Path In Time Research Articles

Fast Algorithm for Cyber-Attack Estimation and Attack Path Extraction Using Attack Graphs with AND/OR Nodes

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra’s shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O(nm), where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem.

Parallel multiple-chain DRAM probabilistic model for ultrasonic pulse velocity of concrete performance in the pipe of arch bridge

The existing models for predicting the compaction state of concrete inside steel tubes based on ultrasonic wave velocity have a major limitation in that they cannot comprehensively consider concrete compressive strength, instar, and various sources of uncertainties, both subjective and objective. To overcome this limitation, the study introduced a probability model for ultrasonic wave velocity in the concrete performance inside arch bridge steel tubes, based on the parallel multiple-chain Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Initially, the study considers the influence of concrete compressive strength and instar, establishing a simplified deterministic model for predicting concrete compactness inside the tubes. Building on this, the study incorporates material stochastic uncertainty and cognitive uncertainty, deriving analytical expressions for a probabilistic prediction model of concrete compactness. Furthermore, using parallel processing alongside the DRAM algorithm, the study explores how the number of parallel chains and the order of acceptance probabilities influence the probabilistic model parameters. The study selected and updated the posterior distribution information of the probabilistic model parameters. Finally, the effectiveness of the model was validated using experimental data. As shown in the analysis, with better prediction accuracy and lower dispersion, the model not only selects the optimal Markov chain iteration path in a short time, but also corrects the parameters affecting the prediction accuracy of the existing model, and also provides a probabilistic method for correcting the confidence intervals of the existing model.

Design of nature-inspired patterns on knitted fabrics for directional transportation of sweat and study of their performances

The human body in a hot environment or during intense training will sweat a lot. Traditional cotton fabrics are soft and breathable, but they tend to absorb moisture easily and evaporate it slowly, which makes people feel cold and wet. Sweat staying on fabrics for a long time could also cause bacteria to grow. Thus, it is necessary to transport the excessive sweat from the gathering section to the collection points along the surface paths in time to minimize such problems. The mechanism of directional water transport of desert beetles, cacti, nepenthes, and river meanders was referenced in this paper. The paths in dot, tree, V-shape, and river meander patterns were designed and manufactured from the inspiration of these natural architectures. The paths were coated on the surfaces of selected knitted fabrics with different structures through a one-step coating process. The optimal recipe of hydrophobic solution was determined when 0.1 g hydroxyethyl-cellulose (HEC) was added into MS-S002 solution. The results showed that sweat movement is mainly influenced by FL, FWD, FG, and knitted structure. The continuous and enclosed paths such as tree and river contribute to a better sweat moving track than those of dot and V-shape ones. Terry structure is more suitable for surface sweat transport than plain and pique. The highest movement speed of the terry structure with a tree pattern reaches 0.43 cm/s, followed by that of the terry structure with river pattern 0.35 cm/s. The coated fabrics also have ideal moisture and air permeability for wearing.

Path Through Specified Nodes and Links in a Network

A constrained shortest route problem in graph theory is about determination of a shortest path between two given nodes of the network that also visits a given set of specified nodes or a set of specified links before arriving to the destination. These earlier approaches did not consider specified elements containing both nodes and links of the given network. This paper finds a shortest path joining the origin node to the destination node, which is constrained to pass through a set of ‘K’ specified elements of the given network, where K1 number of elements represent nodes, 0<K1<K, and K2 number of elements represent links, where K1+K2=K. Alternatively, if the specified elements representing nodes are contained in the set Sn and the remaining elements representing links by the set Sl, and the set of specified elements denoted by the set Se, then Se={Sn ∪ Sl}. Such restricted path will also have real-life applications. Depending upon the configuration of the specified elements, these constrained paths may have loops. The approach discussed in this paper is a heuristic approach, which finds the required constrained path in a real time.

The Basics of Evolution Strategies: The Implementation of the Biomimetic Optimization Method in Educational Modules.

