Deadlock Conditions Research Articles

The critical impact of remote pilot modelling in evaluation of detect-and-avoid systems explained for ACAS Xu

Detect-and-avoid (DAA) systems for remotely piloted aircraft systems (RPAS) can provide remain well clear (RWC) guidance as well as shorter term resolution advisories (RAs) for collision avoidance, which are both provided in the vertical and horizontal planes. Simulation-based studies for large sets of encounter scenarios are used in the development and evaluation of DAA systems, which encompass safety and operational acceptability of the DAA supported operations. Given the key role of the remote pilot (RP) in responding to RWC guidance and RAs, a RP model is an essential element in such simulations. This paper describes the development of a RP model for evaluation of encounter scenarios involving the ACAS Xu DAA system. The model describes RP situation awareness (perception, comprehension, projection) as basis for decision-making, modes for responding to RAs and/or RWC guidance, response delays, response strengths, and the flight control actions. The RP model includes deterministic and stochastic settings. It is integrated in a simulation environment for encounters of manned and/or unmanned aircraft, the involved DAA and airborne collision avoidance systems, the surveillance and communication systems, and the human operators. Simulation results are provided for a set of encounters between pairs of RPAS both having ACAS Xu for various configurations of the RP model, and for cases with and without sensor errors. The results show that there can be large differences between the results of deterministic and Monte Carlo simulations, indicating that limited sensor errors can have a large impact on the nonlinear system dynamics. Furthermore it is shown that deadlock conditions can exist where the RPAS show oscillatory behaviour and do not manage to effectively pass each other, dependent on the encounter geometry and RP model settings. It is advised to perform a broad sensitively study for RP performance and to study extending the scope of DAA systems to include guidance for efficiently returning to mission without triggering new conflicts.

Cooperative task allocation with simultaneous arrival and resource constraint for multi-UAV using a genetic algorithm

In recent years, the application of multi-UAV cooperation systems has expanded across various domains. Enhancing the coordination performance of multi-UAV systems can be achieved through task allocation methods, typically relying on a hierarchical structure. This paper proposes a novel approach using a modified genetic algorithm (GA) to address the integrated task allocation and path planning problems for multi-UAV attacking multi-target. To create a more realistic mission scenario, multiple constraints, such as resource requirement and simultaneous target arrival, are considered. The modified GA incorporates tailored crossover and mutation operators that ensure compliance with the aforementioned constraints. Furthermore, an unlocking strategy is devised to prevent the occurrence of a chromosome deadlock condition, in which several UAVs become stuck in an infinite waiting state. Through simulation results, the modified GA is demonstrated to effectively delivers feasible solutions to the coupled task allocation and path planning problems, preserving the integrated nature of the optimization process. Monte Carlo simulations are conducted to highlight the superiority of the proposed method in comparison to conventional approaches.

Web Based Banker Algorithm Simulation

This study aims to analyze and evaluate the Banker algorithm, which is used in operating systems to manage resources in a multiprogramming computing environment. This study uses an analytical and experimental approach to study the performance of the Banker algorithm in managing resource allocation and preventing deadlock conditions. The data was obtained through computer simulations and statistical analysis was used to compare results with other resource allocation algorithms. This research is expected to provide useful insights for operating system designers in selecting and implementing efficient algorithms to manage resources in complex computing environments.

Addressing deadlock in large‐scale, complex rail networks via multi‐agent deep reinforcement learning

