Saga Research Articles

Observations and modeling of spectral line asymmetries in stellar flares

Context. Stellar flares are energetic events occurring in stellar atmospheres. They have been observed on various stars using photometric light curves and spectra. On some cool stars, flares tend to release substantially more energy than solar flares. Spectroscopic observations have revealed that some spectral lines exhibit asymmetry in their profile in addition to an enhancement and broadening. Asymmetries with enhanced blue wings are often associated with coronal mass ejections, while the origin of red asymmetries is currently not well understood. A few mechanisms have been suggested, but no modeling has been performed so far. Aims. We observed the dMe star AD Leo using the 2-meter Perek telescope at Ondřejov observatory, with simultaneous photometric light curves. In analogy with solar flares, we modeled the Hα line emergent from an extensive arcade of cool flare loops and explain the observed asymmetries using the concept of coronal rain. Methods. We solved the non-LTE (departures from local thermal equilibrium) radiative transfer in Hα within cool flare loops taking the velocity distribution of individual rain clouds into account. For a flare occurring at the center of the stellar disk, we then integrated radiation emergent from the whole arcade to obtain the flux from the loop area. Results. We observed two flares in the Hα line that exhibit a red wing asymmetry corresponding to velocities up to 50 km s−1 during the gradual phase of the flare. Synthetic profiles generated from the model of coronal rain have enhanced red wings that are quite compatible with observations.

Different Path Planning Techniques for an Indoor Omni-Wheeled Mobile Robot: Experimental Implementation, Comparison and Optimization

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.

ON AN ALGORITHM FOR SOLVING A BOUNDARY VALUE PROBLEM FOR FREDHOLM INTEGRO-DIFFERENTIAL EQUATIONS

ON AN ALGORITHM FOR SOLVING A BOUNDARY VALUE PROBLEM FOR FREDHOLM INTEGRO-DIFFERENTIAL EQUATIONS

Near-ultraviolet continuum modeling of the 1985 April 12 great flare of AD Leo

White-light stellar flares are now reported by the thousands in long-baseline, high-precision, broad-band photometry from missions like Kepler, K2, and TESS. These observations are crucial inputs for assessments of biosignatures in exoplanetary atmospheres and surface ultraviolet radiation dosages for habitable-zone planets around low-mass stars. A limitation of these assessments, however, is the lack of near-ultraviolet spectral observations of stellar flares. To motivate further empirical investigation, we use a grid of radiative-hydrodynamic simulations with an updated treatment of the pressure broadening of hydrogen lines to predict the λ ≈ 1800 − 3300 Å continuum flux during the rise and peak phases of a well-studied superflare from the dM3e star AD Leo. These predictions are based on semi-empirical superpositions of radiative flux spectra consisting of a high-flux electron beam simulation with a large, low-energy cutoff (≳ 85 keV) and a lower-flux electron beam simulation with a smaller, low-energy cutoff (≲ 40 keV). The two-component models comprehensively explain the hydrogen Balmer line broadening, the optical continuum color temperature, the Balmer jump strength, and the far-ultraviolet continuum strength and shape in the rise/peak phase of this flare. We use spatially resolved analyses of solar flare data from the Interface Region Imaging Spectrograph, combined with the results of previous radiative-hydrodynamic modeling of the 2014 March 29 X1 solar flare (SOL20140329T17:48), to interpret the two-component electron beam model as representing the spatial superposition of bright kernels and fainter ribbons over a larger area.

