Multidimensional Matrix Research Articles

Innovative fault diagnosis and early warning method based on multifeature fusion model for electric vehicles

Fault diagnostics and early warning are crucial to the safe operation of lithium-ion batteries, and despite partial progress, it is still extremely difficult to solve the problem in a high-dimensional parameter space with complex failure causes. In this article, an innovative fault diagnosis and early warning method based on multifeature fusion model is designed for quantitative and qualitative comprehensive analysis and evaluation of the battery operating state information and the complex internal safety evolution trajectory. Firstly, to form a multi-dimensional fault feature matrix, a real vehicle dataset consisting of 400 vehicles is constructed using typical vehicle fault data from the cloud platform. Then, the original data is noise-reduced and pre-processed by applying initial bias correction and anomalous signal identification methods. Moreover, based on the equivalent circuit model and data-driven algorithm, 13 features including internal short circuit internal resistance, inconsistency accumulation risk, and principal element extraction index are designed from three dimensions: threshold, statistics and model. Finally, the multiple features are combined with random forest (RF) algorithm for hybrid fusion decision to achieve better generalization ability and suppress the overfitting risk of the algorithm. The results show that the proposed feature extraction and fusion decision methods can identify abnormal states and hazard levels in a timely and accurate manner, and this RF-based classification, warning and evaluation framework shows the promise of machine learning algorithms for interpretable early warning studies of battery failures, which can use more modal features to assess the hazard levels of actual operating battery systems in end-cloud collaboration scenarios.

Making co-creation operational: A RECONECT seven-steps-pathway and practical guide for co-creating nature-based solutions

Co-creation as a practice of collaborative product or service development is not a novel concept, however its application in the field of nature-based solutions (NBS) requires a certain level of knowledge, expertise and capacity building to ensure a shared understanding and collaborative dialogue. NBS are defined as cost-effective and ecosystem-based solutions to solving sustainability challenges and climate-change pressures through embedding a more citizen-oriented engagement within its implementation. Although some co-creation principles and guidelines are scientifically well elaborated, only a few of them was put into practice. Thus there is still a need for making them clearer and more feasible to a broad range of stakeholders from the non-scientific community. This problem is mostly caused by the lack of easy-to use framework / strategy for organizing a co-creation and selecting the appropriate co-creation activity for the certain purpose of the NBS realization process. It includes making a right decision on what particular tools, how and with what groups of stakeholders can be applied in a certain case and by the availability of particular resources. For this purpose, a stepwise pathway/guide on how participatory approach can be incorporated into the whole process of NBS co-creation was developed within the RECONECT project. The main innovative contribution of this work is to propose the following ready-to-use solutions:•detailed seven-steps-co-creation pathway and practical recommendations for NBS design and implementation;•multidimensional and comprehensive decision-making matrix consisting of 88 tools for selecting the most suitable solution (tool) according to the particular co-creation goal, available resources and capacities;•additional resources to facilitate and operationalise the co-creation process at each stage of NBS development, offered in the form of a Toolbox.

Local electrochemical and thermal investigation of a segmented reversible solid oxide fuel cell and electrolyzer

A novel setup has been designed and mounted to investigate the local electrochemical and thermal behavior of a cell from a 4-cell short stack under reversible operation solid oxide fuel cell/solid oxide electrolyzer (SOFC/SOE). It consists in the partition of the cell's oxygen electrode into 20 segments, where each segment is controlled by a specific electronic load. This allows to control the segments independently in galvanostatic or potentiostatic mode. The configuration also enables local electrochemical impedance spectroscopy (EIS) measurements of each segment. 20 thermocouples were added to measure the local temperature of the segments in order to gain a deeper insight into the cell thermal distribution. This helps correlating the local thermal and electrochemical responses to the operating parameters. This specific setup allowed mapping the cell's local behavior based on a multidimensional operating conditions matrix, including current density, temperature, feed gas composition, sweep gas flow and polarization. Moreover, a durability test of more than 1,000 h helped investigating the local long-term degradation in a reversible (day SOE, night SOFC) configuration. These results are of significant importance towards optimal operation of a reversible SOC with enhanced durability.

