Path Prediction Algorithm Research Articles

Dual-Reinforcement-Learning-Based Attack Path Prediction for 5G Industrial Cyber–Physical Systems

5G industrial cyber-physical systems have attracted substantial research interests due to their capability in the interconnection of everything. However, integrating the 5G network may expose systems to more potential risks. To reveal attack propagation, an attack path prediction approach based on dual reinforcement learning is proposed. First, a dual-network model is established, incorporating the security constraints for attacks against the 5G network into the attack graph. Second, employing reinforcement learning, Q-value updating functions and reward mechanisms based on topology and vulnerability are designed. Finally, an optimal attack path prediction algorithm is developed. Unlike traditional methods, the proposed approach does not rely on the monotonicity assumption that a system component has only one vulnerability, enabling it to accurately predict the optimal attack paths. Our simulation results demonstrate that the proposed approach can identify possible attack sources and paths from a 5G industrial cyber-physical system.

Robust Pedestrian Detection and Path Prediction using Improved YOLOv5

In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challengesdue to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study.

Joint Trust Management and Sharing Provisioning in IoV-Based Urban Road Network

Internet of Vehicles (IoV) is a novel technology to enhance the safety, intelligence, and efficiency of traffic systems, where vehicles can exchange critical information with other vehicles, roadside units, pedestrians, and cloud platforms. However, the dynamic network topology, high speed, and exposed communication links inevitably pose security threats to IoV. It is pivotal to establish a trust management and trust-sharing mechanism between vehicles to guarantee the safety of IoV. This paper proposes a distributed trust management scheme to discriminate malicious vehicles utilizing the machine learning technology Random Forest (RF). With the help of the sliding time window technology, the trust degree of vehicles can be comprehensively evaluated through the CART trees according to the current and historical records. To further improve the security of communication processes, we also introduce a lightweight cryptography mechanism. In addition, a trust-sharing mechanism based on path prediction algorithm is proposed to guarantee the consistency of trust information in the network. Finally, extensive simulations are conducted to demonstrate the feasibility and efficiency of the proposed scheme.

안전한 차선 변경을 위한 센서 융합 기반 주변 차량의 경로 예측 알고리즘

안전한 차선 변경을 위한 센서 융합 기반 주변 차량의 경로 예측 알고리즘

Fracture propagation using the radial point interpolation method

AbstractThis work presents a crack path prediction algorithm combined with the radial point interpolation method (RPIM), a meshless method. To allow easier implementation in existing structural analysis software, this algorithm is numerically compatible with finite element method (FEM) formulations. The proposed RPIM formulation uses the triangular elements of a FEM mesh as the background integration grid, allowing also to combine both formulations more easily. Thus, with the developed methodology, the RPIM can be integrated directly in a FEM software or use the same CAD tools to build the discretization meshes. Because the RPIM shape functions possess the delta Kronecker property, all the numerical techniques available for the FEM can be applied to enforce the natural and essential boundary conditions.The developed algorithm uses the maximum tangential stress criterion to determine the crack propagation direction, and the crack paths obtained with it corresponded well to previous research.

A surface roughness based damage index for predicting future propagation path of microstructure-sensitive crack in pure nickel

Predicting the future propagation path of small cracks under fatigue loading remains an unsolved engineering problem due to its sensitivity to various microstructure features (e.g., grain orientation, size, etc.). These microstructure features introduce localized plastic deformations and strain distributions that drive crack initiation and propagation. Since plastic deformation is manifested as surface topography changes, this paper investigates surface roughness parameters for future crack path prediction. A compound damage index that accounts for the accumulation of surface topography changes over multiple fatigue intervals was developed, based which a crack path prediction algorithm was formulated. The predicted future crack paths at two crack arresting locations were confirmed by the actual crack paths at later fatigue cycles, without a prior knowledge of the local microstructures. In addition, statistical analysis of the damage indices was performed to establish the confidence of the prediction. This study demonstrated that surface topography imaging is an efficient and effective technique for studying the propagation behaviors of microstructure-sensitive cracks. The agreement between the predicted and actual crack paths suggests that surface topography changes may contain vital quantitative information about the driving force for fatigue crack propagation.

