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Computed tomography (CT) imaging is widely applied to identify tumors and internal injuries in the body. CT image retargeting involves reducing the size of less important areas (like normal organs) either horizontally or vertically, while maintaining the size of key areas (such as affected organs) in the CT image. This retargeting process can significantly improve the way CT images are displayed, which can be advantageous for further educational tasks, e.g., optimally displaying CT images to medical students. This paper focuses on effectively merging multiple channels of perceptual visual features for retargeting CT images with intricate spatial layouts. The key objective is to develop an active learning model that identifies where human attention is directed in a scenery. To capture both semantically and visually important elements in each CT image, we use the BING objectness descriptor, which quickly and accurately localize objects or their parts across multiple scales. Herein, multi-channel low-level features describe each object-aware patch. In an effort to characterize how humans perceive important scenic elements, we propose a locality-preserved and interactive active learning (LIAL) approach. This method sequentially generates gaze shift paths (GSP) for each CT image. Due to the high dimensionality of such GSP feature, a distribution-preserved feature selection (DPFS) is designed to acquire a few highly discriminative GSP features. Last, these refined GSP features are combined by a Gaussian mixture model (GMM) to retarget CT images. Extensive empirical studies demonstrate the power of the designed method.

We propose a framework for multi-age group gait video translation in which, for the first time, individuality-preserving aging patterns in walking style are learnt. More specifically, we build our framework on an existing multi-domain image translation model. Because the existing multi-domain image translation model was originally designed for a still image, we extend it to gait video by introducing a motion-augmented network architecture with three streams, where gait period, period-normalized phase-synchronized gait video, and its frame difference sequence are each input to one stream. We then train the network to ensure three aspects: aging effect (using an age group classification loss), individuality preservation (using a reconstruction loss), and gait realism (using an adversarial loss). Our framework quantitatively and qualitatively outperforms state-of-the-art age progression/regression methods on the largest gait database, OULP-Age, with respect to both age group classification and identity recognition.

Hot spot temperature (HST) is an important parameter reflecting the working state of the traction transformer, and the load characteristics have a great influence on the HST. The current researches rarely consider the load characteristics of transformers in-depth, and most of the research on the temperature field focuses on steady load. Therefore, a prediction model of HST for the traction transformer considering the load characteristics is established by the finite element simulation in this paper. Considering the impact and nonlinearity of traction load, the influence of the load characteristics on the HST is studied. Analysis of the load curve revealed that the steady and step load combination could well simulate the traction load. Based on the analysis of traction transformer load characteristics, the temperature field coupling calculation of traction transformer is carried out according to the VI normative duty class. On this basis, the influence of load rate and time on HST is studied. The present findings show that the method can predict the HST of traction transformer under different working conditions and provide a reference for load dispatching of traction transformer. The simulation data are in good agreement with the test results, and the average relative error is 3.4%. The results verify the effectiveness of the proposed method.

In recent years, methanol has been proposed as a chemical indicator to assess the aging condition of cellulose insulation in oil-immersed power transformers. However, the formation mechanisms of methanol during cellulose degradation are not clear enough. In this paper, such formation mechanisms were studied using molecular dynamics simulation. Three main formation pathways of methanol were found and discussed. Analysis of the Mayer bonds of cellulose revealed that each atom of cellulose contributes differently to methanol formation. The methylol groups on the 5-carbon atom of the 1-pyran ring and 4-pyran ring of cellulose are more readily detached to form methanol molecules. In addition, changes in the amount of methanol molecule with simulation time were investigated. The results indicated that the separate existence of initial moisture and oxygen has no significant effect on the formation of methanol, while the co-existence of initial moisture and initial oxygen catalyzes the conversion of cellulose degradation products to methanol. The findings reported in this paper can provide a valuable theoretical basis for further studies on methanol as an indicator to evaluate the residual life of cellulose insulation.