AC Series Arc Fault Diagnosis Method Based on AO-VMD Multidimensional Feature Extraction

In view of the difficulty in determining the number of mode components K and penalty factor α in VMD, which leads to poor signal decomposition effect and low diagnosis and recognition rate due to insufficient feature extraction in AC arc fault, an arc fault diagnosis method based on the Aquila algorithm was proposed to optimize the multivariate feature extraction of variational mode decomposition. First of all, the arc fault test platform was set up to consider the resistive, inductive, capacitive, and other household loads were considered to obtain the normal and fault current data. Second, the optimal K and α parameters were obtained by AO-VMD optimization and then decomposed into intrinsic mode functions (IMFs) by substituting them into VMD. Then, the time domain characteristics of the IMF2 component, the Energy Entropy of IMFs, and the Fuzzy Entropy of the kurtosis maximum IMFk component were extracted respectively, and the multidimensional fault characteristic matrix was constructed. Finally, the random forest (RF) model was used to accurately identify arc faults. The experiment shows that the average fault recognition rate of each type of load is above 99%, which has an excellent diagnostic effect.

Co-embedding of edges and nodes with deep graph convolutional neural networks

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method.

Raman spectrum model transfer method based on Cycle-GAN

The disparity in hardware quality among various models of Raman spectrometers gives rise to variations in the acquired Raman spectral data, even when the same substance is collected under identical external conditions. Conventionally, models constructed using data obtained from a particular instrument exhibit issues such as limited applicability or poor performance when deployed to different instruments. Currently, numerous model transfer algorithms grounded in chemometrics have been developed, all aiming to establish a mapping relationship capable of transforming spectral data from the source domain to the target domain. With the advancement of deep learning techniques, the utilization of deep learning enables the effective resolution of nonlinear mapping relationships between two spectral vectors. In the field of image translation, the Cycle-Consistent Adversarial Networks, Cycle-GAN, has already achieved mutual transformation between two distinct style images. However, due to images being multidimensional matrix data, unlike one-dimensional spectral data vectors, we have constructed a deep learning network based on Cycle-GAN for vector-to-vector transformation. This network allows the direct conversion of spectral data from the source domain to the target domain, without requiring parameter adjustments or other operations. Compared with traditional chemometric methods, our method is more intelligent and efficient. Finally, the cosine similarity between the source domain data and the transformed target domain data exceeds 99%.

Shared sustainability: An analysis of the belt and road initiative agenda network of united nation news website

The Belt and Road Initiative (BRI) is a global initiative that is gaining increasing attention, participation, and communication globally. However, most existing studies on BRI-related communication are based on BRI communication as the background. As an important theory of communication, network agenda-setting (NAS) theory is used to focus on an increasing number of objects that are rarely studied by international organizations. The United Nations (UN) news website, which is the official website of the largest international governmental organization, is available in nine languages (as of May 7, 2021), including Chinese, English, French, Russian, Arabic, Spanish, Portuguese, Swahili, and Hindi, providing news reports based on audience research. Thus, this study analyzed the BRI agenda network of UN's nine languages web pages, calculates BRI-related elements and correlation strength, and forms a multidimensional matrix in conjunction with the NAS theory. Finally, based on the summary statistics of the BRI agenda network, the UN news website agenda network was summarized and formed. The correlation strength of nine languages and 26 content pieces was analyzed using statistical principal components and cluster analysis. The result showed that the BRI agenda network of the UN news website had a strong link with the UN's sustainable development goals. Note that the BRI agendas in different languages can influence each other, making sustainability a crucial element for both BRI and the UN. In addition, the NAS theory can work within the same organization. However, there is still room for improvement in BRI's sustainable global discourse communication, such as audience analysis, language-ability building, and diversification of communication bodies and methods.

