Scanning Algorithm Research Articles

Improved voltage scanning algorithm based MPPT algorithm for PV systems under partial shading conduction

Maximum Power Point Tracking (MPPT) algorithms are crucial for maximizing power extraction from photovoltaic (PV) systems. Traditional MPPT methods often exhibit suboptimal performance under partial shading conditions. Hence, advanced MPPT algorithms have been developed to enhance efficiency in such scenarios. The voltage scanning-based MPPT algorithm is notable for its superior performance under partial shading, characterized by high tracking speed and efficiency. This study introduces a novel enhancement to this method—namely, a voltage skipping algorithm designed to further improve tracking speed. Unlike conventional scanning techniques, this approach dynamically calculates skipping voltages during operation, eliminating the need for prior knowledge of PV panel characteristics. Performance evaluation was conducted using a MATLAB/Simulink model of a PV system comprising 4 series panels and a boost converter, subjected to simulations under 5 distinct partial shading scenarios. Comparative analyses with established optimization algorithms such as particle swarm optimization, cuckoo search algorithm, and grey wolf optimization highlight the proposed method's effectiveness in terms of tracking speed and maximum power output. The proposed algorithm has worked with high efficiency of 99.28 %, 99.61 %, 99.13 %, 99.16 % and 99.58 % in 5 different scenarios, respectively. By using the proposed method, a significant superiority has been achieved over other methods, with tracking speeds of 0.26s, 0.22s, 0.2s, 0.22s and 0.26s, respectively.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

A real time data-driven dynamic glasius bionic neural network path planning algorithm for polar under-ice feature scanning by “Xinghai 1000” AUV

Using Autonomous Underwater Vehicle (AUV) to survey polar under-ice feature is crucial for studying polar marine environment and global climate change. Compared with seafloor terrain scanning, it faces several challenges, such as uncertain under-ice environment, lack of reliable positioning methods, and stringent constraints on recovery location. Thus, taking “Xinghai 1000” as analysis object, this paper proposes a real time data-driven dynamic glasius bionic neural network path planning algorithm (RDGPP), which consists of an online bi-level path planning module. In global planning module, dynamically hierarchical strategy is used to realize task area layering. On this basis, global coverage path is planned by combining neuron activity value updated which utilize real-time environmental information obtained by FLS sonar with multivariant strategy. Moreover, due to AUV recovery mode limitation, return strategy has been devised in multivariant strategy to ensure AUV automatic return to launch point, thereby reducing energy usage. In local planning module, Flagged bit and GBNN-driven A-star algorithm (FGA) is introduced to minimize deadlock escape time for AUV. Simulation experiments verify RDGPP algorithm advancement in key indicators such as turning times, running time, etc., and effectiveness is verified by “Xinghai 1000” under-ice experiment.

A new extraction method of GaN switch-HEMT small-signal model with capacitance scanning algorithm

A new extraction method of GaN switch-HEMT small-signal model with capacitance scanning algorithm

An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional Antennas.

Neighbor discovery and tracking with directional antennas in flying ad hoc networks (FANETs) is a challenging issue because of dispersed node distribution and irregular maneuvers in three-dimensional (3D) space. In this paper, we propose an adaptive 3D neighbor discovery and tracking algorithm in battlefield FANETs with directional antennas. With time synchronization, a flying node transmits/receives the neighbor discovery packets sequentially in each beam around it to execute a two-way handshake for neighbor discovery. The transmitting or receiving status of each discovery slot depends on the binary code corresponding to the identification of the node. Discovered neighbor nodes exchange their 3D positions in tracking slots periodically for node tracking, and the maximum tracking period is determined by node velocity, beamwidth, and the minimum distance between nodes. By configuring the relevant parameters, the proposed algorithm can also apply to two-dimensional planar ad hoc networks. The simulation results suggest that the proposed algorithm can achieve shorter neighbor discovery time and longer link survival time in comparison with the random scanning algorithm in scenarios with narrow beamwidth and wide moving area. When the frame length increases, the protocol overhead decreases but the average neighbor discovery time increases. The suitable frame length should be determined based on the network range, node count, beamwidth, and node mobility characteristics.