Methanol is regarded as an available indicator to analyze the aging condition of cellulose insulation. However, the diffusion mechanism of methanol molecules in the paper-oil insulation system is still unclear, since it cannot be explored by using only experiments. Given this issue, the molecular dynamics simulations were performed to investigate the diffusion mechanism of methanol molecules. In the current work, the mean square displacement of methanol during the diffusion process was calculated and analyzed, as well as its trajectory tracking. Then, the research on interaction energies and hydrogen bonds in paper/oil system were conducted. Finally, the effect of temperature on the diffusion of methanol molecules was also studied. Findings revealed that the diffusion of methanol in cellulose insulation is faster than that in insulating oil, besides, the presence of hydrogen bonds affects the diffusion rate of methanol molecules in cellulose insulation and insulating oil. Thus, the present findings could support the investigation of the diffusion mechanisms of methanol molecules, by which the aging condition evaluation of cellulose insulation could be also promoted.

Increasing arc pressure by tens of times with fine pouring tube, a positive shock wave by the pouring arc entering the closed space of the thin tube (electric arc recoil wave). The pressure of electric arc recoil wave is 8 to 23 times than that of the arc tube pouring wave. Since the recoil wave has more pressure than the arc tube pouring wave, and their directions are opposite, the tube poring arc is driven away from the tube channel by the recoil wave. The arc completely emptying and a large scale truncation to the electric arc are formed. Therefore, the truncation arc interrupts the lightning current, and recombusts to form a new tube pouring wave and backwash wave. The arc truncation and recombustion are caused by arc tube pouring and electric arc recoil alternately, and forming the intermittent lightning discharge, a large decrease in the amplitude of lightning current, steepness and discharge time. The results show that the amplitude of lightning current can reduce from 67.14 kA to 35.1 kA, and the lightning current steepness can decrease 77.3%. This attenuation method of lightning current amplitude could significantly reduce the lightning current flowing into the ground, the induced overvoltage and the ohm potential difference. Additionally, the problem of lightning strike step voltage in high soil specific resistivity areas and electromagnetic induced overvoltage can be solved by the method in this paper. The probability of original fault is reduced, and the operation reliability is enhanced. The method can lower the cost of ground network and improve the lightning protection performance.

The quick development of smart grids coupled with IoT devices has opened breaches of security leading to cyberattacks done by attackers with different purposes. In this study, a behavior model is proposed to investigate the risk of cyber attacks on power grids, where the utility value is determined by subjective attack attitude and characteristics of candidate targets firstly, and then the behaviors of attack target selection and attack resource allocation are described by the probability response and utility attenuation model respectively based on data analysis of historical events. The simulation results on RTS79 system indicate that the risk and the vulnerable nodes of the power grid vary with the characteristics of attack behaviors and characteristic attributes of targets, which should be considered in the cyber security defense dynamically.

In recent decades, moisture evaluation of transformer oil-immersed cellulose insulation has been widely studied by using the frequency domain spectroscopy (FDS) technique. However, due to the difficulties in sample preparation, most of the existing evaluation methods are based on a small number of samples, which greatly limits the evaluation accuracy of these methods. To accurately evaluate moisture inside transformer oil-immersed cellulose insulation, a novel method combining the FDS technique and the exponential decay model is proposed. In current work, the exponential decay model is introduced to analyze the FDS data, which can be used to assess the quantitative relationship between moisture and extracted parameters. Then, the FDS curves with different moisture can be predicted by using these parameters to form a curve database. Findings reveal the relative error of evaluation results in lab condition is less than 10.57% when using the proposed curve database. In that respect, the feasibility of the proposed database is proved for moisture evaluation of transformer oil-immersed cellulose insulation.

Frequency domain spectroscopy (FDS) technique is widely applied in the condition assessment of the oil-paper system in power transformers. However, the synergistic effect generated by moisture and temperature on the FDS data cannot be analyzed by the existing model since the single independent variable (moisture or temperature) is considered in the construction of the model. To quantify such the synergistic effect, a novel method that utilized for normalizing (or standardizing) the FDS curve is reported based on the theory of the power series and fitting analysis. The present findings reveal that the reported method is capable of predicting the dielectric loss (tanδ) curve under diverse test conditions, in which the average error is less than 7%. The synergistic effect can be also explored by using the extracted feature parameters. The potential application is then proved to make up for the measurement errors during the FDS test, the findings are expected to promote the moisture analysis of the transformer insulation.

