Inconsistency Problem Research Articles

A non-zero-sum investment and reinsurance game between two mean–variance insurers with dynamic CVaR constraints

This paper is devoted to investigating a non-zero-sum game between two competing insurers. The insurers can diversify their insurance risks by purchasing proportional reinsurance and investing their collected premiums into a financial market composed of one risk-free asset and one stock. The reinsurance premiums charged by the reinsurer follow the generalized mean–variance premium principle. Moreover, the dynamic CVaR constraints are incorporated in the game problem to control risks. With the dynamic mean–variance objective, we introduce two forward deterministic auxiliary processes to represent the expectations of the insurers’ wealth processes and transform the original time inconsistent game problem into a standard time consistent game problem with two state variables for each insurer. By adopting the dynamic programming principle and the Lagrange duality method, we derive the Nash equilibrium investment–reinsurance strategies for the two insurers. Finally, the effects of several important model parameters on the optimal policies are analyzed by numerical examples.

Bearing fault diagnosis based on spatial filtering constraint feature extraction and deep network

As the core component of a gas turbine, the health condition of the main bearing has a crucial impact on the safe operation of the gas turbine. However, the distribution inconsistency problem existing in the time series data and the nonlinear coupling effect between the data will affect the extraction of characteristic fault features, leading to a decrease in diagnostic accuracy. To solve the above problems, this paper proposes a bearing fault diagnosis method based on frequency domain distribution filter and deep learning. Specifically, a sinusoidal Mel filter bank is designed to extract the fault features of low-frequency vibration signals based on the distribution principle of the traditional Mel filter bank. Then, considering that the fault information contained in other frequency bands of the vibration signals is also not easy to ignore, an inverse sinusoidal Mel filter bank is constructed to further mine the signature fault features of the vibration signals in the middle and high-frequency bands. Finally, the frequency domain distribution filter is proposed by combining the above two filters to reduce the impact of data distribution inconsistency on the accuracy of fault diagnosis. In addition, a deformable convolutional network is applied to further decouple the fault data and improve the spatial separability of the data features. Experiments with inconsistent data distribution are validated on two public datasets, and the diagnostic accuracy reaches 100%; the engineering reliability of the proposed method is verified on the main bearing of a gas turbine, and the accuracy reaches 99.86%.

Life cycle management of the ventilation system of a construction object

Introduction. In the article, the author considers a mechanical ventilation system as a complex technical system that can be effectively controlled based on a model of its life cycle. Like any engineering system (or product, product, project), a ventilation system has its own life cycle, consisting of a set of successive stages. Each stage is characterized by its own types of work and final results that require management decisions.Materials and Methods. The paper uses the method of analyzing the life cycle of complex technical systems, methods of collecting and processing statistical data, as well as methods of systematic and comparative analysis, generalization of scientific and practical results.Results. In the course of the conducted research, the author applied the "life cycle management" approach to ventilation systems and achieved the goal of developing a model of their life cycle, which includes all stages of system development from conception to disposal. Possible ways of managing the life cycle of ventilation systems from the perspective of continuous interconnection of processes are determined.Discussion and Conclusion. Effective life cycle management of a mechanical ventilation system can be implemented through the development of a software product capable of modeling processes and system elements even at the first stages of the life cycle. The software product should eliminate the existing problem of inconsistencies at different stages of work, store information about the object and provide access to it for each participant in the process. Solving the problem of creating a software product will ensure effective management of the entire life cycle of the ventilation system, reduce labor costs and inconsistencies and ensure compliance with modern requirements for operational reliability and energy efficiency of a vital engineering system.

A Data-Driven Method for Constructing the Spatial Database of Traditional Villages—A Case Study of Courtyard Residential Typologies in Yunnan, China

A digital civilization rising on silicon-based technology is predicted to replace carbon-based spaces with virtual ones, leading to the decline of rural regionalism. Reconciling the benefits of globalization through data-driven methods while preserving the locality of rural areas is crucial. The extensive collection of traditional villages in the Yunnan Province possesses abundant natural heritages and diverse regional cultural resources. However, the existing assessment and identification index system for these traditional villages primarily relies on qualitative measures, which lack a consistent and quantifiable data system that can provide scientific, data-driven analysis. This study focuses on the mainstream types of traditional villages in the Yunnan Province as its research subject. Based on empirical research methods and architectural typology principles, two data acquisition and vectorization procedures are implemented, combining tilt photography and digital mapping technology. This approach addresses the inconsistency problem of the current system’s complex information and multiple data. This study aims to establish an efficient, objective, and consistent method of generating a database that comprehensively represents the environmental and architectural characteristics of the villages. Once established, this database could serve as a consistent and objective basis for subsequent quantitative analyses across different types of villages. Hence, setting up a consistent, quantifiable database is the first step to future data-driven policymaking, which is essential for sustainable preservation. In conclusion, the repeatability and reproducibility of these research findings are intrinsically tied to the meticulous methodology employed in the process of data acquisition and vectorization. The valid result was demonstrated after the verifying of the correlation analysis. Finally, a challenge to the universality came from adding different worldwide villages to the database and revealing underlying patterns.