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Porous materials are of great interest in multiple applications due to their usefulness in energy conversion devices and their ability to modify structural and diffusive properties. Geometric tortuosity plays an important role in characterizing the complexity of a porous medium. The literature on several occasions has related it as a parameter dependent on porosity only. However, due to its direct relationship with the morphology of the medium, a deeper analysis is necessary. For this reason, in the present study, the analysis of the geometric tortuosity is proposed considering the porosity and the pore size distribution. Geometric tortuosity in artificially generated digital porous media is estimated using the A-star algorithm and the Pore Centroid method. By performing changes in the size of the medium and the distribution of the pore size, results are obtained that indicate that the geometric tortuosity does not only depend on the porosity. By maintaining the same porosity, the geometric tortuosity increases if the pore size is reduced. Similarly, these pore size effects are greater if the size of the medium is reduced. The A-star algorithm was found to be more suitable to characterize the majority of paths within the half-pore. On the other hand, to increase the size, the Pore Centroid method is the most appropriate. Finally, three types of correlations were generated relating tortuosity with porosity and pore size. All the correlations were determined with 95% of interval confidence.

A fuzzy based approach to enhance the lifespan of sensor network

The use of wireless sensor networks (WSNs) for data collection is widespread. The resource constraint is an important factor in WSN communications design. The issue arises naturally in WSNs as a result of uneven energy consumption caused by multi-hop routing and dynamic network models, which substantially affects network lifetime. The nodes are dispersed over distant sensing areas and are powered by finite or limited energy batteries that are difficult to replace. The energy of nodes is reduced as a result of changes in network topology or the network’s lifespan and the main intention of this research is to figure out how to make sensor networks last longer. The suggested study work focuses on a specific routing strategy for WSNs that employs the AO-star algorithm with a Fuzzy approach and link stability for extending the network lifetime. The technique chooses the optimum routing path by the sensing point to the receiving node based on how much energy is consumed, the smallest number of nodes with the shortest latency, and lower transmission loads with higher throughput. To compare the proposed strategy’s efficiency in energy consumption balancing and network lifespan enhancement, the proposed technique may achieve a 30% longer average network lifetime than the A-star algorithm.

Research on Path-Planning Algorithm Integrating Optimization A-Star Algorithm and Artificial Potential Field Method

A fusion pathfinding algorithm based on the optimized A-star algorithm, the artificial potential field method and the least squares method is proposed to meet the performance requirements of path smoothing, response speed and computation time for the path planning of home cleaning robots. The fusion algorithm improves the operation rules of the traditional A-star algorithm, enabling global path planning to be completed quickly. At the same time, the operating rules of the artificial potential field method are changed according to the path points found by the optimal A-star algorithm, thus greatly avoiding the dilemma of being trapped in local optima. Finally, the least squares method is applied to fit the complete path to obtain a smooth path trajectory. Experiments show that the fusion algorithm significantly improves pathfinding efficiency and produces smoother and more continuous paths. Through simulation comparison experiments, the optimized A-star algorithm reduced path-planning time by 60% compared to the traditional A-star algorithm and 65.2% compared to the bidirectional A-star algorithm path-planning time. The fusion algorithm reduced the path-planning time by 65.2% compared to the ant colony algorithm and 83.64% compared to the RRT algorithm path-planning time.

UAV Path Planning Model Based on R5DOS Model Improved A-Star Algorithm

In order to solve the problems of large amounts of calculation and long calculation times of the A-star algorithm in three-dimensional space, based on the R5DOS model, this paper proposes a three-dimensional space UAV path planning model. The improved R5DOS intersection model is combined with the improved A-star algorithm. Together, they construct a local search process, and the R5DOS path planning model is established by reducing the number of search nodes. The path planning model is simulated through MATLAB software and the model can greatly reduce the number of nodes and computational complexity of the A-star algorithm in three-dimensional spaces, while also reducing the calculation time of the UAV. Finally, we compare the improved A-star algorithm with the original A-star algorithm and the geometric A-star algorithm. The final fitting result proves that the improved A-star algorithm has a shorter computation time and fewer node visits. Overall, the simulation results confirm the effectiveness of the improved A-star algorithm and they can be used as a reference for future research on path planning algorithms.