The ECOSOPHY concept in discourses of language education: a cross-cultural perspective

This paper focuses on studying the peculiarities of the linguocognitive actualization of the concept of ecosophy in language education discourses with an emphasis on its cross-cultural ontology in the English language teaching and learning environments of Argentina, Côte d'Ivoire, the Congo, Ethiopia, Kenya, Nigeria, Peru, Tanzania, Togo, Uganda, Ukraine. On the premise of analysing the multidisciplinary essence of ecosophy as deep ecology philosophy, within the methodological framework of cognitive linguistics, ecolinguistics and linguoculturology, the authors substantiate the status of ecosophy as a universal fundamental conceptual constituent of linguodidactic discourses of the twenty-first century, oriented at biospheric egalitarianism to realize the Sustainable Development Goals formulated by the United Nations as the blueprint to secure a better future. As a result of the conducted psycholinguistic experiment centered around elicitation techniques, such quantitatively dominant culturally universal components of the concept of ecosophy as harmony, resilience, and sustainability, together with the conceptual facets content and level, as the basis of better language education cross-culturally have been revealed. Our further conceptual and linguistic analysis has enabled our construing a multi-dimensional matrix as a representation of the concept researched. The data obtained have proved that the conceptual dimension content comprises the conceptual commonalities social life, economics, politics, peace/war/safety, environment, culture, whereas the dimension level embraces the concepts individual/identity, community, state. The matrix serves as a tool for explicating major conceptual highlights of ecosophy in language instruction discourses across eleven countries, thus perspectively contributing to the development of more efficient, culturally relevant linguodidactic methods and media.

Retracted: Research on the Academic Impact of Sports in the Multidimensional Matrix Evaluation Framework

Retracted: Research on the Academic Impact of Sports in the Multidimensional Matrix Evaluation Framework

MKConv: Multidimensional feature representation for point cloud analysis

Despite the remarkable success of deep learning, an optimal convolution operation on point clouds remains elusive owing to their irregular data structure. Existing methods mainly focus on designing an effective continuous kernel function that can handle an arbitrary point in continuous space. Various approaches exhibiting high performance have been proposed, but we observe that the standard pointwise feature is represented by 1D channels and can become more informative when its representation involves additional spatial feature dimensions. In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix. Unlike standard point convolution, MKConv proceeds via two steps. (i) It first activates the spatial dimensions of local feature representation by exploiting multidimensional kernel weights. These spatially expanded features can represent their embedded information through spatial correlation as well as channel correlation in feature space, carrying more detailed local structure information. (ii) Then, discrete convolutions are applied to the multidimensional features which can be regarded as a grid-structured matrix. In this way, we can utilize the discrete convolutions for point cloud data without voxelization that suffers from information loss. Furthermore, we propose a spatial attention module, Multidimensional Local Attention (MLA), to provide comprehensive structure awareness within the local point set by reweighting the spatial feature dimensions. We demonstrate that MKConv has excellent applicability to point cloud processing tasks including object classification, object part segmentation, and scene semantic segmentation with superior results.

Virtual Machine and Integrated Developer Environment for Sleptsov Net Computing

Modern computing is a path of violations and transformations coming from an intrinsically concurrent application domain into a sequence of instructions and then back to concurrency with OpenMP, MPI and CUDA/OpenCL. Why we create so many difficulties? Sleptsov Net Computing (SNC) maps a task into an appropriate computing structure implemented as a re-configurable multidimensional sparse matrix of computing memory. It has entirely graphical mass parallel language for concurrent programming and a framework of techniques for concurrent program verification to develop reliable software. Estimated efficiency of SNC is higher than 50% compared to actual less that 1% efficiency of the most powerful supercomputers. It yields hyper-performance capable of efficient control of hyper-sonic objects, colliders, thermonuclear reaction. This paper presents an open source prototype VM and IDE for SNC with a view on upcoming hardware implementation of the corresponding computer.

Development of playfair cryptosystem based on generation a multi-dimensional key matrix

Playfair is considered as one of the classical encryption symmetric methods, it has a limitation of using just 5×5 matrix, which means only 25 English letters could be represented. In this work, a 2D and 3D method is adopted as an expanded matrix that encompass the overall American standard code for information interchange (ASCII) codes in a permuted manner for all symbols of any language. Any sort of the multi-dimensional matrix will enhance the security by increasing the complexity on the attacker to try 256! patterns of keys probabilities instead of 25!. The key-matrix is generated from the chaotic maps for some control parameters as patterns of non-repeating random numbers from 0 to 255 equivalent to their ASCII code values. The security of the proposed method not rely only on the number of key probabilities, but exceed that to: matrix dimensionality, encryption/decryption algorithms, initial chaotic parameters, and key-matrix values permutation. The efficiency of the proposed cryptosystem has been investigated when tested on 784 samples according to security measurements in which the obtained number of pixels change rate (NPCR) (99.609) is very close to the ideal value, while the correlation plotting close to zero (0.00058) and entropy near from 8 (7.9998).

Smart bridge maintenance using cluster merging algorithm based on self-organizing map optimization

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners.

Analysis MiHoYo Business Model Competitiveness

People’s purchasing capacity rises along with that of the national economic strength. At the same time, because of the rapid development of the Internet and mobile phones becoming a common commodity, mobile games have gradually gained the spotlight and integrated into people’s daily lives. The industry of mobile games has expanded tremendously, which has led to fierce competition under the promotion of capitalism. Small- to medium-sized enterprises in the game development industry now face strenuous difficulties in moving forward with the expansion. In this article, we appraise MiHoYo Co., Ltd., a digital game development and publishing company based in Shanghai, regarding its development so far in the industry; using theories and methods in business administration, we analyze the competitive advantages of MiHoYo. MiHoYo has released best-selling video games with themes centered around renowned intellectual properties, which form a multi-dimensional matrix of products that enhances its lead in competition; at reasonable prices; MiHoYo’s original intellectual properties have attracted a large number of users with high consumer stickiness. Moreover, MiHoYo has a culture of prioritizing technology and setting clear goals, and it has successfully released promising products while gathering experience in business management. The success of MiHoYo under the challenges posed by the game development industry allows other small- to medium-sized enterprises to see development directions in a new light. Lastly, suggestions are presented for the future progress of MiHoYo.

Utilization Quality Evaluation and Barrier Factor Diagnosis of Rural Residential Areas in Agricultural Regions of the Northeast Plain: A Case Study of Wangkui County, Heilongjiang Province, China

Conducting quality evaluations of rural residential areas and effectively improving their utilization levels is an important aspect of correctly handling the relationship between humans and the land and achieving high-quality rural developments. Taking Wangkui County, Heilongjiang Province, as an example, this study aimed to achieve the “intensive, humanistic, and green” development of rural residential areas. An evaluation index system of utilization quality was constructed using three aspects: intensive land utilization, human settlement environment quality, and ecological environment quality. The comprehensive evaluation results were classified using a multidimensional combination matrix and targeted optimization plans were proposed. Additionally, an obstacle diagnosis model was constructed to identify the factors that hinder the high-quality utilization of rural residential areas. The results demonstrated the following: (1) The utilization quality of the rural residential areas in the study area was mainly at a medium level, followed by low and high levels, with proportions of 20.18%, 51.38%, and 28.44%, respectively. The utilization levels gradually decreased from the town centers to the surrounding areas. (2) Based on the evaluation results, there were 23 combinations of rural residential areas in the study area, which were classified into four types: coordinated control, key development, single leading, and transforming and upgrading. Optimization plans were proposed for the different types. (3) From the perspective of identifying the barrier factors, the top five factors that hindered the high-quality utilization of rural residential areas were the traffic land density, aggregation index, green-coverage rate of built-up areas, completeness of public service facilities, and the proportion of secondary and tertiary industrial land area. This study provides a significant reference for the evaluation of the utilization quality of rural residential areas in plain agricultural regions to effectively raise their levels of intensive land utilization, improve their settlement environments, enhance their ecological quality, and achieve a development of high quality.

