROM AUTHOR]

This paper introduces an enhanced solid-state transformer topology for a medium-voltage (MV) utility grid. The main objective of the current study is to develop an improved flyback converter equipped with a quasi z-source converter (qZ_iFC) having a high-voltage conversion capability for the integration of low-input voltage to the DC link of an MV modular multilevel converter (MMC). The system integrates the quasi z-source and flyback converters by operating their existing switches complementary. Furthermore, the high-gain qZ_iFC allows for a reduction in the rated voltage of the input, as well as the use of a high-frequency transformer (HFT) with a unity turns ratio that provides galvanic isolation between the input and the output ports. Thus, using an HFT just for isolation purposes without voltage gain improves the system efficiency. In addition, a controller for (i) qZ_iFC which is regulating complementary switches to prevent the shoot-through current from reaching the HFT resulting in saturation; and (ii) a controller for MMC to produce MV-level AC voltage for loads are suggested. The performance of the proposed system was evaluated for several operating conditions. Results show that the proposed SST smoothly performs the power flow between the ports during steady-state and transient conditions. The power flow capabilities and efficiency values validate the viability and effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique's performance. The results show that the online SFRA could be of interest for monitoring induction motors' health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a critical step in many imaging applications. Automatic segmentation has gained extensive concern using a convolutional neural network (CNN). However, the traditional CNN-based methods fail to extract global and long-range contextual information due to local convolution operation. Transformer overcomes the limitation of CNN-based models. Inspired by the success of transformers in computer vision (CV), many researchers focus on designing the transformer-based U-shaped method in medical image segmentation. The transformer-based approach cannot effectively capture the fine-grained details. This paper proposes a dual encoder network with transformer-CNN for multi-organ segmentation. The new segmentation framework takes full advantage of CNN and transformer to enhance the segmentation accuracy. The Swin-transformer encoder extracts global information, and the CNN encoder captures local information. We introduce fusion modules to fuse convolutional features and the sequence of features from the transformer. Feature fusion is concatenated through the skip connection to smooth the decision boundary effectively. We extensively evaluate our method on the synapse multi-organ CT dataset and the automated cardiac diagnosis challenge (ACDC) dataset. The results demonstrate that the proposed method achieves Dice similarity coefficient (DSC) metrics of 80.68% and 91.12% on the synapse multi-organ CT and ACDC datasets, respectively. We perform the ablation studies on the ACDC dataset, demonstrating the effectiveness of critical components of our method. Our results match the ground-truth boundary more consistently than the existing models. Our approach gains more accurate results on challenging 2D images for multi-organ segmentation. Compared with the state-of-the-art methods, our proposed method achieves superior performance in multi-organ segmentation tasks. [ABSTRACT FROM AUTHOR]

The energy exchange between series and shunt converters is the basis for the powerful functioning of the unified distributed power flow controller, which can be realized through the 3rd harmonic current. In this paper, based on the established 3rd harmonic equivalent network of unified distributed power flow controller power system, the influence of the additional loss caused by the 3rd harmonic current on the line transmission capacity is analysed. The mathematical relationship between the neutral point voltage of the transformer and the 3rd harmonic current is constructed, and it is simplified according to the different 3rd harmonic currents. By analysing the power relationship of the 3rd harmonic equivalent network, it is found that there is a contradiction between improving the 3rd harmonic output efficiency of the shunt single‐phase converter and reducing the neutral point insulation voltage of the transformer. The constraint conditions of the 3rd harmonic current are simulated by the advanced digital power system simulator. Finally, the method of obtaining the economical and safe 3rd harmonic current value range is summarized, which provides a theoretical basis and reference for the device parameter design. [ABSTRACT FROM AUTHOR]

This paper proposes a transformer type dc power flow controller (DCPFC) based on stacks composed of sub‐modules (SMs). The converter can perform dc power flow control independently on point‐to‐point as well as multi‐terminal connection, which can be applied to either high voltage or medium voltage dc grids. In order to explain the operation principle, different operating states of the proposed DCPFC are analysed. With phase‐shift modulation and the closed‐loop current control, the proposed DCPFC can achieve bidirectional power flow regulation. Following the theoretical analysis of the proposed DCPFC, simulation results have verified the feasibility of bidirectional power flow control. A downscaled experimental prototype is also built to demonstrate the operation principle and performance of the proposed DCPFC. [ABSTRACT FROM AUTHOR]

