Search Results (660)

To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF₆ circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for the breakdown voltage of mixed gases is derived based on the synergistic effect. Considering the influence of contact gap on electric field distortion, an adaptive switching strategy is designed to quantify the dynamic relationship among operation times, insulation strength degradation, and electric field distortion. Then, multi-round switching-on and switching-off tests are carried out under the condition of fixed single-arc ablation amount, and the laws of voltage–current, gas decomposition products, and pre-breakdown time are obtained. The test data are processed by the least squares method, adaptive switching algorithm, and machine learning method. The results show that the coincidence degree of the pre-breakdown time obtained by the adaptive switching algorithm and the test value reaches 90%. Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. It can also explain the physical mechanism of the decay of insulation degradation rate with the number of operations. Compared with the machine learning method, this algorithm has stronger generalization ability, effectively overcoming the defects of difficult interpretation of physical causes and the poor engineering adaptability of the black box model. Full article

For data-intensive applications like edge AI and image processing, we present a new reconfigurable 8T SRAM-based in-memory computing (IMC) macro designed for high-performance and energy-efficient operation. This architecture mitigates von Neumann limitations through numerous major breakthroughs. We built a new architecture with an adjustable capacitance array to substantially increase the multiply-and-accumulate (MAC) engine’s accuracy. It achieves 10–20 TOPS/W and >95% accuracy for 4–10-bit operations and is robust across PVT changes. By supporting binary and ternary neural networks (BNN/TNN) with XNOR-and-accumulate logic, a dual-mode inference engine further expands capabilities. With sub-5 ns mode switching, it can achieve up to 30 TOPS/W efficiency and >97% accuracy. In-memory Hamming error correction is implemented directly using integrated XOR circuitry. This technique eliminates off-chip ECC with >99% error correction and >98% MAC accuracy. Machine learning-aided co-optimization ensures sense amplifier dependability. To ensure CMOS compatibility, the macro may perform Boolean logic operations using normal 8T SRAM cells. Comparative circuit-level simulations show a 31.54% energy efficiency boost and a 74.81% delay reduction over other SRAM-based IMC solutions. These improvements make our macro ideal for real-time AI acceleration, cryptography, and next-generation edge computing, enabling advanced compute-in-memory systems. Full article

Overmilking occurs when the teat cups remain attached to the udder during milking, even though there is little or no milk flow. This puts pressure on the teat tissue and reduces milk production due to longer milking times, meaning fewer cows are milked per hour. Therefore, the correct removal of the teat cup at the end of mechanical milking is crucial for the milking process. The aim of this study was to describe the factors influencing automatic teat cup removal (ATCR) at the end of mechanical milking and to demonstrate its importance for udder health, milk production and milk quality. There are considerable differences between milking system suppliers and countries regarding the minimum removal of the teat cup at the end of the milking process. However, to ensure good milk quality, prevent teat damage and reduce the risk of mastitis, it is important to shorten the working time of the milking machine on the udder in both automatic and conventional milking systems. For this reason, several studies have shown that increasing the milk flow switch point effectively reduces milking time, especially in automatic milking systems where dairy cows are milked more than twice a day. However, when the ATCR setting was increased above 0.5 kg·min⁻¹, milk production decreased, and the number of somatic cells in the milk produced increased. Therefore, the use of ATCR at a milk flow rate of 0.2 kg·min⁻¹ significantly increased milk production, improved milk quality and maintained udder health when a low vacuum level (34–36 kPa) was used in milking machines such as MultiLactor and StimuLactor (Siliconform, Germany). In conclusion, ATCR at a milk flow of 0.2–0.3 kg·min⁻¹ is a useful level to achieve various goals on dairy farms when a low vacuum of 34–36 is used in the milking machine. If the milking machine uses a higher vacuum, it is possible to program a higher ATCR at a milk flow of up to 0.5 kg·min⁻¹. Full article

Background: Long non-coding Ribonucleic Acids (lncRNAs) can be localized to different cellular compartments, such as the nuclear and the cytoplasmic regions. Their biological functions are influenced by the region of the cell where they are located. Compared to the vast number of lncRNAs, only a relatively small proportion have annotations regarding their subcellular localization. It would be helpful if those few annotated lncRNAs could be leveraged to develop predictive models for localization of other lncRNAs. Methods: Conventional computational methods use q-mer profiles from lncRNA sequences and train machine learning models such as support vector machines and logistic regression with the profiles. These methods focus on the exact q-mer. Given possible sequence mutations and other uncertainties in genomic sequences and their role in biological function, a consideration of these variabilities might improve our ability to model lncRNAs and their localization. Thus, we build on inexact q-mers and use machine learning/deep learning techniques to study three specific problems in lncRNA subcellular localization, namely, prediction of lncRNA localization using inexact q-mers, the issue of whether lncRNA localization is cell-type-specific, and the notion of switching (lncRNA) genes. Results: We performed our analysis using data on lncRNA localization across 15 cell lines. Our results showed that using inexact q-mers (with q = 6) can improve the lncRNA localization prediction performance compared to using exact q-mers. Further, we showed that lncRNA localization, in general, is not cell-line-specific. We also identified a category of LncRNAs which switch cellular compartments between different cell lines (we call them switching lncRNAs). These switching lncRNAs complicate the problem of predicting lncRNA localization using machine learning models, showing that lncRNA localization is still a major challenge. Full article