Development of Sensor Registry System-Based Predictive Information Service Using a Grid.

A sensor registry system (SRS) registers sensor metadata and provides them for a seamless semantic process. Recently, network coverage information-based SRS (NC-SRS) was developed to provide sensor information filtering by combining path prediction and network coverage checks. However, the NC-SRS has problems caused by issues such as termination of OpenSignal service and pre-building road segments. Therefore, this paper proposes a sensor registry system-based predictive information service (SRS-PIS) using a grid. SRS-PIS predicts a path based on the grid, checks the network coverage, and filters the sensor. This paper presents a grid-based real-time path prediction algorithm and an algorithm for grouping network service-disabled areas. To obtain network coverage information, we constructed and implemented a grid-based coverage map through experiment to measure the signal strength. As an evaluation, we compared features among SRS-based systems and SRS-PIS, and compared advantages and disadvantages between segment-based and grid-based methods.

UAV Path Prediction for CD&R to Manned Aircraft in a Confined Airspace for Cooperative Mission

In this paper, a new path prediction approach for unmanned aerial vehicles (UAVs) for conflict detection and resolution (CD&R) to manned aircraft in cooperative mission in a confined airspace is proposed. Path prediction algorithm is established to estimate UAV flight trajectory to predict conflict threat to manned aircraft in time advances (front-end process of CD&R system). A hybrid fusion model is formulated based on three different trajectory prediction conditions considering scenarios in geographical conditions to aid the generation of appropriate resolution advisory of conflict alert. It offers a more precise CD&R system for manned and unmanned aircraft in cooperative rescue missions.

Motion priors based on goals hierarchies in pedestrian tracking applications

In this paper, the problem of automated scene understanding by tracking and predicting paths for multiple humans is tackled, with a new methodology using data from a single, fixed camera monitoring the environment. Our main idea is to build goal-oriented prior motion models that could drive both the tracking and path prediction algorithms, based on a coarse-to-fine modeling of the target goal. To implement this idea, we use a dataset of training video sequences with associated ground-truth trajectories and from which we extract hierarchically a set of key locations. These key locations may correspond to exit/entrance zones in the observed scene, or to crossroads where trajectories have often abrupt changes of direction. A simple heuristic allows us to make piecewise associations of the ground-truth trajectories to the key locations, and we use these data to learn one statistical motion model per key location, based on the variations of the trajectories in the training data and on a regularizing prior over the models spatial variations. We illustrate how to use these motion priors within an interacting multiple model scheme for target tracking and path prediction, and we finally evaluate this methodology with experiments on common datasets for tracking algorithms comparison.

Triple‐stage path prediction algorithm for real‐time mission planning of multi‐UAV

A triple-stage path prediction algorithm is proposed for real-time mission planning. In every planning horizon, each unmanned aerial vehicle (UAV) utilises the A* algorithm first to estimate the path to every task. Then, the estimated result serve as the input of the cluster method to generate the quasi-optimal task assignment. The shortest path to the assigned task is further calculated using the A* algorithm. Finally, it is smoothed to obtain the flyable reference path to guide the UAV by using the cubic B-spline curve. Numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm.

경로 예측 알고리즘의 빠른 투영 후보 선택을 위한 경로 단편 관리 구조

1. 조사의 개요 = 1 가. 조사의 개요 = 1 나. 조사의 목적 = 1 다. 조사 방법 = 1 라. 조사 내용 = 2 2. 조사 결과 = 3 가. 일반적 특성 = 3 나. 청소년 아르바이트 현황 = 4 1) 아르바이트 경험 여부 = 4 2) 향후 아르바이트 경험 의향 여부 = 5 3) 아르바이트 하는 이유와 하지 않은 이유 = 6 4) 가구 소득과 아르바이트 경험과의 관련성 = 8 5) 아르바이트의 종류 = 9 6) 아르바이트 허용 여부 = 11 다. 청소년 아르바이트 노동환경 실태 = 12 1) 근로 계약서 작성 및 부모 동의서 제출 실태 = 12 2) 평균 노동 일수, 시간, 임금 실태 = 13 3) 최저임금 미만 비율 = 14 4) 임금 분포 = 15 5) 휴식시간 및 휴일 확보 = 16 6) 탈의실 및 휴게공간 확보 = 17 7) 노동재해(산재) 경험 및 치료비 부담 = 18 8) 주관적 노동 강도 = 21 9) 부당한 대우 경험률 및 대처 방법 = 23 라. 아르바이트 권리에 대한 인지정도 = 26 1) 최저임금에 대한 인지율 및 인지 경로 = 26 2) 권리보호법 및 산재보험 인지율 = 28 3) 권리보호법 교육 필요 인지율 = 29 3. 요약 및 결론 = 30 가. 설문조사 결과 요약 = 30 나. 결론 및 제언 = 32 4. <부록> 2010년 설문지 = 35

MobiFeed: A location-aware news feed framework for moving users

A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to estimate a mobile user's locations based on a path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. We propose a heuristic algorithm as well as an optimal algorithm for the location-aware news feed scheduler. The performance of MobiFeed is evaluated through extensive experiments using a real road map and a real social network data set. The scalability of MobiFeed is also investigated using a synthetic data set. Experimental results show that MobiFeed obtains a relevance score two times higher than the state-of-the-art approach, and it can scale up to a large number of geo-tagged messages.

Mobile User Motion Detection and Traffic Road Management

The vision of next generation networks is to offer a real time services such that traffic situation on the major or minor roads. While 3G networks are based on wide area cell concept, 4G will be a hybrid netwo rk that use wide area network with Ad -hoc network. In this paper we propose two algorithms, the first algorithm describes how information is disseminate in our system Virtual Explore (VE). VE is a motion detection system that able to predict the mobile use r destination. The second algorithm is �± -path prediction algorithm that predicts the destination of mobile user based on his previous movements ,we expect that our proposed system will reduce the traffic rate between (40 -50%) in the case of accidents or inthe congested locations. Our analytical results show that the prediction accuracy in our work will vary from 20 -40% if we maintains a prediction correctness in the range 40-50%. Considering our goal the prediction accuracy is considered a good results incomparison with other works in the same context.

R2-D2

Path prediction is presently an important area of research with a wide range of applications. However, most of the existing path prediction solutions are based on eager learning methods which commit to a model or a set of patterns extracted from historical trajectories. Such methods do not perform very well in dynamic environments where the objects' trajectories are affected by many irregular factors which are not captured by pre-defined models or patterns. In this demonstration, we present the "R2-D2" system that supports probabilistic path prediction in dynamic environments. The core of our system is a "semi-lazy" learning approach to probabilistic path prediction which builds a prediction model on the fly using historical trajectories that are selected dynamically based on the trajectories of target objects. Our "R2-D2" system has a visual interface that shows how our path prediction algorithm works on several real-world datasets. It also allows us to experiment with various parameter settings.

Unified method for the prediction of rain attenuation in satellite and terrestrial links

In this paper, a semi-empirical method for the prediction of rain attenuation in slant paths and terrestrial links is proposed. The method uses the same simplified model of equivalent rain cell that is the basis for the ITU-R rain attenuation prediction methods but, additionally, the concept of an effective rain rate is introduced. This allows the use of the full rainfall rate distribution for the prediction of the rain attenuation distribution and the unification of the slant path and terrestrial links prediction algorithms. The numerical coefficients in the method’s expressions were derived by multiple non-linear regressions using the experimental data currently available in the ITU-R data banks. Test results indicate that the proposed method provides significant improvement over the current ITU-R methods.