In this paper, an integrated of buck and half‐bridge high step‐down converter utilizing single‐stage driving design for high‐efficiency energy conversion is proposed. The proposed converter has the ability to instantly and synchronously transfer energy from input to output within one conversion period. The proposed topology is capable of lowering the primary side voltage of the transformer, so the turns ratio could be reduced. The coupling rate is therefore better along with the leakage inductance. Furthermore, the duty cycle operated in an extreme situation is not necessary. As a combination of the advantages of high step‐down conversion, lower voltage stresses, and fewer amount of semiconductor elements, the feasibility of the proposed topology is verified by the implementation of a 300 W prototype. The proposed integrated topology utilizes the single‐stage energy transfer control algorithm to verify that the proposed experimental circuit has a full load efficiency of 92.4%, in the case of input voltage 380 V and output voltage 5 V, and the maximum efficiency, occurring at 100 W, is 94.55%. [ABSTRACT FROM AUTHOR]

In this paper, a modular electromagnetic transducer that achieves the optimal transfer of energy from the electric and/or magnetic fields is proposed. Both the magnetic field resonance coupling and the influence of the electric field near the copper transducers of the printed circuit board and inside the FR4-type epoxy material are considered. In our printed arrays of flat transducers, we consider face-to-face capacitances for the study of resonance coupling. Because the space between coil turns is almost double the plate thickness, the coplanar capacitance can be ignored for frequencies under 2 MHz. A radio frequency (RF) transmitter and transducer were built to demonstrate the increased energy transfer efficiency when using both electric and magnetic fields in the near-field region. The transversal leakage flux coupling of a long RF coil was more efficient than a simple axial magnetic field coupling when using pancake transceiver coils. The optimal configuration having one long coil at the base and two or more flat coils as capacitor plates near coil ends generated the highest tandem of magnetic and electrical fields. A power regression tool was used to convert and simplify the transducer current and voltage variation with distance. In this regard, the current change corresponded to magnetic field variation and the voltage change to the electric field variation. New formulas for estimating the near-field region and the self-capacitance of the RF transformer coil are proposed; the optimal function in the frequency domain for a given transducer distance was defined by simulation. [ABSTRACT FROM AUTHOR]

In this paper, a QR flyback converter using a self-driven active snubber (SDAS) was proposed to solve the problem of voltage surge in the switch of QR flyback converters. In the proposed converter, the SDAS consisting of a clamping capacitor and an active switch can be configured in parallel with the main switch or transformer to reduce the voltage surge in the switch. To confirm the steady-state characteristics of the QR flyback converter to which the proposed SDAS is applied, equivalent circuits for each state were constructed, and the equations and characteristics for each state were determined. A 60 W class small AC–DC adapter was constructed to confirm the effectiveness of the proposed converter and the control circuit method, and the experimental results were analyzed. The size of the experimental AC–DC adapter was 74 × 29 × 23   mm , and it had a high power density of 20   W / in 3 or more. The experimental circuit was limited to the high power conversion efficiency of up to 91.56%, and the maximum voltage surge in the switch was approximately 450 V. One of the reasons for such high efficiency is the SDAS circuit, which sufficiently reduces the voltage surge of the QR flyback switch, compared with the RCD clamp circuit, and does not consume power in principle. [ABSTRACT FROM AUTHOR]

Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. Previous works on automated recognition of surgical phases utilized popular methods in computer vision but failed to consider the sequential nature of surgical procedures. In this paper, we propose a method that utilizes calibrated confidence scores to dynamically switch between two Transformer-based models, viz., baseline and a separately trained binary classifier model, depending on the calibrated confidence level. Our method outperforms the baseline model on the publicly available Cholec80 dataset and can be readily applied to a variety of phase recognition methods and applications. [ABSTRACT FROM AUTHOR]

Electricity consumption is increasing rapidly and many countries are looking for ways to cope with the energy crisis. Morever, the world is facing the problem of global warming caused by emissions. Therefore, it is of great importance to operate power systems efficiently. Energy Hub (EH) represents a versatile energy system capable of providing efficient and optimal solutions for the operation of power systems across multiple carriers. This paper examines the optimization of an EH encompassing renewable energy systems (RES) like wind and photovoltaic, combined heat and power (CHP), transformer, absorption chiller, energy storage system (ESS) and furnace with aiming at minimizing the cost. Demand response is an energy sector strategy that entails modifying electricity consumption patterns in reaction to fluctuations in electricity supply or pricing. The objective of demand response programs (DRP) is to curtail or shift electricity consumption during periods of elevated electricity prices. Therefore, Electrical Demand Response Program (EDRP) for electrical demand and the Thermal Demand Response Program (TDRP) for heating demand are incorporated into the EH. The optimization problem is formulated as Mixed Integer Linear Programming (MILP) and solved with CPLEX solver in GAMS. The outcomes of various case studies are compared to ascertain the model's efficiency. [ABSTRACT FROM AUTHOR]

