Hybrid Path Research Articles

Hybrid Path Selection Modeling by Considering Habits and Traffic Conditions

Efficient path guidance is one of the effective ways to improve the utilization of road resources and relieve traffic congestion. Therefore, it is important to identify ways to enable drivers to select path more efficiently. In this paper, we seek to build a pre + during-trip path prediction model based on the impacts of external and internal information on path selection behaviors to enhance path selection efficiency. The prediction process is composed of three parts, namely, pre-trip path prediction, during-trip link prediction, and path adjustment calculation. In the pre-trip path prediction, through RP survey data collected in Changchun, China, we determine the impacts of subjective factor (habits) on path selection using the Markov model. While the Binary Logit model is utilized by the during-trip link prediction to consider the impacts of both subjective factor (travel habits) and objective factor (real-time traffic conditions). The influences of habits and traffic conditions are compared and analyzed. The results indicate that subjective factor has more important influence than objective one. In addition, the verification results also suggest that pre + during-trip path prediction model provides higher forecasting accuracy than the single pre-trip prediction model. These findings are beneficial to uncover the underlying mechanisms of path selection and facilitate the development of strategies to enhance path selection efficiency. Based on the study results, the subjective and objective information that was found to affect path selection can be considered into the path guide system. Moreover, the hybrid prediction model can be applied in the vehicle or mobile navigation App to facilitate the recommendation of the path to travelers as well as to forecast the short-term traffic flow and determine the potential congestion area. Based on the pre-trip path prediction model, the path and the traffic flow distribution in road network can be obtained only from historical travel data.

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.

A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

Cruise ships are widely used in water quality monitoring but suffer from path planning problems in a surface water environment. Solutions to path planning problems have higher requirements of path planning distance and path planning time and the ability of real-time obstacles avoidance. This paper introduces a hybrid path planning method to solve the path planning problem when using unmanned cruise ships. First, a model of the surface water environment with unknown districts is established by the grid method. Then, the global path is planned by the A* algorithm based on such a model, and in unknown areas of the model, the artificial potential filed (APF) is employed for local path planning. The problem of obtaining unreachable targets and falling into a local minimum introduced by the APF is improved by using the optimized repulsion potential field function and adding the directional random escape strategy. The A* algorithm with self-learning ability is proposed for secondary planning and cases where a local path cannot be generated. Finally, the optimal path combined with the global path and the local path is smoothed. The simulation results show that the proposed algorithm has better performance than other algorithms from the aspects of distance cost and time cost.

A Hybrid Path Planning Algorithm for Unmanned Surface Vehicles in Complex Environment With Dynamic Obstacles

Unmanned surface vesssel (USV) has been widely applied due to its advantages in the military reconnaissance and resources exploration. Path planning is one of the critical issues for USV applications, which usually includes global and local path planning methods. However, individual global path planning algorithms may not be easy to detect the dynamic obstacles in the environment, and individual local path planning algorithms may not always guarantee the existence of the feasible solution for the complex environment. Therefore, a hybrid algorithm which effectively combines global and local path planning is proposed in this paper to overcome these drawbacks. The A* algorithm is used in the global path planning to generate a global path for USV to reach the target point. The dynamic window algorithm (DWA) is used in the local path planning to avoid the dynamic obstacles and track the global path by following the local target point which is the intersection of the global and local path planning. The weight coefficient considering sea state is added in the objective function of DWA, where the security of USV can be guaranteed by reducing the weight of velocity and increasing the weight of distance when the sea state level becomes high. Thus, USV can get a global optimal path and reach the target point in complex environment with dynamic obstacles and ocean currents via the proposed hybrid algorithm, and the comparative simulation is carried out to verify the effectiveness and advantage of the proposed method.