AbstractRail freight planning problems pose specific challenges that have attracted the attention of academics and industry professionals for many decades. They involve multiple types of assets (trains, stations, terminals, etc.) and are subjected to structural, operational and safety constraints. Even though various approaches have been proposed, few can address the complexity and size of real‐world scenarios, and decentralized techniques, like multi‐agent systems (MAS), have become more prevalent. The current state of the art in disciplines such as agent technology, reinforcement learning and discrete‐event simulation allows the implementation of complex architectures, with multiple actors interacting and learning simultaneously. Therefore, this study takes advantage of these current advances and proposes an innovative approach to real‐time traffic management problems in freight railway networks through multi‐agent deep reinforcement learning (MADRL). This study was motivated by the decision‐making scheduling problems arising in the Hunter Valley Coal Chain (HVCC), located in New South Wales, Australia. The MADRL algorithm uses as the training environment the simulation model currently utilized for capacity planning of the HVCC, allowing experiments with actual data. Thus, we enhanced the simulation model to accommodate a MAS with intelligent agents representing system elements, such as trains, dump stations, and load points. Furthermore, these agents act in a decentralized fashion based on local observations, constituting a partially‐observed Markov decision process (dec‐POMDP). Three variations of the MADRL approach are presented: a baseline model, an extended model, and one that directly addresses deadlocks. Finally, we present a transfer learning method that improves deadlock resolution and leverages performance. In the experiments, we explore specific, complex scenarios arising in the HVCC, where trains frequently face deadlock conditions. The baseline model outperforms a first‐come‐first‐serve (FCFS) based heuristic used by HVCC's simulation model and a genetic algorithm in instances with up to 60 trains – but fails in more complex scenarios. On the other hand, the most advanced model, which addresses deadlocks via transfer learning, always finds feasible solutions and produces policies that outperform the FCFS‐based heuristic in 94% of the instances.

MRCN: Throughput-Oriented Multicast Routing for Customized Network-on-Chips

The relentless proliferation of Big Data and artificial intelligence has compelled computing platform architectures to evolve into heterogeneous multicores for greater energy efficiency. A customized network-on-chip (NoC) supporting interconnection diversity is pivotal for the asymmetric data-access traffic requirements of modern heterogeneous multicore system-on-chip (SoC). A significant portion of on-chip data access comprises single-source multi-destination (SSMD) traffic, which supports barrier synchronization, multi-threading, cache coherency protocols, and deep neural network (DNN) acceleration. By amortizing SSMD traffic, multicast routing is essential for effectively utilizing communication bandwidth. One of the primary concerns in supporting multicast routing in NoCs is to circumvent the additional deadlock conditions caused by branch operations among the active routers. However, it is challenging to implement the throughput-optimized multicast routing in irregular topology-based NoCs because the deadlock conditions become highly complicated, and the Hamiltonian path required to apply the labeling rule may not exist. Two important observations were identified regarding multicast routing in customized NoCs: 1) Even if the NoC lacks a Hamiltonian path, deadlock-freedom can be guaranteed by restricting branch operations to a specific destination. 2) A variable path diversity in a custom topology can be leveraged in routing path allocation and branch. Based on these properties, this study proposes a deadlock-free and throughput-enhanced multicast routing for customized NoC (MRCN). MRCN ensures deadlock freedom by utilizing extended routing and router labeling rules. Furthermore, destination router partitioning and traffic-aware adaptive branching are incorporated to reduce packet routing hops and disperse channel traffic. The effectiveness of MRCN was verified using Noxim, a well-known cycle-accurate NoC simulator, under various topologies and traffic patterns. The simulation revealed that MRCN improved the average latency by 13.98 % and the throughput by 12.16 % under the saturated traffic conditions over the previous multicast routings in customized NoCs.

RESEARCH ON SHIP SURVIVAL PREDICTION: AN INITIATIVE MODEL FOR ALL IN THE SHIP DURING COVID PANDEMIC CRISIS

There are lots of increasing crisis during the covid pandemic including sheries, transportation ,people survival, industrial harms etc. Also previously many disasters came across sea so we need to aware of all those circumstances and situations. So in this paper I used the algorithm and deadlock condition concept to overcome some of the major problems earlier happened. Thus the basic denition of deadlock in this ship survival paper is that to continuosly check the resources availability and processes running ,observing the crowd of ship and all the shipping materials and informations of shipping. Shipping also creates sometime deadlock condition across the sea while travelling into different canals subways and during covid pandemic the most important thing is the demand of oxygen and its related vaccines, medicines. Thus we must have sufcient management risk analysis technique to handle evacuation through the ship and onboarding to the ship safely.

Implementation of intelligent navigational techniques for inter-collision avoidance of multiple humanoid robots in complex environment

Over the past few decades, research in humanoid robots has been amplified rapidly. This paper displays the attainment of an optimum steering angle to avoid hurdles and reach the target with minimum effort. To achieve this objective, three steps optimization procedure is considered. A hybridization of regression analysis (RA), cell decomposition (CD), and whale optimization algorithm (WOA) are designed and implemented in humanoid NAO for prime trajectory with the least computational cost. RA supplies the first set of steering angles to CD based on the training in the given workspace. CD provides a second set of steering angles to WOA, which results in an optimum steering angle based on its characteristics of hunting prey. The proposed RA-CD-WOA algorithm is evaluated in simulated and experimental workspaces for a single NAO. The proposed algorithm and its standalone algorithms are compared for several iterations, demonstrating the requirement for hybridization. It is also examined for multiple NAOs on a common platform that may lead to a deadlock condition during navigation. To elude this condition, the dining philosopher controller is integrated with the base algorithm, which results in prioritizing a NAO and solving the problem. Further, the RA-CD-WOA algorithm is compared with an existing technique that displays its robustness and effectiveness for robot navigation.

Multi-objective optimization technique for trajectory planning of multi-humanoid robots in cluttered terrain

Humanoid robots hold a decent advantage over wheeled robots because of their ability to mimic human exile. The presented paper proposes a novel strategy for trajectory planning in a cluttered terrain using the hybridized controller modeled on the basis of modified MANFIS (multiple adaptive neuro-fuzzy inference system) and MOSFO (multi-objective sunflower optimization) techniques. The controller works in a two-step mechanism. The input parameters, i.e., obstacle distances and target direction, are first fed to the MANFIS controller, which generates a steering angle in both directions of an obstacle to dodge it. The intermediate steering angles are obtained based on the training model. The final steering angle to avoid obstacles is selected based on the direction of the target and additional obstacles in the path. It is further works as input for the MOSFO technique, which provides the ultimate steering angle. Using the proposed technique, various simulations are carried out in the WEBOT simulator, which shows a deviation under 5% when the results are validated in real-time experiments, revealing the technique to be robust. To resolve the complication of providing preference to the robot during deadlock condition in multi-humanoids system, the dining philosopher controller is implemented. The efficiency of the proposed technique is examined through the comparisons with the default controller of NAO based on toques produces at various joints that present an average improvement of 6.12%, 7.05% and 15.04% in ankle, knee and hip, respectively. It is further compared against the existed navigational strategy in multiple robot systems that also displays an acceptable improvement in travel length. In comparison in reference to the existing controller, the proposed technique emerges to be a clear winner by portraying its superiority.

An Experimental Execution Of Deadlock Algorithms

A deadlock condition occurs when a set of processes requests the same resource which is currently being held/allocated to some other processes. The process which is in deadlock state will wait for a large amount of time and they will never terminate their executions and the resources which are held by that set of processes cannot be made available to any other processes which may sometimes lead to a system failure. There are methods for handling deadlocks such as deadlock prevention or avoidance, ignoring the problem altogether, and deadlock detection and recovery. Also, various conditions may help prevent deadlock to some extent but to completely eradicate deadlock from the system an efficient operating system needs to be developed with efficient synchronization algorithms. In this paper we have provided a practical implementation of deadlock scenario as well as deadlock concepts which includes Mutual Exclusion, Hold and Wait, No Preemption, Circular Wait, Process Termination, Selecting Victim, Rollback, and starvation. Java Programming language is utilized for developing all algorithms which we have presented in this paper.

Job Insecurity in Private Education Sector Considering COVID-19 Pandemic: Bangladesh Panorama

Job insecurity is one of the vexed questions here in the COVID-19 pandemic situation. Private sector employees are agonizing mostly due to this problem. In the midst of July within three months of the crisis, about 13 percent of people have become unemployed in the country due to the Covid-19 pandemic. The educational institutions are closed in Bangladesh from 17 March 2020 due to this crisis. So the private education sector job holders are in a precarious situation in terms of their job security. In this study, we investigate the vacillation of job security for private education employees. For this purpose we questioned 100 private education sector employees such as School, College and University to find out the actual scenarios. Most of the private educational institution depends on its own income from the student’s tuition fees; hence the trouble to manage it for the deadlock condition and their employees are also in a financial dilemma. Though some of them are started online programs in the true picture, they will suffer as the ballgame advancing. Bangladesh's government announces thirty thousand core incentives to face economic hazards. The particular employees in this stratum are benefited from trivial brink. Some lay off strategy can save both the victimized party.