Control optimisation of automated guided vehicles in container terminal based on Petri network and dynamic path planning

• The task optimization and path planning of vehicles considering dynamic obstacle avoidance remain to be the urgent problems to be solved, so as to realize intelligence of container handling in container terminals. • The scheduling framework model of automated guided vehicle is established based on heuristic rules and OOTCSPN with vehicle behavior pattern as the top-level object, which can direct the traffic order and position sequence of the automated guided vehicle fleet. • Based on A* fusion DWA, path planning algorithm of automated guided vehicles is designed. Experiments show that the improved algorithm can improve vehicle operation efficiency, plan the route more smoothly and achieve random obstacle avoidance. To solve the problem of path optimisation and obstacle avoidance of automated guided vehicles (AGV) in the horizontal transportation of container terminals, this study proposed three operation modes of terminal vehicles and summarised four vehicle behaviour modes. Considering the vehicle behaviour pattern as the top-level object, based on heuristic rules and an object-oriented timed coloured stochastic Petri network (OOTCSPN) algorithm, a scheduling framework model of the AGV was established to optimise vehicle scheduling. A path planning model for three vehicle operation modes was designed for AGV path planning and real-time random obstacle avoidance based on the A-star(A*) algorithm fusion dynamic window approach (DWA). The simulation results indicate that the container handling efficiency increased by 13.2% using the improved algorithm. The scaled-down experiment confirms that the proposed method makes the planned path smoother and can also achieve real-time obstacle avoidance.

The Great Flare of 2021 November 19 on AD Leonis

We present a detailed analysis of a superflare on the active M dwarf star AD Leonis. The event presents a rare case of a stellar flare that was simultaneously observed in X-rays (with XMM-Newton) and in the optical (with the Transiting Exoplanet Survey Satellite, TESS). The radiated energy in the 0.2 − 12 keV X-ray band (1.26 ± 0.01 × 1033 erg) and the bolometric value (EF, bol = 5.57 ± 0.03 × 1033 erg) place this event at the lower end of the superflare class. The exceptional photon statistics deriving from the proximity of AD Leo has enabled measurements in the 1 − 8 Å GOES band for the peak flux (X1445 class) and integrated energy (EF, GOES = 4.30 ± 0.05 × 1032 erg), which enables a direct comparison with data on flares from our Sun. From extrapolations of empirical relations for solar flares, we estimate that a proton flux of at least 105 cm−2 s−1 sr−1 accompanied the radiative output. With a time lag of 300 s between the peak of the TESS white-light flare and the GOES band flare peak as well as a clear Neupert effect, this event follows the standard (solar) flare scenario very closely. Time-resolved spectroscopy during the X-ray flare reveals, in addition to the time evolution of plasma temperature and emission measure, a temporary increase in electron density and elemental abundances, and a loop that extends into the corona by 13% of the stellar radius (4 × 109 cm). Independent estimates of the footprint area of the flare from TESS and XMM-Newton data suggest a high temperature of the optical flare (25 000 K), but we consider it more likely that the optical and X-ray flare areas represent physically distinct regions in the atmosphere of AD Leo.

Ship Collaborative Path Planning Method Based on CS-STHA

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

Optimalization Route to Tourism Places in West Java Using A-STAR Algorithm

Various algorithms can be used in the problem of finding the optimal route, one of which is the A-STAR Algorithm. The characteristic for recording routes that have been evaluated is one of the advantages of the A-STAR Algorithm. This study focuses on finding the optimal route to tourism places in West Java Province. In the application of the A-STAR Algorithm, distance data and density data are used from each line segment in West Java Province. The heuristic values used are converted from density data. The A-STAR algorithm is implemented using Python so that the optimal route to tourism places in West Java Province is obtained.