Adaptive Hybrid Path Search MFO Algorithm for Optical Network Unit Placement in Fiber-Wireless Access Network

Fiber-Wireless (Fi-Wi) access network is an emerging solution for high bandwidth and wide coverage network which satisfy the demands of many applications. Research work is going on in this field on several issues and some are Optical Network Unit (ONU) placement, routing, survivability; energy consumption etc. ONU placement problem plays important role as it makes network economic and allows optimal use of resources. In this research work, Adaptive Hybrid Path Search (AHPS) - Moth-Flame Optimization algorithm has been proposed to position multiple ONUs, optimally in the network. The comparative study of different variants of MFO algorithm implementing logarithmic, Archimedes’, hyperbolic and AHPS are presented for ONU placement problem. The outcomes of the simulation are based on different configuration of users and ONUs. It has been reported that the proposed algorithm provides most optimal result among all the MFO variants.

Model checking for hybrid branching-time logics

We consider hybridisations of the full branching time logic CTL⁎ and its prominent fragments CTL, CTL+ and FCTL+ through the addition of nominals, binders and jumps. We formally define three types of hybridisations by restricting the interplay between hybrid operators and path formulas contrary to previous proposals in the literature which ignored potential problems with a formal semantics. We then investigate the model checking problem for these logics obtaining expression complexities from PSpace -completeness to non-elementary decidability and data complexities ranging from NLogSpace to PSpace. Lastly, we identify a family of fragments of these hybrid logics, called the bounded fragments, that (in some cases) have the same model checking complexity as their non-hybrid counterparts.

Generation 1.5 of Russian Israelis: integrated but distinct

ABSTRACTOver the last 5 years, we conducted several ethnographic studies on the social, cultural, and political locations of the young Israeli adults who belong to Generation 1.5 of the ex-Soviet immigrant wave of the 1990s (those who moved to Israel as older children or adolescents). These studies showed that despite successful instrumental integration in the local middle classes in terms of educational and occupational mobility, these 1.5ers keep distinct and ambivalent social and cultural identities that form a third, hybrid path between the Russian and Israeli scripts. As opposed to their “silent” parents, young immigrants organize to protest against what some of them see as their second-class citizenship in the Jewish state. To expand this collective portrait beyond small groups and key activists, we conducted a national online survey among Russian Israeli 1.5ers that was completed by 650 respondents. In the article we report the selected survey findings, focusing on perceived economic mobility and discrimination, intergenerational relations, Jewish identity, political leanings, civic participation, language patterns, cultural interests, and transnational ties to their homelands.

Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

Practical algorithm for stochastic optimal control problem about microbial fermentation in batch culture

How to add glycerol to maximize production of 1,3-propanediol (1,3-PD) is a critical problem in process control of microbial fermentation. Most of the existing works are focusing on modelling this process through deterministic-based differential equations. However, this process is not deterministic, but intrinsically stochastic considering nature of interference. Thus, it is of importance to consider stochastic microorganism. In this paper, we will modelling this process through stochastic differential equations and maximizing production of 1,3-PD is formulated as an optimal control problem subject to continuous state constraints and stochastic disturbances. A modified particle swarm algorithm through integrating the hybrid Monte Carlo sampling and path integral is proposed to solve this problem. The constraint transcription, local smoothing and time-scaling transformation are introduced to handle the continuous state constraints. Numerical results show that, by employing the obtained optimal control governed by stochastic dynamical system, 1,3-PD concentration at the terminal time can be increased compared with the previous results.

Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations

Abstract Presently, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations. In order to carry out these missions in a more efficient way, an intelligent hybrid multi-task allocation and path planning algorithm is required and has been proposed in this paper. In terms of the multi-task allocation, a novel algorithm based upon a self-organising map (SOM) has been designed and developed. The main contribution is that an adaptive artificial repulsive force field has been constructed and integrated into the SOM to achieve collision avoidance capability. The new algorithm is able to fast and effectively generate a sequence for executing multiple tasks in a cluttered maritime environment involving numerous obstacles. After generating an optimised task execution sequence, a path planning algorithm based upon fast marching square (FMS) is utilised to calculate the trajectories. Because of the introduction of a safety parameter, the FMS is able to adaptively adjust the dimensional influence of an obstacle and accordingly generate the paths to ensure the safety of the USV. The algorithms have been verified and evaluated through a number of computer based simulations and has been proven to work effectively in both simulated and practical maritime environments.