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Construction and empirical testing of comprehensive risk evaluation methods from a multi-dimensional risk matrix perspective: taking specific types of natural disasters risk in China as an example

Global climate change, continuous economic development and accelerated urbanisation have increased the frequency and complexity of various incidents. Risk assessment is an important means to deal with different crises and incidents. Based on the systematic analysis of the basic connotation, attribute relationship and main contents of risk assessment, this study extends the traditional two-dimensional risk matrix to multi-dimensional risk matrix. On this basis, this study further proposes four comprehensive risk assessment methods, including total ordinal, weighted average, Euclidean distance and two-norm methods. Empirical research was conducted by selecting three types of natural disasters, regional agro-meteorological disasters, geological disasters and forest fires, in 30 related regions in China between 2010 and 2019. The results indicated that compared to the two-dimensional risk matrix, the multi-dimensional risk matrix can intuitively characterise and compare the all-round risk situation of different regions, periods, types and dimensions, which is more helpful for a systematic analysis and risk comparison. All four evaluation methods proposed in this paper proved able to effectively conduct comprehensive regional risk evaluations with consistent results. However, when considering the fitting coefficient between the results obtained by various evaluation methods and the comprehensive evaluation results, it can be found that the accuracy (i.e. fitting coefficient) of Euclidean distance method is significantly higher than that of the other three methods. In the last 10 years, the temporal order of natural disaster risk in China has been more volatile, with Beijing, Shanxi, Tianjin, Hebei, Liaoning, Ningxia, Zhejiang, Shaanxi, Fujian and Gansu experiencing higher volatility in the ranking of natural disaster risk.

Non-Parametric Tomographic SAR Reconstruction via Improved Regularized MUSIC

Height estimation of scatterers in complex environments via the Tomographic Synthetic Aperture Radar (TomoSAR) technique is still a valuable research field. The parametric spectral estimation approach constitutes a powerful tool to identify the superimposed scatterers with different complex reflectivities, located at different heights in the same range–azimuth resolution cell. Unfortunately, this approach requires prior knowledge about the number of scatterers for each pixel, which is not possible in practical situations. In this paper, we propose a method that analyzes the scree plot, generated from the spectral decomposition of the multidimensional covariance matrix, in order to estimate automatically the number of scatterers for each resolution cell. In this context, a properly improved regularization step is included during the reconstruction process, transforming the parametric MUSIC estimator into a non-parametric method. The experimental results on two data sets covering high elevation towers, with different facade coating characteristics, acquired by the TerraSAR-X satellite highlighted the effectiveness of the proposed regularized MUSIC for the reconstruction of high man-made structures compared with classical approaches.

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information.

Triple-negative breast cancer (TNBC) is one of the more aggressive subtypes of breast cancer. The prognosis of TNBC patients remains low. Therefore, there is still a need to continue identifying novel biomarkers to improve the prognosis and treatment of TNBC patients. Research in recent years has shown that the effective use and integration of information in genomic data and image data will contribute to the prediction and prognosis of diseases. Considering that imaging genetics can deeply study the influence of microscopic genetic variation on disease phenotype, this paper proposes a sample prior information-induced multidimensional combined non-negative matrix factorization (SPID-MDJNMF) algorithm to integrate the Whole-slide image (WSI), mRNAs expression data, and miRNAs expression data. The algorithm effectively fuses high-dimensional data of three modalities through various constraints. In addition, this paper constructs an undirected graph between samples, uses an adjacency matrix to constrain the similarity, and embeds the clinical stage information of patients in the algorithm so that the algorithm can identify the co-expression patterns of samples with different labels. We performed univariate and multivariate Cox regression analysis on the mRNAs and miRNAs in the screened co-expression modules to construct a TNBC-related prognostic model. Finally, we constructed prognostic models for 2-mRNAs (IL12RB2 and CNIH2) and 2-miRNAs (miR-203a-3p and miR-148b-3p), respectively. The prognostic model can predict the survival time of TNBC patients with high accuracy. In conclusion, our proposed SPID-MDJNMF algorithm can efficiently integrate image and genomic data. Furthermore, we evaluated the prognostic value of mRNAs and miRNAs screened by the SPID-MDJNMF algorithm in TNBC, which may provide promising targets for the prognosis of TNBC patients.