Dynamic Secure Key Distribution Based on Dispersion Equalization and Cellular Automata for Optical Transmission

This paper proposes a dynamic secure key distribution scheme based on dispersion equalization and cellular automata (CAs). The scheme effectively eliminates the key inconsistency problem caused by imperfect channel reciprocity, and dynamic key sequences can be conveniently generated with large key space in long-haul optical transmission. In the process of communication, the legitimate parties obtain the secure core parameter from the frequency domain equalizer algorithm, and a final key sequence is generated through CA iterations on the basis of the core parameter. The randomness and reciprocity characteristics of the channel ensure the security and uniqueness of the core parameter and final key sequence. With 10G Baud 16 quadrature amplitude modulation over 400 km standard single-mode fiber transmission, the proposed scheme is verified with a free key error rate and an unlimited key generation rate. The security robustness of this scheme was theoretically analyzed and verified by sweeping the eavesdropper’s tapping position and improving CA operation processing. The proposed key distribution scheme is compatible with the existing transmission system for different signal modulation formats.

Transfer Learning for Indoor Localization Algorithm Based on Deep Domain Adaptation

In application, training data and test data collected via indoor positioning algorithms usually do not come from the same ideal conditions. Changes in various environmental conditions and signal drift can cause different probability distributions between the data sets. Existing positioning algorithms cannot guarantee stable accuracy when facing these issues, resulting in dramatic reduction and the infeasibility of the positioning accuracy of indoor location algorithms. Considering these restrictions, domain adaptation technology in transfer learning has proven to be a promising solution in past research in terms of solving the inconsistent probability distribution problems. However, most localization algorithms based on transfer learning do not perform well because they only learn a shallow representation feature, which can only slightly reduce the domain discrepancy. Based on the deep network and its strong feature extraction ability, it can learn more transferable features for domain adaptation and achieve better domain adaptation effects. A Deep Joint Mean Distribution Adaptation Network (DJMDAN) is proposed to align the global domain and relevant subdomain distributions of activations in multiple domain-specific layers across domains to achieve domain adaptation. The test results demonstrate that the performance of the proposed method outperforms the comparison algorithm in indoor positioning applications.

Regularized Hypothesis-Induced Wasserstein Divergence for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to align the data distributions of source and target domains to learn domain-invariant feature representations. The performance of such representations heavily relies on the measurement of the discrepancy between the distributions. Recent UDA methods utilize hypothesis-based measurements, also known as classifier-induced divergences, to quantify the differences between the two domain distributions, for instance, HΔH-divergence. Nevertheless, classifier-induced divergences face several challenges, including the fake covariate alignment problem and the inconsistency problem, rendering them inadequate for discerning dissimilarities between different categories of output by the classifier. This paper proposes a novel method called Regularized Hypothesis-Induced Wasserstein Divergence-Based Representation Learning for Unsupervised Domain Adaptation (RHWD) to address these issues. Specifically, RHWD introduces a new hypothesis-induced Wasserstein divergence (HWD) to effectively quantify the differences between the two distributions, aiming to overcome the fake covariate alignment problem. Furthermore, RHWD includes a consistency regularization term to mitigate the inconsistency problem. We train RHWD adversarially in three iterative steps. Additionally, we provide empirical evaluations on standard benchmarks, demonstrating the effectiveness of our proposed RHWD.

