Set Of Paths Research Articles

Terminal Trajectory Planning for Synthetic Aperture Radar Imaging Guidance Based on Chronological Iterative Search Framework.

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

Multiple paths of green and low-carbon development in industrial parks: group analysis based on the TOE framework

The green and low-carbon development of industrial parks plays an important role in promoting industrial and technological agglomeration, How to optimise their green and low-carbon development pathways is an important management question to be answered, requiring the examination of multiple path based on a histological perspective. Based on the technology–organisation–environment framework, this study selected 29 state-level economic and technological development zones (ETDZs) in China as research objects, and explored multiple sets of paths using fuzzy set qualitative comparison and necessity-contribution analysis. The study found that: 1) four promotion paths can be adopted for green low-carbon development of the zones: endogenous drive, balanced promotion, core support drive and comprehensive drive; 2) single environmental factor does not constitute a bottleneck hindering the green low-carbon development of the zones, which should be combined with the actual situation of the zones, and the balanced enhancement of the effectiveness of green and low-carbon should be realised through measures such as industrial policy, environmental risk prevention, and performance assessment of green policies. Finally, the study proposes the following policy recommendations: industrial parks should focus on the key role of environmental risk prevention, apply a combination of measures in accordance with their own realities, and appropriate external pressure can help promote green and low-carbon development.

Approximating rolling stock rotations with integrated predictive maintenance

We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.

Signatures of Mesoscopic Transport in Single Non‐Intentionally Doped GaN‐Nanowire Field‐Effect Transistors

In this work, the fabrication and characterization of a fully functional field‐effect transistor (FET) are addressed based on a non‐intentionally doped GaN‐nanowire FET (NW–FET). Universal conductance fluctuations (UCFs) are observed at temperatures below 140 K. In contrast to other reports in literature, UCFs appear in the analyzed NW–FET only under the influence of an electrical field when applying a gate voltage, while no UCF signatures are observed when performing magnetic‐field‐dependent measurements. The reason is the considerable impact of the applied voltage on the narrow conductive channel of the non‐intentionally doped NW. The electrical field influences the Fermi level as well as the width of the depletion region, both changing the effective impurity distribution which determines the set of possible electron paths. The electric‐field‐induced variation of the set of electron paths correlates with a conductance variation, which leads to the occurrence of UCFs. Furthermore, the reliability of determining the phase coherence length from the NW–FET transfer characteristics is analyzed. It is shown that the value of is significantly affected by the choice of the gate voltage range due to the current dependence of the magnitude of the UCFs.

Critical Path-Based Backdoor Detection for Deep Neural Networks.

Backdoor attack to deep neural networks (DNNs) is among the predominant approaches to bring great threats into artificial intelligence. The existing methods to detect backdoor attacks focus on the perspective of distributions in DNNs, however, limited by its ability of generalization across DNN models. In this article, a critical-path-based backdoor detector (CPBD) is proposed, which approaches to detect backdoor attacks via DNN's interpretability. CPBD is designed to efficiently discover the characteristics of backdoors, which distinguish the critical paths in the attacked DNNs. To deal with the intractably large number of neurons, we propose to simplify the neurons, and the preserved key nodes are integrated into a set of critical paths. Thus, a DNN model can be formulated as a combination of several critical paths. Afterward, the detection of backdoors is performed based on the analysis of critical paths corresponding to different classes. Then, combining all the above steps, the CPBD algorithm is integrated to present the results in a standard and systematic manner. In addition, CPBD is able to locate neurons associated with malicious triggers, the combination of which is named as trigger propagation path. Extensive experiments are conducted, which testify the efficiency of the proposed method on multiple DNNs and different trigger sizes.

A general design-oriented resilience measurement and evaluation method for engineering systems: Resilience cube

Resilience is an important property in many engineering systems that may be exposed to shocks. Existing resilience measures primarily focus on changes in system performance during the process of disturbance and recovery. These measures, however, do not directly relate to the composition of a system, which limits the improvement of resilience design. Furthermore, they are also still inadequate in the face of many special situation, such as multi-mode/wave shocks. Therefore, this paper proposes a novel resilience measurement that can be applied to various stage and complex scenarios. First, the concept of the path set is extended to extended path set space (EPSS) to establish the relationship between system design attributes and resilience. Then, resilience cube is defined to measure the system resilience based on EPSS. The measurement represents the properties of the system state itself, including the structure and composition, regardless of the shocks to which the system is exposed. The generality characteristics of the resilience cube are analyzed through different system composition and scenarios. A simulation method of resilience cubes is given for system design with random factors. Finally, the resilience of a maritime unmanned combat system (MUCS) is studied and the superiority of the proposed measurement is proven.

Mechanical properties and field test of lime-soda residue stabilized soil for subgrade

To broaden the sources of subgrade filler and the utilization of Soda Residue (SR), SR was employed to modify clay by adding a small amount of lime for further stabilization, forming a Lime-Soda-Residue-Stabilized Soil (LSRSS). A set of intensive research paths was established, from testing of laboratory mechanical property, mechanism disclosure, and field verification to operational effect. Through Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and Resilient Modulus (MR) experiments, it was concluded that with the increase in SR content, the UCS, CBR, and MR values of LSRSS showed an increasing trend then followed by a decrease, reaching their peak values, respectively of 0.62 MPa, 65.0%, 78.83 MPa, all at 30% SR content. An optimal proportion was determined for LSRSS as 6% lime, 30% SR, and 70% clay. The UCS, CBR, and MR values of optimal proportion all increased with the increase of compaction degree, but increased first and then decreased with the increase of water content. Their maximum values did not correspond to the OWC of 23% but to 27%, called the compaction water content, which was suitable for application in the actual LSRSS subgrade. Field test results showed that the UCS, CBR, and MR values were 0.85 MPa, 86.5%, and 135.7 MPa, which all were higher than the laboratory values, and the long-term road performance was outstanding. The analysis demonstrates that the better strength and road performance of LSRSS are mainly determined by the superior gradation and the reaction of three materials. The laboratory and field test results collectively provide data evidence for excellent performance and lay a solid foundation for the wider application of the LSRSS subgrade.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

Hamiltonian Intervals in the Lattice of Binary Paths

Let $\mathcal{P}_n$ be the set of all binary paths (i.e., lattice paths with upsteps $u = (1,1)$ and downsteps $d = (1,-1)$) of length $n$ endowed with the pointwise partial ordering (i.e., $P \le Q$ iff the lattice path $P$ lies weakly below $Q$) and let $G_n$ be its Hasse graph. For each path $P \in \mathcal{P}_n$, we denote by $I(P)$ the interval which contains the elements of $\mathcal{P}_n$ less than or equal to $P$, excluding the first two elements of $\mathcal{P}_n$, and by $G(P)$ the subgraph of $G_n$ induced by $I(P)$. In this paper, it is shown that $G(P)$ is Hamiltonian iff $P$ contains at least two peaks and $I(P)$ has equal number of elements with even and odd rank. The last condition is always true for paths ending with an upstep, whereas, for paths ending with a downstep, a simple characterization is given, based on the structure of the path.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization for Elastic Optical Satellite Networks

With the increase in global wireless traffic, the use of large-scale satellite networking to provide ubiquitous access is one of the essential trends of future 6G network development. Elastic optical satellite networks (EOSNs) are widely considered a flexible solution for future satellite communication. However, with the continuous proliferation of network devices and users, the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation. Therefore, EOSNs must efficiently address the challenge of allocating scarce bandwidth resources. Effective traffic grooming methods will be applied to EOSNs to solve the problem of bandwidth shortage. This paper proposed a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) to facilitate the bandwidth allocation for EOSNs. Firstly, the nodes of the alternative paths were graded, and the weights of the subsequent hop links were modified. Then, the path was evaluated using link weights, alternative paths were selected in the virtual and physical topologies, respectively, and a path set was constructed. Finally, a resource block evaluation parameter was designed to quantify the quality of candidate resource blocks and rank them. A series of simulations have evaluated the traffic-blocking probability and wavelength utilization under different traffic loads. The link resource was more fully utilized compared with other traffic grooming algorithms. The blocking probability can be reduced by 75%, while wavelength utilization can be improved by 8.1%.

Width Helps and Hinders Splitting Flows

Minimum flow decomposition (MFD) is the NP-hard problem of finding a smallest decomposition of a network flow/circulation X on a directed graph G into weighted source-to-sink paths whose weighted sum equals X . We show that, for acyclic graphs, considering the width of the graph (the minimum number of paths needed to cover all of its edges) yields advances in our understanding of its approximability. For the version of the problem that uses only non-negative weights, we identify and characterise a new class of width-stable graphs, for which a popular heuristic is a O (log Val(X))-approximation (Val(X) being the total flow of X ), and strengthen its worst-case approximation ratio from \(\Omega (\sqrt {m}) \) to Ω ( m /log m ) for sparse graphs, where m is the number of edges in the graph. We also study a new problem on graphs with cycles, Minimum Cost Circulation Decomposition (MCCD), and show that it generalises MFD through a simple reduction. For the version allowing also negative weights, we give a (⌈log ‖ X ‖⌉ + 1)-approximation (‖ X ‖ being the maximum absolute value of X on any edge) using a power-of-two approach, combined with parity fixing arguments and a decomposition of unitary circulations (‖ X ‖ ≤ 1), using a generalised notion of width for this problem. Finally, we disprove a conjecture about the linear independence of minimum (non-negative) flow decompositions posed by [17], but show that its useful implication (polynomial-time assignments of weights to a given set of paths to decompose a flow) holds for the negative version.

Three-Stage Rapid Physical Design Algorithm for Continuous-Flow Microfluidic Biochips Considering Actual Fluid Manipulations

With the continuous development of microfluidic technology, continuous-flow microfluidic biochips (CFMBs) are being increasingly used in the Internet of Things. The automation design of CFMBs has also received widespread attention. The architecture design of CFMBs is divided into a high-level synthesis stage and a physical design stage. Among them, the problem of the physical design stage is very complex. At this stage, the chip architecture is generated based on the device library and a set of flow paths, taking into account the actual fluid manipulations, while minimizing the cost of the chip, such as the number of ports, total length of flow channels, number of flow channel intersections. As fabrication technology advances, the number of devices integrated into CFMBs is increasing. The existing physical design algorithms can no longer meet the design requirements of CFMBs in terms of time. Therefore, we propose a three-stage rapid physical design algorithm for CFMBs considering the actual fluid manipulations. The proposed algorithm includes a port-driven preprocessing stage, a force-directed quadratic placement stage, and a negotiation-based routing stage. In the port-driven preprocessing stage, a port-driven preprocessing algorithm is proposed to generate connection matrices between ports and devices to reduce the number of ports introduced. In the force-directed quadratic placement stage, we model the placement problem as an extremum problem of a quadratic cost function, which mathematically reduces the search space significantly and shortens the running time of the algorithm significantly. In the negotiation-based routing stage, a heuristic negotiation-based routing algorithm and a flow channel strategy that prioritizes the construction of parallel execution are proposed to reduce the running time of the algorithm while ensuring that the number of crossings in the routing solution is close to the optimal solution. Experimental results confirm that our proposed method is able to generate the high-quality solutions quickly. Under general scale problems, compared to the existing method based on ILP, our proposed method achieves a speedup ratio of 23,171 in terms of CPU time and optimizations in terms of number of ports and port reuse of 3.18% and 6.52%, respectively. These optimizations come at the cost of only a slight increase in the number of intersections, the flow length, and the number of flow valves. In addition, our proposed method can effectively solve large-scale problems that cannot be solved by existing method based on ILP.

TargetTree-RRT*: Continuous-Curvature Path Planning Algorithm for Autonomous Parking in Complex Environments

Rapidly-exploring random tree (RRT) has been studied for autonomous parking as it quickly finds an initial path and is easily scalable in complex environments. However, the planning time increases by searching for the path in narrow parking spots. To reduce the planning time, the target tree algorithm, which substitutes a parking goal in RRT with a set (target tree) of backward parking paths, was proposed. However, as it consists of circular and straight paths, it deteriorates parking accuracy because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the TargetTree-RRT* algorithm for complex environments. First, a target tree is designed using clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to initialize a proper target tree that considers obstacles. By integrating with optimal-variant RRT and searching for the shortest path, the proposed TargetTree-RRT* algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments showed that the vehicle parked more accurately, and continuous-curvature paths were obtained more quickly and with higher success rates than those acquired using other sampling-based and other types of planning algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the need to develop a fast and practical path planning algorithm for autonomous parking, not only in simple environments but also in complex environments. Sampling-based planning algorithms have been studied in this area, with the advantages of rapid planning of an initial path and easily reflecting vehicle’s constraints. Nevertheless, in cluttered environments, it needs considerable time to obtain a path for the autonomous vehicle easy for tracking and precise parking. To address the aforementioned issue, this article presents the TargetTree-RRT* algorithm. It finds a path by replacing a parking goal with a set of pre-defined continuous-curvature paths considering cluttered environments. TargetTree-RRT* achieves better performance for real parking scenarios and rapidly obtains a near-optimal parking path, compared to other parking path planners. The proposed algorithm is not limited to autonomous vehicles. It can be applied to other unmanned vehicles or robots, such as autonomous underwater vehicles (AUVs). In any application where the goal of path planning can be replaced with predefined standardized paths, the proposed algorithm can be extended.

A Random Path Sampling-Based Method for Motion Planning in Many Dimensions

In this paper, we present GRPMP (Gaussian Random Paths Motion Planner), a method for solving robot motion planning problems. Compared with traditional sampling-based methods which build large-scale random trees or roadmaps by sampling random nodes in configuration-spaces, GRPMP generates a set of smooth random paths discretized into sparse nodes, using a random path sampler based on Gaussian process, to reduce resource space occupation and improve planning success rate. Employing the high-quality path selecting strategy with the lazy collision checking technique, GRPMP can fast select the collision-free high-quality path that has the lowest cost in the set of smooth random paths, so as to improve the efficiency and quality of the motion planning. We present challenging experiments with GRPMP on pick-and-place tasks, using a humanoid robot, to show that GRPMP has the ability to find high-quality paths with efficiency and stability.

Research on Path Planning for Unmanned Surface Vessels Based on AIS Data

To address the issues of poor endpoint convergence and suboptimal path quality in global path planning for unmanned surface vessels (USVs), this paper proposes an endpoint-convergence oriented improved genetic algorithm based on AIS data. Firstly, utilizing a genetic algorithm with an endpoint-convergence objective function, a set of paths with strong endpoint convergence is selected. Secondly, to overcome the limitations of a single optimization algorithm, a multi-objective path planning approach is employed using a genetic algorithm, thereby improving the distance and trajectory smoothness in global path planning for unmanned ships. Finally, the planned paths are evaluated by comparing them with the real paths from AIS data. The simulation results demonstrate that the proposed method outperforms other traditional algorithms in terms of average path turn count and turn angle, reducing them by an average of 35.41% and 35.72%, respectively. Moreover, the proposed method exhibits the smallest error compared to the real trajectories, with an average reduction of 18.26%. These results validate the effectiveness and rationality of the proposed approach in improving path quality, reducing path turn count, and achieving better alignment with actual trajectories.

Exploration of Digital Transformation Path of Civic Education Management in Colleges and Universities in the Information Age

Abstract In the context of the information age, this article discusses the digital transformation of the management of ideological education in colleges and universities. Based on the humanistic theory and “Internet + education” mode, the article constructs a set of digital transformation paths for Civic and Political Education Management, and establishes a corresponding evaluation system. The research combines the hierarchical analysis and entropy weight methods to assign weights to the evaluation indexes. It uses the gray correlation method and TOPSIS method to calculate the indexes’ positive and negative ideal solution distances to solve the transformation effect score. Applying this path to the case studies of five colleges and universities (A to E) shows that college C has the most significant positive and negative ideal solution distance (0.01016) in developing teachers’ ideological and political competence. In contrast, college B has the smallest positive and negative ideal solution distance (0.00232) in the management system setup. In the assessment of the five universities, the highest score was found in the development of teachers’ civic and political competence (average 0.87017 points). In contrast, the lowest score was found in developing students’ civic and political literacy (average 0.28286 points). The research in this paper provides a practical path and empirical support for the digital transformation of Civic and Political Education Management in colleges and universities, which is of great significance in promoting the modernization of Civic and Political Education in colleges and universities.

When to Switch: Planning and Learning for Partially Observable Multi-Agent Pathfinding.

Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where the environment is only partially observable for each agent, i.e., an agent observes the obstacles and other agents only within a limited field-of-view. Moreover, we assume that the agents do not communicate and do not share knowledge on their goals, intended actions, etc. The task is to construct a policy that maps the agent's observations to actions. Our contribution is multifold. First, we propose two novel policies for solving partially observable MAPF (PO-MAPF): one based on heuristic search and another one based on reinforcement learning (RL). Next, we introduce a mixed policy that is based on switching between the two. We suggest three different switch scenarios: the heuristic, the deterministic, and the learnable one. A thorough empirical evaluation of all the proposed policies in a variety of setups shows that the mixing policy demonstrates the best performance is able to generalize well to the unseen maps and problem instances, and, additionally, outperforms the state-of-the-art counterparts (Primal2 and PICO). The source-code is available at https://github.com/AIRI-Institute/when-to-switch.

Study on the Path of Enhancing the Effectiveness of College Students’ Education and Management in the Context of Informatization

Abstract To cope with the problems existing in the education and Management of students in traditional colleges and universities, improve the management efficiency and realize the informatization of education management, this study proposes a set of optimization paths for the education and Management of students in colleges and universities. In this paper, an intelligent and diversified education management platform is constructed, aiming to strengthen the operation efficiency of the education management team in colleges and universities. By introducing a fuzzy evaluation algorithm to judge the educational management paths of college students and normalizing the input data, empirical evaluation results are derived. Comparative Analysis of the data in the study showed that the optimized educational management path significantly improved students’ daily behavior and mental health status. Students’ average daily online time was effectively controlled between 5.6 and 10.5 hours, the online period was more in line with the healthy work and rest, and the rates of disciplinary behaviors and Internet fraud victimization decreased respectively. At the same time, students’ mental health and learning effects were also improved. In summary, the education and management path for college students proposed in this paper significantly strengthens the management system and comprehensively cultivates students. It provides an effective informatization strategy for education management in colleges and universities.

A digital Jordan surface theorem with respect to a graph connectedness

Abstract After introducing a graph connectedness induced by a given set of paths of the same length, we focus on the 2-adjacency graph on the digital line Z {\mathbb{Z}} with a certain set of paths of length n n for every positive integer n n . The connectedness in the strong product of three copies of the graph is used to define digital Jordan surfaces. These are obtained as polyhedral surfaces bounding the polyhedra that can be face-to-face tiled with digital tetrahedra.