An Advanced Cooperative Path Prediction Algorithm for Safety Applications in Vehicular Networks

Vehicular ad hoc networks (VANETs) are in the heart of current and future automotive research. Most of the current vehicular safety applications are based on sensors installed on the vehicle, e.g., radars and laserscanners. Due to the evolution of wireless networks, there is a tendency to exploit the cooperation among vehicles to enhance road safety through the related applications. Path prediction of a driver's own vehicle and other vehicles is crucial for road safety. Path prediction can assist the driver in having an enhanced perception of the road environment and of the intention of other neighboring drivers. In this paper, an advanced cooperative path prediction algorithm is presented. This algorithm gathers position, velocity, acceleration, heading, and yaw rate measurements from all connected vehicles to calculate their future paths. In addition, map data with regard to the road geometry and, in particularly, the road curvature are used to enhance the path prediction algorithm. Comparative results of the path prediction, with and without wireless communications, are discussed. In addition, the algorithm is adapted for use in the emergency-electronic-brake-lights application. The results of this adaptation are also presented. This paper is another contribution in highlighting the advantages and, at the same time, the challenges of using communications among road users.

RMPG: a realistic mobility pattern generator for the performance assessment of mobility functions

A platform for the simulation of mobility functions (e.g., protocols, application architectures) is presented. This platform, named realistic mobility pattern generator (RMPG), is based on the assumption that real humans exhibit considerable spatial and temporal regularity in their moves. We base our simulation environment on the random way-point algorithm implemented in the network simulation (ns) tool. Through the RMPG, that has been implemented in Java, we performed a series of simulations by considering a path prediction algorithm as a mobility function. We present our results.

Enhanced path prediction for network resource management in wireless LANs

Path prediction is currently being considered for use in the context of mobile and wireless computing toward more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels the user enjoys. Such mechanisms are mostly meaningful in infrastructures like wireless LANs. In this article we present a path prediction algorithm that exploits the machine learning algorithm of learning automata. The decision of the learning automaton is driven by the movement patterns of a single user but is also affected by the aggregated patterns demonstrated by all users. Simulations of the algorithm, performed using the realistic mobility pattern generator, show increased prediction accuracy.

A realistic mobility pattern generator for the performance evaluation of mobile computing mechanisms

AbstractA platform for the simulation of mobility functions (e.g. protocols, application architectures) is presented. This platform, named RMPG, is based on the assumption that real humans exhibit considerable spatial and temporal regularity in their moves. We base our simulation environment on the random way‐point algorithm incorporated in the ns tool. We review other approaches, simulation tools and mobility models. Through the RMPG, that has been implemented in Java, we performed a series of simulations by considering a path prediction algorithm as a mobility function scheme. We present our results. Copyright © 2003 John Wiley &amp; Sons, Ltd.

DEM Flow Path Prediction Algorithm for Geologic Mass Movements

Flow paths for geologic mass movements driven by gravity, such as lahars, floods, mudflows, debris flows, and lava flows, are determined by the local topography. In this work, we consider the problems of predicting the path of a geologic mass movement on a discrete topographic surface that is described by a Digital Elevation Model (DEM). The emphasis of this work is to develop a path finding algorithm that addresses the problems arising from the discrete nature of DEM topography, rather than the physics of mass movements, and improves upon existing approaches employed by commercial and public domain algorithms. We impose three criteria for the algorithm: 1) it must have a rigorous basis in analytic calculus, 2) the decision criteria used within the path finding algorithm must be clearly identified and tested for real flow conditions, and 3) it must be validated by comparing predictions with actual flows. The fundamental assumption of the path finding algorithm is that the definition of ‘downhill’ can be found from the local 3-dimensional gradient to the surface at each point. This definition differs fundamentally from the ‘lowest nearest neighbor’ approach employed by some commercially available software. While conceptually similar to a contour normal approach, we show that integration between contours is necessary to eliminate systematic errors in path orientation. We discuss several specific decision criteria that affect the practical implementation of the algorithm to discrete DEM surfaces: how integration is approximated through a small step size and averaging, our approach to addressing regions of low slope, and how we have accounted for residuals along the path. Finally, we show two convincing examples where the algorithm predicts the paths for two lava flows in Hawaii that traveled several tens of kilometers. The agreement between the predicted and actual flow path is remarkable and validates the algorithm and decision criteria for this type of mass flow.