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High-voltage cables are the main arteries of urban power supply. Cable accessories are connecting components between different sections of cables or between cables and other electrical equipment. The stress in the cold shrink tube of cable accessories is a key parameter to ensure the stable operation of the power system. This paper attempts to explore a method for measuring the stress in the cold shrink tube of high-voltage cable accessories based on ultrasonic longitudinal wave attenuation. Firstly, a pulse ultrasonic longitudinal wave testing system based on FPGA is designed, where the ultrasonic sensor operates in a single-transmit, single-receive mode with a frequency of 3 MHz, a repetition frequency of 50 Hz, and a data acquisition and transmission frequency of 40 MHz. Then, through experiments and theoretical calculations, the transmission and attenuation characteristics of ultrasonic longitudinal waves in multi-layer elastic media are studied, revealing an exponential relationship between ultrasonic wave attenuation and the thickness of the cold shrink tube. Finally, by establishing a theoretical model of the radial stress of the cold shrink tube, using the thickness of the cold shrink tube as an intermediate variable, an effective measurement of the stress of the cold shrink tube was achieved.

The state detection of power cables is very important to ensure the reliability of the power supply. Traditional sensors are mostly based on electric field detection. The operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. First, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Second, the coil-simulation model is established, and it is determined that the optimal number of turns of the coil is 11,000 turns, the diameter of the enameled copper wire is 0.08 mm, and the equivalent magnetic field noise of the sensor is 0.06 pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed, the sensor is assembled, and the experimental circuit is built for the sensitivity test. The results show that the sensitivity of the magnetic field sensor is 327.6 mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, ease of carry, and high reliability.

Ceramic outdoor insulators play an important role in electrical insulation and mechanical support because of good chemical and thermal stability, which have been widely used in power systems. However, the brittleness and surface discharge of ceramic material greatly limit the application of ceramic insulators. From the perspective of sintering technology, flash sintering technology is used to improve the performance of ceramic insulators. In this paper, the simulation model of producing the ceramic insulator by the flash sintering technology was set up. Material Studio was used to study the influence of electric field intensity and temperature on the alumina unit cell. COMSOL was used to study the influence of electric field intensity and current density on sintering speed, density and grain size. Obtained results showed that under high temperature and high voltage, the volume of the unit cell becomes smaller and the atoms are arranged more closely. The increase of current density can result in higher ceramic density and larger grain size. With the electric field intensity increasing, incubation time shows a decreasing tendency and energy consumption is reduced. Ceramic insulators with a higher uniform structure and a smaller grain size can show better dielectric performance and higher flashover voltage.

Recurrent Neural Networks (RNNs) are classical models for processing sequential data, demonstrating excellent performance in tasks such as natural language processing and time series prediction. However, during the training of RNNs, the issues of vanishing and exploding gradients often arise, significantly impacting the model’s performance and efficiency. In this paper, we investigate why RNNs are more prone to gradient problems compared to other common sequential networks. To address this issue and enhance network performance, we propose a method for gradient normalization of network weights. This method suppresses the occurrence of gradient problems by altering the statistical properties of RNN weights, thereby improving training effectiveness. Additionally, we analyze the impact of weight gradient normalization on the probability-distribution characteristics of model weights and validate the sensitivity of this method to hyperparameters such as learning rate. The experimental results demonstrate that gradient normalization enhances the stability of model training and reduces the frequency of gradient issues. On the Penn Treebank dataset, this method achieves a perplexity level of 110.89, representing an 11.48% improvement over conventional gradient descent methods. For prediction lengths of 24 and 96 on the ETTm1 dataset, Mean Absolute Error (MAE) values of 0.778 and 0.592 are attained, respectively, resulting in 3.00% and 6.77% improvement over conventional gradient descent methods. Moreover, selected subsets of the UCR dataset show an increase in accuracy ranging from 0.4% to 6.0%. The gradient normalization method enhances the ability of RNNs to learn from sequential and causal data, thereby holding significant implications for optimizing the training effectiveness of RNN-based models.

Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge (PD) characteristics under such pressure. This work presents the construction of a test platform for electrical trees under varying pressures to observe their growth process. A high-frequency current transformer is used to measure PD patterns during tree growth, enabling analysis of the effect of PD on tree initiation and propagation under pressure. The experimental results demonstrate a significant decrease in tree inception probability and increase in PD inception voltage under pressure. The pressure also influences the tree structure and PD during the treeing process, where the longest tree with a branch-like structure appears under 800 kPa. The effect of pressure on electrical tree and PD characteristics can be attributed to changes in free volume, alterations in air pressure within the tree channels, and the affected charge accumulation.