A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz

This article presents the development and analysis of a hybrid, error correction-based, neural network to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation models, ECC-33, Ericsson 9999, Okumura Hata, and 3GPP's TR 36.942, with a backpropagation Artificial Neural Network (ANN). The network performance was tested along with two optimization techniques - Genetic Algorithm (GA) and Least Mean Square (LMS). Results were compared with data obtained by measurements performed in the vicinity of the Federal University of Rio Grande do Norte (UFRN), in the city of Natal, Brazil. In the end, the hybrid neural network presented the best results, indicating greater similarity with experimental data. The results developed in this research will help to achieve better signal estimation, reducing errors in planning and implementation of LTE and LTE-A systems.

Study on the Effectiveness of Computer-aided Software in Helping College Students Memorize English Words

In this paper, based on cognitive science and foreign language teaching theories, we construct a web-based hybrid learning model, path and platform to cultivate students’ comprehensive language application ability. Meanwhile, we choose 1000 English words suitable for college students to memorize, group them into 24 units with the help of the Mint English memory software and study the memory results of these students. By horizontal and vertical comparison of the test data, we conclude that the web-based software is very effective in helping college students memorize English words.

Dynamic Wavelength Allocation and Analytical Model for Flow Assignment in Optical Packet and Path Integrated Networks

A hybrid optical architecture combining path (circuit) and packet switching can be a good candidate for future optical networks because it exploits the best of both worlds. In this paper, we present a control framework called the Dynamic Optical Wavelength and Flow Allocation Framework (DOWFAF), which can dynamically change the ratio of path and packet wavelengths and the flow size threshold in the hybrid path–packet integrated networks in order to balance the utilization of path and packet subnetworks and maximize the ratio of large flows benefiting from the path switching. We propose an analytical model for calculating the flow size threshold and a feedback control for estimating the wavelength allocation ratio for varying traffic. DOWFAF can be implemented by software-defined networking, which is getting a lot of attention recently. We show that DOWFAF can greatly increase the goodput of large Transmission Control Protocol (TCP) flows for a wide range of traffic, while decreasing the cost and the power consumption.

A Hybrid Path Planning Algorithm for Mobile Robot Based on Sliding Sub Target

Path planning is a difficult and practical problem confronting mobile robot. It is mainly because of a confliction between local and global perspectives of path planning. This paper proposes a hybrid algorithm for path planning to combine both the local path planning and global path planning based on a sliding sub target. The sub target is mounted on the global path, planned by the global path planning algorithm, so that the local path planning algorithm would be guided. By sliding the sub target along the global path, the global environmental information is considered. By using the local path planning to control the motion of the robot, a rapid consideration for the local environment is also combined. Benefit from the combination, both global and local environmental information are considered. Compared with the path planning approaches from scratch, this approach provides a hybrid algorithm for mobile to combine both global and local path planning. Both simulations and experiments have proved the effect.

A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems

In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.

The Budgeted Maximum Coverage Problem in Partially Deployed Software Defined Networks