With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic evolutionary optimization procedure of evolution strategies. We provide three educational modules in work sheets that can be used by teachers and students to improve their understanding of evolution strategies. The educational module "Optimization of a Milk Carton" shows that the material consumption in producing a milk carton can be minimized using an evolution strategy with a mutative step size control. The use of a standard dice and a pocket calculator enables new milk cartons to be generated, with the offspring having the lowest material consumption becoming the parent of the next generation. The other educational modules deal with the so-called brachistochrone problem. The module "Fastest and Shortest Marble Track" provides a construction plan for a marble track whereby students can experimentally compare the "path of shortest length" with the "path of shortest time". The EvoBrach software, is used in the module "Various Marble Track Shapes" to compare the running times of a marble on a straight line, a parabola, and a brachistochrone. In conclusion, the introduction to the biomimetic method of evolution strategies and the educational modules should deepen the understanding of both optimization problems and biological evolution.

Control Systems Design for Autonomous Vehicles: Mathematical Approaches to Path Planning and Trajectory Tracking

Self-driving cars are a big change in the way people get around. They promise to make transportation safer, more efficient, and more convenient. Strong control system design, which includes path planning and trajectory tracking, is a key part of how they work. This essay looks at mathematics methods that are important for reaching these goals. The path planning problem is to find a way to get from where you start to where you want to go while taking into account natural and changing limits. Different methods, like geometric algorithms, optimization techniques, and statistical approaches, are used to find lines that don't collide and maximize things like time, energy, and comfort. Trajectory tracking, on the other hand, is the process of sticking to the planned path even when there are problems or unknowns. Model predictive control, feedback linearization, and adaptive control are some of the control methods that are used to make sure the car stays stable and follows the path that is wanted. One of the hardest parts of controlling a driverless car is balancing the different goals of planning a path and following its current path in real time. Because of this, we need to create combined control systems that can smoothly handle the planning and performance steps while taking into account how the surroundings and car state change over time. Also, the rise of linked and self-driving cars opens the door to joint control methods, in which cars talk to each other and work together to improve safety and traffic flow. Vehicle-to-vehicle contact is used by cooperative route planning algorithms to coordinate moves and avoid crashes, which makes the system more reliable and efficient as a whole.

Research on human-robot interaction for robotic spatial 3D printing based on real-time hand gesture control

With the rapid advancements in three-dimensional (3D) printing, researchers have shifted their focus towards the mechanical systems and methods used in this field. While Fused Deposition Modelling (FDM) remains the dominant method, alternative printing methods such as Spatial 3DP (S-3DP) have emerged. However, the majority of existing research on 3D printing technology has been emphasizing offline control, which lacks the capability to dynamically adjust the printing path in real time. Such an limitation has resulted in a decrease in printing efficiency. Therefore, this paper proposes a human-robot interaction (HRI) method based on real-time gesture control for Robotic Spatial 3DP (RS-3DP). This method incorporates utilization of YOLOv5 and Mediapipe algorithms to recognize gestures and convert the gesture information into real-time robot operations. Results show that this approach offers a feasible solution to address the issue of discontinuous S-3DP nodes because it achieves a gesture-controlled robot movement accuracy of 91 % and an average system response time of approximately 0.54 s. The proposed HRI method represents a pioneering advancement in real-time control for RS-3DP, thereby paving the way for further exploration and development in this field.

A decentralized path planning model based on deep reinforcement learning

The escalating usage of automated guided vehicles (AGVs) on a grand scale accentuates the scalability limitations inherent in traditional, centrally-controlled path planning algorithms. Addressing this challenge, we introduce a pioneering decentralized path planning model, DPPDRL, built upon the foundations of deep reinforcement learning. Drawing inspiration from human path planning and dynamic coupling techniques, DPPDRL imbues AGVs with the autonomy to independently dictate and dynamically adapt their paths in real time. Notably, DPPDRL champions the adoption of diverse policies, thereby facilitating efficient collision avoidance and exploration of potential routes. Simulation-based evidence substantiates critical findings, including remarkable improvements in training speed and scalability, heightened adaptability to environmental variations and agent number changes, and applicability across any number of AGVs, provided a reasonable task distribution. However, DPPDRL's performance becomes limited when tasks are densely concentrated. Addressing this issue through task distribution and hierarchical reinforcement learning is a promising direction for future research. These findings underscore the transformative potential of DPPDRL in managing extensive, dynamic AGV operations.

Emergency Evacuation Path Planning Method for the Large Ships

When an emergency occurs in a large ship, it is necessary to provide a scientific and reasonable evacuation strategy for the passengers in the ship, so as to avoid the loss of life and property caused by untimely evacuation. For the traditional Dijkstra’s algorithm, which only selects the optimal path based on the single index of path length, this paper takes into account the influence of path length, congestion and safety, and selects the optimal path in real time according to the dynamically changing external environment, which is more suitable for the application of emergency evacuation in large ship. The results of simulation experiments also prove the effectiveness of the algorithm proposed in this paper.

Determining Time for completion Construction Projects Using Time List

The main text of civil contracts consists of three main parts: the contract’s subject, the contract’s amount, and the contract’s time. Various techniques are used to specify the amounts of civil projects. The most conventional method is using basic price lists published by Iran’s Planning and Budgeting Organization. By the end of the second phase of studies and design and following the completion of all maps and execution details, as well as the specification of the materials to be consumed, the consulting engineer initially determines the volumes of all executive operations from the map and then uses the prices of the items contained in the price lists published by the organization mentioned above to specify the execution price of each of the working items. In the end, the engineer sums up the costs of executing the items and applies the overhead factor to specify the cost of executing the project to initiate the process of tender. To determining time for completion the construction project, however, there is no computational technique, and the [time for completion process] is usually performed by the order of the employer or by taking into consideration the construction time of similar projects by the consulting engineers. One of the techniques to schedule the project is to use the critical path network technique. Project execution time is the sum of the time allotted to perform various working packages within a critical path. The duration of each work package can be determined by using the items that make up that work package. The time to execute each working item involves a probable distribution similar to a normal distribution, and the most probable time to execute an activity is in the middle of a probable distribution diagram. In the schedule, the duration of the execution of each work package is determined by using the previous experiences of the experts. According to Goldart, when people are asked to estimate the timing of an activity, they apply a safe time to insure themselves against unforeseeable events, though they can perform their activities at shorter times, also .In this study, to determine the execution time of each item included in the price list, the item price breakdown tables that have been published by the Program and Budget Organization have been used, and a list similar to the price list called the time list has been proposed, with the difference that For each item, instead of the implementation cost, the implementation time of that item is included according to the unit of that activity. First, the time list for building items, the price list of mechanical facilities and the price list of electrical facilities have been extracted, and then by using them, the execution time of the items of each work package is determined according to the amount of each item and the number of available resources, and the total times The implementation of work packages is the critical time of project implementation. Finally, the sum of times allotted to execute working packages will be on the critical path of project execution time. It was concluded that time for completion construction project execution by the time list showed a 22.6% reduction [in execution time] compared to the time prediction technique using expert experiences.

Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks.

Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures.

ERROR ANALYSIS OF VISUAL ODOMETRY FOR A SMALL SIZE UNMANNED AERIAL VEHICLE

Abstract. Usage of small size Unmanned Aerial Vehicle (UAV) with autonomous flight capability became widespread at both civil and military applications because of its low costs and ease of use. However, inertial or GPS based positioning and orientation sensors can be disabled at battlefields and the UAV losses its path in a short time. Visual odometry becomes a solution for the aforementioned hassle. Real-time detection of ground control points, matching the same control points at the stereo pair images and correcting the position and orientation data with least square adjustment are the challenges of visual odometry. In this study, error analysis of position and orientation of a UAV flying through mountainous region which cannot obtain any augmenting data for position and orientation is conducted by error propagation law. The flight of UAV is simulated with exact initial position and orientation data. Optic images of ground obtained by nadir directed camera are georeferenced using the digital elevation model of the region, interior and exterior camera parameters. The digital elevation model is used to correct the relief displacement. The stereo-pair image is obtained when the UAV flies 200 m and image coordinates of the common control points are detected by colinearity equations. Position and orientation of UAV is corrected by least square adjustment and the process is repeated for each obtained image acquired at 200 m interval. The analysis revealed that the precisions of horizontal and vertical positioning becomes 75 and 200 meter, respectively. The precision of roll angle becomes worse than 0.5 degree, while the precisions of pitch and yaw angle are estimate to be better that 0.1 degree.