In order to achieve the optimal stator–rotor combinations of conventional flux-switching permanent magnet (FSPM) machines, this paper proposes and analyzes a general principle with prime phase numbers. Based on the coil complementarity concept, the proposed methodology specifically addresses the phase symmetry of back electromotive force (back-EMF) and electromagnetic torque optimization, with comprehensive analysis conducted for two-phase, three-phase, and five-phase configurations. Firstly, the coil-EMF vectors and the concept of coil pairs of conventional FSPM machines are introduced. Then, based on the coil-EMF vectors, an analytical model determining the stator pole and rotor teeth combinations is proposed. Further, the combinations for conventional FSPM machines with prime phase numbers are synthesized and summarized on the basis of the results obtained by the proposed model. To validate the model and combination principles, the FSPM machines satisfying the principles have been verified to exhibit a symmetrical phase back-EMF waveform by finite element analysis (FEA) and experiments on prototypes. In addition, the winding factors of the conventional FSPM machines with different stator pole and rotor teeth combinations are calculated. Full article

The battery pack is a critical component of electric vehicles, with lithium-ion cells being a frequently preferred choice. Lithium-ion cells are known for long life, high power and energy density, and are reliable for a broad range of temperatures. However, these batteries have a drawback of over-voltage, under-voltage, thermal runaway, and especially, state of charge or voltage imbalance. Among these, the cell imbalance is particularly important because it causes an uneven power dissipation in each cell, resulting in non-uniform temperature distribution. This uneven temperature distribution negatively affects the lifetime and efficiency of a battery pack. Cell imbalance is mitigated by cell balancing techniques, of which several methods have been presented over the last few years. These methods consider different power electronics circuits and control approaches to optimise cell balancing characteristics. This paper reviews basic to advanced cell balancing techniques and compares their circuit designs, costs, switching stresses, complexity, sizes, and control techniques to highlight the recent trends and future directions. This paper also compares the recent trend of machine learning integration with basic cell balancing topologies and provides a critical analysis of the outcomes. Full article

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems. Full article

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GERT) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions. Full article

Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts in terms of label generation for supervised methods. One way is to employ an inversion network to convert the seismic shot gathers into a velocity model. The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. To further improve the efficiency, we propose a two-stage training strategy for the self-supervised learning FWI. The first stage is to pretrain the inversion network using a simultaneous source for a large-scale velocity model with high efficiency. The second stage is switched to modeling the separate shot gathers for an accurate measurement of the seismic data to invert the velocity model details. The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. The time-domain 2D acoustic wave equation serves as the physical constraint in this self-supervised framework. Different loss functions are used for the two stages, that is, the waveform loss with time weighting for the first stage (simultaneous source) and the hybrid waveform with time weighting and logarithmic envelope loss for the second stage (separate source). Comparative experiments demonstrate that the proposed approach improves both inversion accuracy and efficiency on the Marmousi2 model, Overthrust model, and BP model tests. Moreover, the method exhibits excellent noise resistance and stability when low-frequency data component is missing. Full article

This paper focuses on the analysis of a new modulation method based on the reverse conduction of metal–oxide–semiconductor field-effect transistors (MOSFETs) for a three-phase voltage-feed full-bridge converter with two-switched transistors. The implementation of the proposed method allows efficient converter performance during regenerative braking of a brushless DC machine. It does not require any additional components such as power switches, sensors, and high-performance microcontrollers. Previously known classical modulation methods were characterised by significantly lower efficiency of the converter due to diode conduction. The operating principle of the modified modulation method is clearly explained in detail with mathematical and simulation analyses presented. The theoretical results obtained were verified experimentally, demonstrating that the maximum efficiency of the converter increased from 88% (for classical modulation) to 95% with the new modulation strategy. The developed solution is dedicated to electric vehicles and enables effective regenerative braking even at low speeds. Full article

This paper deals with an analysis of the operation of switched reluctance machines with a non-uniform air gap. The main focus was on the analysis of the performance of machines with a linearly decreasing air gap. Intensive field calculations made it possible to provide their accurate characteristics, which were then used in simulations of various dynamic states. On this basis, the advantages and disadvantages of machines with a non-uniform air gap were finally discussed from the point of view of applications in efficient high-speed drive systems. Full article

