Alignment Of Clusters Research Articles

SFDA-T: A novel source-free domain adaptation method with strong generalization ability for fault diagnosis

Currently, source free domain adaptation (SFDA) methods are employed to address the issue of inaccessible source domain data (SDD) in transfer learning. However, existing SFDA methods often suffer from overfitting to specific domains, leading to poor generalization ability in the target domain. To address these challenges, this paper proposes a novel SFDA method named SFDA-T for fault diagnosis. Specifically, a Transformer-CNN-based feature extractor is constructed, to mine the transferable feature knowledge of faults in the SDD. The approach reduces the overfitting of the model to domain-specific information and improves model’s generalization ability. In addition, the feature attention loss is designed to calculate attention weights of the sample features to increase the model’s attention to the crucial feature regions in the target domain. A source similarity guided exponential loss is developed to guide target samples based on the decision boundaries of the source domain, facilitating cluster alignment of target sample categories and expanding distances between different categories. Furthermore, a self-training pseudo-labeling constraint is employed to reduce the effect of incorrect label matching and further constrain the model. The results of the experiments on gearboxes and bearings indicate that the proposed method achieves high fault diagnosis accuracy while effectively decoupling from SDD.

Aligned colloidal clusters in an alternating rotating magnetic field elucidated by magnetic relaxation

Precise control at the colloidal scale is one of the most promising bottom-up approaches to fabricating new materials and devices with tunable and precisely engineered properties. Magnetically driven colloidal assembly offers great versatility because of the ability to externally tune particle-particle interactions and to construct a host of particle arrangements. However, despite previous efforts to probe the parameter space, global orientational control in conjunction with two-dimensional microstructural control has remained out of reach. Furthermore, the magnetic relaxation time of superparamagnetic beads has been largely overlooked despite being a key feature of the magnetic response. Here, we take advantage of the magnetic relaxation time of superparamagnetic beads in an alternating rotating magnetic field and show how harnessing this feature facilitates the formation of oriented clusters. The orientation of these clusters can be controlled by field parameters. Using experiments, simulations, and theory, we probe a two-particle system (dimer) under this alternating rotating magnetic field and use its dynamics to provide insights into the collective response that forms clusters. We find that the type of field has significant implications for the dipolar interactions between the colloids because of the nonnegligible magnetic relaxation. Moreover, we find that the competing time scales of the magnetic relaxation and the alternating field generate an anisotropic interaction potential that drives cluster alignment. By exploiting the magnetic relaxation time of magnetic systems, we can tailor new types of interparticle interactions, thereby expanding the capabilities of colloidal assembly in engineering unique materials and devices.

Molecular dynamics method to investigate the interaction energy and mechanical properties of the reinforced graphene aerogel with paraffin as the phase change material in the presence of different external heat fluxes

BackgroundGraphene aerogels (GA), known for their exceptional lightweight and sturdy characteristics, present a promising avenue for improving thermal energy (TE) storage and transfer efficiency. It might be possible to make better thermal management systems in fields like electronics, aerospace, and energy storage by studying how heat flux (HF) affects the strength and stability of graphene aerogels. MethodsThe study used molecular dynamics (MD) simulation to investigate how the mechanical properties of graphene aerogels strengthened with paraffin as phase change material (PCM) change in response to external heat flux (EHF). These simulation methods provided a detailed view of molecular interactions and dynamics at the atomic level, allowing researchers to understand the behavior of materials under various conditions. The change in toughness, interaction energy (IE), Young's modules (YM), and ultimate strength (US) was examined for this reason. Significant findingsThe results indicate that when the HF increased from 0.1 to 0.3 W/m2, the ultimate strength and Young's modules increased from 8.91 and 5.37 GPa to 14.546 and 8.59 GPa, respectively. These values declined when HF increased by more than 0.3 W/m2. When EHF went up to 0.3 W/m², these graphene aerogel properties went up. This was because the atoms moved around more and there were more bonding contacts among the graphene sheets, which made the structure of material stronger. However, at heat flux levels exceeding 0.3 W/m², excessive thermal energy may lead to thermal degradation, causing bond breakage and loss of structural integrity, ultimately resulting in a decrease in these mechanical properties. Also, the results reveal that interaction energy increased from -1522.098 to -1546.325 eV as external HF increased to 0.3 W/m2. The thermal motion of atoms enhanced as the HF increased, enabling closer clustering and better alignment of graphene sheets, thereby strengthening their interactions. This study gave us useful information about how to improve the mechanical properties of graphene aerogels in different HF conditions. This made it more likely that these materials can be used in energy storage systems and thermal management.