This study investigated the adsorption properties of the decomposed gases in locomotive electric transformers: C2H2, CH4, and CO on metal atoms (Ti, Zr, V, and Nb) modified Ti3C2O2 by DFT calculations. The optimal modification structures of metal atoms on Ti3C2O2 were calculated and band structure, adsorption energy, charge transfer, density of states, charge density diagrams, and recovery time were used to analyze the adsorption properties. The results showed that metal atom modifications could enhance the conductivity and surface activity. In the adsorption systems, gas received electrons, and the conductivity was changed after gas adsorption. The adsorption processes of CH4 on modified systems were physical and had an extremely short recovery time. However, new bonds were formed in the adsorption of C2H2 and CO resulting in long recovery times. In essence, Ti, Zr, V, and Nb-doped Ti3C2O2 can be used as gas-sensing materials for CH4 and as adsorbents for C2H2 and CO gases in locomotive electric transformers. [ABSTRACT FROM AUTHOR]

Tasmanian devil optimization (TDO) algorithm represents one of the most recent optimization algorithms that were introduced based on the nature behavior of Tasmanian devil behavior. However, as a recent optimizer, its performance may provide inadequate balance among the exploitation and exploration abilities, especially when dealing with the multimodal and high-dimensional natures of optimization tasks. To overcome this shortage, a novel variant of the TDO, called improved Tasmanian devil optimization (ITDO), is introduced in this paper. In ITDO, two competitive strategies are embedded into TDO to enrich the scope of the searching capability with the aim of improving the diversification and identification of the algorithm. The effectiveness of the ITDO algorithm is examined by validating its performance on CEC 2020 benchmark functions with different landscape natures. The recorded results proved that the ITDO is very competitive with other counterparts. After ITDO exhibited a sufficient performance, then, it was applied to estimate the parameters of the 1 kVA, 230/230 V, single-phase transformer. Some assessment metrics along with convergence analysis are conducted to affirm the performance of the proposed algorithm. The recorded results confirm the competitive performance of the proposed method in comparison with the other optimization methods for the benchmark functions and can identify the accurate parameters for the single-phase transformer as the estimated parameters by ITDO are highly coincident with the experimental parameters. [ABSTRACT FROM AUTHOR]

The biomimetic design provides an adequate solution to attain an excellent design. However, the prototype space for biomimetic design is relatively large, and it becomes more and more challenging to find the required biological prototypes efficiently and accurately. To improve the design efficiency and enrich the biomimetic information, this paper proposes a coupled biological strategies-enabled bidirectional encoder representation from transformers (BERT) model to assist biomimetic design, namely BioDesign. We extract the biological strategies and extract dimensional information from AskNature as a part of the database. The linguistic expression model-BERT helps to search for biological strategy. Based on the coupled biological strategies analysis, the quantitative results of biomimetic strategies are given by BioDesign. Finally, we take the erosion wear-resistant design of the control valve core as an example to demonstrate the utilization based on the proposed BioDesign. The erosion wear experiment demonstrated the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Open, broken, and improperly closed manholes can pose problems for autonomous vehicles and thus need to be included in obstacle avoidance and lane-changing algorithms. In this work, we propose and compare multiple approaches for manhole localization and classification like classical computer vision, convolutional neural networks like YOLOv3 and YOLOv3-Tiny, and vision transformers like YOLOS and ViT. These are analyzed for speed, computational complexity, and accuracy in order to determine the model that can be used with autonomous vehicles. In addition, we propose a size detection pipeline using classical computer vision to determine the size of the hole in an improperly closed manhole with respect to the manhole itself. The evaluation of the data showed that convolutional neural networks are currently better for this task, but vision transformers seem promising. [ABSTRACT FROM AUTHOR]

This task‐force paper documents and summarizes the models of power transformers that have been proposed, used, and validated in the exercise of digital real‐time simulation. Power transformer is one of the most important equipment in power systems, and its modelling for electromagnetic transient simulation has evolved over time, especially in the area of real‐time simulation. The focus of the paper is to document and archive the models that have been well accepted and used for transient analysis in digital real‐time simulation so that readers can use it as a master document for transformer modelling in real‐time simulation studies. It includes both conventional and specialized models of power transformers that have been broadly acknowledged by the power engineering community. The models provided here come with detailed mathematical representation and their implementation techniques. A comparative study is also performed to illustrate the differences in their performances. In the end, an application guideline has been provided to guide the readers to select the appropriate model for their study. [ABSTRACT FROM AUTHOR]

Higher education plays a crucial role in modern society and in emerging energy systems, due to the rising complexity of the phenomena and the interplay among various layers (physical, cyber, social and economic). To cope with this context, universities need to develop new visions and tools for education and training. Real-time simulation (RTS) is emerging as a novel and effective approach to analyzing power system aspects, and this characteristic can be effectively used in power system courses. Hence, after the presentation of the basics about the RTS, the paper will show the advantages of employing RTS for teaching activities. Finally, two examples of didactic activities involving RTS at Politecnico di Torino will be presented. In fact, RTS is part of both multi-disciplinary projects (where students with different backgrounds can face the issues affecting the protection and control of power systems) and the course "Electrical systems and safety", where RTS is used to study the behavior of voltage transformers in saturation. [ABSTRACT FROM AUTHOR]

