Local Maximum Research Articles

Autonomous design of noise-mitigating structures using deep reinforcement learning.

This paper explores the application of deep reinforcement learning for autonomously designing noise-mitigating structures. Specifically, deep Q- and double deep Q-networks are employed to find material distributions that result in broadband noise mitigation for reflection and transmission problems. Unlike conventional deep learning approaches which require prior knowledge for data labeling, the double deep Q-network algorithm learns configurations that result in broadband noise mitigations without prior knowledge by utilizing pixel-based inputs. By employing unified hyperparameters and network architectures for transmission and reflection problems, the capability of the algorithms to generalize over different environments is demonstrated. In addition, a comparison with a genetic algorithm highlights the potential for generalized design in complex environments, despite the algorithms tending to predict local maxima. Furthermore, we examine the impact of hyperparameters and environment types on agent performance. The autonomous design approach offers generalized learning while avoiding restrictions to specific shapes or prior knowledge of the task.

Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Robust Methods for Assessing the Characteristics of Locomotor Activity Based on Markerless Video Capture Data

Introduction. Analysis of locomotor activity is essential in a number of biomedical and pharmacological research designs, as well as environmental monitoring. The movement trajectories of biological objects can be represented by time series exhibiting a complex multicomponent structure and non-stationary dynamics, thus limiting the effectiveness of conventional correlation and spectral time series analysis methods. Recordings obtained using markerless technologies are typically characterized by enhanced noise levels, including both instrumental noise and anomalous errors associated with false estimates of the location of the points of interest, as well as gaps in the trajectories, promoting an urgent need in the development of robust methods to assess the characteristics of locomotor activity.Aim. Development of robust methods for assessing the characteristics of locomotor activity capable of efficient processing of noisy recordings obtained by markerless video-based motion capture systems.Materials and methods. In order to assess the characteristics of locomotor activity, the relative movements of body parts of laboratory animals were analyzed using the stability metrics of the mutual dynamics of their trajectories, their relative delays, as well as the relative duration of the recording fragments when relatively stable mutual dynamics could be observed. The local maxima of the cross-correlation function of two body fragments, the minima of the standard deviation of the difference between their Hilbert phases, as well as their relative delays, were used as the metrics of mutual dynamics.Results. The considered phase metrics were shown to explicitly reflect changes in locomotor activity, while the assessment of time delays using phase metric was shown to be prone to periodic error. The above limitation could be largely overcome using the correlation metrics, assuming that phase and correlation metrics could be combined.Conclusion. The proposed robust methods provide stable estimates of the characteristics of locomotor activity based on markerless video capture recordings, altogether increasing the efficiency of diagnostic procedures and assessment of the therapeutic effect during rehabilitation.

Tunable elastic wave transmission and resonance in a periodically aligned tube-block structure

A tube-block structure is proposed to realize tunable elastic wave transmission and resonance, consisting of periodically aligned circular tubes sandwiched and joined by two blocks. Finite element simulations for a unit structure are carried out to reveal the frequency dependence of the transmission behavior for the normal incidence of longitudinal and transverse waves in the tube-block structure. As a result, the transmission ratios are found to take multiple local maxima at different peak frequencies. Eigenfrequency analysis shows that the local resonances of the tube and the block surfaces occur at the peak frequencies in the transmission ratios. The peak frequencies originating from the local resonance of the tube depend on its radius and thickness, while those from the resonance on the block surfaces are in good agreement with the theoretical relation between the interval of the periodically aligned tubes and the wavelength of the Rayleigh wave. Furthermore, when the tube-block structure is subjected to compressive loading, the deformation shifts the peak frequencies of the transmission ratio corresponding to the local resonance of the tube. This result implies that the proposed structure has the potential to serve as a tunable meta-interface between solid blocks.

Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

Condensate and superfluid fraction of homogeneous Bose gases in a self-consistent Popov approximation

We study the condensate and superfluid fraction of a homogeneous gas of weakly interacting bosons in three spatial dimensions by adopting a self-consistent Popov approximation, comparing this approach with other theoretical schemes. Differently from the superfluid fraction, we find that at finite temperature the condensate fraction is a non-monotonic function of the interaction strength, presenting a global maximum at a characteristic value of the gas parameter, which grows as the temperature increases. This non-monotonic behavior has not yet been observed, but could be tested with the available experimental setups of ultracold bosonic atoms confined in a box potential. We clearly identify the region of parameter space that is of experimental interest to look for this behavior and provide explicit expressions for the relevant observables. Finite size effects are also discussed within a semiclassical approximation.

