Model and Data Agreement for Learning with Noisy Labels

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.Comment: Accepted by AAAI2023 Worksho

Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning

Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity

Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway

Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan

A DFT Calculation of Fluoride-Doped TiO2 Nanotubes for Detecting SF6 Decomposition Components

Gas insulated switchgear (GIS) plays an important role in the transmission and distribution of electric energy. Detecting and analyzing the decomposed components of SF6 is one of the important methods to realize the on-line monitoring of GIS equipment. In this paper, considering the performance limits of intrinsic TiO2 nanotube gas sensor, the adsorption process of H2S, SO2, SOF2 and SO2F2 on fluoride-doped TiO2 crystal plane was simulated by the first-principle method. The adsorption mechanism of these SF6 decomposition components on fluorine-doped TiO2 crystal plane was analyzed from a micro perspective. Calculation results indicate that the order of adsorption effect of four SF6 decomposition components on fluoride-doped TiO2 crystal plane is H2S > SO2 > SOF2 > SO2F2. Compared with the adsorption results of intrinsic anatase TiO2 (101) perfect crystal plane, fluorine doping can obviously enhance the adsorption ability of TiO2 (101) crystal plane. Fluorine-doped TiO2 can effectively distinguish and detect the SF6 decomposition components based on theoretical analysis

Investigation of Gas-Sensing Property of Acid-Deposited Polyaniline Thin-Film Sensors for Detecting H2S and SO2

Latent insulation defects introduced in manufacturing process of gas-insulated switchgears can lead to partial discharge during long-time operation, even to insulation fault if partial discharge develops further. Monitoring of decomposed components of SF6, insulating medium of gas-insulated switchgear, is a feasible method of early-warning to avoid the occurrence of sudden fault. Polyaniline thin-film with protonic acid deposited possesses wide application prospects in the gas-sensing field. Polyaniline thin-film sensors with only sulfosalicylic acid deposited and with both hydrochloric acid and sulfosalicylic acid deposited were prepared by chemical oxidative polymerization method. Gas-sensing experiment was carried out to test properties of new sensors when exposed to H2S and SO2, two decomposed products of SF6 under discharge. The gas-sensing properties of these two sensors were compared with that of a hydrochloric acid deposited sensor. Results show that the hydrochloric acid and sulfosalicylic acid deposited polyaniline thin-film sensor shows the most outstanding sensitivity and selectivity to H2S and SO2 when concentration of gases range from 10 to 100 μL/L, with sensitivity changing linearly with concentration of gases. The sensor also possesses excellent long-time and thermal stability. This research lays the foundation for preparing practical gas-sensing devices to detect H2S and SO2 in gas-insulated switchgears at room temperature

Fluctuating Demand-Oriented Optimization of Train Line Planning Considering Carriage Resources Transfer under Flexible Compositions

The intercity railway is subject to variation and fluctuation in demand both in time and space over a day to a large extent. In that case, more advanced line planning techniques are practically needed to match the non-equilibrium passenger demand. We propose an integer linear programming model for adapting to the fluctuating demand and improving rail line profit, in which the multi-period planning approach and flexible train composition mode are taken into consideration. In particular, we also consider the limitations of the carriage and the dynamic transfer of resources during a finite period to ensure the better implementation of the line planning and subsequent operation plans. Our purpose is to improve on previous line planning models by integrating the multi-period strategic-level line planning decision with resource constraints. Since the problem is computationally intractable for realistic size instances, an improved round heuristic algorithm that is based on linear relaxation is proposed and we compare the round heuristic performance with the commercial solver Gurobi on artificial instances. The numerical experiments that are based on an intercity railway in China certify the effectiveness and applicability of the proposed model and algorithm. We evaluate the impact of different optimization parameters and reserved carriages and the computation results show that in comparison to the fixed composition mode, the proposed approach can improve the utilization efficiency of carriage resources and increase the line profit by 1.9% under the same carriage resource conditions

