Path Update Research Articles

Mitigating Insider Threat’s IP Spoofing through Enhanced Dynamic Cluster Algorithm (EDPU Based HCF)

Insider Threat has always been a major problem to computer security due to unauthorized system misuse by users in an organization. Understanding the concept and the inherent adverse consequences of the insider threat can assist in postulating mitigating approaches and techniques to the menace. Insider intrusion, from researches, experiences and literature have proved to be more expensive and destructive more than external attacks due the comprehensive understanding of the internal operations of the organization by the perpetrator. Many researchers have explored into the unhealthy nature of insider activity with the aim of eliminating the threat, thereby identifying the various categories as theft of intellectual property, fraud, sabotage, espionage. This work tends to address the menace by studying models for detecting, reducing and eliminating the threat through IP Spoofing in order to propose a better model for the intrusion. Certain experimental research through analysis of network data measurement has shown that HCF (Hop Count Filtering) can discover and discard almost 90% of spoofed IP packets but an improvement on this experiment called DPU (Dynamic Path Update) Based Hop Count Filtering has proved to identify and discard more than 90%. This was carried out in Linux Kernel environment to substantiate the effectiveness of its measurements. However, enhancing enhancing the performance of the DPU-based HCF by reducing the packet size of packets at the point of entry in order to decrease the network traffic, and to permanently discard 100% spoofed packets is the research direction of this work

MOLM: Alleviating Congestion through Multi-Objective Simulated Annealing-Based Load Balancing Routing in LEO Satellite Networks

In satellite networks, existing congestion resolution methods do not consider the predictability and stability of paths, leading to frequent path switches and high maintenance costs. In this regard, we propose a novel congestion resolution approach, named MOLM, which introduces a continuous neighbor set during path updates. This set includes nodes capable of establishing sustainable connections with the predecessors and successors of congested nodes. Combined with a multi-objective simulated annealing framework, MOLM iteratively derives an optimal selection from this set to replace congested nodes. Additionally, we employ a Fast Reroute mechanism based on backup paths (FRR-BP) to address node failures. The simulation results indicate that the optimal node endows the new path with optimal path stability and path latency.

Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data

Abstract This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

Improved Sampling of Adaptive Path Collective Variables by Stabilized Extended-System Dynamics.

Because of the complicated multistep nature of many biocatalytic reactions, an a priori definition of reaction coordinates is difficult. Therefore, we apply enhanced sampling algorithms along with adaptive path collective variables (PCVs), which converge to the minimum free energy path (MFEP) during the simulation. We show how PCVs can be combined with the highly efficient well-tempered metadynamics extended-system adaptive biasing force (WTM-eABF) hybrid sampling algorithm, offering dramatically increased sampling efficiency due to its fast adaptation to path updates. For this purpose, we address discontinuities of PCVs that can arise due to path shortcutting or path updates with a novel stabilization algorithm for extended-system methods. In addition, we show how the convergence of simulations can be further accelerated by utilizing the multistate Bennett's acceptance ratio (MBAR) estimator. These methods are applied to the first step of the enzymatic reaction mechanism of pseudouridine synthases, where the ability of path WTM-eABF to efficiently explore intricate molecular transitions is demonstrated.

Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance

Electric vehicles (EVs) not only serve as significant loads for the power grid but also play a crucial role in the operation of the traffic. Their travel and charging behaviors have an impact on both the power grid and the road network. In order to address the potential impacts of a large-scale deployment of EVs on the power grid and the exacerbation of traffic congestion, this paper first establishes a dynamic road network model based on graph theory and time-varying traffic data combined with a road impedance model. Then, the spatio-temporal distribution characteristics of EV travel are modeled. Furthermore, by incorporating real-time road network data, the traditional Dijkstra’s algorithm for finding the optimal path is improved. At each node, the current real-time road impedance is used as the objective for EV path updates, thus accurately capturing the energy consumption of the EVs. Finally, using a standard testing problem on a typical working day based on data from a real case, the impacts of EV travel and charging behaviors on power distribution network operation and traffic congestion are analyzed under scenarios with no guidance and guidance for the shortest travel time. The results show that this method can significantly reduce the time cost by approximately 18% in travel time, which is of particular concern to users. This method balances the load of the charging stations, elevates the voltage level within the safety requirement of 7%, and simultaneously alleviates traffic congestion near the stations.

