Joint Path Research Articles

Edge Learning With Unmanned Ground Vehicle: Joint Path, Energy, and Sample Size Planning

Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Things (IoT). However, due to the limited transmit power at IoT devices, collecting the sensing data in EL systems is a challenging task. To address this challenge, this article proposes to integrate unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices’ energy consumption, and the number of samples for different jobs? This article further proposes a graph-based path planning model, a network energy consumption model, and a sample size planning model that characterizes F-measure as a function of the minority class sample size. With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. To this end, it is proved that each IoT device should be served only once along the path, thus the problem dimension is significantly reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)-based algorithm is derived, which converges in expectation to the optimal solution to the JPESP problem. Simulation results under different task scenarios show that our optimization schemes outperform the fixed EL and the full path EL schemes.

Joint Optimization of Path Planning and Resource Allocation in Mobile Edge Computing

With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity $L$ L -step lookahead branch scheme (referred to as $L$ L -step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed $L$ L -step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

Heterogeneous Networks in 5g using Joint Path Selection and Efficient Self Organization Protocol

Tree networks are systematically spread across multiple networks. This project proposes a framework for effective self-organization for 5 G heterogeneous networks. All nodes in this network are classified into two types: network nodes, and non-network nodes. Network nodes are able to broadcast packets to neighboring nodes. A tree-based network can then be obtained from one layer to another. The topology is dynamically balanced to balance energy consumption and extend the service life of the network. To test the current technique we perform tests with it. Results from the simulation show our proposed protocol can easily create a stable tree-based network. As the network size rising, the time for self-organization, average hop and error ratio for packets does not change any more. In comparison, packet success rates in effective self-organization protocol are significantly higher compared to AODV and DSDV. Non-network nodes collect the packets being transmitted and decide whether to access the network. We use various metrics such as number of child nodes, hop, contact distance and residual energy to meet the available sink nodes weight during the self-organizing process, the node with highest weight is selected as sink node. Non-network nodes will be transformed to network nodes if they effectively join the network.

Heterogeneous Networks in 5g using Joint Path Selection and Rate Allocation Framework

One of the major challenges in evolving wireless cellular networks whose return network is constrained by capability and heterogeneity (wired, wireless, and hybrid) is the design of distributed management mechanism. This study proposes a new method of managing wireless network interference with the ability to detect return network. The proposed approach helps Macro-cell User Equipment (MUE) to maximize its output using adjacent small cell base stations to maximize its uplink. Considering the radio access network and backhaul (possibly heterogeneous), this issue is represented as a non-cooperative game between MUE that attempts to maximize its compensation for delay levels. A new distributed learning algorithm is proposed to solve this problem, which uses the algorithm to autonomously choose the best uplink transmission strategy, provided a limited amount of available knowledge. The algorithm's convergence is demonstrated, and its output is studied. The simulation results show that, in comparison to the existing reference algorithm, The proposed approach has substantial efficiency benefits for various forms of backhaul in terms of average output and delay in MUE.

Greater lifestyle engagement is associated with better age-adjusted cognitive abilities.

Previous evidence suggests that modifiable lifestyle factors, such as engagement in leisure activities, might slow the age-related decline of cognitive functions. Less is known, however, about which aspects of lifestyle might be particularly beneficial to healthy cognitive ageing, and whether they are associated with distinct cognitive domains (e.g. fluid and crystallized abilities) differentially. We investigated these questions in the cross-sectional Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data (N = 708, age 18–88), using data-driven exploratory structural equation modelling, confirmatory factor analyses, and age-residualized measures of cognitive differences across the lifespan. Specifically, we assessed the relative associations of the following five lifestyle factors on age-related differences of fluid and crystallized age-adjusted abilities: education/SES, physical health, mental health, social engagement, and intellectual engagement. We found that higher education, better physical and mental health, more social engagement and a greater degree of intellectual engagement were each individually correlated with better fluid and crystallized cognitive age-adjusted abilities. A joint path model of all lifestyle factors on crystallized and fluid abilities, which allowed a simultaneous assessment of the lifestyle domains, showed that physical health, social and intellectual engagement and education/SES explained unique, complementary variance, but mental health did not make significant contributions above and beyond the other four lifestyle factors and age. The total variance explained for fluid abilities was 14% and 16% for crystallized abilities. Our results are compatible with the hypothesis that intellectually and physically challenging as well as socially engaging activities are associated with better crystallized and fluid performance across the lifespan.