Study of stress-strain relationship of hydraulic asphalt concrete at low to intermediate temperatures based on experimental data regression

The conventional principal structural theory modeling is constrained by the underlying assumptions that make the established constitutional model incapable of accurately describing the stress–strain relationship of asphalt concrete. Subsequently, in this study, the traditional modeling idea was changed and the projection pursuit regression (PPR) method was used to analyze triaxial test data obtained from asphalt concrete. The PPR models utilized to calculate deviatoric stress and volumetric strain were developed and the validity and applicability of the PPR model and this method were verified. The results show that the modeling method avoids the problem of inconsistency between a priori model assumptions and objective reality. Asphalt concrete has complex strain softening, strain hardening, shear shrinkage, and shear expansion characteristics. The model employed to calculate deviatoric stress and volumetric strain based on PPR method is superior to the E–B and “Nanshui” models in its capacity to reflect the characteristics of strain softening and shear deformation. The results calculated using the PPR model can accurately describe the stress–strain relationship of asphalt concrete. Studying the stress–strain relationship of asphalt concrete from the perspective of the examination of data breaks through the constraints of traditional modeling ideas, which has certain theoretical significance. The research results can provide a new solution for non-hypothesis modeling of nonlinear data and accurately describing the stress–strain relationship of asphalt concrete.

Possibilistic distribution distance metric: a robust domain adaptation learning method.

The affective Brain-Computer Interface (aBCI) systems, which achieve predictions for individual subjects through training on multiple subjects, often cannot achieve satisfactory results due to the differences in Electroencephalogram (EEG) patterns between subjects. One tried to use Subject-specific classifiers, but there was a lack of sufficient labeled data. To solve this problem, Domain Adaptation (DA) has recently received widespread attention in the field of EEG-based emotion recognition. Domain adaptation (DA) learning aims to solve the problem of inconsistent distributions between training and test datasets and has received extensive attention. Most existing methods use Maximum Mean Discrepancy (MMD) or its variants to minimize the problem of domain distribution inconsistency. However, noisy data in the domain can lead to significant drift in domain means, which can affect the adaptability performance of learning methods based on MMD and its variants to some extent. Therefore, we propose a robust domain adaptation learning method with possibilistic distribution distance measure. Firstly, the traditional MMD criterion is transformed into a novel possibilistic clustering model to weaken the influence of noisy data, thereby constructing a robust possibilistic distribution distance metric (P-DDM) criterion. Then the robust effectiveness of domain distribution alignment is further improved by a fuzzy entropy regularization term. The proposed P-DDM is in theory proved which be an upper bound of the traditional distribution distance measure method MMD criterion under certain conditions. Therefore, minimizing P-DDM can effectively optimize the MMD objective. Secondly, based on the P-DDM criterion, a robust domain adaptation classifier based on P-DDM (C-PDDM) is proposed, which adopts the Laplacian matrix to preserve the geometric consistency of instances in the source domain and target domain for improving the label propagation performance. At the same time, by maximizing the use of source domain discriminative information to minimize domain discrimination error, the generalization performance of the learning model is further improved. Finally, a large number of experiments and analyses on multiple EEG datasets (i.e., SEED and SEED-IV) show that the proposed method has superior or comparable robustness performance (i.e., has increased by around 10%) in most cases.

Finiteness of N=4 Super-Yang–Mills Effective Action in Terms of Dressed N=1 Superfields

We argue in favor of the independence on any scale, ultraviolet or infrared, in kernels of the effective action expressed in terms of dressed N=1 superfields for the case of N=4 super-Yang–Mills theory. Under “scale independence” of the effective action of dressed mean superfields, we mean its “finiteness in the off-shell limit of removing all the regularizations”. This off-shell limit is scale independent because no scale remains inside these kernels after removing the regularizations. We use two types of regularization: regularization by dimensional reduction and regularization by higher derivatives in its supersymmetric form. Based on the Slavnov–Taylor identity, we show that dressed fields of matter and of vector multiplets can be introduced to express the effective action in terms of them. Kernels of the effective action expressed in terms of such dressed effective fields do not depend on the ultraviolet scale. In the case of dimensional reduction, by using the developed technique, we show how the problem of inconsistency of the dimensional reduction can be solved. Using Piguet and Sibold formalism, we indicate that the dependence on the infrared scale disappears off shell in both the regularizations.

A structural consensus representation learning framework for multi-view clustering

Learning a structural consensus representation is crucial for various multi-view tasks, such as multi-view clustering, multi-view classification, etc. However, this task is challenging due to the inconsistent structure problem caused by the view-level bias of multi-view data. To address this issue, we propose a structural consensus representation learning (SCRL) framework, which contains two cascading representation training processes, to learn and refine structural consensus representation. A Consensual Joint Multi-AutoEncoder is developed which estimates a consensual structure shared among all views and learns each view representation guided by the consensual structure in a unique process. By applying EM (Expectation–Maximization)-style optimization, the view representations and the consensual structure are optimized iteratively. Then, we devise a Hybrid Contrastive Refining Net, which contains two contrastive refining components to fine-tune the learnt representations by further eliminating inconsistent view representations within the same view and from different views. The proposed SCRL framework is able to debias the learning of view representations and provide structural consensus representations for multi-view clustering. Extensive experiments and analysis on several real datasets show the effectiveness of our proposed SCRL framework.