A Design Method for Semi-Rigid Steel Frame via Pre-Established Performance-Based Connection Database

With decades of research, semi-rigid beam-to-column connections have been widely accepted. However, most studies have been restricted to the local connection level, leaving system-oriented analysis and design methods with a meager investigation, which leads to the fact that the active use of semi-rigid connections in practice is rare. This study aims to provide a system-level design method to bridge the gap between element and connection design, and the two main contributions are to propose a method for designing semi-rigid steel frames by pre-establishing a performance-based connection database and to formulate refined classification criteria for connection performance levels. In this method, the frame design is transformed into finding an appropriate matching of performance requirements between elements and connections. The classification criteria for connection performance levels are based on the assumption that the structural responses (stability, resistance, and deformation) are only slightly affected by the properties of connections within the same level. The emphasis is on the rotational stiffness and moment resistance of the connection. Finally, the results of examples indicate that the connection database is portable and can be applied to various frames, avoiding the repetitive design for connections in different projects. In addition, tuning the performance requirements of the connection can greatly reduce the number of design variables compared to tuning its geometry, and more importantly, it provides designers with a clearer update path, which can significantly shorten the process of trial-and-error and quickly arrive at the final design.

Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated. An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm (DADCS) is proposed, whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system. Firstly, considering the information fusion rules and different working modes of the netted radar system, a two-factor jamming effectiveness evaluation function is constructed, detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode, respectively. Then a nonconvex optimization model for cooperatively jamming netted radar system is established. Finally, a dynamic adaptive discrete cuckoo search algorithm (DADCS) is constructed by improving path update strategies and introducing a global learning mechanism, and a three-step solution method is proposed subsequently. Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.

Event-triggered cooperative path following control of multiple underactuated unmanned surface vehicles with complex unknowns and actuator saturation

We propose an event-triggered cooperative control scheme to address the path following problem of multiple underactuated unmanned surface vehicles (USVs) under complex unknowns and actuator saturation. The control law combines the line-of-sight navigation principle, backstepping technology, an uncertainty and disturbance estimator (UDE), an auxiliary system, a high-gain observer (HGO) and an event-triggered mechanism (ETM) to realize the path following control of individual USVs. A path update law is established based on the synchronization scheme. Our control strategy features the following two aspects: (i) the UDE and HGO are jointly constructed to estimate complex unknowns of USV formation with actuator saturation, and (ii) an ETM is employed to reduce the update frequency of the controller. The system stability is proved using the Lyapunov method. Numerical simulations and comparison with other control strategies demonstrate the feasibility and effectiveness of the proposed strategy.

Event-triggered containment control of networked underactuated unmanned surface vehicles with finite-time convergence

This study investigates the containment problem of networked underactuated unmanned surface vehicles (USVs) guided by multiple virtual leaders using an event-triggering control mechanism. Each USV is subject to external disturbance and internal model uncertainties. First, a finite-time observer is introduced to identify the model uncertainties and the unmeasured USV velocities. Then, a guidance law is designed to generate a series of virtual reference points for each USV so that these points are contained within the convex hull spanned by the virtual leaders. Meanwhile, the path update law for each virtual leader is proposed. Next, to steer the USVs towards the corresponding reference points, an event-triggered-based control law is developed for each USV using the observed data, in which the event-triggering technique is applied in the channel of sensor-to-controller to reduce communication burden while ensuring the similar control performance with the continuous mechanism. Besides, a first-order filter is introduced to provide the derivatives of the virtual control laws. Finally, stability analysis shows that the closed-loop system is finite-time stable and all error signals converge to a small bound within finite time. Comparative simulations are provided to show the effectiveness of the proposed event-triggered containment control method.

SPaFE: A Crowdsourcing and Multimodal Recommender System to Ensure Travel Safety in a City

Existing navigation applications such as Google Maps, Apple maps provide a core service to users to find the shortest path or the path that takes the least amount of time towards a user’s destination. The applications and other research efforts have been sought to include other features such as 3D maps, neighboring facilities, traffic information, and multi-modal alternate route suggestion based on user constraints. None of these, however, take into account an important factor: "user safety while commuting". Although the common perception of street crime is that it is primarily a problem in the third-world countries, current popular hashtags or topics (e.g. “blacklifematters”) indicate that it is now prevalent in other parts of the world as well. Even existing multi-modal alternative route recommender systems are incapable of adapting to a dynamic set of safety features, are unable to provide new safe path updates in response to real-time commuter responses, and take little or no account of historical on-road events when designing the safe algorithm. In light of the above background, we begin by developing a generic framework, namely On-road Risk Minimization Problem (ORMP).We then introduce a dynamic population-based algorithm, that we call Safe Path for Everyone (SPaFE), that solves ORMP using multi-modal historical and crowdsourced data. Finally, our extensive empirical results demonstrate that SPaFE markedly outperforms the state-of-the-art.

An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image

The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.

A Basketball Technique Balance Control Technology Based on Ant Colony Algorithm

Aiming at the problems of low accuracy, poor stability, and effect of balance control in the current basketball skill balance control process, a basketball skill balance control method based on ant colony algorithm is proposed. The principle and characteristics of ant colony algorithm are analyzed, the mathematical model of ant colony algorithm is established, and the implementation steps of ant colony algorithm are given. According to the balance and stability control principle of basketball skills, this paper constructs the balance and stability control model of basketball skills. By establishing the knee-ankle-foot joint analysis model and using the ant colony algorithm, the basketball movement path is constructed. Through the fitness function, select the basketball motion path nodes and update the pheromone, obtain the relationship between the internal stress and displacement of the body under different force directions, and obtain the influence law of the effective knee joint stress with the varus and internal rotation, so as to achieve the balance and stability of the basketball skills sexual control analysis. The experimental results show that the proposed method has better stability and effect of basketball technique balance control and can effectively improve the accuracy of basketball technique balance control.

Solving in real-time the dynamic and stochastic shortest path problem for electric vehicles by a prognostic decision making strategy

The adoption of Electric Vehicles (EVs) has substantially increased during the last decade, creating the need for customized EV-oriented routing strategies capable of using the enormous amount of historical, and real-time, traffic data that is collected through Intelligent Transport Systems (ITSs). Existing EV routing algorithms, however, concentrate mostly on the usage historical data to compute offline optimal paths, whereas the use of real-time traffic information to compute en-route path updates is still an almost unexplored topic; mainly due to its inherent computational challenges. This research effort proposes a Prognostic Decision Making (PDM) strategy to solve in real-time the Electric Vehicle Dynamic Stochastic Shortest Path Problem (EV-DSSPP); aiming at the simultaneous utilization of historical and real-time traffic data. Factors such as recurring and non-recurring traffic congestion, elevation, velocity and EV’s parameters are incorporated into the decision-making process. The proposed strategy has two hierarchical functional layers. The lower layer consists of a fast-computing routing algorithm that, by construction, guarantees a real-time execution. The higher layer organizes the periodic en-route execution of the first layer to compute en-route path updates during a trip. This strategy can hence serve as an expert router that works jointly with an ITS to assist EV drivers on route. The proposal is validated through a simulation study based on real-world traffic data collected in Santiago, Chile. The results show that periodic en-route path updates can generate a reduction in both travel time and energy consumption, which evidences the benefits of incorporating real-time traffic information into the EV-routing problems.

Event-triggered distributed coordinated control of networked autonomous surface vehicles subject to fully unknown kinetics via concurrent-learning-based neural predictor

This paper is concerned with distributed coordinated control of networked autonomous surface vehicles (ASVs) guided by multiple parameterized paths. The model of each ASV is subject to fully unknown kinetics, including environment disturbances, model uncertainties, and unknown input coefficients. A coordinated control method is presented for multiple ASVs to achieve a containment formation based on event-triggered mechanisms. Specifically, an event-triggered kinematic control law is firstly developed based on aperiodic communication. The desired path during the triggering interval is predicted by the last event-triggered velocity. Then, an event-triggered kinetic control law is developed, a third-order linear tracking differentiator is introduced to take the derivative of the kinematic control law, and the control input is updated only if the predefined event is triggered. A concurrent-learning-based neural predictor is constructed by using historical data such that model uncertainties and unknown input coefficients are identified simultaneously. Next, an event-triggered path update law is constructed by using a path-maneuvering design and the aperiodic information received from the network. The proposed method ensures the input-to-state stability of the resulting closed-loop system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed distributed coordinated control for networked ASVs by using event-triggered mechanisms and concurrent-learning-based neural predictors.

Distributed Containment Maneuvering of Uncertain Multiagent Systems in MIMO Strict-Feedback Form

This paper investigates the distributed containment maneuvering problem for uncertain nonlinear multiagent systems in multiple-input multiple-output (MIMO) strict-feedback form. The follower agents are driven to achieve a collective motion guided by multiple parameterized paths, and a dynamic behavior can be independently prescribed for the group during maneuvering. A containment maneuvering controller is developed by utilizing a modular design method enabling decoupled estimation and control. Specifically, an estimator module is constructed by utilizing an echo state network to identify the unknown nonlinearities. Next, a controller module is constructed by employing a modified dynamic surface control method where a second-order nonlinear tracking differentiator is introduced to extract the derivative information of the virtual control law. Subsequently, a path update law is derived such that the virtual leaders are synchronized, and the desired speed profile for the group can be specified independently. By using a small-gain theorem and cascade stability theory, the entire closed-loop system is proved to be input-to-state stable, and the containment maneuvering errors are uniformly ultimately bounded. An application for the formation control of marine surface vehicles is provided to show the efficacy of the proposed controller for containment maneuvering of uncertain nonlinear MIMO strict-feedback systems.

Finite-time unknown observer based coordinated path-following control of unmanned underwater vehicles

In this paper, a coordinated path-following control (CPFC) scheme is proposed for multiple unmanned underwater vehicles (UUVs) under undirected communication links. Each UUV is subject to complex unknowns involving model parameter perturbations and time-varying external disturbances. In light of individual path-following control, the coordinated guidance laws are developed to guide UUV surge velocities and yaw velocities, simultaneously. By virtue of graph theory, the neighbor information based path update laws are further employed to achieve synchronized tracking. Within the kinetics controller design, a finite-time unknown observer (FTUO) is utilized to assist in cancelling the lumped unknowns within a short time. Additionally, the FTUO-based distributed robust controllers are developed for UUVs and guarantee the guidance velocity signals generated by coordinated guidance can be successfully tracked. Strict stability analysis is proved in the sense of Lyapunov. Finally, simulation results and comparisons substantiate the efficacy and superiority of the developed CPFC scheme.

A new robust delayless subband adaptive filtering algorithm with variable step sizes for active control of broadband noise

Conventional delayless subband active noise control (ANC) systems can be degraded by the unexpected impulsive disturbances in the reference signal and/or the residual error signals despite the fact that the step sizes have been normalized by the variances of subband signals. Another inherent drawback is that an undesirable delay is introduced into the weight update path by using analysis filter banks, which reduces the upper bound of the step sizes thus limits the convergence rate. This paper proposes a novel robust and effective ANC algorithm configured in the delayless subband architecture for broadband noise cancellation. Instead of real-time providing threshold(s) on the reference and/or error signal as applied in most of the existing ANC algorithms for robustness, an online tuning scheme of bounded step sizes for the adaptive learning is developed for better convergence and outlier suppression. Box-constraint and time-averaging scheme deployed in the step size tuning guarantee robustness without requiring very accurate a priori information of the noises which might be corrupted by the impulses in real-world applications. In particular, this paper presents the stability and convergence analysis of the proposed closed-loop ANC system. Moreover, the computational complexity advantage is justified when compared to other state-of-the-art robust ANC algorithms. Numerical simulations are performed to validate the enhanced performance of the proposed algorithm for various colored noises with or without impulsive interferences.

Research on Public Facility Renewal Design System Under the Background of Future Community Construction

Under the background of future communities construction, this article analyzes the systematic update path to construct future community public facilities by taking the renewal design of the community public facilities as the breakthrough point on the basis of interpretation of the future communities in Zhejiang Province, with humanization, compounding and ecologicalization taken as the value orientation. In view of the problems that it is difficult to achieve breakthrough changes in the living environment space of old residential areas, such as single functional structure, outdated public facilities and lack of systematic design, With the collaborative analysis of the three major factors of “people, environment, and facilities”, a SWOT strategy model is established to clarify the advantages, disadvantages, opportunities and threats in updates in future community public facilities; Combining the renewal path of necessary, selective, adaptive and interventional facilities, this article updates the design of practical cases to derive the correlation between the needs of the crowd and different types of public facilities in order to build a public facility system that contains complex spatial model, diverse humanities and green communities ecology.

Fault-tolerant and Congestion Balanced Routing Algorithm for 2D Mesh NoCs

With the number of cores and nodes in networks-on-chips (NoCs) growing, the node fault occurrence probability is increasing. Although the existing turn model can route packets around the fault area and avoid deadlocks, a large traffic load is generated in the non-rightmost column of the fault region. This paper presents a novel fault-tolerant and congestion balanced (FTCB) routing algorithm that chooses a lower load area as the optimal router path by calculating the maximum path channels to balance traffic load and avoid network congestion. Two methods are proposed to calculate path channels for the fault-free mesh and the fault mesh. The improved odd-even turn rule is introduced to calculate path channels for the fault-free mesh. To balance the network load, free buffer length information is added to path channel calculations, which reflects the global perception. For the non-fault region, we update path channels by using the back formulas from the destination node to the source node. In a fault region, the modified calculation rules of path channels and fault-location odd-even turn rules are given. Compared to the other two related works, the throughput of the FTCB algorithm is improved by 6.92% and 10.7%. Meanwhile, the traffic load of FTCB is decreased to some degree in whole mesh, which shows the FTCB routing algorithm can obviously improve network load balanced, saturation throughput and network latency.