Kink Sum for Long-Memory Small Matrix Path Integral Dynamics.

The small matrix decomposition of the real-time path integral (SMatPI) allows for numerically exact and efficient propagation of the reduced density matrix (RDM) for system-bath Hamiltonians. Its high efficiency lies in the small size of the SMatPI matrices employed in the iterative algorithm, whose size is equal to that of the full RDM. By avoiding the storage and multiplication of large tensors, the SMatPI algorithm is applicable in multistate systems under a variety of conditions. The main computational effort is the evaluation of path sums within the entangled memory length to construct the SMatPI matrices. A number of methods are available for this task, each with its own favorable parameter regime, but calculations with strong system-bath coupling and long memory at low temperatures remain out of reach. The present paper evaluates the path sums by binning the paths (in forward time only) based on their amplitudes, which depend on the number and type of kinks they contain. The algorithm is very efficient, leading to a dramatic acceleration of path sums and significantly extending the accessible memory length in the most challenging regimes.

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Abstract. In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.

Research on optimal path planning technology for industrial robots based on improved IDE algorithm

Abstract Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

Method for planning the way of UGV using a modification of dynamic bi-directional RRT algorithm.

The path planning problem of unmanned autonomous ground vehicles has always been an acute problem in the field of autonomous ground robotic systems research. From the point of view of the complexity of the tasks during the conduct of combat operations in the urbanized space of the densely built-up city with a constantly changing landscape, the tasks entrusted to the UGV are constantly becoming more difficult, and the scenarios of the use of the UGV demonstrate a diversified trend in the development of new management and control systems. The rapidity of conducting modern military operations in an urbanized space appears as a complex and multifaceted task. Considering the complexity of the UGV movement process, at the current stage of the development of robotic systems, the general trend is to abandon remote control of robotic complexes with the transition to automatic modes, which requires the development and implementation of algorithms for automatic interaction and movement of military mobile robotic systems. To solve the problems of the Rapidly-Exploring Random Tree *Fixed Nodes algorithm regarding its low speed for obtaining track junctions and the impossibility of using it in a dynamic environment when planning a UGV path. To solve the problem of accelerating the acquisition of collision-free paths in real time in two-dimensional space, it is proposed to apply a modified dynamic bidirectional RRT* algorithm with reference nodes. The algorithm is a modification of Rapidly-Exploring Random Tree *Fixed Nodes using a bidirectional greedy search method to speed up and solve the problem of the unidirectional Rapidly-Exploring Random Tree algorithm regarding its slow search speed, as well as the difficulties of decision making in a narrow environment caused by blind random sampling. In the case of dynamic disturbance movement, taking advantage that reference nodes do not require much calculation in planning, in the process of iterative path optimization, the proposed algorithm updates the map information in real time and repairs the damaged output path to complete the dynamic path planning.

Islam in Yamal: features of integration in the Arctic zone

On the basis of the analysis of sources, works of researchers, interviews, field studies, participant observation, features of the Islamic religion in the Yamalo-Nenets Autonomous Okrug are traced. During the Middle Ages the territory of Yamal was included in trade with the countries of the East through the “fur route”, as evidenced by a significant number of archaeological finds of oriental objects, and later it was formally part of such Muslim states as the Tyumen and Siberian Khanates, but the presence of Muslims in Yamal was minimal. However, since the middle of the 20th century the territory of the Yamalo-Nenets Autonomous Okrug began to attract migrants who created communities in cities and in shift camps. The majority of believers, despite the difference in places of exodus, adhere to Islam traditional for Russia (Hanafi and Shafi'i madhhabs), building relationships with authorities, representatives of other faiths and worldviews. They believe that it is only through dialogue that relations can be harmonized, while respecting the traditions and norms that have developed in the district. The specificity of the confession of Islam in the conditions of the Arctic is also obvious. The radicals tried to instill into the minds of fellow believers that extremist ideology and terrorist acts should become a tool in building a caliphate. For such propaganda and crimes, other Islamists are serving sentences (including life imprisonment), having different conditions for observing rituals. Security in the district, which is of strategic importance in political and economic terms, should take into account both the peculiarities of the organization of the local society and its experience in countering extremism. The article explores how Islam in Yamal passed a path in a short time that took centuries for other regions of the world.

