Neighbor Position Research Articles

Ultra-short-term global horizontal irradiance forecasting based on a novel and hybrid GRU-TCN model

The need for energy is increasing globally due to a several factors, including population growth and economic development. Achieving this energy demand in the face of global warming and the depletion of fossil fuels requires the use of renewable energy. Photovoltaic energy is one of the renewable energy sources that is widely used in many nations across the world. Photovoltaic (PV) energy integration into the grid has significant benefits for the environment and economy, but at high penetration levels, its intermittent nature makes system stability difficult to maintain. Accurate ultra-short-term global horizontal irradiance forecasting is necessary in order to guarantee the most optimal use of photovoltaic power production sources. For GHI forecasting, a novel GRU-TCN-based model is proposed in this paper. It is composed of two neural networks: a temporal convolutional network and a gated recurrent unit. After extracting the temporal features from time-series solar irradiance data using GRU, the spatial features are obtained from the correlation matrix of different meteorological variables of the target and its neighbor position using TCN. Univariate and multivariate GRU-TCN models have been used for GHI ultra short-term forecasting. This paper compares the univariate and multivariate GRU-TCN models with TCN, LSTM, and GRU models based on three evaluation metrics in order to investigate how different combinations of variables affect the accuracy of the GRU-TCN models for one-step forecasting. The findings indicate the adoption of a univariate model using historical data of GHI is suitable to obtain reliable forecasting with 23.02 (W/m2) in MAE as opposed to the best multivariate model that achieved 25.67 (W/m2) in MAE. According to the results, the proposed model outperforms the other models assessed and offers a practical alternative for ultra-short-term GHI forecasting.

Short-Range Order in Gallium Solid Solutions in α-Iron

Short-range order in soft magnetic FeGa alloys containing from 3 to 25 at % Ga was studied using nuclear gamma resonance (Mössbauer) spectroscopy. The Mössbauer spectra were analyzed via fitting with subspectra corresponding to different configurations of the neighborhoods of the Fe atoms with Ga in the first and second coordination shells. It has been shown that in samples of alloys containing from 3 to 17 at % gallium, the short-range order is almost independent of the heat treatment conditions (quenching from a paramagnetic state or exposure in a ferromagnetic state) and is characterized by the presence of pairs of Ga atoms in the position of the second neighbors (B2-type clusters). At a Ga content of 17 to 21 at %, the portion of B2 clusters turns out to be significantly higher after quenching than after annealing, which correlates with the observed effect of heat treatment on the magnitude of magnetostriction. As the Ga concentration (21–25 at %) further increases, the observed features in the distribution of Ga atoms indicate the appearance and growth of D03 phase regions.

Archerfish foraging success varies with immediate competition level but not group size.

Group living can lead to kleptoparasitism, the theft of resources by competitors. Under such conditions, foragers may alter their behavior to minimize competition. However, it is unclear how such behavioral changes impact foraging performance. Archerfish (Toxotes spp.) are a good model for investigating the behavioral responses to kleptoparasitism, as their hunting method (shooting waterjets at insects perched above the water) leaves them vulnerable to theft. They must hit the target prey with sufficient force to dislodge it; thus, the prey may land some distance away from the shooter. Kleptoparasitism rates increase with group size in archerfish, and individuals alter their behavior around conspecifics. We investigated whether group size affected shooting success, using 7-spot archerfish T. chatareus. We considered a fish's shot to be successful if it knocked a fly, placed on a transparent platform above the tank, into the water. The probability of shooting success was modeled as a function of group size, aiming duration, nearest neighbor distance and position, and trial number. We found no effect of group size, aiming duration, or nearest neighbor distance or position on shooting success. Shooting success increased as trials progressed, likely due to the fish becoming more familiar with the task. We also found no change in the kleptoparasitism rate between group sizes. Instead, the likelihood of the shooter consuming the prey depended on the types of competition present at the time of shooting. We suggest that archerfish shooting behavior can be influenced by the presence of conspecifics in ways not previously considered.