Modeling and Analysis of Communication Subsystem design Process for Wireless Sensor Networks based on Petri Net

Wireless Sensor Networks (WSNs) have numerous applications in the field of engineering. These networks have variety of design constraints and energy is most important one. As energy harvesting is not possible in most of the cases, energy conservation is the only option available to the researchers. Energy efficient communication is the key to energy conservation as most of the energy is spent in transfer of data from one node to another. This article is devoted to development of energy efficient communication system design process model based on Petri Net for Wireless Sensor Networks. This model not only facilitates to examine the dynamicity in design process but also evaluates the design process for any deadlock conditions. Based on this model, radiating element array for communication in sensor networks can be designed and developed and therefore can be used to improve the overall energy efficiency of the network.

Petri Net Based Modeling and Property Analysis of Distributed Discrete Event System

PetriNet is an imperative and handy language used for modeling and analysis of discrete event system (DES) i.e. a dynamic system that progress according to unexpected occurrence of events at probably unknown, asymmetrical interval of time. This concept provides an interface for analysis of behavioral and structural properties like liveness, boundedness and cover-ability tree of discrete event systems. These properties are not only necessary for proving the correctness of system model but also helpful in checking the deadlock conditions in a system. As a graph Petri Net is used for modeling and mathematically, it can be used for analysis of the system. In this paper, we have first modeled various DES like computation model and communication model using Petri Nets and then analyzed their properties using MATLAB. These DES models have applications in almost every domain of science and engineering.

Implementation of Banker’s Algorithm Using Dynamic Modified Approach

Banker’s algorithm referred to as resource allocation and deadlock avoidance algorithm that checks for the safety by simulating the allocation of predetermined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for all other pending processes. It needs to know how much of each resource a process could possibly request. Number of processes are static in algorithm, but in most of system processes varies dynamically and no additional process will be started while it is in execution. The number of resources are not allow to go down while it is in execution. In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that prevents the system to fall in unsafe state. It also give details about all the resources and processes that which one require resources and in what quantity. This also allocates the resource automatically to the stopped process for the execution and will always give the appropriate safe sequence for the given processes.

Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.

Minimum Vertex Cover of Circulant Graph having Hamiltonian Path and its Application

Objective: The Minimum Vertex Cover of a circulant graph for m≥2 obtained from the complete graph K2m+1 and K2m+2 have been discussed. Methods: Minimum Vertex Cover is a NP Complete problem. Various properties to find out the Minimum Vertex Cover of different types of circulant graphs of even and odd values of m≥2 have been studied. Findings: After studied the Minimum vertex cover we find two theorems for the graph K2m+1 and K2m+2 and results are also observed. An algorithm also developed to find the Minimum Vertex Cover. Application: An application of minimum vertex cover has been cited to avoid the deadlock condition in process graph with an example.

Algebraic Modeling and Verification of Web Service Composition

In order to provide a rigorous and sound foundation for formal reasoning about Web services, algebraic modeling is one of the important techniques used as is witnessed from the Web service literature. However, the algebraic modeling approach for Web services (Web service algebra) is still in its infancy. To further facilitate the algebraic modeling of Web services, in this paper, we propose a composition algebra based on the notion of recursive composition. The proposed algebra is fully capable to verify the presence of behavioral equivalences and deadlock conditions in a Web service composition scenario. The main motivation for proposing Web service composition algebra is to capture the recursive nature of composition which cannot be done using traditional approaches like model checking and Petri net.