Solid-state laser (266 nm) as an alternative to ArF excimer laser (193 nm) for corneal reshaping: Comparative numerical study of the thermal effect.

Laser corneal reshaping is a safe and effective technique utilized to treat common vision disorders. An advanced laser delivery system equipped with a pulsed UV laser with specific parameters is used to ablate parts of the cornea surface to correct the existing refractive error. The argon fluoride (ArF) excimer pulsed gas laser at 193 nm is the most employed type in the commercial devices for such treatments. This laser is generated using a mixture of Argon, Fluorine, and a significant amount of Neon gases. However, due to the ongoing Russian-Ukraine war, the availability of Neon gas is currently very limited, as this region is considered the primary supplier of pure Neon gas. Consequently we suggest replacing the common ArF laser source in the commercial devices with a solid-state (forth harmonic neodymium-doped yttrium aluminum garnet laser at 266 nm). This replacement uses the same operation parameters, optics, and scanning algorithm. Parameters from five commercial devices (Zeiss MEL 90, Technolas TENEO 317, Alcon Wave Light EX 500, Schwind Amaris 750 s, OptoSystems MICROSCAN VISUM) were compared with those of the i-ablation device, a research device that uses a 266 nm laser source. Our goal is to reduce production costs through a simple modification that has a significant impact. Consequently, the present study aims to find an alternative laser source for the current ArF laser without exchanging the complete system's design. This recommendation is based on a numerical simulation study. The thermal effect on a human cornea model was numerically evaluated using finite-element solutions of Pennes' bioheat equation on the COMSOL platform by applying two laser wavelengths. The results demonstrated that changing the laser source significantly impacts the thermal effect, even with the same laser settings. All studied devices showed a reduction in the thermal effect to below 40°C, compared with nearly 100°C under ordinary conditions.

Identification of digital device hardware vulnerabilities based on scanning systems and semi-natural modeling

Objectives. The development of computer technology and information systems requires the consideration of issues of their security, various methods for detecting hardware vulnerabilities of digital device components, as well as protection against unauthorized access. An important aspect of this problem is to study existing methods for the possibility and ability to identify hardware errors or search for errors on the corresponding models. The aim of this work is to develop approaches, tools and technology for detecting vulnerabilities in hardware at an early design stage, and to create a methodology for their detection and risk assessment, leading to recommendations for ensuring security at all stages of the computer systems development process.Methods. Methods of semi-natural modeling, comparison and identification of hardware vulnerabilities, and stress testing to identify vulnerabilities were used.Results. Methods are proposed for detecting and protecting against hardware vulnerabilities: a critical aspect in ensuring the security of computer systems. In order to detect vulnerabilities in hardware, methods of port scanning, analysis of communication protocols and device diagnostics are used. The possible locations of hardware vulnerabilities and their variations are identified. The attributes of hardware vulnerabilities and risks are also described. In order to detect vulnerabilities in hardware at an early design stage, a special semi-natural simulation stand was developed. A scanning algorithm using the Remote Bitbang protocol is proposed to enable data to be transferred between OpenOCD and a device connected to the debug port. Based on scanning control, a verification method was developed to compare a behavioral model with a standard. Recommendations for ensuring security at all stages of the computer systems development process are provided.Conclusions. This paper proposes new technical solutions for detecting vulnerabilities in hardware, based on methods such as FPGA system scanning, semi-natural modeling, virtual model verification, communication protocol analysis and device diagnostics. The use of the algorithms and methods thus developed will allow developers to take the necessary measures to eliminate hardware vulnerabilities and prevent possible harmful effects at all stages of the design process of computer devices and information systems.

ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.

Highly reliable DHR-based polar compilation code communication method

The advent of fifth-generation mobile communication technology, 5G, has raised the bar for data transmission rates, information security, and reliability. Polar codes, the channel coding scheme in the 5G communication standard, encounter issues of singularity, limitations, and passivity in the face of unknown attacks. Therefore, this study introduces a highly reliable DHR-based polar coding communication approach. Drawing parallels between cyber attacks and channel noise, the method incorporates reconfigurable executors, decoding decisions, strategy scheduling, and other mechanisms to enable active defense. The True Relatively Axiom (TRA) is employed to theoretically validate the method’s heterogeneity, redundancy, and dynamics. Comparative simulation experiments with traditional polar decoding algorithms, such as SCL, SC, BP, SCAN, and SSC decoding algorithms, demonstrate that the proposed method not only improves secure transmission efficiency but also guarantees secure and reliable transmission across various attack scenarios. Our code is available at https://github.com/yuluzi/my-code.git.

