Tactical demand and capacity balancing using incremental search in spatio-temporal graphs with flight uncertainty

Hydrilla, an invasive submerged macrophyte that is classified as a noxious weed in the U.S., can quickly spread into extensive monospecific infestations, excluding other native macrophytes and disrupting entire lake ecosystems. In Florida, infestation has increased tenfold in just three years, consuming over 60% of total management costs and requiring millions of dollars in annual control efforts. Traditional monitoring methods, such as field sampling, provide accurate localized assessments but are expensive and time-consuming. This study leverages Sentinel-2 satellite imagery, introducing the Submerged Aquatic Vegetation Index for Hydrilla (SVIH), a novel three-band index utilizing the green (G, 560 nm), red-edge 1 (RE1, 705 nm), and shortwave infrared 1 (SWIR1, 1610 nm) bands to distinguish hydrilla from water and emergent aquatic vegetation (EAV) in two Florida lakes. The index, coupled with other vegetation indices, was validated using in situ measurements of hydrilla abundance levels, confirming its strong ability to accurately distinguish hydrilla. At the highest abundance level, SVIH produced the highest Mathew correlation coefficients (MCCs), i.e., >0.86 for Lake Yale (2021), and >0.60 (2020) and >0.68 (2021) for Lake Apopka, using three thresholding methods. For Apopka (2022), other tested indices such as MFI and FAI yielded high MCC values along with high recall using incremental search threshold. However, these indices could not distinguish EAV from SAV in the eastern regions of Lakes Apopka and Yale, where EAV was dominant. These findings encourage the use of SVIH for routine hydrilla detection and mapping, facilitating improved management, conservation efforts, and targeted herbicide applications.

The advancement of sequencing technology has led to a rapid increase in the amount of DNA and protein sequence data; consequently, the size of genomic and proteomic databases is constantly growing. As a result, database searches need to be continually updated to account for the new data being added. However, continually re-searching the entire existing dataset wastes resources. Incremental database search can address this problem. One recently introduced incremental search method is iBlast, which wraps the BLAST sequence search method with an algorithm to reuse previously processed data and thereby increase search efficiency. The iBlast wrapper, however, must be generalized to support better performing DNA/protein sequence search methods that have been developed, namely MMseqs2 and Diamond. To address this need, we propose iSeqsSearch, which extends iBlast by incorporating support for MMseqs2 (iMMseqs2) and Diamond (iDiamond), thereby providing a more generalized and broadly effective incremental search framework. Moreover, the previously published iBlast wrapper has to be revised to be more robust and usable by the general community. iMMseqs2 and iDiamond, which apply the incremental approach, perform nearly identical to MMseqs2 and Diamond. Notably, when comparing ranking comparison methods such as the Pearson correlation, we observe a high concordance of over 0.9, indicating similar results. Moreover, in some cases, our incremental approach, iSeqsSearch, which extends the iBlast merge function to iMMseqs2 and iDiamond, provides more hits compared to the conventional MMseqs2 and Diamond methods. The incremental approach using iMMseqs2 and iDiamond demonstrates efficiency in terms of reusing previously processed data while maintaining high accuracy and concordance in search results. This method can reduce resource waste in continually growing genomic and proteomic database searches. The sample codes and data are available at GitHub and Zenodo (https://github.com/EESI/Incremental-Protein-Search; DOI: 10.5281/zenodo.14675319).

We investigate the synthesis of policies for high-level agent programs expressed in Golog, a language based on situation calculus that incorporates nondeterministic programming constructs. Unlike traditional approaches for program realization that assume full agent control or rely on incremental search, we address scenarios where environmental nondeterminism significantly influences program outcomes. Our synthesis problem involves deriving a policy that successfully realizes a given Golog program while ensuring the satisfaction of a temporal specification, expressed in Linear Temporal Logic on finite traces (LTLf), across all possible environmental behaviors. By leveraging an expressive class of first-order action theories, we construct a finite game arena that encapsulates program executions and tracks the satisfaction of the temporal goal. A game-theoretic approach is employed to derive such a policy. Experimental results demonstrate this approach's feasibility in domains with unbounded objects and non-local effects. This work bridges agent programming and temporal logic synthesis, providing a framework for robust agent behavior in nondeterministic environments.

Finding the shortest path in a network is a classical problem, and a variety of search strategies have been proposed to solve it. In this paper, we review traditional approaches for finding shortest paths, namely, uninformed search, informed search and incremental search. The above traditional algorithms have been put to successful use for fixed networks with static link costs. However, in many practical contexts, such as transportation networks, the link costs can vary over time. We investigate the applicability of the aforementioned benchmark search strategies in a simulated transportation network where link costs (travel times) are dynamically estimated with vehicle mean speeds. As a comparison, we present performance metrics for a reinforcement learning based routing algorithm, which can interact with the network and learn the changing link costs through experience. Our results suggest that reinforcement learning algorithm computes optimal paths dynamically.

This paper presents a new heuristic search that determines the feasibility of scheduling a set of jobs on a single machine where each job is characterized by its release date, processing time, and deadline. uses incremental search and restart strategies to scale the best-first-search heuristic F (Laalaoui and M'Hallah, in: IEEE symposium on evolutionary scheduling and combinatorial optimisation (SSCI) 2022, IEEE, Singapore, 2022). Experimental results provide computational evidence of the superiority of for large-scale instances. In fact, outperforms the IBM ILOG CP constraint programming solver, the CPLEX mixed integer programming solver, and existing heuristics. It finds feasible solutions for many instances with 100,000 jobs in less than one minute.

In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs.

We present a lazy incremental search algorithm, Lifelong-GLS (L-GLS), along with its bounded suboptimal version, Bounded L-GLS (B-LGLS) that combine the search efficiency of incremental search algorithms with the evaluation efficiency of lazy search algorithms for fast replanning in problem domains where edge evaluations are more expensive than vertex expansions. The proposed algorithms generalize Lifelong Planning A* (LPA*) and its bounded suboptimal version, Truncated LPA* (TLPA*), within the Generalized Lazy Search (GLS) framework, so as to restrict expensive edge evaluations only to the current shortest subpath when the cost-to-come inconsistencies are propagated during repair. We also present dynamic versions of the L-GLS and B-LGLS algorithms, called Generalized D* (GD*) and Bounded Generalized D* (B-GD*), respectively, for efficient replanning with non-stationary queries, designed specifically for navigation of mobile robots. We prove that the proposed algorithms are complete and correct in finding a solution that is guaranteed not to exceed the optimal solution cost by a user-chosen factor. Our numerical and experimental results support the claim that the proposed integration of the incremental and lazy search frameworks can help find solutions faster compared to the regular incremental or regular lazy search algorithms when the underlying graph representation changes often.

An asymptotically optimal path-planning guarantees an optimal solution if given sufficient running time. This research proposes a novel, fast, asymptotically optimal path-planning algorithm. The method uses five smart sampling strategies to improve the probabilistic road map (PRM). First, it generates samples using an informed search procedure. Second, it employs incremental search techniques on increasingly dense samples. Third, samples are generated around the best solution. Fourth, generated around obstacles. Fifth, it repairs the found route. This algorithm is called the Smart PRM (Smart-PRM). The Smart-PRM was compared to PRM, informed PRM, and informed rapidly-exploring random tree*-connect. Smart-PRM can generate the optimal path for any test case. The shortest distance between the start and goal nodes is the optimal path criterion. Smart-PRM finds the best path faster than competing algorithms. As a result, the Smart-PRM has the potential to be used in a wide variety of applications requiring the best path-planning algorithm.

Fine-grained damage detection of cement concrete pavement based on UAV remote sensing image segmentation and stitching

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as “ funnels”. We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory; and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility; helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.

Automatic semantic modeling of structured data sources with cross-modal retrieval

Driving line-based two-stage path planning in the AGV sorting system

Expected polynomial-time randomized algorithm for graph coloring problem

Abstract The problem of optimally locating Automatic Vehicle Identification (AVI) sensors on a traffic network for travel time estimation has been a topic of growing interests in recent years. Even though great progresses have been made on AVI sensor deployment for path‐level travel time estimation, very few contributions exist in the literatures that address the AVI sensor deployment for link‐level travel time estimation on an urban network. In this paper, considering the link travel time estimation, two deployment sub‐problems are addressed: (1) where to deploy a certain number of AVI sensors? (2) What is a cost‐effective number of AVI sensors to deploy? To address the first problem, a potential game of sensors is developed to find their optimal locations which maximize the objective function that consists of estimation coverage and estimation accuracy. Then, based on the optimal locations, an incremental search method is proposed to find the optimal number of sensors considering the cost. The case in Shanghai shows the proposed game‐theoretic method is superior to other two heuristic algorithms. Moreover, compared to the real‐world sensor locations, the optimally redeployed locations improve both the estimation coverage and estimation accuracy. The case in Xuancheng City validates the proposed incremental search uses less computations to find an optimal number that close to the global optimal number solved from the brute‐force search.

Pushing diversity into higher dimensions: The LID effect on diversified similarity searching

ABSTRACT Objectives The study aimed to (1) apply a data-mining approach to league-wide microtechnology data to identify absolute velocity zone thresholds and (2) apply the respective velocity zones to microtechnology data to examine the locomotor demands of elite match-play. Methods League-wide microtechnology data were collected from elite male rugby league players representing all National Rugby League (NRL) teams (n = 16 teams, one excluded due to a different microtechnology device; n = 4836 files) over one season. To identify four velocity zones, spectral clustering with a beta smoothing cut-off of 0.1 was applied to each players’ instantaneous match-play velocity data. Velocity zones for each player were calculated as the median while the overarching velocity zones were determined through an incremental search to minimise root mean square error. Results The velocity zones identified through spectral clustering were 0–13.99 km · h−1 (i.e., low velocity), 14.00–20.99 km · h−1 (i.e., moderate velocity), 21.00–24.49 km · h−1 (i.e., high velocity) and >24.50 km · h−1 (i.e., very-high velocity). Conclusions The application of spectral clustering (i.e., a data-mining method) to league-wide rugby league microtechnology data yielded insights into the distribution of velocity data, thereby informing the cut-off values which best place similar data points into the same velocity zones. As the identified zones are representative of the intensities of locomotion achieved by elite male rugby league players, it is suggested that when absolute zones are used, the consistent application of the identified zones would facilitate standardisation, longitudinal athlete monitoring as well as comparisons between teams, leagues and published literature.

In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20% of coverage extension can be accomplished by deploying IRS with the proposed method.

Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments

A planetary exploration rover has been used for scientific missions or as a precursor for a future manned mission. The rover’s autonomous system is managed by a space-qualified, radiation-hardened onboard computer; hence, the processing performance for such a computer is strictly limited, owing to the limitation to power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This study, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such an incremental search is subjected to the trade-off between trajectory optimality and computational burden. Therefore, we introduce the trajectory-quality growth rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain, and the planning stop criterion is estimated. Furthermore, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce the computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Furthermore, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.