The First Complete Mitochondrial Genome of the Flathead Cociella crocodilus (Scorpaeniformes: Platycephalidae) and the Phylogenetic Relationships within Scorpaeniformes Based on Whole Mitogenomes

Complete mitochondrial genomes (mitogenomes) are important molecular markers for understanding the phylogenetics of various species. Although recent studies on the mitogenomes of the Scorpaeniformes species have been greatly advanced, information regarding molecular studies and the taxonomic localization of Platycephalidae is still sparse. To further analyze the phylogeny of Platycephalidae, we sequenced the complete mitogenome of Cociella crocodilus of the Platycephalidae family within Scorpaeniformes for the first time. The mitogenome was 17,314 bp in length, contained two ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), 13 protein-coding genes (PCGs), and two typical noncoding control regions (the control region (CR) and origin of the light strand (OL)). All PCGs used standard initiation codons ATG, apart from cox1. The majority of the tRNA genes could be folded into cloverleaf secondary structures, whereas the secondary structure of tRNASer (AGN) lacked a dihydrouridine (DHU) stem. The CR contained several conserved sequence blocks (CSBs) and eight tandem repeats. In addition, the phylogenetic relationship based on the concatenated nucleotides sequences of 13 PCGs indicated that the Platycephalidae species are relatively basal in the phylogenetic relationships of Scorpaeniformes. Our results may not only advance the origin and the evolution of Scorpaeniformes, but also provide information for the genetic evolution and taxonomy of the teleostean species

Fluctuating Demand-Oriented Optimization of Train Line Planning Considering Carriage Resources Transfer under Flexible Compositions

The intercity railway is subject to variation and fluctuation in demand both in time and space over a day to a large extent. In that case, more advanced line planning techniques are practically needed to match the non-equilibrium passenger demand. We propose an integer linear programming model for adapting to the fluctuating demand and improving rail line profit, in which the multi-period planning approach and flexible train composition mode are taken into consideration. In particular, we also consider the limitations of the carriage and the dynamic transfer of resources during a finite period to ensure the better implementation of the line planning and subsequent operation plans. Our purpose is to improve on previous line planning models by integrating the multi-period strategic-level line planning decision with resource constraints. Since the problem is computationally intractable for realistic size instances, an improved round heuristic algorithm that is based on linear relaxation is proposed and we compare the round heuristic performance with the commercial solver Gurobi on artificial instances. The numerical experiments that are based on an intercity railway in China certify the effectiveness and applicability of the proposed model and algorithm. We evaluate the impact of different optimization parameters and reserved carriages and the computation results show that in comparison to the fixed composition mode, the proposed approach can improve the utilization efficiency of carriage resources and increase the line profit by 1.9% under the same carriage resource conditions

Research on Current Distribution Strategy Based on Interleaved Double Boost Converter

In the new energy DC microgrid system, most of the new energy outputs DC power with a low voltage level and a large fluctuation range, which cannot be directly connected to the network. It needs to be boosted by a DC–DC converter, then connected to the power grid or supplied with a DC load. On the premise that the traditional DC–DC converter cannot meet the requirements of high-power applications, the interleaved dual boost converter (IDBC) has been widely used because of its low input current ripple, low device stress and high-power density. It is necessary to maintain the current balance of each phase of the converter during a heavy load and to improve the efficiency during a light load. This paper analyzes the working principle of the six-phase IDBC and reduces the high order model to the low order model according to the symmetry. Due to the current imbalance caused by the mismatch of the parasitic parameters of each phase, two current distribution strategies are designed for different operating. To balance the current of each phase when the converter is overloaded, the relationship between the phase current, parasitic parameters and duty cycle is analyzed based on the state space average method. The estimated parasitic parameters are used to obtain the duty cycle compensation of each phase to eliminate the current imbalance. In addition, to improve the overall efficiency of the converter when the converter connects with a light load, the optimal power distribution coefficient is obtained according to the parasitic parameters to achieve the optimization of the input power, so as to improve the efficiency of the converter. Finally, the simulation results verify the feasibility and effectiveness of the proposed control strategy