Petri Net Decomposition Approach to Deadlock-Free and Non-Cyclic Scheduling of Dual-Armed Cluster Tools

Semiconductor cluster tools are the integrated equipment to process a variety of silicon wafers for the fabrication of microelectronic components. The cluster tool system consists of several loadlock modules, processing chambers, and material handling armed robots for transferring wafers between them. Most scheduling problems for dual-armed cluster tools adopt cyclic scheduling with the assumption of swap sequence of the robot arm. A deadlock-free and non-cyclic scheduling is highly required to improve total throughput of cluster tools for various types of wafer flow patterns and various changes of equipment configurations. In this paper, we propose a Petri net decomposition approach to derive a near-optimal solution of deadlock-free and non-cyclic scheduling of dual-armed cluster tools to reduce the computational complexity. A timed Petri net model is introduced to represent a non-cyclic scheduling model for dual-armed cluster tools. In order to obtain a deadlock-free and non-cyclic schedule efficiently, we propose a deadlock avoidance control policy that restricts the markings to prevent unmarked siphons. Deadlock condition for the dual-armed cluster tools is characterized by the structure of Petri net model. The performance of the non-cyclic scheduling model by the proposed method is compared with that of the cyclic scheduling model. Computational results show the effectiveness of the non-cyclic scheduling model compared with the cyclic scheduling model for multiple wafer flow patterns.

Towards the design of efficient nonbeacon-enabled ZigBee networks

This paper presents experimental results of the communication performance evaluation of a prototype ZigBee-based patient monitoring system commissioned in an in-patient floor of a Portuguese hospital (HPG –Hospital Privado de Guimara˜es). Besides, it revisits relevant problems that affect the performance of nonbeacon-enabled ZigBee networks. Initially, the presence of hidden-nodes and the impact of sensor node mobility are discussed. It was observed, for instance, that the message delivery ratio in a star network consisting of six wireless electrocardiogram sensor devices may decrease from 100% when no hidden-nodes are present to 83.96% when half of the sensor devices are unable to detect the transmissions made by the other half. An additional aspect which affects the communication reliability is a deadlock condition that can occur if routers are unable to process incoming packets during the backoff part of the CSMA-CA mechanism. A simple approach to increase the message delivery ratio in this case is proposed and its effectiveness is verified. The discussion and results presented in this paper aim to contribute to the design of efficient networks, and are valid to other scenarios and environments rather than hospitals.

Modeling and simulation of traffic lights and controller unit systems by colored Petri net

We know that increasing fuel consumption, high environmental pollution and time wasting are the visible results of high traffic density. Developing new directions or even highways is an alternative way to solve this problem. It may add several junctions. Therefore, we provided a method which declines the waiting time behind the red color in the first phase through controlling Fuzzy logic traffic light and following a fair performance. The density queue behind the red color and also the density of passing cars are the arrival parameters in this method. This Fuzzy controller system with receiving those two traffic parameters in regular condition provided timing operation according to the level of traffic or in front of a special traffic mode that will change traffic light condition. Junction controlling problem is raised in the second phase which has specific importance, because their usual operation will cause problems if they remain without control. This not only does not help to traffic managing, but also leads traffic towards deadlock conditions. Therefore, the second phase started through sending the time parameter to the control center (which is timing by Fuzzy traffic light). Then the control center sends appropriate output to the next traffic light after necessary validation of parameter value. We simulate Fuzzy intelligent traffic lights and junction controller systems by CPN Modeling tool to investigate the accuracy of system performance. Key words: Fuzzy traffic light, colour petri net, control center, controller systems, fuzzy logic, controller unit.

Research on Tool Flow Deadlock Based on Petri Net and Graph Theory

Part flow and tool flow were the two mainly aspects of the workflow in flexible manufacturing system. During the past decades part flow deadlock was much more considered than that of tool flow. Tool flow deadlock problem in the condition that the production procedure can be interrupted was studied in this paper. The model of machine process based on the Petri net was set up and the definition of tool flow deadlock was proposed. Necessary and sufficient conditions for deadlock and potential deadlock were discussed according to graph theory method. A case was illustrated and showed that this method was to be correct and effective.