STRATEGI HUMAS UNTUK MEMPERBAIKI CITRA LEMBAGA PENDIDIKAN

ABSTRACTPublic relations serves as a distinctive management and supports the coaching, maintenance of the joint pathway between the organization and the public, concerning communication activities, understanding, acceptance and cooperation, involving management in dealing with problems, helping management to be able to face public opinion.Creating a positive image for an institution or education, the role of Public Relations is also involved in many things such as making strategies and programs interesting to be able to compete and still exist both in a profit-oriented institutions and services to the community or nonprofit. The success of a Public Relations will determine the success or failure of vision and mission in an education or institution.Looking at the above background contains the formulation of the problem: 1) How the public relations devinisi and research ? 2) What is the negative and positive impact of public relations in educational institutions? 3) How to Step by step analysis of persuasive and contributive strategy in developing public relations in educational institutions.The results of the theory analysis can be summarized as follows: 1) Public Relations is a typical management function, which supports coaching and maintenance, the joint path between the organization and its Public about communication, understanding and cooperation. Engage and assist management to know and respond to public opinion, establish and emphasize management's responsibility to serve the public as well as to support management in making positive use of experiments, using sound and ethical research and communication techniques as the primary means. 2) Negative influence, may occur due to ethical abuse in public relations activities and the positive influence that can result from the implementation of the code of ethics. 3) Steps - persuasive and contributive strategy in building Public Relations effectively by improving internal and external quality. So that Public Relations remain in good harmony with institutions and society.�Keywords: Public relations Strategy, Improving Education Institution image

Multi-homotopy class optimal path planning for manipulation with one degree of redundancy

Redundant manipulators have an infinitely large set of joint paths that yield a desired end-effector path in the task space. A unique joint path can be obtained by minimizing a global cost function. Prior optimal control methods minimize a global cost function to find a local minimum within a homotopy class. Many possible locally optimal joint paths are in different homotopy classes. This paper presents an algorithm that effectively searches the solution space and finds many locally optimal paths in all relevant homotopy classes. The path with the lowest cost is very likely the globally optimal path. The algorithm is demonstrated in a case study for which the globally optimal path would be impossible to find using traditional methods.

National Internationalization Processes: SME on the way to Eastern Europe

Going alone on an adventure tour can be hazardous. Good friends help, if only through their advice. The same holds true for small businesses aiming for major international markets. They rely on information, support services and networks that are jointly built up with business partners. Business communities therefore follow a joint path of gradual internationalization that resembles the internationalization of firms described by Uppsala scholars. We present a model that integrates business networks with the internationalization process model to explain international business of small and medium size enterprises (SME). While global multinationals may be able to tap into several clusters and networks, SMEs are to a large extent part of the internationalization process of their national economy. Our survey evidence points to differences in the home country environment that explain why Danish firms, relative to Austrian firms, are less active. Our case evidence sheds more light on the dynamics of entry, as events in the network, and expansion of the network, motivate increased increased commitment.

Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

Based on the self-balance of free-floating two-wheel self-balancing pendulum robot system, an optimal path planning of two-wheel self-balancing pendulum robot is proposed. Firstly, the corner trajectory of the two-wheel self-balancing pendulum robot is parameterized by quantum particle swarm optimization (QPSO), and the objective function is designed according to the base attitude and the control accuracy of the robot’s terminal position and attitude. The optimal path planning problem of the two-wheel self-balancing pendulum robot system is transformed into the optimization problem of the non-linear system. Quantum particle swarm optimization (QPSO) algorithm is used to solve the non-linear optimization problem, and the goal of optimal path planning is achieved. The experimental results show that the proposed method can converge to the global optimal value with fast convergence speed and less adjustment parameters. The planned joint path satisfies the range of corner, angular velocity, and angular acceleration. The joint path is smooth and suitable for the control of two-wheel self-balancing pendulum robot.

Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mm-wave Networks

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, we investigate the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, in this work we aim at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, we propose a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: path selection and rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, we provide a comprehensive performance analysis and convergence proofs for the proposed solution. Numerical results show that our approach ensures reliable communication with a guaranteed probability of up to $99.9999\%$, and reduces latency by $50.64\%$ and $92.9\%$ as compared to baseline models. Furthermore, the results showcase the key trade-off between latency and network arrival rate.