A New Centralized Equalizer with a Simpler Control Strategy for Series-Connected Batteries

The centralized equalizer has a good prospect in the mobile battery system due to its small size and light weight. However, equalizers with a centralized structure generally need a tricky balancing strategy and have an improbable balancing performance. This paper proposes an equalizer with multi-modes. It has a more straightforward balancing strategy than conventional equalizers with a centralized structure. This allows it to deal with the problem of inconsistency in a battery string more effectively without repeated actions of the switch matrix. Moreover, the merits of conventional equalizers with a centralized structure, such as being low-cost and compactness, remain in the proposed scheme. The operating principle, balancing strategy, and balancing efficiency are analyzed in detail. An experimental prototype for a battery string containing four cells is implemented after simulations. Experimental results validate that the proposed equalizer can balance a battery string with an efficiency of 81.7%.

Temporary Asylum: Problems of Public Administration, Statistics and Administrative Legal Regulation

The author examines the current issues of the institution of temporary asylum in the context of the current administrative and legal regulation and public administration practice. The article pays attention to the problems of implementing the institution of temporary asylum known in the literature, associated with the complex administrative procedure for granting temporary asylum status and the long period of consideration of an application for status, as well as the falling demand for refugee and temporary asylum institutions, reflected in statistical data. The object of the study is the public relations arising in the field of forced migration. The subject of the study is the public relations that arise regarding the implementation of public administration in the process of granting temporary asylum status. The purpose of this article is to develop proposals for reforming the administrative and legal regulation of the institution of temporary asylum in the Russian Federation in the system of public management of forced migration in Russia. The scientific novelty of this article consists in conducting a qualitative analysis of the provision of refugee status and temporary asylum based on statistical data from government agencies and other sources in the field of forced migration in Russia. Particular attention is paid to forced migrants from Ukraine in the context of existing problems. A systemic problem of inconsistency between statistical data on the granting of refugee status, temporary asylum and citizenship of the Russian Federation and the actual socio-political situation has been identified. In conclusion, proposals for modernizing the current legislation in the field of forced migration are formulated, including the vesting of statistical powers in the field of forced migration of the Russian Ministry of Internal Affairs, as well as the introduction into administrative and legal practice of a new legal status - “temporary protection”

Task-oriented contrastive learning for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, thereby improving the classification performance of the target domain. Recent methods use contrastive learning to optimize this task, however, these methods only focus on contrastive learning for aspects of domain alignment, which do not actively serve the classification task, resulting in suboptimal solution. To this end, we propose Task-oriented Contrastive Learning for Unsupervised Domain Adaptation (TOCL), which addresses the inconsistency problem of optimizing contrastive learning and classification tasks. We perform feature weighting on the source domain images and extract the task-related features containing semantic information after data augmentation as the source domain positive and negative sample pairs for contrastive learning. At the same time, a delimitation discriminator is introduced to maximize the output difference between two types of random data-augmented samples of the target domain to detect data-augmented samples that are not conducive to classification, and then minimize the difference through the feature generator to generate effective data-augmented features (good for classification). Our extensive experiments on several public datasets show that TOCL has good adaptability and effectiveness, which improves the accuracy of classification results.

Pipelined Stochastic Gradient Descent with Taylor Expansion

Stochastic gradient descent (SGD) is an optimization method typically used in deep learning to train deep neural network (DNN) models. In recent studies for DNN training, pipeline parallelism, a type of model parallelism, is proposed to accelerate SGD training. However, since SGD is inherently sequential, naively implemented pipeline parallelism introduces the weight inconsistency and the delayed gradient problems, resulting in reduced training efficiency. In this study, we propose a novel method called TaylorPipe to alleviate these problems. The proposed method generates multiple model replicas to solve the weight inconsistency problem, and adopts a Taylor expansion-based gradient prediction algorithm to mitigate the delayed gradient problem. We verified the efficiency of the proposed method using the VGG-16 and the ResNet-34 on the CIFAR-10 and CIFAR-100 datasets. The experimental results show that not only the training time is reduced by up to 2.7 times but also the accuracy of TaylorPipe is comparable with that of SGD.