A novel contact optimization algorithm for endomicroscopic surface scanning.

Probe-based confocal laser endomicroscopy (pCLE) offers real-time, cell-level imaging and holds promise for early cancer diagnosis. However, a large area surface scanning for image acquisition is needed to overcome the limitation of field-of-view. Obtaining high-quality images during scanning requires maintaining a stable contact distance between the tissue and probe. This work presents a novel contact optimization algorithm to acquire high-quality pCLE images. The contact optimization algorithm, based on swarm intelligence of whale optimization algorithm, is designed to optimize the probe position, according to the quality of the image acquired by probe. An accurate image quality assessment of total co-occurrence entropy is introduced to evaluate the pCLE image quality. The algorithm aims to maintain a consistent probe-tissue contact, resulting in high-quality images acquisition. Scanning experiments on sponge, ex vivo swine skin tissue and stomach tissue demonstrate the effectiveness of the contact optimization algorithm. Scanning results of the sponge with three different trajectories (spiral trajectory, circle trajectory, and raster trajectory) reveal high-quality mosaics with clear details in every part of the image and no blurred sections. The contact optimization algorithm successfully identifies the optimal distance between probe and tissue, improving the quality of pCLE images. Experimental results confirm the high potential of this method in endomicroscopic surface scanning.

SCAN Algorithm to Optimize Dynamic Instructional Information Network for Predicting Student Behavior

With the integration of work, study and life on campus taking shape gradually in major universities across China, the concept of education from classroom to life is more widely accepted and the smart education model has become a trend in education informatization. However, most of the existing studies are not applicable to student behaviour data in the campus environment, and the temporal as well as cyclical characteristics in the data are not accessible in the student behaviour prediction problem. In this study, a hypergraph-based dynamic campus behaviour information network is designed to address the needs of the student behaviour prediction problem, and a student campus behaviour prediction algorithm is proposed in the dynamic campus behaviour information network-based student behaviour prediction problem. The effectiveness and rationality of the algorithm is verified through experiments with real campus data sets. The experimental results demonstrated that the periodic nature of the data acquired by the cycle gated cyclic unit module needs to be built on top of the snapshot gated cyclic unit module to help the algorithm achieve better results. The area under the curve of the algorithm proposed in the study achieves more advantageous results on both the 21-day and 35-day datasets. The cycle gated cyclic unit module of the student campus behaviour prediction algorithm proposed in this study can more effectively extract the cyclic features present in the dynamic campus behaviour information network and better accomplish the prediction of student campus behaviour.

Optimal Scanning Pattern for Initial Free-Space Optical-Link Alignment

Since free-space optical links (especially fully photonic ones) are very challenging to accurately align; scanning algorithms are used for the initial search and alignment of the transceivers. The initial alignment aims to intercept the optical beam so that it hits a position-sensitive detector. However, this operation can be very time-consuming (depending on the system parameters, such as transceiver parameters, distance between transceivers, divergence of the transmitter, angle of view of the receiver, etc.). A spiral scan is used as the most widespread pattern for scanning. This article examines the effects of system parameters (e.g., global navigation satellite systems and compass accuracy) on the angular area of uncertainty that must be scanned to find the optical beam. Furthermore, several types of spiral pattern are compared depending on the time of the scan execution and the required number of points for scanning the given uncertainty area. The cut hexagonal spiral scan achieved the best results as it required 18.1% less time than the common spiral scan for the presented transceiver.

Deephullnet: a deep learning approach for solving the convex hull and concave hull problems with transformer

ABSTRACT Convex and concave hulls originating from computational geometry are widely applied in practice. For instance, to determine the boundaries of a geographical area within a group of cities, convex hulls can represent the approximate boundaries of the areas. Concave hulls can accurately describe the shape of the area. The traditional methods for solving two-dimensional convex hull problems include the Jarvis March algorithm and the Graham scanning algorithm. The K-nearest neighbours and alpha-shape methods are designed for solving the concave hull problem. Other than traditional algorithms, we consider using deep neural networks to handle the convex and concave hull problems. General neural networks cannot deal with the problem whose output is a variable-length sequence. Our study provides a machine-learning approach for solving the convex hull and concave hull problems. We combine the Pointer network (Ptr-Net) with the Transformer model and propose the DeepHullNet. The trained DeepHullNet can provide a feasible solution in the majority of cases. The experimental results show that DeepHullNet outperforms the original Ptr-Net regarding accuracy. Compared with traditional methods, DeepHullNet can generate a good solution quickly, and the running time is shortened by nearly 100 times based on GPU.

Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways.

Study on circular scanning for cross-scale micro/nanoscratching machining

Large-scale scanning probe can facilitate fabricating cross-scale micro/nano structures. However, the processing of complex two-dimensional patterns usually encounters challenges including numerous machining feature points and probe jumps, which results in low efficiency, as well as poor machining quality. Therefore, a circular scanning method based on corner point extraction (CSCE) was proposed in this study for programming the probe path and reducing the number of probe jumps. Before the machining, the target structures or images were converted into raw data points through image processing, and then the data was refined by corner point extraction for obtaining the machining points. Subsequently, the machining points were arranged in the order of a circular scanning algorithm to get the probe path. Using CSCE, the probe jump was only 1 time for processing an H-shaped pattern. By comparison with the circular scanning method based on intersection point extraction (CSIE) for the H pattern, the number of machining points in CSCE was reduced from 82 to 12, and the machining time was shortened from 17.15 s to 3.17 s. Consequently, CSCE can enable efficient and high-quality fabrication of cross-scale micro/nanostructures.

FEBIAD ionization development via a web-app for multidimensional characterization

The ISAC-FEBIAD is an electron impact ion source typically used to ionize radioactive molecules or isotopes of elements beyond the reach of either surface or laser ion sources. The FEBIAD’s key tuning parameters are the cathode temperature defining the number of electrons created; the anode voltage establishing the electron energy; and the magnetic field controlling the electron density inside the anode volume. However, these parameters are typically scanned in a small and limited range when optimizing the source. Recent investigations have shown the need to explore the entire range of operational values accessible by the power supplies, not only due to the intrinsic variations from source to source but also to operate the source at optimal settings. To address this, a scanning algorithm has been implemented as a web interface thanks to the High-Level-Application (HLA) infrastructure available at TRIUMF. The ion beam intensity during both offline and online commissioning of the web app are presented here as contour plots. The optimal settings found for stable 20Ne are confirmed as the optimal settings for radioactive 18Ne. The main takeaway, however, is that the optimal ion source parameters differ between singly-charged, doubly-charged, and molecular species. This development demonstrate and facilitate the need for element and charge state-specific parameter optimization. Additionally, the results highlight the possibility of parameter optimization to enhance the ratio of the species of interest to co-ionized contamination.

An improved immersed boundary method with local flow pattern reconstruction and its validation

This study introduces an immersed boundary (IB) method based on coefficient array transformations of discrete equations for local cells and local flow pattern reconstruction, for the simulation of turbulent flow and combustion chemistry inside combustors with complex structure. This IB method is combined with a geometric scanning algorithm that traverses each fluid grid point in the vicinity of the wall, and based on the exact wall positions and normal vectors obtained from the scanning, the coefficient matrices of the individual grid points and their discrete forms of the governing equations are transformed, and the boundary conditions are added implicitly and exactly. The effectiveness of the method is validated through simulations of a cylinder, a gas turbine model combustor [Meier et al., “Spray and flame structure of a generic injector at aeroengine conditions,” in Proceedings of the ASME 2011 Turbo Expo: Power for Land, Sea, and Air (American Society of Mechanical Engineers, 2011), pp. 61–72 and Freitag et al., “Measurement of initial conditions of a kerosene spray from a generic aeroengine injector at elevated pressure,” Atomization Sprays 21, 521 (2011)], and a specific aero-engine combustor, demonstrating precision comparable to traditional body-fitted mesh approaches, especially for complex combustor structures. The simulation demonstrates that the IB method achieves accuracy comparable to a fitted grid when it provides boundary information of similar quality and detail for control equations. The locally reconstructed IB method introduced in this paper successfully delivers high-precision boundary conditions, making it valuable for practical engineering applications.

