Singular Value Decomposition Analysis Research Articles

A Compact Stepped Frequency Continuous Waveform Through-Wall Radar System Based on Dual-Channel Software-Defined Radio

Software-defined radio (SDR) has high flexibility and low cost. It conforms to the miniaturization, lightweight, and digitization trends in through-wall radar systems. Stepped frequency continuous waveform (SFCW) is commonly used in through-wall radar, which has high resolution and strong anti-interference ability. This article develops an SFCW through-wall radar system based on a dual-channel SDR platform. Without changing hardware structure and complicated accessories, a phase compensation method of solving the phase incoherence problem in a low-cost dual-channel SDR platform is proposed. In addition, this article proposes a wall clutter mitigation approach by means of singular value decomposition (SVD) and principal component analysis (PCA) framework for through-wall applications. This approach can process the wall clutter and noise ‌efficiently,‌ and then extract the target subspace to obtain location information. The experimental results indicate that the proposed windowing-based SVD-PCA approach is effective for the developed radar system, which can ensure the accuracy of through-wall detection. It is also superior to the traditional methods in terms of the image quality of range profiles or signal-to-noise ratio (SNR).

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-square approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal components analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6 dB signal-to-noise ratio improvement over standard denoising methods for signal corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

Load recognition of connecting-shaft rotor system under complex working conditions

A method for qualitatively recognizing the load of the rolling equipment's connecting-shaft rotor system is proposed in this paper due to the complexity of rolling production conditions and the limitations of single source response signals. The method is oriented towards fusing the vibration and motor's current information. First, singular value decomposition and wavelet packet analysis are used to preprocess the two types of response signals. Then, the Bayesian estimation method in feature-level fusion achieves qualitative recognition and analysis of rotor system load types. Corresponding load experiments are completed on a load recognition test platform based on vibration and the motor's current signals. The research results show that the load recognition method based on fusion information can recognize the type of load excitation with a recognition accuracy of 91.7 %, higher than other single-source response signal methods. Therefore, the feasibility of the aforementioned theoretical methods is verified.

Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

Deep Kinetic Energy Response to the Variability of the Kuroshio Extension

AbstractThe transfer of energy from the upper to deep oceans is a well‐known and challenging subject in physical oceanography. This research investigates the intricate relationship between surface and deep ocean currents. Utilizing nearly 2 years of observations from the Kuroshio Extension System Study (KESS), our study unveils the deep‐ocean kinetic energy response to the Kuroshio Extension (KE) variability. We introduce the Kuroshio Extension Jet Path Index (KEJPI), which identifies two distinct modes of the KE jet on an intra‐seasonal timescale. Our findings reveal a strong correlation (0.73) between KEJPI and the mean kinetic energy of deep‐ocean geostrophic circulation, suggesting that the KE jet's large amplitude meanders have a significant impact on deep‐ocean kinetic energy. Singular Value Decomposition (SVD) analysis further unveils a co‐evolving spatial pattern between upper and lower ocean kinetic energy. We investigate the dynamic vertical coupling (DVC) mechanism by examining the coherent variation among the sea surface height (SSH), 15°C isotherm (Z15), deep pressure anomaly, and abyssal flow. The KE jet migration can induce net divergence and convergence within the water column, which in turn generates deep‐ocean quasi‐geostrophic currents. These currents show a marked increase in kinetic energy, reaching levels three times higher than the background. This DVC‐driven kinetic energy can further cascade into near‐inertial and high‐frequency internal waves, contributing to abyssal mixing. Our study underscores the role of large current system instabilities in transferring energy to the deep ocean and facilitating deep mixing processes.

Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging.

The inherent ambiguity in reconstructed images from coherent diffraction imaging (CDI) poses an intrinsic challenge, as images derived from the same dataset under varying initial conditions often display inconsistencies. This study introduces a method that employs the Noise2Noise approach combined with neural networks to effectively mitigate these ambiguities. We applied this methodology to hundreds of ambiguous reconstructed images retrieved from a single diffraction pattern using a conventional retrieval algorithm. Our results demonstrate that ambiguous features in these reconstructions are effectively treated as inter-reconstruction noise and are significantly reduced. The post-Noise2Noise treated images closely approximate the average and singular value decomposition analysis of various reconstructions, providing consistent and reliable reconstructions.