MODIFICATION OF A QUANTUM-INSPIRED GENETIC ALGORITHM FOR NUMERICAL OPTIMIZATION USING QUDIT UNDER CONDITIONS OF SIMULATING QUANTUM DECOHERENCE

The genetic algorithm for numerical optimization (GA) of the metaheuristic class is a method for finding optimal solutions based on the biological principles of natural selection and variability. GA is characterized by high operating speed, resistance to noise in the data, low probability of hitting the local extremum of the multimodal objective function, as well as the simultaneous application of probabilistic and deterministic rules for generating search space points. An alternative to the classical GA is the quantum-inspired genetic algorithm for numerical optimization (QIGA), which has advantages that are unattainable for GA by using the concepts and principles of quantum computing. The article proposes a new approach to the implementation of a quantum-inspired genetic numerical optimization algorithm for searching for the global maximum of the objective function, based on modeling the functioning of the GA by simulating the execution of quantum calculations based on qudit in the conditions of the existence of quantum decoherence in the era of noisy medium-scale quantum algorithms. For this purpose, to carry out quantum operations of rotating the states of multilevel quantum systems, the paper presents a density matrix based on Heisenberg-Weyl operators as an analogue of the Bloch sphere for qudits. The simulation of quantum decoherence is interpreted from the point of view of the influence of extraneous noise emanating from the environment on the qudit and is presented as the use of a normal random variable with zero mathematical expectation and unit variance in quantum gates. At the same time, the work presents detailed pseudocodes of the functioning of both the most modified quantum-inspired genetic algorithm for numerical optimization and its individual operations. Testing is carried out by conducting computational experiments with the implementation of a modified algorithm on two-dimensional and multidimensional functions of test optimization problems, as well as when solving an applied optimization problem of planning hybrid flow production in the manufacturing industry based on financial costs and solving the problem of increasing forecasting accuracy based on compact extreme learning machines. The experimental results demonstrate the superiority of the new algorithm over QIGA and classical optimization algorithms in the accuracy of the solution, the speed of convergence with the target value of the global maximum and the execution time of the algorithm.

In Migratio Noncovalent Fluorophore Labeling of Proteins by Propidium Iodide in Sodium Dodecyl Sulfate Capillary Gel Electrophoresis.

Sodium dodecyl sulfate capillary gel electrophoresis is one of the frequently used methods for size-based protein separation in molecular biology laboratories and the biopharmaceutical industry. To increase throughput, quite a few multicapillary electrophoresis systems have been recently developed, but most of them only support fluorescence detection, requiring fluorophore labeling of the sample proteins. To avoid the time-consuming derivatization reaction, we developed an on-column labeling approach utilizing propidium iodide for the first time in SDS-CGE of proteins, a dye only used before for nucleic acid analysis. As a key ingredient of the gel-buffer system, the oppositely migrating positively charged propidium ligand in migratio complexes with the SDS-proteins, therefore, supports in situ labeling during the electrophoretic separation process, not requiring any extra pre- or postcolumn derivatization step. A theoretical treatment is given to shed light on the basic principles of this novel online labeling process, also addressing the influence of propidium iodide on the electroosmotic flow, resulting in reduced retardation. The concept of propidium labeling in SDS-CGE was first demonstrated using a commercially available protein sizing ladder ranging from 6.5 to 200 kDa with different isoelectric points and post-translational modifications. Considering the increasing number of protein therapeutics on the market next, we focused on the labeling optimization of a therapeutic monoclonal antibody and its subunits, including the addition of the nonglycosylated heavy chain. Peak efficiency and resolution were compared between noncovalent and covalent labeling. The effect of ligand concentration on the effective and apparent electrophoretic mobility, the resulting peak area, and the resolution were all evaluated in view of the theoretical considerations. The best detection sensitivity for the intact monoclonal antibody was obtained by using 200 μg/mL propidium iodide in the separation medium (LOD 2 μg/mL, 1.35 × 10-8 M) with excellent detection linearity over 3 orders of magnitude. On the other hand, the resolution between the biopharmaceutical protein test mixture components containing the intact and subunit fragments of the therapeutic monoclonal antibody was very good in the ligand concentration range of 50-200 μg/mL, but using the local maximum at 100 μg/mL for the nonglycosylated/glycosylated heavy chain pair is recommended. The figures of merit, including precision, sensitivity, detection linear range, and resolution for a sample mixture in hand, can be optimized by varying the propidium iodide concentration in the gel-buffer system, as demonstrated in this paper.

PIN2-mediated self-organizing transient auxin flow contributes to auxin maxima at the tip of Arabidopsis cotyledons.