Credible Gaussian sum cubature Kalman filter based on non-Gaussian characteristic analysis

Toward the pose estimation problem of unmanned aerial vehicles (UAVs) flight over water, a credible Gaussian-sum cubature Kalman filter (CGSCKF) based on non-Gaussian characteristic analysis for nonlinear non-Gaussian system is proposed. In the framework of the proposed scheme, a data characteristic analysis based on multi-peak distribution degree, skewness and kurtosis is applied to evaluate the non-Gaussian intensity of noise, and a Gaussian mixture reduction (GMR) method based on the improved monarch butterfly optimization (IMBO) is proposed to improve the filtering accuracy in the case of weak non-Gaussian noise (Gaussian-like noise). In addition, considering the problem of inconsistency between the practical and the theoretic system model, the process noise and measurement noise are estimated online, and the credibility of filtering result, which is obtained by Gaussian-sum cubature Kalman filter (GSCKF), is measured by comparing the filtering error covariance with the true error covariance. Some comparison simulations and experiments with classical algorithms are provided, justifying that the proposed scheme has better performance in the pose estimation of UAV.

A Symmetric Ring Galaxy Model Explains the Rotation Curve of Galaxies

This paper proposes a symmetric ring galaxy (SRG) model based on the general structure of galaxies. Based on this model, we have obtained results that are consistent with the actual observation results of the galaxy rotation curve, thus solving the problem of inconsistency between the observation results of the galaxy rotation curve and the expected results, which has long plagued the astronomical community. This article also analyzes the root causes of the problem of galaxy rotation curves, and finds that the main reason is that people use unrealistic galaxy models and incorrect calculation methods. Therefore, the dark matter hypothesis and gravitational correction theory that explain the problem of galaxy rotation curves have lost their theoretical basis, and the correctness of these theories undoubtedly faces serious challenges and profound doubts.

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

AbstractThe vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

Research on equalization scheme of lithium-ion battery packs based on consistency control strategy

In order to address the inconsistency problem of series-connected lithium-ion battery groups in practice, a two-level balanced topology based on bidirectional Sepic-Zeta circuit is designed in this article. Two-level equalization topology uses bidirectional Sepic-Zeta circuits both within and between groups, which can achieve the equilibrium between any cells in a battery group and between any battery groups. With the state of charge (SOC) of the battery as the equalization variable, and the equalization control strategy is designed based on the consistency controller and PI controller to achieve fast and efficient equalization by dynamic adjustment of equalization current, and to efficiently decrease the inconsistency of the equalized battery packs. The experiments are conducted by Matlab/Simulink platform. According to the experimental results it can be obtained that compared with the topology based on single Cuk equalizer, the topology based on single Sepic-Zeta equalizer and the adjacent topology based on Sepic-Zeta equalizer, the proposed topology shortens the equilibrium time by about 51.57 %, 51.43 % and 42.24 %, respectively. Under the same operating conditions, the equalization control strategy designed in this article shortens the equilibrium time by about 29.4 % and 11.19 % compared to the conventional fixed-threshold method and the Fuzzy Logic Control (FLC) algorithm, respectively, and also achieves better results in the equalization efficiency and consistency problem, which effectively verifies the feasibility of the equalization scheme in this article.

Joint domain symmetry and predictive balance for cross-dataset EEG emotion recognition

Background:Cross-dataset EEG emotion recognition is an extremely challenging task, since data distributions of EEG from different datasets are greatly different, which makes the universal models yield unsatisfactory results. Although there are many methods have been proposed to reduce cross-dataset distribution discrepancies, they still neglected the following two problems. (1) Label space inconsistency: emotional label spaces of subjects from different datasets are different; (2) Uncertainty propagation: the uncertainty of misclassified emotion samples will propagate between datasets. New Method:To solve these problems, we propose a novel method called domain symmetry and predictive balance (DSPB). For the problem of label space inconsistency, a domain symmetry module is designed to make label spaces of source and target domain to be the same, which randomly selects samples from the source domain and put into the target domain. For the problem of uncertainty propagation, a predictive balance module is proposed to reduce the prediction score of incorrect samples and then effectively reduce distribution differences between EEG from different datasets. Results:Experimental results show that our method achieve 61.48% average accuracies on the three cross-dataset tasks. Moreover, we find that gamma is the most relevant to emotion recognition among the five frequency bands, and the prefrontal and temporal brain regions are the channels carrying the most emotional information among the 62 brain channels. Comparison with existing methods:Compared with the partial domain adaptation method (SPDA) and the unsupervised domain adaptation (MS-MDA), our method improves average accuracies by 15.60% and 23.11%, respectively. Conclusion:Besides, data distributions of EEG from different datasets but with the same emotional labels have been well aligned, which demonstrates the effectiveness of DSPB.