Least-Squares Estimation of Tangent Space for a Triangular Mesh

This paper presents a novel method for computing an orthonormal tangent space at every vertex of a triangular mesh provided with texture coordinates, which is essential in normal mapping. Our method employs least-squares estimation of rotation between two sets of corresponding points: one set from the positions of the 1-ring neighbors of a vertex in texture space and the other set from their corresponding positions in world space. This optimal rotation represents a local space transformation that defines the tangent space at the vertex. Consequently, our method ensures that the resulting tangent space is inherently orthonormal, eliminating the need for intricate operations such as averaging and orthonormalization, which are required in the previous methods. In addition, we introduce texture space alignment of triangles to address vertices on seams, which are inevitable in texture mapping to a closed surface mesh. Experimental results demonstrate the effectiveness of the proposed methods in various experiments.

Structures and Properties of High-Concentration Doped Th:CaF2 Single Crystals for Solid-State Nuclear Clock Materials.

Thorium-doped vacuum ultraviolet (VUV) transparent crystals is a promising candidate for establishing a solid-state nuclear clock. Here, we report the research results on high-concentration doping of 232Th:CaF2 single crystals. The structures, defects, and VUV transmittance performances of highly doped Th:CaF2 crystals are investigated by theoretical and experimental methods. The defect configurations formed by Th and the charge compensation mechanism (Ca vacancy or interstitial F atoms) located at its first nearest neighbor position are mainly considered and studied. The preferred defect configuration is identified according to the doping concentration dependence of structural changes caused by the defects and the formation energies of the defects at different Ca or F chemical potentials. The cultivated Th:CaF2 crystals maintain considerable high VUV transmittance levels while accommodating high doping concentrations, showcasing an exceptional comprehensive performance. The transmittances of 1-mm-thick samples with doping concentrations of 1.91 × 1020 and 2.76 × 1020 cm-3 can reach ∼62% and 53% at 150 nm, respectively. The VUV transmittance exhibits a weak negative doping concentration dependence. The system factors that may cause distortion and additional deterioration of the VUV transmittance are discussed. Balancing and controlling the impacts of various factors will be of great significance for fully exploiting the advantages of Th:CaF2 and other Th-doped crystals for a solid-state nuclear optical clock.

Collective behavior of self-propelled particles with heading estimation via focal observation

Animals, including humans, often achieve velocity alignment by observing their neighbors from their own perspective rather than form a bird’s-eye view. In this study, we explore a particular implementation of this idea, where particles estimate the heading of their neighbors based on their own focal view observations, thereby achieving velocity alignment. Two types of observations are used to estimate the heading of neighbors in this study: the relative position of neighbors at two adjacent moments, and the movement of the focal particle itself. Simulation results show that noise amplitude η arising during observations and the weight parameter s denoting the ratio of noise contained in the two types of observations can induce continuous order–disorder phase transition. The probability density distribution of the noise contained in different models is statistically analyzed, and results show that the type of phase transition mainly depends on the variance of the noise. Furthermore, an optimal value of s is found in our model that makes the model tolerate large noise amplitude. Our findings suggest that the focal view observation plays an important role in collective motion.

Modeling spatial patterns in a moving crowd of people using data-driven approach—A concept of Interplay Floor Field

On the basis of data-driven methodology, especially velocity correlations of pedestrians moving in a crowd, we have proposed a new model of pedestrian dynamics with an easy-adjustable space discretization. The model is based on Cellular Automata (CA) with an adaptive lattice and takes into account proxemics patterns among pedestrians. The proposed model uses agents located on the CA lattice, constructed using the concept of Floor Field (FF), namely, a set of gradient potential fields influencing the movement of agents. In the model, we have proposed three kinds of such fields: Static FF—responsible for navigation to agents’ Points of Interest (POIs), Wall FF—a repulsive influence with obstacles, and Interplay FF—to model agents’ volume and proxemics effects. The third field, which models mutual relations between pedestrians taking into account the rules of proxemics is an added value in the area of crowd modeling and simulation.During the creation of the proposed model, we have based on a set of experimental data provided by Jülich Supercomputing Centre and our previous analysis of spatial patterns and velocity correlations. Thus, the proposed model includes, among other things the most probable positions of the nearest neighbors in a crowd—including the shape of “the forbidden zone” around a pedestrian and the finding of rules explaining observed mutual spatial relations between neighbors in a crowd. In the described model the cell size is an adjustable parameter, with available values from 1 cm to 50 cm. It is possible with the usage of embodied agents which always occupy only one cell of Cellular Automata, actual pedestrian volume is modeled with the Interplay FF. An interesting property of the model is that changing the cell size does not involve changing the rules of the model, e.g. its transition function.

ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

Emergence of Adaptation of Collective Behavior Based on Visual Perception

Unmanned swarms are widespread used in the IoT. The ability of unmanned swarms to achieve adaptive collective behavior in complicated mission scenarios is a prerequisite for meeting mission objectives. However, classical collective behavior models often use the velocity and position of neighbors as inputs to be constructed from a phenomenological perspective. This complicates the construction of unmanned swarms and is incompatible with biological perception. Therefore, this paper proposes an Observation-Orientation-Decision-Action (OODA) framework for the construction of adaptive collective behavior based on visual perception, inspired by biological collective behavior formations. The model contains no explicit alignment, and no information exchange occurs between individuals. Instead, individuals make decisions based purely on the sight distance corresponding to different relative orientations. Based on adaptability evaluation metrics defined at the collective level, experiments including coordinated collective motion, single disturbed individual, single external disturbance, narrow passage, and multiple external disturbances scenarios show that the group can respond adaptively to different scenarios with a stable crystal structure while avoiding collisions. In addition to particle simulations, different scenarios provide validation using the Webots robot simulator. As a result, this approach compensates for the inadequacies of existing models and provides technical support for the application of unmanned swarms in various IoT scenarios.

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered (LPSO) structures, a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered (LPSO) structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300 °C∼500 °C. Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures, which precipitate during the solidification and homogenization process. The stability of Zn/Y clusters is explained by the first principles of density functional theory. The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units, which comprises local fcc stacking sequences upon a tightly packed plane. The presence of solute atoms causes local lattice distortion, thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice, and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions. This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces, providing direct evidence of the transition from 18R to 14H. Growth of the LPSO, devoid of any defects and non-coherent interfaces, was observed separately from other precipitated phases. As a result, the precipitation sequence of LPSO in the solidification stage was as follows: Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO; whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows: 18R LPSO→various metastable LPSO building block clusters→14H LPSO. Of these, 14H LPSO was found to be the most thermodynamically stable structure.

Collective decision-making and spatial patterns in orientation of an endemic ungulate on the Tibetan Plateau.

Group living animals form striking aggregation patterns and display synchronization, polarization, and collective intelligence. Though many collective behavioral studies have been conducted on small animals like insects and fish, research on large animals is still rare due to the limited availability of field collective data. We used drones to record videos and analyzed the decision-making and behavioral spatial patterns in orientation of Kiang (Tibetan wild ass, Equus kiang). Leadership is unevenly distributed among Kiang, with the minority initiating majority behavior-shift decisions. Decisions of individual to join are driven by imitation between group members, and are largely dependent on the number of members who have already joined. Kiang respond to the behavior and position of neighbors through different strategies. They strongly polarize when moving, therefore adopting a linear alignment. When vigilant, orientation deviation increases as they form a tighter group. They remain scattered while feeding and, in that context, adopt a side-by-side alignment. This study reveals partially-shared decision-making among Kiang, whereby copying neighbors provides the wisdom to thrive in harsh conditions. This study also suggests that animals' spatial patterns in orientation depend largely on their behavioral states in achieving synchronization.

Physical-Assisted Routing for Proactive Avoidance of Nomadic Obstacles in IoT

With the broadening of the radio spectrum to higher frequency bands, wireless links are more prone to blockages by nomadic obstacles. However, existing routing schemes mostly follow the network-oriented design principle, which makes it difficult to react quickly to sudden obstruction. This article proposes PAR, a novel physical-assisted routing scheme for the Internet of Things. PAR takes a physical-oriented viewpoint attempting to mitigate unexpected link blockages by leveraging sensor observations of obstacles at individual nodes. It analyzes the signatures of sensor measurements to directly estimate the positions and sizes of obstacles and to proactively infer remedial routing decisions even before the transmission failure occurs. During the network deployment time, PAR lets each node learn and calibrate the geographic distribution of neighbors with respect to its sensor measurements by exchanging the sensor observations of a mobile guidance obstacle. During the runtime, an obstacle bypassing algorithm is then developed to find the shortest detour routes by comparing the local sensor measurements with the calibrated positions of neighbors to immediately resume data forwarding. We evaluate the efficacy of PAR using simulation and a real-world testbed. It is observed that PAR significantly improves the success rate while reducing redundant hops and routing control overhead.

Effects of tungsten on vacancy aggregation behavior and its induction for interstitial and vacancy migration in tantalum–tungsten alloys: A first-principles study

Tantalum–tungsten (Ta–W) alloys have been considered candidates for plasma-facing materials (PFMs) in advanced nuclear fusion design due to their excellent thermal and mechanical properties. Herein, the effects of tungsten (W) on the clustering and migration of vacancies and interstitials in Ta are studied using first-principles calculations. We find that W can attract vacancies at the second nearest neighbor position, causing the original non-clustered W to gather around the vacancies, forming an octahedral cluster structure. However, as the number of W atoms increases, W exerts a progressive inhibitory effect on the formation of vacancy clusters. Furthermore, this work confirms that W induces the migration of vacancies, thus significantly improving the mobility and recombination ability of vacancies. Similarly, W also induces the migration of interstitial atoms in addition to reducing the interstitial rotation threshold energy, leading to an evolution of Ta–Ta interstitial to Ta–W interstitial. Therefore, it can be speculated that W not only plays a vital positive role in the combination between interstitials and vacancies, but also can effectively inhibit the formation of voids by adding W, and then improving the anti-irradiation ability of Ta–W alloy material under neutron irradiation.

Development of Experimental Multi-Robot System for Network Connectivity Controls

This paper reports some results of network connectivity control experiments using a multi-robot system which we developed. Although a lot of connectivity control algorithms for a multi-robot network are proposed, almost all of them are verified only on computer simulations or using experimental robots with centralized sensors and controllers. To execute experimental verifications of connectivity control algorithms on a distributed robotic system, we developed an experimental multi-robot system. Hardware installed on the robot and information flow from sensors to actuators are detailed. Some results of measurement experiments are shown to estimate accuracy to detect a neighbor position. Then, results of connectivity control experiments using the developed multi-robot system are discussed.

Spherical Formation Tracking Control of Nonlinear Second-Order Agents With Adaptive Neural Flow Estimate.

This article addresses the spherical formation tracking control problem of nonlinear second-order vehicles moving in flowfields under both undirected networks and directed, strongly connected networks. Different from the previous adaptive estimate of the time-invariant parameters of flowfields, the flowfields under our consideration are spatial and absolutely unknown dynamics. Adaptive neural networks (ANNs) with the novel cooperative adaptive algorithms are proposed to approximate the flowfield acting on the channel of each vehicle's velocity (i.e., the mismatched flowfield) and the flowfield pushing the acceleration (i.e., the matched flowfield), respectively. For the purpose of avoiding the complex derivation derived from backstepping, the novel first-order filters are generated by dynamic surface based on barrier functions and relative positions of neighbors. The proposed control algorithms and adaptive upgrade law are fully distributed without using any global information of the graph. The uniform boundedness is analyzed in the Lyapunov sense. Simulation results are given to verify the theoretical analysis.