Directional auxin transport and formation of auxin maxima are critical for embryogenesis, organogenesis, pattern formation, and growth coordination in plants, but the mechanisms underpinning the initiation and establishment of these auxin dynamics are not fully understood. Here we show that a self-initiating and -terminating transient auxin flow along the marginal cells (MCs) contributes to the formation of an auxin maximum at the tip of Arabidopsis cotyledon that globally coordinates the interdigitation of puzzle-shaped pavement cells in the cotyledon epidermis. Prior to the interdigitation, indole butyric acid (IBA) is converted to indole acetic acid (IAA) to induce PIN2 accumulation and polarization in the marginal cells, leading to auxin flow toward and accumulation at the cotyledon tip. When IAA levels at the cotyledon tip reaches a maximum, it activates pavement cell interdigitation as well as the accumulation of the IBA transporter TOB1 in MCs, which sequesters IBA to the vacuole and reduces IBA availability and IAA levels. The reduction of IAA levels results in PIN2 down-regulation and cessation of the auxin flow. Hence, our results elucidate a self-activating and self-terminating transient polar auxin transport system in cotyledons, contributing to the formation of localized auxin maxima that spatiotemporally coordinate pavement cell interdigitation.

Approach to Detect Windows Malware Based on Malicious Tendency Image and ResNet Algorithm

Timely detection of self-replicating malware in the high market share Windows operating system can effectively prevent personal or corporate financial losses. The form and characteristics of malware are constantly evolving, leading to a concept drift issue that gradually decreases the effectiveness of traditional detection methods. Therefore, we propose WinMDet, a Windows malware detection method based on malicious tendency image and ResNet algorithm. First, to tackle the complexity and difficulty in accurately characterizing malware features, WinMDet retains detailed malware features and encodes them into malicious tendency images to better describe malware across different periods. Secondly, WinMDet utilizes previously generated malicious tendency images to train the initial detection model. Then, to alleviate the issue of malware concept drift, WinMDet employs Local Maximum Mean Discrepancy (LMMD) as the criterion for model transfer, enhancing the initial detection model’s ability to distinguish between malware and benign software. We conducted a comprehensive evaluation of WinMDet using common metrics such as accuracy, precision and recall. The results indicate that WinMDet performs remarkably well in terms of accuracy, exceeding 82%. Additionally, significant improvements were observed in precision and recall, surpassing 82.42% and 82.06%, respectively. After employing our LMMD-based transfer method, the initial detection model improved the detection accuracy of malware in 2021 and 2022 by approximately 4.22% to 8.06%. The false negative rate decreased by at most 4.34%, and the false positive rate decreased by at most 4.61%.

Advanced Method for Improving Marine Target Tracking Based on Multiple-Plot Processing of Radar Images

Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide.

Bimodalność i długoterminowe trendy wartości ekstremalnych temperatury powietrza

Histograms of air temperature with a bimodal shape are commonly observed in many regions of the world. In this study, we investigate the causes of bimodality in the histograms of daily temperature series (minimum, average, and maximum) for selected climatological stations in Slovakia. Our findings suggest that in the Central European region, the bimodal shape of air temperature histograms is mainly due to the latent heat of freezing, as the surface of snow and ice and the air are thermally coupled. The asymmetry in the air temperature histograms is due to the lower mass heat capacity of ice compared to water and air. The energy-intensive latent heat of conversion of ice to water (and vice versa) results in the more frequent occurrence of ground-layer air temperatures around the freezing point, leading to the formation of the observed local maximum. This has far-reaching implications, such as the calculation of the annual mean air temperature at climatological stations. When calculating the average air temperature, negative temperatures should be given less weight than positive temperatures. Temperatures around 0-6°C should be given higher weight. This may also explain why Arctic regions are experiencing more significant warming than equatorial regions. In the second part of this paper, we analyze the long-term trends of selected temperature indices for the climatological station at Hurbanovo (Slovakia) from 1871 to 2020. Our results indicate statistically significant changes in all temperature indices, with indices related to cold temperatures increasing more significantly than those associated with high temperatures. Finally, study examines theoretical probability distributions to estimate T-year temperatures for temperature indices at the Hurbanovo climate station in Slovakia. The analysis includes three time periods (1901–1960, 1961–2020, and 1991–2020) and reveals significant changes in temperature indices at the Hurbanovo station. The 100-year temperature of TN,min was –35.75°C in 1901–1960, –28.69°C in 1961–2020, and –26.52°C in 1991–2020. The 100-year temperature of TX,max was 39.4°C in 1901–1960 and 39.63°C in 1961–2020. TN,min showed the most significant changes, with the 100-year temperature increasing by up to 7.06°C in 1961–2020 and up to 9.23°C in 1991–2020.

Attenuation of non-stationary random noise in ground penetrating radar data based on time-varying filtering

The attenuation of random noise is significant in the enhancement of the signal-to-noise ratio in ground penetrating radar (GPR) data. However, the non-stationary nature of random noise poses significant challenges to most conventional denoising methods. We propose a time-varying bandpass filter (TVBF) based on the local maxima multiple synchrosqueezing transform (LMMSST), referred to as TVBFLMMSST, for the attenuation of non-stationary random noise in GPR data. The LMMSST method is introduced to achieve highly compressed time–frequency representation (TFR). By integrating an instantaneous frequency trajectory detection algorithm, we design the TVBF to successfully separate useful signals from random noise. The efficacy of the TVBFLMMSST is validated through synthetic and field experiments, demonstrating its capability to effectively isolate components of useful signals and noise signals. Compared to classical denoising methods, TVBFLMMSST exhibits robust performance in attenuating random noise while preserving the amplitude of useful signals.

Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data

Background: Integrating multi-omics data for cancer classification brings complementary biological insights while also facing challenges such as data integration, gene grouping, and adaptive weight construction. Objective: This paper aims to address the challenges faced by the cancer subtype classification and gene screening based on multi-omics data. Methods: Multinomial logistic regression with adaptive regularization (MLRAR) was proposed by integrating DNA methylation, gene mutation, and RNA-seq information. A data preprocessing strategy that effectively utilizes multi-omics information was presented, and the local maximum quasiclique merging (lmQCM) algorithm was implemented to group genes. Biological pathway information was utilized to evaluate the significance of gene groups, while the significance of each gene within a group was evaluated by integrating mutation information, information theory, and methylation information. Results: Compared to MRlasso, MRGL, MSGL, MROGL, AMRSOGL, and AGLRMR, the proposed method yielded improvements in subtype classification accuracy of breast cancer by 2.6%, 2.9%, 3.5%, 2.3%, 2.0%, and 1.8%, respectively. In addition, MLRAR also achieved significant improvements in ovarian cancer by 8.2%, 5.0%, 6.8%, 5.2%, 12.7%, and 6.3%, respectively. Conclusion: The proposed method can effectively deal with data integration, gene grouping, and adaptive weight construction.

Manipulation of Cooper-Pair Tunneling via Domain Structure in a van der Waals Ferromagnetic Josephson Junction.

Ferromagnetic Josephson junctions play a key role in understanding the interplay between superconductivity and ferromagnetism in condensed matter physics. The magnetic domain structures of the ferromagnet in such junctions can significantly affect the tunneling of the superconducting Cooper pairs due to the strong interactions between Cooper pairs and local magnetic moments in the ferromagnetic tunnel barrier. However, the underlying microscopic mechanism of relevant quasiparticle tunneling processes with magnetic domain structures remains largely unexplored. Here, the manipulation of Cooper-pair tunneling in the NbSe2/Cr2Ge2Te6/NbSe2 ferromagnetic Josephson junction is demonstrated by using a multidomain ferromagnetic barrier with anisotropic magnetic moments. The evolution of up-, down-magnetized domain and Bloch domain structures in Cr2Ge2Te6 barrier under external magnetic fields leads to the enhancement of the critical tunneling supercurrent and an unconventional dual-peak feature with two local maxima in the field-dependent critical current curve. The phenomenon of magnetic-field-modulated critical tunneling supercurrent can be well explained by the competition between the coherence length of tunneling Cooper pairs and the size of magnetic domain walls in Cr2Ge2Te6 barrier. This kind of ferromagnetic Josephson junction provides an intriguing material system for manipulating Cooper-pair tunneling by tuning the local magnetic moments within magnetic Josephson junction devices.

Chess Game Rook‐Icon Movement‐Based Photovoltaic Array Reconfiguration for Higher Shade Dispersion and Improved Global Power Peaks Under Partial Shading Conditions: An Experimental Study

Solar photovoltaic (PV) system generates low power because of nonuniform sun irradiation compared to standard test conditions. Partial shading conditions (PSCs) can arise from various factors, with passing clouds, trees, telecommunication towers, bird droppings, and similar elements being among the most prevalent causes. Under the shading scenario, multiple global and local maxima power points exist on the P–V curves. Herein, the chess game rules for the “Rook” icon are followed to attain the optimal placement of integer numbers (1–9) in the 9 × 9 size game puzzle matrix. The integer number‐based game puzzles such as Su‐Do‐Ku, symmetric matrix, and proposed Rook's movement‐game theory (RMGT) to rearrange the PV modules in arrays are investigated and compared with the standard arrangements such as series‐parallel, total‐cross‐tied during PSCs. The suggested RMGT puzzle‐based arrangement is able to achieve higher power at global maximum power point, performance enhancement, fill factor, and low power loss. As a result, the experimental study proves the superiority of RMGT configuration compared to conventional methodologies under the shading scenarios. The proposed RMGT‐based PV array is tested and obtained the performance higher side as, , , , and compared to existing reconfiguration methodologies.

Assessment of Multi-Physical Fields Reconstruction Approaches in Three-Dimensional Supersonic Flow with Shocks

The paper evaluates the application of several multi-physical fields reconstruction approaches based on the Tomographic Particle Image Velocimetry (PIV) in three-dimensional supersonic flows with shock waves, including the pressure, temperature, and density. The MacCormack method and the Flux Vector Splitting method with outstanding performance in two-dimensional supersonic flow are extended into three-dimensional forms. The conventional Poisson method is also considered because of its widespread application and high accuracy in subsonic and transonic flow. All of these approaches are established by the conservative Navier-Stokes equations and solved by the time-marching iteration process, combined with the perfect gas law and adiabatic flow assumption. The performances are evaluated by numerical velocimetry data. To simulate the typical three-dimensional features, the cases of uniform freestream at Mach 0.78 and 3 past a cone with a 20° half-angle are selected to obtain a sufficient spanwise velocity component. The results confirm the feasibility of the PIV-based reconstruction methods in the conical flow field. The conventional Poisson method performs well only in the subsonic case while the Flux Vector Splitting method has a better performance in supersonic flow, including higher accuracy, stability, and efficiency, with a low-level root-mean-square error of 0.449% and local maximum relative error of 1%.

A novel adaptive and modified controller for tracking global peak under partial shading conditions

The present state of Maximum Power Point Tracking (MPPT) techniques face challenges in accurately tracking the Global Maximum Power Point (GMPP) under non-uniform conditions. Swift transitions between operational points, significant transient fluctuations, and imprecise tracking contribute to notable power losses. To address these issues, a novel modified Perturb and Observe (P&O) algorithm is proposed. It mitigates persistent oscillations and enhances tracking accuracy in photovoltaic (PV) systems. Compared to traditional P&O and incremental conductance methods, the proposed algorithm achieves approximately 9.52 % higher average MPPT efficiency. Even under challenging conditions like partial shading, it maintains superior performance with faster tracking and less complexity. The algorithm ensures proper power sharing, achieving an average maximum stabilized power efficiency of 98.48 % with a tracking time below 0.06 s. Notably, it exhibits a 16.5 % increase in power increment across various shading scenarios. Additionally, the proposed method complies with IEC 60904-3 standards, minimizing voltage and current deviations during irradiance dynamics. MATLAB/Simulink and hardware experiments confirm the efficacy of the algorithm, demonstrating improved stability and reduced settling time. The proposed method outperforms existing algorithms, exhibiting the lowest mismatch power loss and achieving a remarkable 15.08 % enhanced power increment. This research contributes to advancing MPPT techniques, offering improved performance and stability in PV systems.

A new method for individual treetop detection with low-resolution aerial laser scanned data

In the past decade, the use of three-dimensional forest information from airborne Light Detection and Ranging (LiDAR) has become widespread in forest inventories. Accurate Individual Treetop Detection (ITD) and crown boundary delineation using LiDAR data are critical for obtaining precise inventory metrics. To address this need, we introduced a novel growing tree region (GTR)-driven ITD method that utilizes canopy height models (CHM) derived from very low-resolution airborne LiDAR data. The GTR algorithm consists of three key stages: (i) preserving all height layers through incremental cutting and stacking of CHM; (ii) employing a three-layer concept to identify individual treetops; and (iii) refining the detected treetops using a distance-based filter. Our method was tested in five temperate forests across Central Europe and was compared against the widely-used local maxima (LM) search combined with an optimized variable window filtering (VWF) technique. Our results showed that the GTR method outperformed LM with VWF, particularly in forests with high canopy density. The achieved root mean square accuracies were 74% for the matching rate, 19% for commission errors, and 27% for omission errors. In comparison, the LM with the VWF method resulted in a matching rate of 71%, commission errors of 20%, and omission errors of 31%. To facilitate the application of our algorithm, we developed an R package called TREETOPS, which seamlessly integrates with the lidR package, ensuring compatibility with existing treetop-based segmentation methods. By introducing TREETOPS, we provide the most accurate open-source tool for detecting treetops using low-resolution LiDAR-derived CHM.