The present “condition-based maintenance decision making” of power transformers will cause large financial losses to electric enterprises, because of not having taken reliability and economy into consideration. To solve this problem, a maintenance decision-making model with the consideration of reliability and economy was established to choose the best maintenance strategy for oil-filled transformers. With the corrected parameters of operating environment and maintenance records, a condition assessment model including DGA test, oil test, and the electrical test was proposed to decide the comprehensive health index of transformers. After establishing the relationship between the fault rate and the comprehensive health index, a reliability evaluation model was formed, which can simulate the impact of different maintenance types. Taking the reliability and economy operation of transformers into consideration, a particle swarm optimization method was developed to solve the optimization model and select the best maintenance strategy according to the current condition of transformers. Two cases were studied and the results demonstrate that the proposed model offers an improved maintenance strategy.

The ReaxFF molecular dynamics simulation and Monte Carlo method were adopted to analyze the pyrolysis process of cellulose in insulating papers from the perspective of microscopic atom. Molecular dynamics failed to continuously describe the motion behavior of an atom. According to this principle, the system can only calculate the atomic state on the node and then move the atom by a time step to continue calculating the atom. This paper would put forward the optimal step setting method of cellulose thermal decomposition in insulating paper: setting one step every other 0.1 fs. Specifically, for small molecules with a simple structure, such as H2O, the step size was set to 0.4 fs or less, while the step size of macromolecules with complex structures (CH2O2) should be set to 0.2 fs or less. In addition, the relationship between the step size and the temperature to which the system was heated was given as well in this paper. In previous literatures, empirical values were used to set the step size. This study would not only provide a theoretical basis for the study on the bond formation and fracture process of cellulose pyrolysis products, but also offer the data and guidance for related fields in the future, thus rendering an efficient simulation process.

The flue gas desulfurization of coal-fired power plants is currently the main mean to control the emission of sulfur dioxide in China. How to select desulfurization technology that meets both technical and economic requirements becomes the issue of this paper. To deal with problems in evaluating indexes with linguistic evaluation information in the optimization of flue gas desulphurization technologies, a new synthetic evaluation model for flue gas desulphurization technologies based on the cloud model and the kernel vector space method was proposed in this paper. The main contribution of this paper is as follows. First, the comprehensive evaluation index system includes technology, economy, and environmental performance. The technology indicators are qualitative index; economic and environmental indicators belong to quantitative index. In this case, the reasonable transformation from qualitative concepts to quantitative indication was accomplished and the cloud model was used to represent the natural language evaluation information. Second, the subjective weights were determined by the analytic hierarchy process and the objective weights were determined by the entropy method so that the comprehensive weights can be obtained by the method of combining the additional principle with both subjective and objective weights' information. Finally, the priority membership of evaluation objects with the kernel vector space theory was calculated to achieve the optimization of flue gas desulphurization technologies. By comparing and analyzing six different representative flue gas desulphurization technologies, it can be proved that the model has advantages of objectivity, simplicity, effectiveness, and implementation simplicity, and can provide a reliable basis for making a decision in the optimization of flue gas desulphurization technologies.

In this paper, a set of dissolved gas analysis (DGA) new feature combinations is selected as input from the mixed DGA feature quantity, and an improved krill herd (IKH) algorithm optimized support vector machine (SVM) transformer fault diagnosis model is established to solve the problem that the single characteristic gas or characteristic gas ratio, which are utilized as the DGA feature quantity cannot fully reflect the transformer fault classification. The following work has been done in this paper: 1) IEC TC 10 fault data and other 117 sets of fault data in China are preprocessed in order to reduce the influence on the diagnosis results causing by the edge data in the fuzzy area; 2) the SVM parameters and 11 features are encoded by a binary code technique; 3) a preferred DGA feature set for fault diagnosis of power transformers is selected by genetic algorithm (GA) and SVM, and; 4) IKH is utilized to optimize the parameters of SVM. Combining with cross-validation principle, a transformer fault diagnosis model based on IKH algorithm to optimize SVM is established. The fault diagnosis results based on the new fault sample show that the proposed DGA feature set to increase the accuracy by 26.78% and 10.83% over the DGA full data and IEC ratios. Moreover, the accuracy of IKHSVM is better than the GASVM, back-propagation neural network (BPNN), and particle swarm optimization optimized support vector machine (PSOSVM), the accuracy rates are 85.71%, 75%, 64.29%, and 71.43%, which proves the validity of the proposed fault diagnosis model.

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency.