Deadlock States Research Articles

Learning Heterogeneous Relation Graph and Value Regularization Policy for Visual Navigation.

The goal of visual navigation is steering an agent to find a given target object with current observation. It is crucial to learn an informative visual representation and robust navigation policy in this task. Aiming to promote these two parts, we propose three complementary techniques, heterogeneous relation graph (HRG), a value regularized navigation policy (VRP), and gradient-based meta learning (ML). HRG integrates object relationships, including object semantic closeness and spatial directions, e.g., a knife is usually co-occurrence with bowl semantically or located at the left of the fork spatially. It improves visual representation learning. Both VRP and gradient-based ML improve robust navigation policy, regulating this process of the agent to escape from the deadlock states such as being stuck or looping. Specifically, gradient-based ML is a type of supervision method used in policy network training, which eliminates the gap between the seen and unseen environment distributions. In this process, VRP maximizes the transformation of the mutual information between visual observation and navigation policy, thus improving more informed navigation decisions. Our framework shows superior performance over the current state-of-the-art (SOTA) in terms of success rate and success weighted by length (SPL). Our HRG outperforms the Visual Genome knowledge graph on cross-scene generalization with ≈ 56% and ≈ 39% improvement on Hits@ 5* (proportion of correct entities ranked in top 5) and MRR * (mean reciprocal rank), respectively. Our code and HRG datasets will be made publicly available in the scientific community.

Deadlock control and hybrid social spider scheduling algorithm for two-stage assembly permutation flowshop with limited buffers

This paper focuses on the deadlock control and scheduling problem of two-stage assembly permutation flowshop with limited buffers. In such a system, all parts are first processed in a permutation flowshop, then assembled into final products. The objective is to find a feasible schedule, including a manufacturing subschedule and an assembly subschedule, to minimize makespan. Owing to limited buffers, not only may the blocking phenomenon appear in the system operation, but also some manufacturing subschedules may lead to deadlock states, making them unavailable. To obtain a feasible schedule, this work first proposes a deadlock control or an availability detection and amendment (ADA) algorithm to determine whether a manufacturing subschedule is available, and convert unavailable ones to available ones. Then by embedding ADA into a social spider algorithm (SSA), a hybrid social spider algorithm (HSSA) is developed for computing feasible schedules. In HSSA, an improved heuristic search algorithm is proposed to generate a better initial population, and a simplified variable neighborhood search algorithm is designed and incorporated into HSSA to enhance its searching ability. Finally, HSSA is tested on instances, showing its superiority over SSA in performance. The effect of buffer size on the scheduling objective is discussed.

Reliability and Performance Measurement of Safety-Critical Systems Based on Petri Nets: A Case Study of Nuclear Power Plant

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

Ferritin-based fusion protein shows octameric deadlock state of self-assembly

Ferritin is a universal protein complex responsible for iron perception in almost all living organisms and has applications from fundamental biophysics to drug delivery and structure-based immunogen design. Different platforms based on ferritin share similar technological challenges limiting their development – control of self-assembling processes of ferritin itself as well as ferritin-based chimeric recombinant protein complexes. In our research, we studied self-assembly processes of ferritin-based protein complexes under different expression conditions. We fused a ferritin subunit with a SMT3 protein tag, a homolog of human Small Ubiquitin-like Modifier (SUMO-tag), which was taken to destabilize ferritin 3-fold channel contacts and increase ferritin-SUMO subunits solubility. We first obtained the octameric protein complex of ferritin-SUMO (8xFer-SUMO) and studied its structural organization by small-angle X-ray scattering (SAXS). Obtained SAXS data correspond well with the high-resolution models predicted by AlphaFold and CORAL software of an octameric assembly around the 4-fold channel of ferritin without formation of 3-fold channels. Interestingly, three copies of 8xFer-SUMO do not assemble into 24-meric globules. Thus, we first obtained and structurally characterized ferritin-based self-assembling oligomers in a deadlock state. Deadlock oligomeric states of ferritin extend the known scheme of its self-assembly process, being new potential tools for a number of applications. Finally, our results might open new directions for various biotechnological platforms utilizing ferritin-based tools.

Potential field-based formation tracking control for multi-UGV system with detection behavior and collision avoidance

This paper investigates the time-varying formation tracking and collision avoidance problems for multi-unmanned ground vehicle (UGV) system which consists of omnidirectional vehicles on directed topology. To prevent the failure of collision avoidance caused by the lack of information interaction when the directed graph is not unilaterally connected, partial vehicles are elected as observers and their detection behaviors represented by sectors are realized by potential field and yaw control. Firstly, an improved repulsive potential field force is designed, and two composite potential field forces are proposed for single-to-single detection and single-to-multiple detection situations. Inspired by the equilibrium of forces, it is proven that the reconstructed potential field force which is not affected by the complexity of the collision situation can eradicate the local minima or deadlock states. Secondly, in order to generate detection behaviors according to a basic communication graph, an algorithm is put forward. Then, the protocol of position and velocity control, as well as the protocol of yaw control, are presented respectively. The former ensures the realization of formation tracking, while the latter, together with composite potential field forces, makes the detection behavior continuous. Moreover, according to Lyapunov-based theory, it is proven that the given method can achieve time-varying formation tracking and collision avoidance. Finally, simulations demonstrate the theoretical results.

Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.

Automatic Routing Reconfiguration for Fault Tolerance in Smart Manufacturing Plants

This paper focuses on the parts routing problem in a reconfigurable manufacturing plant, in presence of potential faults and uncertainty on the job scheduling and duration. The plant is modeled as a directed graph, where the nodes represent either transportation modules or machines, and the edges represent the allowed transitions between adjacent nodes. The parts move across the plant along predefined sequences of nodes, therefore the system state tracks the progress of the parts along such sequences and the control inputs are the transitions to be activated to command the parts movement. Provided the sequences, the proposed method automatically generates feedback control rules for deadlock avoidance, which are employed by a path following strategy to compute the suitable control inputs, complying with given temporal-logic constraints and avoiding deadlock states. Additionally, the approach is extended to deal with faults affecting the transportation modules via the selection of new feasible sequences and the online reconfiguration of the system state. Finally, the proposed approach is tested in high-fidelity simulations, showing high computational efficiency and throughput.

In-process feedback by detecting deadlock based on EEG data in exercise of learning by problem- posing and its evaluation

Feedback on learning activities is one of the most important issues in achieving adaptive learning. In this study, we propose a mechanism for solving this problem by detecting the deadlock state of a learner during a learning activity and providing feedback to eliminate such a state. Feedback on the products of learning activities (we call it “after-process feedback”) has been implemented in numerous interactive and adaptive learning environments. However, feedback during an activity (we call it “in-process feedback”) has rarely been implemented. In-process feedback is considered to be much better than after-process feedback when learners have difficulty or become frustrated with the learning material during the learning process. The difficulty in implementing in-process feedback lies in the timing and content of the feedback. It has been pointed out that the detection of a deadlock must be achieved as early as possible; otherwise, it reduces the learning motivation of the learner. Therefore, we focused on electroencephalograph (EEG) data, which are difficult to cheat and can clearly detect the state of the learner. By combining EEG data with machine learning, we developed a model for detecting when a learner is stuck, allowing us to detect the timing. After that, we generate the proper feedback by estimating the knowledge state of the learner based on the knowledge structure and task response status. We implemented and evaluated the in-process feedback approach in a learning environment posing arithmetic word problems.

Performance measurement of safety-critical systems based on ordinary differential equations and Petri nets: A case study of nuclear power plant

This article proposes a novel approach to measure the performance of Safety-Critical Systems (SCS). Such systems contain multiple processing nodes that communicate with each other is modeled by a Petri nets (PN). The paper uses the PN for the performance evaluation of SCS. A set of ordinary differential equations (ODEs) is derived from the Petri net model that represent the state of the system, and the solutions can be used to measure the system's performance. The proposed method can avoid the state space explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to Shutdown System (SDS) of Nuclear Power Plant (NPP). We obtained 99.887% accuracy of performance measurement, which proves the effectiveness of our approach.

Maximally Permissive Deadlock and Livelock Avoidance for Automated Manufacturing Systems via Critical Distance

The problem under consideration in this paper is how to avoid deadlocks and livelocks in the paradigm of Petri nets (PNs). Although deadlock and livelock avoidance has been extensively studied in existing literature, fewer results can be applied to practical automated manufacturing systems (AMSs) due to expensive computations and reduced permissiveness. In this paper, we propose an efficient and maximally permissive control scheme to avoid deadlocks and livelocks by using critical distance. From the perspective of the reachability graph of a PN, critical distance is equal to the maximum number of transitions among all transition sequences from any critical state to its corresponding deadlock or livelock state. First, we show how to calculate the critical distance of a PN in an efficient way by using a portion of reachable states. Then, local reachability graphs are established based on critical distance to avoid deadlocks and livelocks. The provided policy is maximally permissive since only the necessary minimum number of illegal transitions are forbidden at each state. Several representative examples are presented to illustrate our approach. Note to Practitioners—Deadlock and livelock avoidance is a crucial problem in automated manufacturing systems (AMSs). Various methods have been proposed to deal with deadlocks and livelocks by researchers and practitioners. However, there exist fewer works that focus on deep exploration regarding how long a deadlock or livelock will occur from a safe state. As a consequence, the existing works suffer from the disadvantages of being too aggressive or conservative, i.e., checking the whole state space or forbidding many acceptable states when avoiding deadlocks and livelocks. Through the analysis of a partial of reachable states, this paper derives the critical distance of Petri nets modeling AMSs, which is equal to the maximum number of transitions among all transition sequences from any critical marking to its corresponding deadlock or livelock marking. All deadlocks and livelocks, if exist, can be detected at any given marking within the critical distance. Our approach not only is maximally permissive but also explores only a partial of states, thereby mitigating state explosion problem.

Intersection traffic deadlock formation and its probability: A petri net‐based modeling approach

Although traffic deadlock at an intersection is a common phenomenon during rush hours, the deadlock formation metrics such as formation probability and duration have not been derived yet. As Petri Net (PN)-based approach can formulate the deadlock formation in analytical form and has been applied in numerous traffic flow studies, the PN is employed to investigate the deadlock formation probability and duration. First, each of the four main components of an intersection is formulated, namely vehicle trajectory, traffic signal, vehicle arrivals, and conflict behaviour, as a Petri Net (or IntersectionPN), and name them CellularPN, TrafficLightPN, DemandPN, and ConflictPN, respectively. Together, the entire intersection becomes an IntersectionPN, on which the system's three classes of states are defined, namely the deadlock state, the trap state, and the live state. The deadlock occurrence is formulated as an integer programming problem, while the formation probability and duration are studied by constructing a reachability graph that leads to the Markov chain of the system. A case study shows that the developed model is able to reproduce an intersection deadlock, and that the focus is on two priority behaviours: “First Enter First Serve” and “Pure Stochastic”. The results show that different behaviours lead to different deadlock formation metrics and that, when the saturation flow rate degrades (due to events such as severe weather conditions), a deadlock will occur more quickly. The research results provide the analysis tool for probabilistic deadlock formation. It can also be applied to the intersection deadlock prevention and control.

Temporal and Spatial Structure of Collective Pass-Chaining Action Performed by Japanese Top-Level Field Hockey Players.

In real hockey or soccer games, scoring opportunities usually occur quite rarely, and thus, for most of the duration of a game, the ball is drifting between the two goals. This pass-chaining situation can be regarded as the stable state of the offense–defense interaction. In the current study, temporal and spatial structure of this dynamical state was unveiled via quantification of the “defensive pressure distribution” on the pass trajectory, which was modeled as a non-linear function of the distance between the defender(s) and a given position on the pitch. Two groups, i.e., a top-level group and a less-skilled group, of Japanese collegiate hockey players were asked to play in 3-on-3 small-sided games between players of the same skill levels. When both the top-level and less-skilled players succeeded in passing the ball, there were no skill-level differences observed in the defensive pressure distribution on the pass trajectory. In these cases, the defenders put a certain level of pressure on the middle of the pass trajectory when the passer received a previously released pass, and later, when he released the ball to a teammate, the defenders approached the position at which the passer released the ball to intensively press on the passer. However, in the cases wherein they failed to thread the ball, clear differences were observed between the groups in terms of the defensive pressure distribution on the pass trajectory. In particular, for the less-skilled group, extremely intensive defensive pressure was put on the overall regions of the pass trajectory heavily concentrated on the timing at which the passer released the pass. This unique pressure distribution emerged for the less-skilled group because of their long ball-keeping duration (longer than 1 s and also longer than that for the top-level group), i.e., from the moment the passer received the ball, to the moment he released it to the next attacker. Thus, for top-level hockey players, a short time constant (less than 1 s) for the passing action will enable the passers to avoid extremely intensive defensive pressure, and enable the emergence of a dynamically stable attack–defense deadlock state through continuous chaining of the pass.

Robot Path Planning Using Improved Ant Colony Algorithm in the Environment of Internet of Things

It is a research topic of practical significance to study the path planning technology of mobile robot navigation technology. Aiming at the problems of slow convergence speed, redundant planning path, and easy to fall into local optimal value of ant colony algorithm in a complex environment, a robot path planning based on improved ant colony algorithm is proposed. First, the grid method is used to model the path environment, which marks each grid to make the ant colony move from the initial grid to the target grid for path search. Second, the ant colony is divided according to different planning tasks. Let some ants explore the way first, and carry out basic optimization planning for the map environment. The antecedent ants mark the basic advantage on a target value of the path with pheromone concentration so as to guide the subsequent route-finding operation of the main ant colony. Finally, in order to avoid the individual ants falling into a deadlock state in the early search, the obstacle avoidance factor is increased, the transition probability is improved, and the amount of information on each path is dynamically adjusted according to the local path information, so as to avoid the excessive concentration of pheromones. Experimental results show that the algorithm has high global search ability, significantly speeds up the convergence speed, and can effectively improve the efficiency of mobile robot in path planning.

Formal modelling and verification of high interactive honeypot using coloured Petri nets

Honeypot is an active defence mechanism intended to mimic a computer system concealing its identity to misguide attackers. The mechanism traps an attacker and collects intrusion information as they trespass a network environment and cause a menace for their interest. The paper proposes a honeypot tool with deadlock and livelock states to strengthen the defence mechanism and engage the attacker for a longer period. The proposed work aims to present the formal analysis of a honeypot using coloured petri nets tool. The three core components of the honeypot, such as data capture, control and collection, are included in the formal modelling to study the behavioural properties of a honeypot in a deadlock and livelock state. The main objective is to emphasise the working of high interaction honeypot in deadlock or livelock states under an attack surface. The honeypot's formal model verification using a state-space tool in coloured petri net determines that an attacker wedged in a deadlock or livelock state fails to navigate further to fulfil malicious intent, thereby deceiving an attacker for a longer period.

Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Technologies used in ICS and Smart Grid are overlapping. The most discussed attacks on ICSs are Stuxnet and Black energy malware. The anatomy of these attacks not only pointed out that the security of ICS is of prime concern but also demanded to execute a proactive approach in practicing ICS security. Honeypot is used to implement defensive measures for security. The Honeynet group released Honeypot for ICS labelled as Conpot in 2013. Though the Conpot is low interactive Honeypot, it emulates processes of different cyber-physical systems, typically Smart Grid. In the literature, the effectiveness of Honeypot operations was studied by challenging limitations of the existing setup or proposing new variants. Similar approaches are followed for Conpot evaluation. However, none of the work addressed a formal verification method to verify the engagement of Honeypot, and this makes the presented work unique. For proposed work, Coloured Petri Net (CPN) tool is used for formal verification of Conpot. The variants of Conpot are modelled, including initial state model, deadlock state model and livelock model. Further evaluation of these models based on state space analysis results confirmed that Conpot could lure an attacker by engaging him in an infinite loop and thereby limiting the scope of the attacker from exploring and damaging the real-time systems or services. However, in the deadlock state, the attacker’s activity in the conpot will be restricted and will be unable to proceed further as the conpot model incorporates deadlock loop.