Due to their compact structure and low laying cost, three-core power cables are widely used for power distribution networks. The three-phases of such cables are distributed symmetrically with a 120° shift to each other. Phase current is an important parameter to reflect the operation state of the power system and three-core cable. Three-core symmetrical power cables use a common shield, leading to magnetic field cancelation outside the cable during steady operation. Thus, traditional magnetic-based current transformers cannot measure the phase current on three-core cable non-invasively. In order to measure the phase current more conveniently, a phase current measurement method for three-core cables based on a magnetic sensor is proposed in this paper. Nonlinear equations of a phase current and the magnetic field of a measuring point are constructed. The calculated magnetic field distribution of the three-core cable is verified using a finite element simulation. The effectiveness of the measurement method is further validated through experiments. This proposed method is able to conveniently detect the phase current of three-core power cables, which can help cable maintenance.

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

Background Our study aimed to investigate the association between social isolation and chest pain, and to evaluate the relationship between social isolation and all‐cause mortality stratified by the presence or absence of chest pain among participants. Methods This study incorporated data from the 2001–2018 National Health and Nutrition Examination Survey (NHANES) from the whole of the United States, adopting a hybrid design integrating cross‐sectional and longitudinal cohort methodologies. Weighted logistic regression modeling was employed to examine the association between social isolation and chest pain. Additionally, a weighted Cox proportional hazards model was utilized to estimate hazard ratio (HR) and 95% confidence interval (CI) for all‐cause mortality. Results Analysis revealed a notable association between social isolation scores and chest pain. Following covariate adjustment, individuals with social isolation scores of 3 or 4 exhibited a 52% higher likelihood of reporting chest pain compared to the reference category. Compared with the socially integrated group, the socially isolated group had an approximately 31% increased risk of chest pain. Multivariate Cox regression analysis showed that, after adjusting for all covariates, the risk‐adjusted ratio (HR) for death was 2.17 for participants with social isolation scores of 3 to 4 and 2.06 for those with chest pain. Compared with the socially integrated group, the risk‐adjusted ratio for death in the socially isolated group was 1.65 among all participants and 1.48 among those with chest pain and 2.43 in the group without chest pain. High social isolation was associated with an increased risk of death, regardless of the presence of chest pain. Conclusions Our findings demonstrate a significant association between social isolation and increased prevalence of chest pain, identifying specific high‐risk subgroups. Importantly, social isolation was linked to elevated all‐cause mortality in both individuals with and without chest pain, highlighting its independent prognostic significance across these clinical subgroups.

The Unrelated Parallel Machines Scheduling Problem (UPMSP) with sequence-dependent setup times has been widely applied to cloud computing, edge computing and so on. When the setup times are ignored, UPMSP will be a NP problem. Moreover, when considering the sequence related setup times, UPMSP is difficult to solve, and this situation will be more serious in the case of high-dimensional. This work firstly select the maximum completion time as the optimization objective, which establishes a mathematical model of UPMSP with sequence-dependent setup times. In addition, an improved firefly algorithm with courtship learning is proposed. Finally, in order to provide an approximate solution in an acceptable time, the proposed algorithm is applied to solve the UPMSP with sequence-dependent setup times. The experimental results show that the proposed algorithm has competitive performance when dealing with UPMSP with sequence-dependent setup times.

Cloud computing is an efficient technology to serve the requirement of big data applications. Minimizing the makespan of the cloud system while increasing resource utilization is important to reduce costs. In this case, task scheduling is a challenging task to meet the requirement because it requires both effectiveness and efficiency. This article proposes a task scheduler with several discrete variants of the particle swarm optimization (PSO) algorithm for task scheduling in cloud computing. In order to evaluate the performance, these approaches were compared with three well-known heuristic algorithms on task scheduling problems. Experiment results demonstrate the efficiency and effectiveness of the proposed approaches. For the proposed PSO-based scheduler, an appropriate choice is to use the logarithm decreasing strategy to provide an optimal scheduling scheme. The average makespan of the proposed PSO-based scheduler that adopts logarithm decreasing strategy is reduced by 19.12%, 21.42% and 15.14% relative to the compared gravitational search algorithm, artificial bee colony algorithm and dragonfly algorithm respectively.