Due to the large installed base of distributed legacy networks, software defined networking (SDN) nodes may be required to coexist with legacy nodes to form hybrid networks. It has been shown that such a hybrid network has better performance than a pure legacy network due to smarter transmission scheduling. Despite such advantages, limited budgets continue to hinder the rapid adaptation of SDNs. Only a part of the network can be upgraded at a time especially for large-scale networks. In this paper, we define the minimum percentage of SDN nodes in a path, and paths with at least one SDN node, as the hop coverage and path coverage, respectively. We intend to evaluate the relationship between cost and coverage in the partially deployed SDNs. We formulate SDN node selection as four optimization problems with hop/path coverage and cost as objectives and constraints, respectively, and vice-versa. We propose two heuristic solutions: 1) maximum number of uncovered path first (MUcPF) and 2) maximum number of minimum hop covered path first (MMHcPF), to these NP-hard problems. Through a MATLAB experiment, we show that MUcPF is significantly better in terms of economy and efficiency to establish a hybrid path between every pair of hosts in the network. In particular, it required 5%–15% less investment to achieve 100% path coverage compared to other algorithms. The results show the coverage consistency of MMHcPF on each individual path along with gains in terms of cost and efficiency. It takes 5%–20% less investment to achieve certain hop coverage target compared to other existing algorithms.

A Hybrid Path Planning Algorithm for Indoor Mobile Robot Using Hierarchy Reinforcement Learning

This paper focuses on the path planning for a mobile robot which is operated in indoor environment. Since the layout of indoor environment is a hybrid structure of known and unknown, this paper presents a hybrid algorithm which uses the Max-Q method and the option method together. Firstly, a novel task graph and high level definition are presented to divide sub-tasks. Then, the appropriate definitions of states, actions and options could let a robot fulfill a task. Finally, an angle parameter is employed in the reward function to ensure a robot select a shorter path and adjust orientation timely. In the series of simulations, a robot can arrival any position successfully with random initial positions and directions. Moreover the results show that a robot can overcome the local minimal problem with our hybrid method. Key-words: mobile robot, Hierarchical Reinforcement Learning, path planning, hybrid method

Ultra-High-Throughput VLSI Architecture of H.265/HEVC CABAC Encoder for UHDTV Applications

Ultra high definition television (UHDTV) imposes extremely high throughput requirement on video encoders based on High Efficiency Video Coding (H.265/HEVC) and Advanced Video Coding (H.264/AVC) standards. Context-adaptive binary arithmetic coding (CABAC) is the entropy coding component of these standards. In very-large-scale integration implementation, CABAC has known difficulties in being effectively pipelined and parallelized, due to the critical bin-to-bin data dependencies in its algorithm. This paper addresses the throughput requirement of CABAC encoding for UHDTV applications. The proposed optimizations including prenormalization, hybrid path coverage and lookahead rLPS to reduce the critical path delay of binary arithmetic encoding (BAE) by exploiting the incompleteness of data dependencies in rLPS updating. Meanwhile, the number of bins BAE delivers per clock cycle is increased by the proposed bypass bin splitting technique. The context modeling and binarization components are also optimized. As a result, our CABAC encoder delivers an average of 4.37 bins per clock cycle. Its maximum clock frequency reaches 420 MHz when synthesized in 90 nm. The corresponding overall throughput is 1836 Mbin/s that is 62.5% higher than the state-of-the-art architecture.

Flexibility, Argumentation and Confrontation. How Deliberative Minipublics Can Affect Policies on Controversial Issues

Over the last few decades, deliberative minipublics have been used as inclusive and innovative practices to integrate traditional policy-making. Because of their policy-oriented aim, but also owing to the usually not legally-binding nature, some scholars have recently pointed out the importance of understanding how and to what extent they actually manage to influence the decisions of public authorities, especially when they deal with highly controversial issues, such as locally unwanted land use or ethic matters.This article has the aim of offering a contribution in this direction, by presenting the findings of a comparative analysis of two deliberative processes promoted by public authorities to integrate decision-making in order to deal with highly controversial issues. The two processes had significantly different impacts on the final policies. The analysis, conducted by means of ethnographic and qualitative methods, has shown that how the minipublics were designed and how deliberation was put in practice were crucial aspects in determining the effective influence of the minipublics on policy decisions. In particular, the use of a strictly deliberative design on such contentious issues has proved to be counterproductive, because it has generated distortions and produced institutional conflicts, while the use of a hybrid path, although not free from problems, has proved to be a more effective solution.