The PAU survey: photometric redshift estimation in deep wide fields

ABSTRACT We present photometric redshifts (photo-z) for the deep wide fields of the Physics of the Accelerating Universe Survey (PAUS), covering an area of $\sim$50 deg$^{2}$, for $\sim$1.8 million objects up to $i_{\rm {AB}}\lt 23$. The PAUS deep wide fields overlap with the W1 and W3 fields from CFHTLenS and the G09 field from KiDS/GAMA. Photo-z are estimated using the 40 narrow bands (NB) of PAUS and the broad-bands (BB) of CFHTLenS and KiDS. We compute the redshifts with the SED template-fitting code bcnz, with a modification in the calibration technique of the zero-point between the observed and the modelled fluxes, that removes any dependence on spectroscopic redshift samples. We enhance the redshift accuracy by introducing an additional photo-z estimate ($z_{\textrm {b}}$), obtained through the combination of the bcnz and the BB-only photo-z. Comparing with spectroscopic redshift estimates ($z_{\textrm {s}}$), we obtain a $\sigma _{68} \simeq 0.020$ for all galaxies with $i_{\rm {AB}}\lt 23$ and a typical bias $|z_{\textrm {b}}\!-\!z_{\textrm {s}}|$ smaller than 0.01. For $z_{\textrm {b}} \sim (0.10\!-\!0.75)$, we find $\sigma _{68} \simeq (0.003\!-\!0.02)$, this is a factor of $10\!-\!2$ higher accuracy than the corresponding BB-only results. We obtain similar performance when we split the samples into red (passive) and blue (active) galaxies. We validate the redshift probability $p(z)$ obtained by bcnz and compare its performance with that of $z_{\textrm {b}}$. These photo-z catalogues will facilitate important science cases, such as the study of galaxy clustering and intrinsic alignment at high redshifts ($z \lesssim 1$) and faint magnitudes.

Local atomic configuration and dynamic properties of liquid Ag-Si eutectic alloy by ab initio molecular dynamics

The structural evolution and dynamic properties of liquid Ag89Si11 eutectic alloy during rapid cooling were explored by ab initio molecular dynamics. The calculated pair distribution functions and structure factors at 1173 K agree well with the experimental reference data. The comprehensive analyses including Honeycutt-Andersen index, Voronoi tessellation, and atomistic cluster alignment reveal a gradual enhancement of chemical short-range order with the decreasing temperature and a dominant structural shifting towards perfect and distorted icosahedra. Additionally, the dynamic properties of liquid Ag89Si11 eutectic alloy were obtained and the diffusion coefficients follow Arrhenius relationship.

Philosophical Dimensions of Digital Transformation and Their Impact on the Future

This article aims to highlight digital transformation within the context of an in-depth philosophical analysis from the perspectives of epistemological, axiological, ethical, and anthropological dimensions. The period of general philosophical interpretation of the practical-everyday status of digitalisation has evolved into a need for a comprehensive characterisation of the essence of the digital space. The tasks of the article involve organising various philosophical characteristics of digital transformation and aligning them within the scientific worldview paradigm of the future. The article's methodology is based on a qualitative study of the problem of digitalisation within the scientific-philosophical discourse of the last five years. A varied sampling in the literature review has facilitated the analysis of most philosophical dimensions of digitalisation in its contemporary manifestation. The scientific-philosophical synergetic approach is intended to ensure a holistic understanding of the essence of digitalisation as a socio-cultural phenomenon and to optimise the use of its practical structural-technological characteristics. The research results demonstrate the alignment of thematic clusters within the philosophical dimension of digitalisation. The initial dominance of epistemological themes related to the innovation of the digital space is gradually being transformed into a more balanced philosophical interpretation with the actualisation of axiological, ethical, and anthropological contexts. The impact of digital products on individuals and society requires evaluation and understanding how to regulate their activity. Therefore, a review of the literature dedicated to the philosophical aspects of digitalisation indicates a quantitative increase in works of a value-ethical and anthropocentric nature. Philosophy is dynamically shaping the content and format of the structural analysis of digital transformation as a critical sociocultural phenomenon of modernity. At the same time, prospects for philosophical research into the digital space are emerging, which involve outlining short-term tactics for understanding digital transformation in the ethical and anthropological segments and long-term strategies for the axiological and epistemological dimensions. Conclusion: Digital transformation has reached a large scale and intensity within the sociocultural dimension and requires a comprehensive worldview interpretation from science, philosophy, and cultural perspectives. A complete philosophical characterisation of digital transformation should consist of epistemological, axiological, ethical, and anthropological justifications for digitalisation's institutional-organisational and structural-functional aspects.

