Enabling Scalable and Efficient Low-Altitude Airspace Utilization for Dense Urban Operations

Coverage path planning is one of the important issues for robot mowers, which brings great convenience to our life. In this paper, hierarchical partitioning strategy is employed to fulfill robot's environment modeling and the double heuristic optimization algorithms have been adopted to plan optimal coverage paths. Ant Colony Optimization (ACO) is used for global path planning in the upper layer, and Tabu Search (TS) is used for local coverage planning in the lower layer. Finally, simulation experiments are carried out and the results show the proposed method obtains satisfactory coverage path planning.

Military vehicle object detection technology in complex environments is the basis for the implementation of reconnaissance and tracking tasks for weapons and equipment, and is of great significance for information and intelligent combat. In response to the poor performance of traditional detection algorithms in military vehicle detection, we propose a military vehicle detection method based on hierarchical feature representation and reinforcement learning refinement localization, referred to as MVODM. First, for the military vehicle detection task, we construct a reliable dataset MVD. Second, we design two strategies, hierarchical feature representation and reinforcement learning-based refinement localization, to improve the detector. The hierarchical feature representation strategy can help the detector select the feature representation layer suitable for the object scale, and the reinforcement learning-based refinement localization strategy can improve the accuracy of the object localization boxes. The combination of these two strategies can effectively improve the performance of the detector. Finally, the experimental results on the homemade dataset show that our proposed MVODM has excellent detection performance and can better accomplish the detection task of military vehicles.

This paper addresses the partitioning and scheduling problems in mapping multi-stage regular iterative algorithms onto fixed size distributed memory processor arrays. We first propose a versatile partitioning model which provides a unified framework to integrate various partitioning schemes such as "locally sequential globally parallel", "locally parallel globally sequential" and "multi-projection". To alleviate the run time data migration overhead--a crucial problem to the mapping of multi-stage algorithms, we further relax the widely adopted atomic partitioning constraint in our model such that a more flexible partitioning scheme can be achieved. Based on this unified partitioning model, a novel hierarchical scheduling scheme which applies separate schedules at different processor hierarchies is then developed. The scheduling problem is then formulated into a set of ILP problem and solved by the existing software package for optimal solutions. Examples indicate that our partitioning model is a superset of the existing schemes and the proposed hierarchical scheduling scheme can outperform the conventional one-level linear schedule.

Logic simulation of complex VLSI models is very time consuming. Simulation speed can be increased by model partitioning and assigning the resulting parts to simulator instances which cooperate over a loosely coupled system. We have developed a distributed framework, parallelMAP, that implements a hierarchical model partitioning strategy. It can serve both as production environment in VLSI design and as an experimental test bed for algorithm development. In this paper, we describe the possibilities that parallelMAP offers for the modular construction of partitioning processes, starting from basic sequential and parallel modules. Experimental experiences refer to IBM processor models comprising from 1.5 x 105 to 2.5 x 106 elements at gate level.

An enhanced method for improving the accuracy of small failure probability of structures

The Vertical Takeoff and Landing Aircraft (VTOL) is capable of both low-speed hovering and high-speed flight. This paper proposes an autonomous motion planning and anti-wind disturbance control algorithm for the VTOL, implemented spatial and temporal joint motion planning for VTOL, under the limitation of maximum flight speed of the aircraft, VTOL can achieve fully autonomous flight in complex environments, which can realize the autonomous flight of a single aircraft in a complex and dense environment, achieve real-time perception and avoidance of static and dynamic obstacles. The information generated by motion planning includes the position, velocity, and acceleration information of the aircraft, and proposes a wind disturbance estimation and active control strategy in a wind disturbance environment, accurate estimation of wind disturbance is achieved through a state observer, and the wind disturbance was suppressed through nonlinear error feedback control law, implemented wind disturbance estimation and suppression for aircraft in wind disturbance environments, and achieved complete autonomous motion planning and trajectory tracking of VTOL in a complex and wind disturbance environment. Our work can provide a basis for the collaborative control of multiple UAVs.

A package query returns a package---a multiset of tuples---that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package queries to Integer Linear Programs (ILPs) and developed the SketchRefine algorithm for package query processing. While this algorithm was an important first step toward supporting prescriptive analytics scalably inside a relational database, it struggles when the data size grows beyond a few hundred million tuples or when the constraints become very tight. In this paper, we present Progressive Shading, a novel algorithm for processing package queries that can scale efficiently to billions of tuples and gracefully handle tight constraints. Progressive Shading solves a sequence of optimization problems over a hierarchy of relations, each resulting from an ever-finer partitioning of the original tuples into homogeneous groups until the original relation is obtained. This strategy avoids the premature discarding of high-quality tuples that can occur with SketchRefine. Our novel partitioning scheme, Dynamic Low Variance, can handle very large relations with multiple attributes and can dynamically adapt to both concentrated and spread-out sets of attribute values, provably outperforming traditional partitioning schemes such as kd-tree. We further optimize our system by replacing our off-the-shelf optimization software with customized ILP and LP solvers, called Dual Reducer and Parallel Dual Simplex respectively, that are highly accurate and orders of magnitude faster.

A primary challenge of agent-based policy learning in complex and uncertain environments is escalating computational complexity with the size of the task space (action choices and world states) and the number of agents. Nonetheless, there is ample evidence in the natural world that high-functioning social mammals learn to solve complex problems with ease, both individually and cooperatively. This ability to solve computationally intractable problems stems from both brain circuits for hierarchical representation of state and action spaces and learned policies as well as constraints imposed by social cognition. Using biologically derived mechanisms for state representation and mammalian social intelligence, we constrain state-action choices in reinforcement learning in order to improve learning efficiency. Analysis results bound the reduction in computational complexity due to state abstraction, hierarchical representation, and socially constrained action selection in agent-based learning problems that can be described as variants of Markov decision processes. Investigation of two task domains, single-robot herding and multirobot foraging, shows that theoretical bounds hold and that acceptable policies emerge, which reduce task completion time, computational cost, and/or memory resources compared to learning without hierarchical representations and with no social knowledge.

This paper presents a strategy for improving motion planning of an unmanned helicopter flying in a dense and complex city-like environment. Although Sampling Based Motion planning algorithms have shown success in many robotic problems, problems that exhibit ldquonarrow passagerdquo properties involving kinodynamic planning of high dimensional vehicles like aerial vehicles still present computational challenges. In this work, to solve the kinodynamic motion planning problem of an unmanned helicopter, we suggest a two step planner. In the first step, the planner explores the environment through a randomized reachability tree search using an approximate line segment model. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In the second step, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a dynamically feasible path. Numerical simulations in 3D indicate the ability of the method to provide real-time solutions in dense and complex environments.

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.

This article studies on the analysis of safety characteristics of flight situation in complex low-altitude airspace with Agent-based simulation. Several aircraft behavior models are proposed taking account of complex low-altitude environment and general aviation flight characteristics, including an individual aircraft behavior model, a multi-flight behavior model, and an individual interaction model. And, some flight situation indicators are introduced to be used to analyze safety characteristics, such as flight volume, average speed, and flight conflict. In addition, a mixed flight situation simulation environment for complex low-altitude airspace is built with Agent technology and NetLogo. Based on the simulation environment, the relationship and influence rules among these flight situation indicators are found with flight situation evolution process. The results show a non-linear relationship between flight volume and flight conflict and conflict time; as flight volume increases, the fluctuation of average separation decreases, while the average speed-change increases first, but decreases with continuous increase in the volume, meanwhile the average heading-change gradually stabilizes after initial increase. It is also found that a reasonable setting of flight parameters is beneficial for the smooth operation of low-altitude airspace.

We introduce the design of a parallel library for MPI based on the Linda programming paradigm, called Eilean. It provides a scalable distribution of the tuple space through a hierarchical (or cluster) partitioning scheme, and tuple type specific access/distribution policies. Portability of the library is achieved using the message passing standard MPI as the underlying communication system. The hierarchical distribution provides a static, yet general partition of the tuple space through the available processors. With such general structure, the run-time system, aided by programmer hints, can map tuples closely to the processes which use them. To accomplish the tuple mapping task, Eilean treats tuples differently according to their use within the application. The required information about tuple access patterns is explicitly given by the programmer and used during run-time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, a methodology for mapping algorithms onto heterogeneous suite of supercomputers is presented. A n approach for selecting an optimal suite of computers for solving problems with diverse computational requirements, called Heterogeneous Optimal Selection Theory (HOST), is presented. HOST is an extension t o Augmented Optimal Selection Theory in two ways: It incorporates heterogeneous parallelism embedded in the tasks, and it reflects the costs associated in using various fine grain mapping strategies at individual machine level. The proposed mapping methodology is based on the Cluster-M programming paradigm. For the mapping purpose, the input format, assumed in HOST, is modeled in terms of Hierarchical Cluster-M specification and representation. For a given problem, a hj'ierarchical Cluster-M specification is generated t o indicate the execution of concurrent tasks at different stizges of the computation. This specification is then mapped onto the Hierarchical ClusterM representation ofthe underlying heterogeneous suite of supercomputers.

The explosive growth of Internet of Things (IoT) devices has generated considerable data in diverse networks. This poses serious challenges in collecting timely information and managing frequency resources optimally. In particular, unauthorized access, measurement constraints, and variable channel conditions cause interference, performance degradation, and security compromise, especially in distributed IoT systems. This research presents a comprehensive system for improving data collection in heterogeneous IoT networks. Using complex mathematical models and machine learning algorithms, the system aims to increase the efficiency of frequency resource utilization and reduce interference in network access. A Q-based reinforcement learning method is designed along with an intelligent MAC protocol. Simulation results show that this method increases channel utilization efficiency by 25%, reduces interference probability by 30% compared to traditional methods such as ALOHA, and provides a flexible and scalable solution for frequency resource management. The performance of the proposed system is significantly better than traditional methods, increasing channel utilization efficiency by 25% and reducing the probability of interference by 30%. The system's self-learning capability enables effective frequency resource management even in complex and dense environments. This research presents an innovative method for data collection in IoT networks that combines machine learning and mathematical modeling, providing a secure and scalable solution for the next generation of heterogeneous networks. This system paves the way for designing more stable and efficient networks in various fields, including smart cities and industries.

An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies