arXiv subjects

Richard Green

Publications and source records attributed to Richard Green.

At least 37 records · Page 2Linked to original sources

arXiv2025

Missing Money and Market-Based Adequacy in Deeply Decarbonized Power Systems with Long-Duration Energy Storage

The ability of deeply decarbonised power systems to ensure adequacy may increasingly depend on long-duration energy storage (LDES). A central challenge is whether capacity markets (CMs), originally designed around thermal generation, can provide efficient investment signals when storage becomes a central participant. While recent studies have advanced methods for accrediting variable renewables and short-duration storage, the effectiveness of these methods in CMs with substantial LDES penetration remains largely unexplored. To address this gap, we extend a two-stage stochastic equilibrium investment model by endogenising continuous, duration-based capacity accreditation for storage and apply it to a Great Britain-based case using 40 years of weather-driven demand and renewable profiles under varying emission limits. Results show that well-calibrated CMs can sustain near-efficient investment and mitigate revenue volatility, but their effectiveness diminishes in deeply decarbonized systems, underscoring both their potential and the regulatory challenges of supporting large-scale LDES.

eess.SY↗

arXiv2025

The Discovery of Little Red Dots in the Local Universe: Signatures of Cool Gas Envelopes

JWST observations have revealed a population of high-redshift "little red dots" (LRDs) that challenge conventional AGN models. We report the discovery of three local LRDs at $z = 0.1$-$0.2$, initially selected from the SDSS database, with follow-up optical/near-IR spectroscopy and photometry. They exhibit properties fully consistent with those of high-redshift LRDs, including broad hydrogen and helium emission lines, compact morphologies, V-shaped UV-optical SED, declining near-IR continua, and no significant variability. Two sources were targeted but not detected in X-rays with statistical significance. All three sources show blue-shifted He I absorption, while two exhibit H$α$ and Na D absorption lines. We detect full Balmer and Paschen line series in all three objects, along with abundant narrow [Fe II] emission in two. The emission line analyses suggest narrow lines originate from AGN-powered, metal-poor regions with minimal dust; broad lines come from inner regions with exceptionally high density or atypical dust properties; and [Fe II] emission arises from dense gas between broad and narrow-line regions. One of our objects, J1025+1402 (nicknamed $The~Egg$), shows extremely high equivalent width Na D, K I, and Ca II triplet absorption lines, along with other potential low-ionization absorption features, suggesting the presence of a cool ($\sim$5000 K), metal-enriched gas envelope. The optical/near-IR continua of these LRDs are also consistent with theoretical models featuring an atmosphere around black holes. The WISE-detected IR emission is consistent with weak dust emission of $T \sim 10^2-10^3$ K. We propose a conceptual model consisting of a largely thermalized cool-gas envelope surrounding the central black hole and an extended emission line region with high-density outflowing gas to explain the observed properties of these local LRDs.

astro-ph.GA↗

arXiv2025

Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images

Traditional stereo matching algorithms like Semi-Global Block Matching (SGBM) with Weighted Least Squares (WLS) filtering offer speed advantages over neural networks for UAV applications, generating disparity maps in approximately 0.5 seconds per frame. However, these algorithms require meticulous parameter tuning. We propose a Genetic Algorithm (GA) based parameter optimization framework that systematically searches for optimal parameter configurations for SGBM and WLS, enabling UAVs to measure distances to tree branches with enhanced precision while maintaining processing efficiency. Our contributions include: (1) a novel GA-based parameter optimization framework that eliminates manual tuning; (2) a comprehensive evaluation methodology using multiple image quality metrics; and (3) a practical solution for resource-constrained UAV systems. Experimental results demonstrate that our GA-optimized approach reduces Mean Squared Error by 42.86% while increasing Peak Signal-to-Noise Ratio and Structural Similarity by 8.47% and 28.52%, respectively, compared with baseline configurations. Furthermore, our approach demonstrates superior generalization performance across varied imaging conditions, which is critcal for real-world forestry applications.

cs.CV↗

arXiv2025

YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications

Manual pruning of radiata pine trees poses significant safety risks due to extreme working heights and challenging terrain. This paper presents a computer vision framework that integrates YOLO object detection with Semi-Global Block Matching (SGBM) stereo vision for autonomous drone-based pruning operations. Our system achieves precise branch detection and depth estimation using only stereo camera input, eliminating the need for expensive LiDAR sensors. Experimental evaluation demonstrates YOLO's superior performance over Mask R-CNN, achieving 82.0% mAPmask50-95 for branch segmentation. The integrated system accurately localizes branches within a 2 m operational range, with processing times under one second per frame. These results establish the feasibility of cost-effective autonomous pruning systems that enhance worker safety and operational efficiency in commercial forestry.

cs.CV↗

arXiv2025

Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems

UAV-based autonomous forestry operations require rapid and precise tree branch segmentation for safe navigation and automated pruning across varying pixel resolutions and operational conditions. We evaluate different deep learning methods at three resolutions (256x256, 512x512, 1024x1024) using the Urban Street Tree Dataset, employing standard metrics (IoU, Dice) and specialized measures including Thin Structure IoU (TS-IoU) and Connectivity Preservation Rate (CPR). Among 22 configurations tested, U-Net with MiT-B4 backbone achieves strong performance at 256x256. At 512x512, MiT-B4 leads in IoU, Dice, TS-IoU, and Boundary-F1. At 1024x1024, U-Net+MiT-B3 shows the best validation performance for IoU/Dice and precision, while U-Net++ excels in boundary quality. PSPNet provides the most efficient option (2.36/9.43/37.74 GFLOPs) with 25.7/19.6/11.8 percentage point IoU reductions compared to top performers at respective resolutions. These results establish multi-resolution benchmarks for accuracy-efficiency trade-offs in embedded forestry systems. Implementation is available at https://github.com/BennyLinntu/PerformanceTreeBranchSegmentation.

cs.CV↗

arXiv2025

Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications

Autonomous UAV forestry operations require robust depth estimation methods with strong cross-domain generalization. However, existing evaluations focus on urban and indoor scenarios, leaving a critical gap for specialized vegetation-dense environments. We present the first systematic zero-shot evaluation of eight state-of-the-art stereo methods--RAFT-Stereo, IGEV, IGEV++, BridgeDepth, StereoAnywhere, DEFOM (plus baseline methods ACVNet, PSMNet, TCstereo)--spanning iterative refinement, foundation model, and zero-shot adaptation paradigms. All methods are trained exclusively on Scene Flow and evaluated without fine-tuning on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury forestry dataset captured with ZED Mini camera (1920x1080). Performance reveals scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D, 0.83-1.07 px on KITTI; DEFOM: 0.35-4.65 px across benchmarks), while iterative methods maintain cross-domain robustness (IGEV++: 0.36-6.77 px; IGEV: 0.33-21.91 px). Critical finding: RAFT-Stereo exhibits catastrophic ETH3D failure (26.23 px EPE, 98 percent error rate) due to negative disparity predictions, while performing normally on KITTI (0.90-1.11 px). Qualitative evaluation on Canterbury forestry dataset identifies DEFOM as the optimal gold-standard baseline for vegetation depth estimation, exhibiting superior depth smoothness, occlusion handling, and cross-domain consistency compared to IGEV++, despite IGEV++'s finer detail preservation.

cs.CV↗

arXiv2025

Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices

q-bio.OT↗

arXiv2025

Call to Protect the Dark and Quiet Sky from Harmful Interference by Satellite Constellations

The growing number of satellite constellations in low Earth orbit (LEO) enhances global communications and Earth observation, and support of space commerce is a high priority of many governments. At the same time, the proliferation of satellites in LEO has negative effects on astronomical observations and research, and the preservation of the dark and quiet sky. These satellite constellations reflect sunlight onto optical telescopes, and their radio emission impacts radio observatories, jeopardising our access to essential scientific discoveries through astronomy. The changing visual appearance of the sky also impacts our cultural heritage and environment. Both ground-based observatories and space-based telescopes in LEO are affected, and there are no places on Earth that can escape the effects of satellite constellations given their global nature. The minimally disturbed dark and radio-quiet sky is crucial for conducting fundamental research in astronomy and important public services such as planetary defence, technology development, and high-precision geolocation. Some aspects of satellite deployment and operation are regulated by States and intergovernmental organisations. While regulatory agencies in some States have started to require operators to coordinate with their national astronomy agencies over impacts, mitigation of the impact of space objects on astronomical activities is not sufficiently regulated. To address this issue, the CPS urges States and the international community to take steps to protect the dark and quiet sky as specified in this paper.

astro-ph.IM↗

arXiv2024

Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration

This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to SGBM, deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process using deep neural networks is applied to approximate optimal depth values. This methodology facilitates precise branch detection and distance measurement, addressing critical challenges in the automation of pruning operations. The results demonstrate notable advancements in both accuracy and efficiency, underscoring the potential of deep learning to drive innovation and enhance automation in the agricultural sector.

cs.CV↗

arXiv2024

Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models

Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera, enabling precise detection and trimming of branches. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection, while the Semi-Global Matching algorithm is integrated to provide reliable distance estimation. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning. Experimental results demonstrate that the combined implementation of YOLO and SGBM enables the drone to accurately detect branches and measure their distances from the drone. This research not only improves the safety and efficiency of pruning operations but also makes a significant contribution to the advancement of drone technology in the automation of agricultural and forestry practices, laying a foundational framework for further innovations in environmental management.

cs.CV↗

arXiv2024

Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis

This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912 percent was found for the calculated relative weight distribution difference, and an accuracy of 83.33 percent was achieved on diagnoses.

cs.CV↗

arXiv2024

Gemini Near Infrared Spectrograph -- Distant Quasar Survey: Rest-Frame Ultraviolet-Optical Spectral Properties of Broad Absorption Line Quasars

We present the rest-frame ultraviolet-optical spectral properties of 65 broad absorption line (BAL) quasars from the Gemini Near Infrared Spectrograph-Distant Quasar Survey (GNIRS-DQS). These properties are compared with those of 195 non-BAL quasars from GNIRS-DQS in order to identify the drivers for the appearance of BALs in quasar spectra. In particular, we compare equivalent widths and velocity widths, as well as velocity offsets from systemic redshifts, of principal emission lines. In spite of the differences between their rest-frame ultraviolet spectra, we find that luminous BAL quasars are generally indistinguishable from their non-BAL counterparts in the rest-frame optical band at redshifts $1.55 \lesssim z \lesssim 3.50$. We do not find any correlation between BAL trough properties and the H$β$-based supermassive black hole masses and normalized accretion rates in our sample. Considering the Sloan Digital Sky Survey quasar sample, which includes the GNIRS-DQS sample, we find that a monochromatic luminosity at rest-frame 2500 A of $\gtrsim 10^{45}$ erg s$^{-1}$ is a necessary condition for launching BAL outflows in quasars. We compare our findings with other BAL quasar samples and discuss the roles that accretion rate and orientation play in the appearance of BAL troughs in quasar spectra.

astro-ph.GA↗

arXiv2024

Sustainable Skies and the Earth-Space Environment

The rapid launch of hundreds of thousands of satellites into Low Earth Orbit will significantly alter our view of the sky and raise concerns about the sustainability of Earth's orbital space. A new framework for sustainable space development must balance technological advancement, protection of space environments, and our capacity to explore the Universe.

astro-ph.IM↗

arXiv2024

Topological plasma oscillations in the solar tachocline

We study the properties of plasma oscillations in the solar tachocline using shallow-water magnetohydrodynamic equations. These oscillations are expected to correlate with solar activity. We find new qualitative features in the equatorial spectrum of magnetohydrodynamic oscillations associated with magneto-Rossby and magneto-Yanai waves. By studying this spectrum in terms of band theory, we find that magneto-Kelvin and magneto-Yanai waves are topologically protected. This highlights the important role of these two classes of waves, as robust features of the plasma oscillation spectrum, in the interpretation of helioseismological observations.

astro-ph.SR↗

arXiv2023

Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization

This paper introduces Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree point cloud. Smart-Tree uses a sparse voxel convolutional neural network to extract the radius and direction towards the medial axis of each input point. A greedy algorithm performs robust skeletonization using the estimated medial axis. Our proposed method provides robustness to complex tree structures and improves fidelity when dealing with self-occlusions, complex geometry, touching branches, and varying point densities. We evaluate Smart-Tree using a multi-species synthetic tree dataset and perform qualitative analysis on a real-world tree point cloud. Our experimentation with synthetic and real-world datasets demonstrates the robustness of our approach over the current state-of-the-art method. The dataset and source code are publicly available.

cs.CV↗

arXiv2023

Gemini Near Infrared Spectrograph -- Distant Quasar Survey: Augmented Spectroscopic Catalog and a Prescription for Correcting UV-Based Quasar Redshifts