Host associations of Campylobacter jejuni and Campylobacter coli isolates carrying the L-fucose or d-glucose utilization cluster

Campylobacter was considered asaccharolytic, but is now known to carry saccharide metabolization pathways for L-fucose and d-glucose. We hypothesized that these clusters are beneficial for Campylobacter niche adaptation and may help establish human infection.We investigated the distribution of d-glucose and L-fucose clusters among ∼9600 C. jejuni and C. coli genomes of different isolation sources in the Netherlands, the United Kingdom, the United States of America and Finland. The L-fucose utilization cluster was integrated at the same location in all C. jejuni and C. coli genomes, and was flanked by the genes rpoB, rpoC, rspL, repsG and fusA, which are associated with functions in transcription as well as translation and in acquired drug resistance. In contrast, the flanking regions of the d-glucose utilization cluster were variable among the isolates, and integration sites were located within one of the three different 16S23S ribosomal RNA areas of the C. jejuni and C. coli genomes. In addition, we investigated whether acquisition of the L-fucose utilization cluster could be due to horizontal gene transfer between the two species and found three isolates for which this was the case: one C. jejuni isolate carrying a C. coli L-fucose cluster, and two C. coli isolates which carried a C. jejuni L-fucose cluster. Furthermore, L-fucose utilization cluster alignments revealed multiple frameshift mutations, most of which were commonly found in the non-essential genes for L-fucose metabolism, namely, Cj0484 and Cj0489. These findings support our hypothesis that the L-fucose cluster was integrated multiple times across the C. coli/C. jejuni phylogeny.Notably, association analysis using the C. jejuni isolates from the Netherlands showed a significant correlation between human C. jejuni isolates and C. jejuni isolates carrying the L-fucose utilization cluster. This correlation was even stronger when the Dutch isolates were combined with the isolates from the UK, the USA and Finland. No such correlations were observed for C. coli or for the d-glucose cluster for both species. This research provides insight into the spread and host associations of the L-fucose and d-glucose utilization clusters in C. jejuni and C. coli, and the potential benefits in human infection and/or proliferation in humans, conceivably after transmission from any reservoir.

Multi-day neuron tracking in high-density electrophysiology recordings using earth mover’s distance

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Scalable and parameter-free fusion graph learning for multi-view clustering

Multi-view clustering aims to capture the consistency and complementary information present in view-specific data to achieve clustering alignment. However, existing multi-view clustering methods often rely on different regularization terms to quantify the importance of various views, which inevitably introduces additional hyperparameters. It is challenging to fine-tune these additional parameters in real-world applications. Additionally, these methods suffer from high time complexity and impose substantial constraints when applied in large-scale scenarios. To address these limitations, we propose a parameter-free and time-efficient graph fusion method for multi-view clustering that can integrate view-specific graphs and directly generate clustering labels. Specifically, we introduce an anchor strategy and generate bipartite graphs on different views to enhance efficiency. Subsequently, we employ a self-weighted graph fusion strategy to merge the view-specific bipartite graphs. Finally, we propose a new solver to handle these problems, enabling the structured bipartite graphs to directly indicate clustering results. In contrast to previous clustering methods, our approach does not introduce any additional parameters and entirely relies on self-weighting for the fusion of view-specific graphs. As a result, our proposed method exhibits linear computational complexity to the data scale. Extensive experimental results on various benchmark datasets demonstrate the effectiveness and efficiency of our approach. Our code is available at https://github.com/DuannYu/MvSST.

Unsupervised domain adaptation with weak source domain labels via bidirectional subdomain alignment

Unsupervised domain adaptation (UDA) enables knowledge transfer from a labeled source domain to an unlabeled target domain. However, UDA performance often relies heavily on the accuracy of source domain labels, which are frequently noisy or missing in real applications. To address unreliable source labels, we propose a novel framework for extracting robust, discriminative features via iterative pseudo-labeling, queue-based clustering, and bidirectional subdomain alignment (BSA). The proposed framework begins by generating pseudo-labels for unlabeled source data and constructing codebooks via iterative clustering to obtain label-independent class centroids. Then, the proposed framework performs two main tasks: rectifying features from both domains using BSA to match subdomain distributions and enhance features; and employing a two-stage adversarial process for global feature alignment. The feature rectification is done before feature enhancement, while the global alignment is done after feature enhancement. To optimize our framework, we formulate BSA and adversarial learning as maximizing a log-likelihood function, which is implemented via the Expectation–Maximization algorithm. The proposed framework shows significant improvements compared to state-of-the-art methods on Office-31, Office-Home, and VisDA-2017 datasets, achieving average accuracies of 91.5%, 76.6%, and 87.4%, respectively. Compared to existing methods, the proposed method shows consistent superiority in unsupervised domain adaptation tasks with both fully and weakly labeled source domains.