Efficient static analysis and verification of featured transition systems

A Featured Transition System (FTS) models the behaviour of all products of a Software Product Line (SPL) in a single compact structure, by associating action-labelled transitions with features that condition their presence in product behaviour. It may however be the case that the resulting featured transitions of an FTS cannot be executed in any product (so called dead transitions) or, on the contrary, can be executed in all products (so called false optional transitions). Moreover, an FTS may contain states from which a transition can be executed only in some products (so called hidden deadlock states). It is useful to detect such ambiguities and signal them to the modeller, because dead transitions indicate an anomaly in the FTS that must be corrected, false optional transitions indicate a redundancy that may be removed, and hidden deadlocks should be made explicit in the FTS to improve the understanding of the model and to enable efficient verification—if the deadlocks in the products should not be remedied in the first place. We provide an algorithm to analyse an FTS for ambiguities and a means to transform an ambiguous FTS into an unambiguous one. The scope is twofold: an ambiguous model is typically undesired as it gives an unclear idea of the SPL and, moreover, an unambiguous FTS can efficiently be model checked. We empirically show the suitability of the algorithm by applying it to a number of benchmark SPL examples from the literature, and we show how this facilitates a kind of family-based model checking of a wide range of properties on FTSs.

A process calculus approach to detection and mitigation of PLC malware

We define a simple process calculus, based on Hennessy and Regan's Timed Process Language, for specifying networks of communicating programmable logic controllers (PLCs) enriched with monitors enforcing specification compliance at runtime. We define a synthesis algorithm that given an uncorrupted PLC returns a monitor that enforces the correctness of the PLC, even when injected with malware that may forge/drop actuator commands and inter-controller communications. Then, we strengthen the capabilities of our monitors by allowing the insertion of actions to mitigate malware activities. This gives us deadlock-freedom monitoring: malware may not drag monitored controllers into deadlock states. Our enforcing monitors represent a formal mechanism for prompt detection of malicious activities within PLCs. Finally, we illustrate our results by implementing in Simulink a non-trivial Water Transmission Network (WTN) system, and testing the effectiveness of our monitors in detecting and mitigating three different attacks targeting the PLCs of our WTN.

Algorithm and model for improve the avoiding of deadlock with increasing efficiency of resource allocation in cloud environment

Recently, cloud computing has been the most popular method among all other promising technologies that provides the service for many users at the same time. all users can reach and use the resource that is provided by the Virtual Machines (VMs). Hence users process, data must arrive to the resources to perform as a disturbed system, which provides dynamical scale services and virtualized resources. As a result of limitation of resources, the deadlock status may occur in such type of systems. In this paper, we have designed an algorithm that could increase the efficiency of deadlock avoidance. Our technique will use the attribute of execution time in the process to get better resource allocation in additional to the checking system to stay in safe state and free of deadlock.

An Evolutionary Game Research on Cooperation Mode of the NEV Power Battery Recycling and Gradient Utilization Alliance in the Context of China’s NEV Power Battery Retired Tide

Recycling and gradient utilization (GU) of new energy vehicle (NEV) power batteries plays a significant role in promoting the sustainable development of the economy, society and environment in the context of China’s NEV power battery retirement tide. In this paper, the battery recycling subjects and GU subjects were regarded as members in an alliance, and an evolutionary game model of competition and cooperation between the two types of subjects was established. Evolution conditions and paths of the stable cooperation modes between these two were explored. Suggestions were proposed to avoid entering a state of deadlock and promote the alliance to achieve the “win-win” cooperation mode of effective resource recovery and environmental sustainability. The results revealed four types of certain situations, two types of uncertain situations, and one type of deadlock situation for the evolution of alliance cooperation. The factors of the market environment are evident in not only changing the evolution paths and steady-states of the alliance but also in breaking the evolution deadlock. However, the sensitivity of the members in the alliance to different types of parameters varies greatly. It is difficult for the government to guide the formation of an ideal steady-state of cooperation or break the deadlock of evolution by a single strategy, such as subsidies or supervision. The combination of subsidy-and-supervision or phased regulation should be adopted. Only increasing subsidies is likely to weaken the function of the market and have a counterproductive effect.

Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.