Human Action Recognition in Unconstrained Trimmed Videos Using Residual Attention Network and Joints Path Signature

Action recognition has been achieved great progress in recent years because of better feature representation learning and classification technology like convolutional neural networks (CNNs). However, most current deep learning approaches treat the action recognition as a black box, ignoring the specific domain knowledge of action itself. In this paper, by analyzing the characteristics of different actions, we proposed a new framework that involves residual-attention module and joint path-signature feature (JPSF) representation framework. The path signature theory was developed recently in the field of rough path and stochastic analysis, which provides a very efficient way to analyze any temporal sequence data. The proposed n-fold joint path signature features entail the Euclidean distances between joints and respective angles. For our experiment, JPSF for three modalities of joints (spatial location, bi-folds and tri-folds) are computed over the temporal length of action sequence. Then all these PSF are concatenated and fed to a CNN to give the recognition result. Experiments on three benchmark datasets, J-HMDB, HMDB-51 and UCF-101, indicate that our proposed method achieves state-of-the-art performance.

A Joint Allocation and Path Selection Scheme for Downlink Transmission in LTE-Advanced Relay System with Cooperative Relays

A Joint Allocation and Path Selection Scheme for Downlink Transmission in LTE-Advanced Relay System with Cooperative Relays

Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level

The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions.

Multiperiod Coverage Path Planning and Scheduling for Airborne Surveillance

In this paper, optimal surveillance mission plans are developed to cover disjoint areas of interest (AOIs) over an extended time horizon using multiple aerial vehicles. AOIs to be covered are divided into a number of cells. To promptly update information collected from AOIs and to ensure persistent surveillance, each cell is to be revisited within a time slot. Joint path planning and temporal scheduling is formulated as a combinatorial optimization with the proposal of novel objective functions: 1) maximizing the minimum number of nonrepeatedly covered cells in a sliding-window fashion and 2) maximizing the total number of covered cells in the mission plan. A multiobjective evolutionary algorithm (MOEA) with a specific chromosome representation and custom genetic operators, in which the constraint that each cell be revisited within a time slot is transformed into the third objective to handle infeasibility, is developed. The initial single-period paths are generated by solving a series of orienteering problems. The initial population is obtained by connecting these single-period paths and selecting the take-off time for each flight. Three mutation moves are proposed to enable revisiting in a single-period path and rescheduling of take-off time. The solutions converge in the MOEA and are selected by a weighted-sum model according to user preferences in decision making. Simulation results on different mission scenarios and different criteria show the superiority of the proposed algorithm. The algorithm is done offline ahead of the missions and requires modest computational resources.

Techniques of reordering traces in the event logs in business process management tasks

The problem of formation of input data for construction of models of business processes by methods of the intellectual analysis of processes is studied. It is shown that event logs of real information systems do not always meet the requirements of the ordering of input data on processes, that is, a log can have one track with events from different business processes. The tasks of analysis of trace characteristics in the business process event log and the development of methods for forming process events information order tracks organized on the basis of the comparison of event attributes and on the basis of comparison with the invariants of the event attributes of the business process logs are organized in the processes of the log of events events of the process management information system. The first method generates business process log lines based on pairwise comparison of event attributes based on the criterion for maximizing the number of shared attribute values for this pair. The method sequentially selects events from the common path for several business processes, and after pairwise comparison of the attributes with the events of the traces of specific business processes, determines the belonging of the next event to the path of the corresponding process. When comparing, the end event of the business process is also detected. The second method generates log lines based on the comparison of the invariant of the event attributes and the event, which is analyzed by the criterion of maximizing the weight of the values of the common attributes. Unlike the first method, when choosing a new business process, an invariant is created in the form of a sum of weights of the values of the attributes of the log of the business process. The scales of attribute values reflect the number of occurrences of these values in the execution of the business process. This allows you to take into account the history of the implementation of the business process in implementing the method. In practical terms, both proposed methods allow the formation of a set of business process event logs that are executed in parallel in a format suitable for use of methods and technologies of the intellectual analysis of proces ses. The first method has a lower accuracy. However, its advantage is the ability to use in the presence of only a joint path of several business processes, without the previously known orderly traces log of each business process. The second method allows you to increase the accuracy of the highlighting of the event path for each business process. The disadvantage of this method is that it requires a priori formation of the invariant of the attributes of the business process events.