Variable controlling factors lead to contrasting patterns of volcanism in the Changbaishan volcanic area (Tianchi-Longgang), China-North Korea: Insights from morphometry and spatial-temporal analyses

The coexistence of monogenetic and polygenetic volcanoes is a common phenomenon in volcanic areas. However, the genetic relationship between monogenetic and polygenetic systems and the factors controlling their distinct eruptive styles are not well understood. In active volcanic areas, analysing the clustering and vent alignment of monogenetic volcanoes, as well as examining the geomorphology and relative ages of scoria cones, offers quantitative insights into magma supply rates, volcano type distribution, and volcanic development trends. Our study presents geomorphological and spatio-temporal analyses of the co-existing monogenetic volcanoes in the Longgang Volcanic Field (LVF) and those associated with a polygenetic volcano (Tianchi) in the Changbaishan Volcanic Area, China. The distance between the two volcanic areas is around 150 km. Monogenetic vents in the LVF exhibit greater density compared to the dispersed system associated with Tianchi. The LVF vents also show better alignment, particularly in the direction of pre-existing basement faults (NE-SW, NW-SE and EW). By using scoria cone morphometric parameters and features, we estimated the relative ages and erupted volumes of monogenetic volcanoes in the LVF and the Tianchi area. We classified the cones of the two volcanic systems into five eruptive periods and found that, despite similar magma sources and output rates over approximately 870 kyr, differing numbers of scoria cones across age classes suggest that Tianchi's magma system influences its associated monogenetic volcanic plumbing. Furthermore, the continuous rise in output rates of monogenetic volcanoes in the Tianchi area highlights the increasing magma supply sustaining Tianchi volcano. Together, these interpretations are consistent with the two systems being controlled by different factors: the Tianchi monogenetic volcanic system is more controlled by magmatism, whereas the LVF is more strongly controlled by local tectonic structures, alongside an increasing magma supply causing the formation of progressively larger individual volcanoes. In volcanic areas, analysing monogenetic volcanoes' spatial-temporal distribution, volumes and recurrence rate provides a framework to evaluate magma supply rates and tectonic associations, which are key to the development of different volcano types.

Detailed characterizations of proton permeability properties through commercial Nafion® films under various acidic electrolytes

Nafion® film has been widely used as a representative cationic ion exchange membrane for electrochemical systems including water splitting. However, detailed H+ permeability properties have been poorly understood thus far. We here attempt to relate the H+ permeability with the material properties of Nafion® films. For that, four samples representing two casting types were selected to study the H+ permeability. We conducted the pretreatment to see the effect of the new alignment of the water clusters on the H+ permeability. Although the studied membranes show individual transport features, we observed the increased H+ permeability from 0.05 to 0.5 M concentrations in acidic electrolytes. Such a feature is caused by the increased chemical-potential gradient along with the osmotic de-swelling. We also found that a H+ diffusion channel is associated with the ionic cluster size of the films, which can explain the higher H+ permeability after the pretreatment. Lastly, the individual H+ permeability was obtained when measuring at different electrolytes, revealing that the H+ permeability is strongly influenced by the anionic mobility rather than the acidic dissociation constants. Our results provide a fundamental understanding of the H+ transports through the commercial Nafion® membrane, which gives a guideline for the rational design of ion exchange membranes.

B-modes from galaxy cluster alignments in future surveys

Intrinsic alignment (IA) of source galaxies represents an important contaminant for upcoming cosmic shear surveys. In particular, it is expected on general grounds that IA contains a B-mode while the weak lensing signal does not. Thus, a detection of B-modes offers the possibility to study directly the IA signal of the sources. Galaxy clusters exhibit strong IA and are therefore a natural candidate to look for a B-mode signal. We forecast the signal-to-noise ratio (SNR) for B-modes from IA of galaxy clusters in the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We use a perturbative model for the IA multipoles based on the Effective Field Theory of Intrinsic Alignments (EFT of IA), which has recently been validated against N-body simulations. We forecast SNR ≈12 and find that this detectability is not significantly impacted by different analysis choices. Lastly, we also apply our forecast to clusters in the redMaPPer SDSS and DESY1 samples. We find SNR ≈5 and SNR ≈3, respectively, suggesting a detection is within reach, provided accurate redshift information is available.