Consensus Control in Heterogeneous Nonlinear Multiagent Systems With Position Feedback and Switching Topologies

The primary challenge in designing a consensus algorithm under switching topologies is to deal with discontinuous interactive information. At present, most consensus control results are limited to either agents with mismatched uncertainties in a time-invariant interaction environment or integrator dynamics in a dynamically changing interaction environment. This work addresses consensus problems of heterogeneous nonlinear multiagent systems without these limitations. We propose a novel, simple, yet effective higher-order filter to yield differentiable control variables employing neighbor position information and discontinuous topology information. By applying this filter and defining a suitable auxiliary error, we develop distributed consensus controllers for second-order and uncertain higher-order systems. One of the salient attributes of the proposed methodology is to allow each agent to require minimal local feedback, i.e., only the position information of itself and its neighboring agents. Theoretical and simulation verifications of the scheme guaranteeing asymptotic convergence of consensus and the boundedness of each signal in the closed-loop system are carefully examined.

Modulating of electronic states and magnetic polarization in monolayered 1T-HfSe2 under non-metal atom and transition metal atom doping

Doping is very effective in regulating the properties of two-dimensional materials. Based on the first principles of density functional theory, the effects of the electronic and magnetic properties on the 1T-HfSe2 monolayer with single doping of non-metallic atoms (NM) and transition metal atoms (TM) and co-doping of metal and non-metallic atoms are systematically studied. We find that N, P and As exhibit non-magnetic metal properties. In contrast, F, Br and I exhibit magnetic semiconductor properties. The F-doped system retains the magnetic semiconductor properties under (−6%–10%) strain, and the band gap varies from 0.131 eV to 1.122 eV. Magnetic moments are generated outside the Ni atom doping system for metal atom doping. For metal and non-metal atoms doping, due to the interaction between TM and NM, found that not only can it produce massive magnetic moment and stimulate the initially did not produce a magnetic moment of non-metal atoms magnetic moment. The influence of increasing doping concentration on performance was further considered. It was found that the nearest neighbor position of the metal atom and non-metal atom was the most structurally stable, and the magnetic moment also changed when the distance between the metal atom and non-metal atom changed. These results have a specific significance for developing spintronic devices based on HfSe2.

Finite-time robust containment control for autonomous surface vehicle with input saturation constraint

In this paper, a finite-time containment robust control scheme is proposed for autonomous surface vehicle with input saturation constraint. Firstly, to achieve finite-time containment control, a saturated distributed equivalent control algorithm is proposed based on the position and velocity measurements of neighbors and a part of leaders; In light of the tools from algebraic graph theory and homogeneous theory, it is theoretically proven that all the follower will converge to the convex hull formed by the virtual leaders in finite-time; Then, in order to suppress the influence of external disturbance, a nonlinear integral sliding mode surface based on equivalent control law is designed. Combined with finite-time Lyapunov stability theory, a finite-time cooperative control law is proposed, which makes the system tend to sliding surface in finite-time from any initial state; Finally, simulation results are presented to verify the effectiveness of the proposed control scheme.

Structural and Electrical Properties of Samarium-doped Ceria Electrolyte

Effect of dopant on structural and electrical properties of samarium-doped ceria (SDC) electrolyte was investigated using statistical moment method. Our results showed the lattice expansion due to doping and local distortion resulting from preferential distribution of oxygen vacancies around dopants. The ionic conductivity of SDC crystals is in more than three orders of magnitude larger than that of pure ceria. However, the presence of dopant in cation edge and the first nearest neighbor positions around migrating vacancy gives rise to the blocking and trapping effects on the vacancy transport. The maximum value of the ionic conductivity was found for the samples with the dopant concentration x = 0.15. These results can be compared to the one of the published data of boththe theoretical and experimental investigations.

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures.

A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle ("all chemistry is local"), using information about the position of an atom's neighbors to predict a per-atom energy. In this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML's nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force-that of the central atom-is added to the growing training set, preventing the noisy and irrelevant information from the piece's boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.