Separation of fast and slow shear waves and prediction of fracture parameters based on non-orthogonal assumptions

SUMMARY Natural fractures play a significant role in oil and gas reservoirs. Accurate predictions of fracture parameters are vital in reservoir prediction and oil and gas development. The birefringent phenomenon of shear waves in fractured media makes shear wave splitting (SWS) analysis an important tool in formulating fracture predictions. The traditional SWS analysis method is based on an orthogonal assumption of fast and slow shear waves. However, in an orthotropic medium composed of a background vertical transversely isotropic medium and a set of vertical fractures, fast and slow shear waves are not necessarily orthogonal. This causes the traditional SWS analysis method to fail. To solve this problem, we proposed an SWS analysis algorithm with a non-orthogonal assumption of fast and slow shear waves in this study. First, we introduced a parameter (difference angle) to characterize the angle between slow shear waves and the normal polarization directions of the fast shear waves. Subsequently, based on the traditional two-parameter scanning algorithm, a parameter was added to facilitate three-parameter scanning. In addition, we derived an expression for the two-parameter scanning objective function using the non-orthogonal assumption. Two-parameter scanning can accurately extract fast and slow wave time delay data, but it cannot determine an accurate fast shear wave polarization direction. Therefore, we optimized the three-parameter scanning algorithm as follows: first, we used two-parameter scanning to obtain the fast and slow wave time delays and then performed further scanning to determine the polarization direction of the fast shear wave and difference angle. The optimization algorithm significantly improved the computational efficiency. Subsequently, we tested the accuracy of this method using synthetic single-trace and three-component vertical seismic profile data. We demonstrated the implementation process of the three-parameter scanning method using actual data, separated fast and slow shear waves, and predicted fracture parameters. The final fracture parameters were verified.

DoppelGanger++: Towards Fast Dependency Graph Generation for Database Replay

A database replay system (DRS) captures workloads on a production system and then replays them in a test system to test various system changes, avoiding any risk before realizing them in production. The dependency graph generation in a DRS is crucial in preserving output determinism while maximizing concurrency. The state-of-the-art dependency graph generation algorithm deployed in a commercial DBMS uses a generate-and-prune strategy. It first generates a dependency graph by performing backward scans for each request in a workload. It then prunes all redundant edges using an expensive, transitive reduction algorithm. However, we notice that this generates a large dependency graph that contains many redundant edges and its worst-case time complexity is quadratic to the number of requests in a workload. In order to solve these challenging problems, we formally propose four classes of dependency graphs for DRSs. We then present a stateful single forward scan algorithm, SSFS, to generate any class of dependency graphs by performing a single scan over all requests while succinctly maintaining states. Here, states refer to information that is stored and maintained for efficient dependency graph generation. We also propose the parallel SSFS to utilize the computation power with multi-core CPUs while balancing the loads. We implemented our DRS in a leading commercial DBMS. Extensive experiments using the TPC-C, SD benchmarks, and a real-world customer workload show that our DRS significantly improves the dependency graph generation time by up to two orders of magnitude, compared to the state-of-the-art.

Сonceptual Foundations оf Applying Innovative 3d Scanning Technologies іn the Context of Maintaining Airworthiness of State Aviation Equipment

The goal of this article is to analyze the use of modern 3D scanning technologies and additivemanufacturing in addressing practical needs to maintain airworthiness of state aviation equipment. The unified algorithm for 3D scanning of complex technical objects which is independent of specific methods and equipment types has been developed as well as the detailed description of each step of the algorithm. Innovative optical methods of 3D scanning technologies are explored, and a comprehensive methodology that utilizes these optical 3D scanning methods for determining the potential improvement of identified resource indicators of aviation equipment is proposed.