A horizontal single-axis tracking bracket with an adjustable tilt angle and its adaptive real-time tracking system for bifacial PV modules

An efficient photovoltaic (PV) tracking system enables solar cells to produce more energy. However, commonly-used PV tracking systems experience the following limitations: (ⅰ) they are mainly applied to single-sided PV panels; (ⅱ) they employ conventional astronomical algorithms that cannot adjust the tracking path in real time according to variable weather. In this study, a model of horizontal single-axis tracking bracket with an adjustable tilt angle (HSATBATA) is developed, and the irradiance model of moving bifacial PV modules is designed, which considers the mounting height, spacing and ground shading of PV panels. Furthermore, an adaptive real-time tracking (ARTT) algorithm is put forward to obtain the optimal tracking path for PV cells, which considers the energy consumption of tracking motors, the front and back irradiance of solar cells, cell temperature and ambient wind speed. The test results indicate that the presented ARTT algorithm enhances the energy of PV modules by 32.7 % and 7.5 %, respectively, compared to the fixed bracket and the conventional tracking algorithm. Additionally, the number of motor starts of the PV tracking system is reduced by 71.7 % compared with that of the conventional algorithm, which greatly contributes to extending the service life of PV tracking brackets and lowering the cost of electricity. Present study will help to improve the theoretical research system of PV tracking bracket construction, irradiance modeling of moving bifacial modules, and intelligent tracking algorithms.

부모의 긍,부정 양육태도와 자녀의 행복감의 관계에서 부모와의 대화시간의 매개효과 : 초등학생, 중학생 집단에 따른 다집단 분석

This study attempted to verify the mediating effect of positive and negative parenting attitudes on their children’s happiness through conversation time with parents and to determine whether there are differences between elementary and middle school students in such effects. To this end, the response data of elementary and middle school students collected in the third year (2020) of the Korean Children and Youth Panel Survey conducted by the Korea Youth Policy Institute were used. The analysis was conducted using structural equations and M-plus. The analysis results are as follows: First, in the case of elementary school students, positive parenting attitudes significantly affected happiness overall. In addition, in the case of middle school students, support for autonomy among positive parenting attitudes positively affected happiness. Second, in the case of middle school students, warmth and structural provision among positive parenting attitudes positively affected happiness by mediating conversation time with parents. Third, the mediating path of conversation time was not significant for autonomy support and coercion in common for all ages. Fourth, rejection of negative parenting attitudes in all ages negatively affected happiness by negatively mediating conversation time. Also, the inconsistency negatively affected happiness by negatively mediating conversation time. In conclusion, this study confirmed the positive effect of a positive parenting attitude on happiness rather than the amount of conversation time with parents. Moreover, it proved that a positive parenting attitude (warmness, structure provision) for middle school students positively affects happiness by mediating conversation time. These results are of great significance in that they provided evidence for the necessity of parental education considering the age of children.

Self-Directed Mobile Robot Navigation Based on Functional Firefly Algorithm (FFA)

This paper proposes an optimized mobile robot navigation strategy using a functional firefly algorithm (FFA) and choice function. This approach has two key advantages: first, the linear objective function performs efficiently with the single degree and finite-order polynomial time operation, and second, the cartesian constraint performs compactly with the chosen degree of freedom on the finite interval. This functional approach optimizes the size of operational parameters in context with key size, operation time, and a finite range of verification. The choice function achieves parameter order (size) reduction. The attraction characteristic of fireflies is represented by the choice function for optimizing the choice between low and high intensities of fireflies. In 2D and 3D environments, the proposed robot navigation performs well in an uncertain environment with static and dynamic obstacles. This efficiency includes the robot’s speed as determined by the choice function’s minimum path lengths. The collision-free path is achieved by the non-void family of non-void sets. The obtained results are optimal in terms of path length and navigational time. The proposed controller is also compared with the other existing controllers, and it is observed that the FFA gives the shortest path in less time for the same environmental condition.