Quasars at $z~{\gtrsim}~1$ most often have redshifts measured from rest-frame ultraviolet emission lines. One of the most common such lines, C IV $λ1549$, shows blueshifts up to ${\approx}~5000~\rm{km~s^{-1}}$, and in rare cases even higher. This blueshifting results in highly uncertain redshifts when compared to redshift determinations from rest-frame optical emission lines, e.g., from the narrow [O III] $λ5007$ feature. We present spectroscopic measurements for 260 sources at $1.55~{\lesssim}~z~{\lesssim}~3.50$ having $-28.0~{\lesssim}~M_i~{\lesssim}~-30.0$ mag from the Gemini Near Infrared Spectrograph - Distant Quasar Survey (GNIRS-DQS) catalog, augmenting the previous iteration which contained 226 of the 260 sources whose measurements are improved upon in this work. We obtain reliable systemic redshifts based on [O III] $λ5007$ for a subset of 121 sources which we use to calibrate prescriptions for correcting UV-based redshifts. These prescriptions are based on a regression analysis involving C IV full-width-at-half-maximum intensity and equivalent width, along with the UV continuum luminosity at a rest-frame wavelength of 1350 A. Applying these corrections can improve the accuracy and the precision in the C IV-based redshift by up to ${\sim}~850~\rm{km~s^{-1}}$ and ${\sim}~150~\rm{km~s^{-1}}$, respectively, which correspond to ${\sim}~8.5$ Mpc and ${\sim}~1.5$ Mpc in comoving distance at $z~=~2.5$. Our prescriptions also improve the accuracy of the best available multi-feature redshift determination algorithm by ${\sim}~100~\rm{km~s^{-1}}$, indicating that the spectroscopic properties of the C IV emission line can provide robust redshift estimates for high-redshift quasars. We discuss the prospects of our prescriptions for cosmological and quasar studies utilizing upcoming large spectroscopic surveys.

astro-ph.GA↗

arXiv2023

Gemini Near Infrared Spectrograph - Distant Quasar Survey: Prescriptions for Calibrating UV-Based Estimates of Supermassive Black Hole Masses in High-Redshift Quasars

The most reliable single-epoch supermassive black hole mass ($M_{\rm BH}$) estimates in quasars are obtained by using the velocity widths of low-ionization emission lines, typically the H$β$ $\lambda4861$ line. Unfortunately, this line is redshifted out of the optical band at $z\approx1$, leaving $M_{\rm BH}$ estimates to rely on proxy rest-frame ultraviolet (UV) emission lines, such as C IV $\lambda1549$ or Mg II $\lambda2800$, which contain intrinsic challenges when measuring, resulting in uncertain $M_{\rm BH}$ estimates. In this work, we aim at correcting $M_{\rm BH}$ estimates derived from the C IV and Mg II emission lines based on estimates derived from the H$β$ emission line. We find that employing the equivalent width of C IV in deriving $M_{\rm BH}$ estimates based on Mg II and C IV provides values that are closest to those obtained from H$β$. We also provide prescriptions to estimate $M_{\rm BH}$ values when only C IV, only Mg II, and both C IV and Mg II are measurable. We find that utilizing both emission lines, where available, reduces the scatter of UV-based $M_{\rm BH}$ estimates by $\sim15\%$ when compared to previous studies. Lastly, we discuss the potential of our prescriptions to provide more accurate and precise estimates of $M_{\rm BH}$ given a much larger sample of quasars at $3.20 \lesssim z \lesssim 3.50$, where both Mg II and H$β$ can be measured in the same near-infrared spectrum.

astro-ph.GA↗

arXiv2023

Seeing the Fruit for the Leaves: Towards Automated Apple Fruitlet Thinning

Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet thinning decisions. Thinning requires accurately measuring the true crop load for individual apple trees to provide optimal thinning decisions on an individual basis. A challenging task due to the dense foliage obscuring the fruitlets within the tree structure. This paper presents the initial design, implementation, and evaluation details of the vision system for an automatic apple fruitlet thinning robot to meet this need. The platform consists of a UR5 robotic arm and stereo cameras which enable it to look around the leaves to map the precise number and size of the fruitlets on the apple branches. We show that this platform can measure the fruitlet load on the apple tree to with 84% accuracy in a real-world commercial apple orchard while being 87% precise.

cs.RO↗