The continuous demand for faster processing systems, driven by the rise of artificial intelligence, has exposed limitations in traditional transistor-based electronics, including quantum tunneling, heat dissipation, and switching delays due to challenges in further miniaturization. This study explores optical systems as a promising alternative, leveraging the speed of photons over electrons. Specifically, we design and simulate optical NAND and NOR logic gates using a two-dimensional photonic crystal structure with a square lattice. Symmetrical waveguides are used for the input paths to make the structure relatively more straightforward to fabricate. A key innovation is the ability to realize both gates within a single structure by adjusting the phases of the input sources. To optimize the phase parameters efficiently, we employ the ML-FOLD (Meta-Learning and Formula Optimization for Logic Design) optimization formula, which outperforms traditional methods and machine learning approaches in terms of computational efficiency and data requirements. Through finite-difference time-domain (FDTD) simulations, the proposed optical structure demonstrates successful implementation of NAND and NOR gate logic, achieving high contrast ratios of 4.2 dB and 4.8 dB, respectively. The results validate the effectiveness of the ML-FOLD method in identifying optimal configurations, offering a streamlined approach for the design of all-optical logic devices. Full article

In the rapidly advancing digital era, the demand for miniaturized and high-performance electronic devices is increasing, particularly in applications such as wireless communication, unmanned aerial vehicles, and healthcare devices. Radio-frequency microelectromechanical systems (RF MEMS), particularly RF MEMS switches, play a crucial role in enhancing RF performance by providing low-loss, high-isolation switching and precise signal path control in reconfigurable RF front-end systems. Among different configurations, electrothermally actuated bistable lateral RF MEMS switches are preferred for their energy efficiency, requiring power only during transitions. This paper presents a novel approach to improve the RF performance of such a switch through structural modifications and machine learning (ML)-driven optimization. To enable efficient high-frequency operation, the H-clamp structure was re-engineered into various lateral configurations, among which the I-clamp exhibited superior RF characteristics. The proposed I-clamp switch was optimized using an eXtreme Gradient Boost (XGBoost) ML model to predict optimal design parameters while significantly reducing the computational overhead of conventional EM simulations. Activation functions were employed within the ML model to improve the accuracy of predicting optimal design parameters by capturing complex nonlinear relationships. The proposed methodology reduced design time by 87.7%, with the optimized I-clamp switch achieving −0.8 dB insertion loss and −70 dB isolation at 10 GHz. Full article

Switched Reluctance Machines (SRMs) are gaining increasing traction within the industrial sector, primarily due to their inherently simple and robust structure. Nevertheless, SRMs are characterized by two major drawbacks—high torque ripple and strong radial forces—both of which render them less suitable for applications requiring smooth operation, such as Electric Vehicles (EVs). To address these limitations, researchers and designers focus on optimizing these critical performance metrics during the design phase. In recent years, the concept of System-Level Design Optimization (SLDOM) has been introduced and applied to SRM drive systems, where both the machine and the controller are simultaneously considered within the optimization framework. This integrated approach has shown significant improvements in mitigating the aforementioned issues. This paper aims to review the existing literature concerning the SLDOM applied to SRMs, highlighting the key methodologies and findings from studies conducted in recent years. Despite its promising outcomes, the adoption of SLDOM remains limited due to its high computational cost and complexity. In response to these challenges, the paper discusses complementary techniques used to enhance the optimization process, such as search space and computational time reduction strategies, along with the associated challenges and potential solutions. Finally, two critical directions for future research are identified, which are expected to influence the development of the SLDOM and its application to SRMs in the coming years. Full article

Multispecific antibodies have redefined the immunotherapeutic landscape in hematologic malignancies. Bispecific antibodies (BsAbs), which redirect cytotoxic T cells toward malignant targets via dual antigen engagement, are now established components of treatment for diseases such as acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and multiple myeloma (MM). Clinical trials of agents like blinatumomab, glofitamab, mosunetuzumab, and teclistamab have demonstrated deep and durable responses in heavily pretreated populations. Trispecific antibodies (TsAbs), although still investigational, represent the next generation of immune redirection therapies, incorporating additional tumor antigens or co-stimulatory domains (e.g., CD28, 4-1BB) to mitigate antigen escape and enhance T-cell persistence. This review provides a comprehensive evaluation of BsAbs and TsAbs across hematologic malignancies, detailing molecular designs, mechanisms of action, therapeutic indications, resistance pathways, and toxicity profiles including cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), cytopenias, and infections. We further discuss strategies to mitigate adverse effects and resistance, such as antigen switching, checkpoint blockade combinations, CELMoDs, and construct optimization. Notably, emerging platforms such as tetrafunctional constructs, checkpoint-integrated multispecifics, and protease-cleavable masking designs are expanding the therapeutic index of these agents. Early clinical evidence also supports the feasibility of applying multispecific antibodies to solid tumors. Finally, we highlight the transformative role of artificial intelligence (AI) and machine learning (ML) in multispecific antibody development, including antigen discovery, biomarker-driven treatment selection, toxicity prediction, and therapeutic optimization. Together, BsAbs and TsAbs illustrate the convergence of molecular precision, clinical innovation, and AI-driven personalization, establishing a new paradigm for immune-based therapy across hematologic and potentially solid tumor malignancies. Full article