Decentralized federated domain generalization with cluster alignment for fault diagnosis

Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.

Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis

Domain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment, soft large margin clustering, and GE in a unified structural risk minimization framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through 36 tasks on 2 bearing databases.

The intrinsic alignment of galaxy clusters and impact of projection effects

ABSTRACT Galaxy clusters, being the most massive objects in the Universe, exhibit the strongest alignment with the large-scale structure. However, mis-identification of members due to projection effects from the large-scale structure can occur. We studied the impact of projection effects on the measurement of the intrinsic alignment of galaxy clusters, using galaxy cluster mock catalogues. Our findings showed that projection effects result in a decrease of the large-scale intrinsic alignment signal of the cluster and produce a bump at rp ∼ 1 h−1 Mpc, most likely due to interlopers and missed member galaxies. This decrease in signal explains the observed similar alignment strength between bright central galaxies and clusters in the SDSS $\rm redMaPPer$ cluster catalogue. The projection effect and cluster intrinsic alignment signal are coupled, with clusters having lower fractions of missing members or having higher fraction of interlopers exhibiting higher alignment signals in their projected shapes. We aim to use these findings to determine the impact of projection effects on galaxy cluster cosmology in future studies.

On the anisotropic distribution of clusters in the local Universe

ABSTRACT In his 2021 lecture to the Canadian Association of Physicists Congress, P.J.E. Peebles pointed out that the brightest extragalactic radio sources tend to be aligned with the plane of the de Vaucouleur Local Supercluster up to redshifts of z = 0.02 ($d_{\rm MW}\approx 85~\rm {Mpc}$). He then asked whether such an alignment of clusters is anomalous in the standard Lambda cold dark matter (ΛCDM) framework. In this letter, we employ an alternative, absolute orientation agnostic, measure of the anisotropy based on the inertia tensor axial ratio of these brightest sources and use a large cosmological simulation from the flamingo suite to measure how common such an alignment of structures is. We find that only 3.5 per cent of randomly selected regions display an anisotropy of their clusters more extreme than the one found in the local Universe’s radio data. This sets the region around the Milky Way as a 1.85σ outlier. Varying the selection parameters of the objects in the catalogue, we find that the clusters in the local Universe are never more than 2σ away from the simulations’ prediction for the same selection. We thus conclude that the reported anisotropy, whilst noteworthy, is not in tension with the ΛCDM paradigm.

Clumppling: cluster matching and permutation program with integer linear programming.

In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K. Clumppling is available at https://github.com/PopGenClustering/Clumppling.

IC-GAN: An Improved Conditional Generative Adversarial Network for RGB-to-IR image translation with applications to forest fire monitoring

This paper introduces a novel Deep Learning (DL) architecture for inferring temperature information from aerial true-color RGB images by transforming them into Infrared Radiation (IR) domain. This work is motivated by a few facts. First, off-the-shelf contemporary drones are typically equipped only with regular cameras. Second, IR heat-mapping cameras are costly and heavy for payload-limited drones. Third, additional communication channels and power supply would be needed when including IR cameras. Finally, IR cameras provide lower resolution and shorter distance ranges than RGB cameras. Therefore, learning-based translation of aerial IR recordings to RGB images can be extremely useful not only for new tests but also for offline processing of the currently available forest fire datasets with RGB images. We offer an Improved Conditional-Generative Adversarial Network (IC-GAN), where matched IR images are used as a condition to guide the translation process by the generator. The U-Net-based generator is concatenated with a mapper module to transform the output into a stack of diverse color spaces with learnable parameters. To avoid the unnecessary penalization of pixel-level disparities and achieve structural similarity, we include clustering alignment to the loss function. The proposed framework is compared against several state-of-the-art methods, including U-Net, Efficient U-Net, GAN, and Conditional-GAN from both subjective (human perception) and objective evaluation perspectives. The results support our method’s efficacy, demonstrating a significant improvement of around 6% in PSNR, 15% in UQI, 9% in SSIM, and 23% in IoU metrics.