Fast ultrasonic ablation monitoring: An innovative approach using ultrasound RF signals and singular value decomposition

Microwave ablation (MWA) can rapidly lead to tumor cell necrosis by heating local tissues using electromagnetic field energy, making it a highly efficient modality widely utilized in clinical ablation surgeries. However, the lack of timely and accurate intraoperative monitoring methods is a critical issue that urgently needs to be addressed in MWA. This article introduces an innovative approach for real-time monitoring of ablation therapies by the combination of ultrasound radio frequency (RF) signal acquisition and singular value decomposition (SVD), which breaks through the limitations of current monitoring techniques during MWA procedures. Due to the difference in acoustic scattering properties between the gas generated in high-temperature ablated region and the non-ablated tissues, the current new method aimed to apply SVD analyses to ultrasound RF signals scattered from the tissues, through which the range of gas generation could be determined and then the ablation area could be estimated. Eventually, high-precision differentiation between ablated and non-ablated tissues could be achieved, enabling real-time adjustment and improvement of MWA strategies. Validated through ex vivo experiments on pig liver, this technique demonstrated a relative error in monitoring the size of ablation region of approximately 7%, indicating a strong correlation with actual ablation outcomes. The study indicated the proposed method should have great potential to improve the precision and safety of MWA therapy.

Impact of sea surface temperature in the Arabian Sea on the variability of Summer Monsoon Rainfall over Pakistan Region

The seas and oceans are essential in preserving energy in the Earth, Ocean, and atmosphere system. Assessing past trends and expected sea surface temperature (SST) shifts is critical for future climate scenarios within this framework. Previous studies have emphasized the importance of SST in the emergence and intensification of heavy rainfall events in the South Asian basin and the development of heat waves in the coastal regions of South Asia. This study has focused to investigate the relationship between SST of the Arabian Sea and mean rainfall in South Asia using Singular Value Decomposition Analysis (SVDA) for the July, August, and September (JAS) season, as most of the region's rainfall is in this season. The first mode accounts for 69 % of the covariance between Arabian Sea SST and regional JAS rainfall, with three dominant SVDA modes explaining 96 % of the total squared covariance. The degree to which the connected fields are correlated has been investigated using Root Mean Squared Covariance. By heterogeneous correlation maps, it is concluded how significant is the impact of one field (SST) on the other (rainfall), which validates our hypothesis that the SST of the Arabian Sea affects variation in precipitation. The empirical results from the SVDA are consistent with those of the composite diagrams. Dry and wet years have been defined and analyzed to examine the impact of SST on regional JAS rainfall further. Vertically integrated moisture transport reveals a significant difference in moisture transport over the Arabian Sea between wet and dry years during the JAS season. Water vapor transport is much stronger during wet years compared to dry years.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia.

Dengue has become a public health concern in Indonesia since it was first found in 1968. This study aims to determine dengue hotspot areas and analyze the spatiotemporal distribution of dengue and its association with dominant climate parameters nationally. Monthly data for dengue and climate observations (i.e., rainfall, relative humidity, average, maximum, and minimum temperature) at the regency/city level were utilized. Dengue hotspot areas were determined through K-means clustering, while Singular Value Decomposition (SVD) determined dominant climate parameters and their spatiotemporal distribution. Results revealed four clusters: Cluster 1 comprised cities with medium to high Incidence Rates (IR) and high Case Densities (CD) in a narrow area. Cluster 2 has a high IR and low CD, and clusters 3 and 4 featured medium and low IR and CD, respectively. SVD analysis indicated that relative humidity and rainfall were the most influential parameters on IR across all clusters. Temporal fluctuations in the first mode of IR and climate parameters were clearly delineated. The spatial distribution of heterogeneous correlation between the first mode of rainfall and relative humidity to IR exhibited higher values, which were predominantly observed in Java, Bali, Nusa Tenggara, the eastern part of Sumatra, the southern part of Kalimantan, and several locations in Sulawesi.

A novel potential cause of extreme precipitation in the northwest China

The northwest China is a climate change-sensitive and ecologically vulnerable area. Under the backdrop of global warming, this region exhibits clear characteristics of warming and wetting. In recent years, the causes of climate change in the northwest China has become a widely-focused topic. Previous research has mainly attributed the increase in precipitation to changes in the westerly belt and enhanced local convective activity caused by global warming. South Asia, beside the Tibetan Plateau from the northwest China, is one of the regions with the fastest growth in global atmospheric pollutant emissions. This study utilizes reanalysis data such as ERA5 and MERRA-2. Statistical methods, including Theil-Sen Median trend analysis and Singular Value Decomposition analysis, are employed to analyze the spatiotemporal characteristics of South Asian aerosols, extreme precipitation in the northwest China, and the correlation between the two. The study reveals the existence of two key aerosol-precipitation response areas. Synthetic analysis of the meteorological elements of the response events in both regions is conducted to explore the possible physical mechanisms behind the correlation between South Asian aerosols and precipitation in the northwest China. The result of this study is to provide a new perspective on the causes of extreme precipitation in the arid region of northwest China.

Association between the relative abundance of phyla actinobacteria, vitamin C consumption, and DNA methylation of genes linked to immune response pathways.

There is an emerging body of evidence that vitamin C consumption can modulate microbiota abundance and can also impact DNA methylation in the host, and this could be a link between diet, microbiota, and immune response. The objective of this study was to evaluate common CpG sites associated with both vitamin C and microbiota phyla abundance. Six healthy women participated in this cohort study. They were divided into two groups, according to the amount of vitamin C they ingested. Ingestion was evaluated using the 24-h recall method. The Illumina 450 k BeadChip was used to evaluate DNA methylation. Singular value decomposition analyses were used to evaluate the principal components of this dataset. Associations were evaluated using the differentially methylated position function from the Champ package for R Studio. The group with higher vitamin C (HVC) ingestion also had a higher relative abundance of Actinobacteria. There was a positive correlation between those variables (r = 0.84, p = 0.01). The HVC group also had higher granulocytes, and regarding DNA methylation, there were 207 CpG sites commonly related to vitamin C ingestion and the relative abundance of Actinobacteria. From these sites, there were 13 sites hypomethylated and 103 hypermethylated. The hypomethylated targets involved the respective processes: immune function, glucose homeostasis, and general cellular metabolism. The hypermethylated sites were also enriched in immune function-related processes, and interestingly, more immune responses against pathogens were detected. These findings contribute to understanding the interaction between nutrients, microbiota, DNA methylation, and the immune response.

Examining the Insight Impacts of Indian Ocean Pressure on Autumntime Precipitation Variability over Tasmania through Singular Value Decomposition Analysis

This study explores further the impacts of the relationship between autumntime mean sea level pressure (MSLP) over the Indian Ocean and autumntime precipitation over Tasmania. We employ a combination of cross-correlation analysis, Singular Value Decomposition (SVD), and covariance measures to unravel this intricate connection. Our results reveal that MSLP over the Indian Ocean significantly influences Tasmania’s autumn precipitation, particularly in the region extending from 125°E eastward and from 35°S southward. This MSLP-precipitation link is statistically significant, characterized by a Pearson’s coefficient of correlation of 0.63 (p < 0.05). In addition, we calculate the Root Mean Square Covariance (RMSC) to gauge the strength of this association. The RMSC value, calculated at 0.33, surpasses the threshold of 0.1, emphasizing a robust and significant connection between autumntime MSLP and precipitation in Tasmania. Our results emphasize that the first leading mode of SVD accounts for 84% of the variability in both MSLP over the Indian Ocean and precipitation in Tasmania. These results contribute to a more profound understanding of the factors governing autumn precipitation in Tasmania and offer valuable insights for meteorological and climate science.

Covariability of decadal surface air temperature variability over Myanmar with sea surface temperature based on singular value decomposition analysis

The Myanmar’ Southeast Asian country is currently experiencing environmental changes, with temperature change being one the major contributing factors. Although many studies have shown the contribution of anthropogenic activities, the factors sustaining the observed increase in air temperature (TEMP) are not fully understood. We examined the interdependence of the surface TEMP with the sea surface temperature (SST) from 1971 to 2020 on a decadal timescale to predict changes in TEMP over a longer time period. Our analysis showed a pronounced interdecadal change in TEMP, with the highest intensity observed in the 2010s. The results show that the dominant modes of the global SST significantly influence the TEMP variation in the region at the decadal time scale. Indian Ocean (IO) SST-singular value decomposition (SVD)1 presents significant positive (negative) correlations in the southeast, and central (southwest) related to warming (cooling) TEMP in the east, northern tip, and northwest (south-Yangon and Taninthayi, and some parts of the north). The second and third modes of SST-SVD are cooling (warming) SST over the east of the Bay of Bengal (BoB), and along the Myanmar coast, which are associated with cooling(warming) TEMP patterns in the region. Significant negative correlations of decadal TEMP at the annual scale with Pacific Decadal Oscillation (PDO) and North Pacific (NP), the MAMJ with Atlantic Multidecadal Oscillation (AMO) and November–December–January–February (NDJF) with PDO were evidenced. Meanwhile, significant positive correlations were obtained between TEMP and AMO (NP) at the annual scale (NDJF), respectively. These findings provide valuable insights into decadal-scale TEMP patterns and their relationships with SSTs, contributing to a better understanding of TEMP variability in Myanmar, which may be helpful in climate prediction. Predicting surface TEMP on a decadal timescale is helpful in environmental management.

Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods

This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.

Improved PVTOL Test Bench for the Study of Over-Actuated Tilt-Rotor Propulsion Systems

In recent years, applications exploiting the advantages of tilt-rotors and other vectored thrust propulsion systems have become widespread, particularly in many novel Vertical Takeoff and Landing (VTOL) configurations. These propulsion systems can provide additional control authority, enabling more complex flight modes, but the resulting control systems can be challenging to design due to the mismatch between the vehicle degrees of freedom and physical input variables. These propulsion systems present both advantages and difficulties because they can exert the same overall forces and moments in many different propulsive configurations. This leads to the traditional non-uniqueness problem when using the inverse dynamics control allocation approach, which is the basis of many popular VTOL control algorithms. In this article, a modified Planar VTOL (PVTOL) test bench configuration, which considers an arbitrary number of co-linear tilting rotors, is introduced as a benchmark for the study of the control allocation problem. The resulting propulsion system is then modeled and linearized in a closed and compact form. This allows a simple and systematic derivation of many of the currently used control allocation approaches. According to the proposed PVTOL configuration, a two-rotor test bench is implemented experimentally and a decoupling control allocation strategy based on Singular Value Decomposition (SVD) analysis is developed. The proposed approach is compared with a traditional input mixer algorithm based on physical intuition. The results show that the SVD-based solution achieves better cross-coupling reduction and preserves the main properties of the physically derived approach. Finally, it is shown that the proposed PVTOL configuration is effective for studying the control allocation problem experimentally in a controlled environment and could serve as a benchmark for comparing different approaches.

Seasonal Variability of Arctic Mid-Level Clouds and the Relationships with Sea Ice from 2003 to 2022: A Satellite Perspective

Mid-level clouds play a crucial role in the Arctic. Due to observational limitations, there is scarce research on the long-term evolution of Arctic mid-level clouds. From a satellite perspective, this study attempts to analyze the seasonal variations in Arctic mid-level clouds and explore the possible relationships with sea ice changes using observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) over the past two decades. For mid-level clouds of three layers (648, 548, and 447 hPa) involved in AIRS, high values of effective cloud fraction (ECF) occur in summer, and low values primarily occur in early spring, while the seasonal variations are different. The ECF anomalies are notably larger at 648 hPa than those at 548 and 447 hPa. Meanwhile, the ECF values at 648 hPa show a clear reduced seasonal variability for the regions north of 80°N, which has its minimum coefficient of variation (CV) during 2019 to 2020. The seasonal CV is relatively lower in the regions dominated by Greenland and sea areas with less sea ice coverage. Analysis indicates that the decline in mid-level ECF’s seasonal mean CV is closely correlated to the retreat of Arctic sea ice during September. Singular value decomposition (SVD) analysis reveals a reverse spatial pattern in the seasonal CV anomaly of mid-level clouds and leads anomaly. However, it is worth noting that this pattern varies by region. In the Greenland Sea and areas near the Canadian Arctic Archipelago, both CV and leads demonstrate negative (positive) anomalies, probably attributed to the stronger influence of atmospheric and oceanic circulations or the presence of land on the sea ice in these areas.

The effect of graph complexity in an energy-based FDI approach

This paper introduces a structured approach for creating node signature graphs in energy graph-based visualization for industrial systems, focusing on anomaly detection. It examines the relationship between graph cardinality and the singular value decomposition analysis applied in the energy graph-based visualization method. The results reveal a trade-of between graph complexity and singular value resolution. The study highlights how graph structure affects energy flow monitoring and anomaly detection. It notably enables fault detection in components not represented as nodes, enhancing the methodology in energy-based system monitoring.