In recent years, supervised video summarization has made tremendous progress with treating it as a sequence-to-sequence learning task. However, traditional recurrent neural networks (RNNs) have limitations in sequence modeling of long sequences, and the use of a transformer for sequence modeling requires a large number of parameters. We propose an efficient U-shaped transformer for video summarization tasks in this paper to address this issue, which we call "Uformer". Precisely, Uformer consists of three key components: embedding, Uformer block, and prediction head. First of all, the image features sequence is represented by the pre-trained deep convolutional network, then represented by a liner embedding. The image feature sequence differences are also represented by another liner embedding and concatenate together to form a two-stream embedding feature in the embedding component. Secondly, we stack multiple transformer layers into a U-shaped block to integrate the representations learned from the previous layers. Multi-scale Uformer can not only learn longer sequence information but also reduce the number of parameters and calculations. Finally, prediction head regression the localization of the keyframes and learning the corresponding classification scores. Uformer combine with non-maximum suppression (NMS) for post-processing to get the final video summarization. We improved the F-score from 50.2% to 53.9% by 3.7% on the SumMe dataset and improved F-score from 62.1% to 63.0% by 0.9% on the TVSum dataset. Our proposed model with 0.85M parameters which are only 32.32% of DR-DSN's parameters. [ABSTRACT FROM AUTHOR]

The voltage rise problem due to the reverse power flow is one of the main obstacles to expanding the photovoltaic systems (PVSs) in distribution networks. In this paper, a decentralized control method for coordinated control of on-load tap changer (OLTC) transformers and PV inverters, is proposed for the voltage regulation of radial distribution networks. In this method, the allowable voltage range is divided into several zones, and appropriate corrective actions are adaptively taken according to each zone. To reduce the tension on the OLTC, first, the reactive power capability of the PV inverters is employed for the voltage regulation, and if it is insufficient, OLTC is used as the last solution. In this regard, the unused capacity of PV inverters is utilized for the reactive power compensation, considering the grid codes reactive power requirements. Furthermore, decentralized control based on a signaling method is used for coordination between OLTC and PVSs, which eliminates the need for communication links. Finally, the effectiveness of the proposed method is validated by the simulation results for the IEEE 33-bus distribution network, and the impact of different reactive power control methods on the number of OLTC operation is investigated. [ABSTRACT FROM AUTHOR]

The progressive application of non-linear loads in distribution systems (DS) increases current harmonics flow in DS's apparatuses, especially distribution transformers (DTs). Since DTs' operating temperature rises due to the harmonics flow, their loading should be reduced such that the hot spot temperature (HST) is preserved under its permissible value. This means that DTs' available capacity is influenced by load harmonic content. In this paper, a novel formulation for DTs' failure rate in the presence of harmonics is presented as a function of load harmonic contents. Using the suggested equivalent failure rate, DTs' available capacity in harmonic polluted DS is mathematically formulated. Additionally, the presence of the harmonic increases the HST, leading to DTs' aging acceleration. Therefore, the impact of harmonic components on DTs' aging is arithmetically modeled. To evaluate the efficacy of the suggested reliability model, it is applied to three distinct DTs having respectively industrial, commercial, and residential loads. The obtained results indicate that the available capacity of DTs with the same rated capacity would be different regarding to their load harmonic contents. On the other hand, it is comprehended from the achieved results that the aging acceleration factor (Faa) of the DTs increases owing to their load harmonic contents. [ABSTRACT FROM AUTHOR]

This paper studies the voltage stability of the Slovak Republic's power system (PS) based on an assessment of the PV curves. The PV curve is a tool for assessing voltage stability, and based on its shape, it is possible to determine weak and strong voltage nodes with the possibility of voltage stability reserve quantification. We present an analysis of the influence of transformers with an automatic voltage regulator (AVR) on the shape of the PV curves and on the magnitude of voltage in the PS. In general, the 400 kV/110 kV transformers equipped with AVRs are critical assets for the PS as they address voltage control in the DS. However, in the case of voltage problems in the TS, the AVR function may worsen the voltage situation across the entire PS. Therefore, we closely analyze the negative effects of the AVR on the PS operation. This impact is clearly proved, and recommendations are given for the transmission system operator (TSO) in order to maintain voltage stability. In addition, the PV curves of the pilot nodes are analyzed very accurately, thereby confirming their importance in the TS in terms of a sufficient reserve of reactive power. The study was conducted in cooperation with the TSO, Slovenská elektrizačná prenosová sústava, a.s. [ABSTRACT FROM AUTHOR]