The battle over the commons in port cities

ABSTRACTThe zone of intersection between land and sea within an urban context has long been viewed as a special type of urban commons. The well-researched port-city interface, however, tells a rather tragic story about the use and management of this valuable resource. This study asks how four major regulatory-institutional issues in the interface – land ownership, activities allowed in port area, planning autonomy, and public access – affect the ability of ports and cities to preserve elements of “commons” in urban coasts? Furthermore, it assesses if and how Elinor Ostrom’s principles for overcoming commons-related tensions, could contribute to the management of port-city conflicts over land-uses in the Mediterranean urban coastline and the various questions that may derive from such an application. For these purposes the study combines a comprehensive literature review with the analysis of planning regulation and in-depth, semi-structured interviews of key-stakeholders in seven port cities across three Mediterranean countries. The common experience in these different cases suggests that through a strategy involving scaling-down spatial decisions or governance, and built-in mechanisms for spatial cooperation, ports and the cities that host them can find a new joint path, which will strengthen local synergies and the quality of urban, public space.

On the evaluation of a general model for optimum revolute cylindrical cylindrical cylindrical path generation

A general optimization model for the dimensional synthesis of defect free revolute cylindrical cylindrical cylindrical (RCCC) joint path generators is formulated, implemented and evaluated in this work. With this optimization model, RCCC dimensions required to approximate precision points are calculated. The model includes constraints to eliminate order, branch and circuit defects, which are common in dyad-based dimensional synthesis. Therefore, the originality of this work is the development of a general optimization model for RCCC path generation that simultaneously considers order, branch, and circuit defect elimination. This work demonstrates both the benefits and drawbacks realized when implementing the optimization model on a personal computer using the commercial mathematical analysis software package Matlab.

Automatic planning of needle placement for robot-assisted percutaneous procedures.

Percutaneous procedures allow interventional radiologists to perform diagnoses or treatments guided by an imaging device, typically a computed tomography (CT) scanner with a high spatial resolution. To reduce exposure to radiations and improve accuracy, robotic assistance to needle insertion is considered in the case of X-ray guided procedures. We introduce a planning algorithm that computes a needle placement compatible with both the patient's anatomy and the accessibility of the robot within the scanner gantry. Our preoperative planning approach is based on inverse kinematics, fast collision detection, and bidirectional rapidly exploring random trees coupled with an efficient strategy of node addition. The algorithm computes the allowed needle entry zones over the patient's skin (accessibility map) from 3D models of the patient's anatomy, the environment (CT, bed), and the robot. The result includes the admissible robot joint path to target the prescribed internal point, through the entry point. A retrospective study was performed on 16 patients datasets in different conditions: without robot (WR) and with the robot on the left or the right side of the bed (RL/RR). We provide an accessibility map ensuring a collision-free path of the robot and allowing for a needle placement compatible with the patient's anatomy. The result is obtained in an average time of about 1min, even in difficult cases. The accessibility maps of RL and RR covered about a half of the surface of WR map in average, which offers a variety of options to insert the needle with the robot. We also measured the average distance between the needle and major obstacles such as the vessels and found that RL and RR produced needle placements almost as safe as WR. The introduced planning method helped us prove that it is possible to use such a "general purpose" redundant manipulator equipped with a dedicated tool to perform percutaneous interventions in cluttered spaces like a CT gantry.

Efficient Clustering and Path Planning Strategies for Robotic Data Collection Using Space-Filling Curves

In this paper, we consider a scenario where a mobile robot is tasked with periodically collecting data from a fixed wireless-sensor network. Our goal is to minimize the total cost of the system, including the motion cost of the robot and the communication cost of the sensors to the robot in realistic fading environments. We propose a strategy that divides the sensors into a number of clusters and uses a mobile robot to visit each cluster in order to wirelessly collect the corresponding data. We then propose a computationally efficient approach to solve this joint path planning and clustering problem by using space-filling curves. More specifically, by utilizing space-filling curves and their locality property, we show how the coupled clustering, stop position selection, path planning, and motion design problems can be solved as a series of convex optimization problems. We further mathematically characterize an upper bound for the total energy consumption of our proposed approach for the case of uniformly distributed sensors, relating it to key motion and communication parameters, such as motor parameters, channel multipath fading and shadowing variances, path-loss exponent, and target bit error rate. Finally, we verify the effectiveness of our framework in a simulation environment. Our results with realistic channel and motion parameters show considerable energy savings.

Explicit LDP for a slowed RW driven by a symmetric exclusion process

We consider a random walk (RW) driven by a simple symmetric exclusion process (SSE). Rescaling the RW and the SSE in such a way that a joint hydrodynamic limit theorem holds we prove a joint path large deviation principle. The corresponding large deviation rate function can be split into two components, the rate function of the SSE and the one of the RW given the path of the SSE. These components have different structures (Gaussian and Poissonian, respectively) and to overcome this difficulty we make use of the theory of Orlicz spaces. In particular, the component of the rate function corresponding to the RW is explicit.