Analysis and Implementation of Augmented Reality Using Markerless and A-Star Algorithm (Case Study: Gedung Kuliah Umum ITERA)

Institut Teknologi Sumatera is a public university in the province of Lampung. Institut Teknologi Sumatera (ITERA) has many buildings, including Gedung Kuliah Umum (GKU). GKU is the largest and widest lecture building in ITERA. GKU has four floors, where each floor has many rooms in it with different functions in each room. As the largest building in ITERA, GKU is often used for various events, including CPNS exams, new student admissions, or for visits from other campuses. Due to the size of this building, this allows visitors from outside ITERA to GKU to experience problems in terms of time to ask questions and difficulty finding various spaces in the GKU Building. This research uses Augmented Reality technology to help make it easier for visitors from outside ITERA to find space quickly and precisely. In its development using several tools, including the ARWaKit SDK. This framework is used on devices with the IoS operating system. In its implementation, it requires a camera on a smartphone to capture existing images and convert them into cyberspace. In the ARWayKit framework, Azure Spatial Anchors have been used which can be used to carry out the mapping process as a markerless method and to optimize the distance from the user's position to the destination location, the a-star algorithm is used. The results obtained from the Variation-2 test were 91.6%.

Load-Balanced Topology Rebuilding for Disconnected Wireless Sensor Networks With Delay Constraint

In inhospitable environments, connectivity recovery plays a significant role in enabling normal data transmission in wireless sensor networks (WSNs). However, existing approaches lack thorough load-balancing and delay-control functions. In this article, we present a load-balanced connectivity recovery (LBCR) strategy to address these problems. This strategy consists of two relay-segment selection approaches. The first one is the delay-controlled connectivity mechanism, in which mobile relays and static relays are employed to connect isolated segments, and the path length of the two kinds of relays is adjusted based on the requirement of data delivery delay. The second one is the load-balanced connectivity mechanism, in which both the intra-segment and the inter-segment traffic distribution are evaluated to balance the traffic load. For intra-segment load equilibrium, we use the A-Star algorithm to calculate the number of disjoint paths of a relay segment, which helps to evaluate the load sharing ability from a micro perspective. For inter-segment load equilibrium, we compare the traffic load of different paths, which helps to evaluate the load sharing ability from a macroscopic angle. Extensive simulations demonstrate the effectiveness and advantage of our strategy in terms of connectivity cost, data collection delay, and system lifespan.

Fluoroscopic Image-Based 3-D Environment Reconstruction and Automated Path Planning for a Robotically Steerable Guidewire.

Cardiovascular diseases are the leading cause of death globally and surgical treatments for these often begin with the manual placement of a long compliant wire, called a guidewire, through different vasculature. To improve procedure outcomes and reduce radiation exposure, we propose steps towards a fully automated approach for steerable guidewire navigation within vessels. In this paper, we utilize fluoroscopic images to fully reconstruct 3-D printed phantom vasculature models by using a shape-from-silhouette algorithm. The reconstruction is subsequently de-noised using a deep learning-based encoder-decoder network and morphological filtering. This volume is used to model the environment for guidewire traversal. Following this, we present a novel method to plan an optimal path for guidewire traversal in three-dimensional vascular models through the use of slice planes and a modified hybrid A-star algorithm. Finally, the developed reconstruction and planning approaches are applied to an ex vivo porcine aorta, and navigation is demonstrated through the use of a tendon-actuated COaxially Aligned STeerable guidewire (COAST).

The CARMENES search for exoplanets around M dwarfs

Context. A challenge with radial-velocity (RV) data is disentangling the origin of signals either due to a planetary companion or to stellar activity. In fact, the existence of a planetary companion has been proposed, as well as contested, around the relatively bright, nearby M3.0 V star AD Leo at the same period as the stellar rotation of 2.23 days. Aims. We further investigate the nature of this signal. We introduce new CARMENES optical and near-IR RV data and an analysis in combination with archival data taken by HIRES and HARPS, along with more recent data from HARPS-N, GIANO-B, and HPF. Additionally, we address the confusion concerning the binarity of AD Leo. Methods. We consider possible correlations between the RVs and various stellar activity indicators accessible with CARMENES. We additionally applied models within a Bayesian framework to determine whether a Keplerian model, a red-noise quasi-periodic model using a Gaussian process, or a mixed model would explain the observed data best. We also exclusively focus on spectral lines potentially associated with stellar activity. Results. The CARMENES RV data agree with the previously reported periodicity of 2.23 days, correlate with some activity indicators, and exhibit chromaticity. However, when considering the entire RV data set, we find that a mixed model composed of a stable and a variable component performs best. Moreover, when recomputing the RVs using only spectral lines insensitive to activity, there appears to be some residual power at the period of interest. We therefore conclude that it is not possible to determinedly prove that there is no planet orbiting in synchronization with the stellar rotation given our data, current tools, machinery, and knowledge of how stellar activity affects RVs. We do rule out planets more massive than 27 M⊕ (=0.084 MJup). Likewise, we exclude any binary companion around AD Leo with M sin i greater than 3–6 MJup on orbital periods <14 yr.

Control and Path Planning for Quadrotor Oriented to Agricultural Support in Annona Muricata Crops

The use of Unmanned Aerial Vehicle (UAV) devices in precision farming is more and more increasing due to the ease of this type of mechanism to traverse terrain, even those difficult to access, a UAV is implemented to optimize performances and monitor crop grow and production; however, these devices present flight issues as GPS signals blocked, decreased battery life, wrong direction and instability on air. For this reason, it is essential to carry out researches to improve the current deficiencies of UAVs, to improve their performance and their implementation in different applications for human assistance. The paper presents results obtained by the design of a control system capable of tracking trajectories built by implementing the A-star algorithm for Quadrotor-type drones. A system of two control techniques used in the tracking and positioning of a course, obtained by using the "A-star" algorithm, is constructed, and analyzed by using MATLAB® as a programming and simulation context. Thanks to the usage of these devices, the autonomy in precision farming systems is increased, generating greater effectiveness and profitability. It has been possible to conclude that the strategy studied in the research project increases the autonomy of the device and the capacity of data collection. In addition, it is an algorithm that does not require extra hardware. Also, its computational processing consumption depends only on the distance between the origin and target points.

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

Tower Defense is a game genre that uses a pathfinding algorithm. Pathfinding is a way to find a path from one point to another; pathfinding itself has many variants with different scenarios. One of these algorithms is the A-Star algorithm, a well- known and commonly used method for game pathfinding. Another algorithm is the Flow Field algorithm, which is an algorithm that is not widely known and has become the topic in this following research. This research will be carried out by comparing the time required by both algorithms to reach the target point from starting point, and time comparison will be carried out in 3 different scenarios. The result of the research is that the Flow Field algorithm reaches the target faster than A-Star Pathfinding in every scenario carried out in the simulation. This research concludes that the Flow Field algorithm can compete with the A-Star algorithm to find paths in the Tower Defense game.

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

A-star Optimization with Heap-sort Algorithm on NPC Character

Objectives: To find out whether the A-star algorithm that is optimized with the heap-sort algorithm can reach the target destination faster than the native Astar when applied on NPC to find the path. Methods: Comparisons are made by implementing an optimized A-star algorithm with heap-sort and native Astar on an NPC in a dungeon-crawling game genre by applying five different scenarios, where each scenario has a different start position of the NPC, and the target goal is set. The comparison is made by measuring the time required by both algorithms to reach the target goal in milliseconds with Unity engine software and C# language. Findings: From the results of experiments and calculation, it was found that the A-star algorithm optimized with the heap-sort algorithm was 50%-80% faster than using only the A-star algorithm in cases that was applied alone on NPC in dungeon-crawling game. Novelty: The novelty in this research is the optimization of the A-star algorithm with heap-sort, and then that algorithm is compared with the native A-star algorithm that is applied to NPCs in the dungeon-crawling game genre. Keywords: AStar; Heap Sort; NonPlayable Character; Optimization Algorithm; Pathfinding