Minimum Path Research Articles

Investigating the min‐cost minimum fleet problem through taxi data analysis

AbstractThe optimization of urban traffic efficiency and reduction of pollution through minimizing the number of taxis has become a topic of increasing interest. However, the problem of determining the minimum fleet that considers both time and distance efficiency has received limited attention. Furthermore, little research has been done on how this problem is influenced by factors such as city size and travel demand (i.e., the number of trips). This study extends the minimum fleet problem to the min‐cost minimum fleet problem, and proposes a method to address the problem by constructing a “dispatch network” and finding the minimum weight and minimum path cover of it. A comparative analysis of the method is conducted with the taxi trajectory data of three cities, New York, Chengdu, and Chicago, and the results show a reduction in the off‐load travel time of vehicles compared to the previous method, while maintaining the same fleet size. With the introduction of an emissions model and its combination with our methodology, emissions can also be minimized. Moreover, the relationship between min‐cost minimum fleet and city size and travel demand (number of trips) is studied. Experimental results indicate that fleet size and idle time (distance) vary linearly with the number of trips. Meanwhile, city size also has an essential impact on the algorithm's performance. Furthermore, these results indicate that the min‐cost minimum fleet will help small cities to achieve higher efficiency (smaller fleet and less connection time) than large cities. The acceleration of urbanization is driving changes in the transportation system. Our findings are expected to have significant implications for transport planning, resource conservation, and reducing pollution.

3D reconstruction of the scapula from biplanar X-rays for pose estimation and morphological analysis.

Patient-specific scapular shape in functional posture can be highly relevant to clinical research. Biplanar radiography is a relevant modality for that purpose with already two existing assessment methods. However, they are either time-consuming or lack accuracy. The aim of this study was to propose a new, more user-friendly and accurate method to determine scapular shape. The proposed method relied on simplified manual inputs and an upgraded version of the first 3D estimate based on statistical inferences and Moving-Least Square (MLS) deformation of a template. Then, manual adjustments, with real-time MLS algorithm and contour matching adjustments with an adapted minimal path method, were added to improve the match between the projected 3D model and the radiographic contours. The accuracy and reproducibility of the method were assessed (with 6 and 12 subjects, respectively). The shape accuracy was in average under 2mm (1.3mm in the glenoid region). The reproducibility study on the clinical parameters found intra-observer 95% confidence intervals under 3mm or 3° for all parameters, except for glenoid inclination and Critical Shoulder Angle, ranging between 3° and 6°. This method is a first step towards an accurate reconstruction of the scapula to assess clinical parameters in a functional posture. This can already be used in clinical research on non-pathologic bones to investigate the scapulothoracic joint in functional position.

Minimum dose path planning method for virtual nuclear facilities based on navigation mesh

The operation, maintenance and decommissioning of nuclear facilities have radiation risks. When workers work in the radiation area, it is necessary to carry out radiation protection through path planning. In order to optimize the dose field path planning algorithm, a virtual verification platform for dose field path planning is established based on the Unreal Engine. The platform includes virtual environment, dose field, and personnel dose assessment modules, which can meet the needs of dose field path planning algorithm research and optimization. Considering the complexity and variability of the environment in the operation process, a new dose minimum path planning algorithm based on navigation mesh is designed. Based on this algorithm, the number of grids for path planning in complex or large scenes is significantly reduced, and the calculation time of the algorithm is reduced while ensuring the optimal dose. The simulation results show that the minimum dose path planning method based on navigation mesh can effectively reduce path planning time and ensure the safety of personnel during the decommissioning of nuclear facilities.

Regret-Index Neural Network for Finding the Maximum Data Flow of Self-Powered Wireless Sensor Networks

There are many complex situations in self-powered wireless sensor networks. First, the signal fluctuation will cause the upper limit of the data transmission volume between the sensor and the base station to change at different times. In addition, with long-term use, equipment failure will cause the loss of some data during transmission, and the rate of data loss is usually undetermined. Based on the above complexity, to determine the maximum data flow of self-powered wireless sensor networks, the fuzzy-wastage time-varying maximum flow problem (FTMFP) is introduced in this paper. Existing algorithms are not suitable for solving the FTMFP due to the uncertainty and time-variation in the problem. To solve this problem, a regret-index neural network (RINN) algorithm is proposed in this paper. The unique minimum regret-index path selection mechanism of the RINN algorithm improves the accuracy when obtaining solutions. The parallel computing and training-free mode of the RINN algorithm improves the computational speed. The Utah channel impulse response wireless sensor network and the large-scale New York City road monitoring network are employed to evaluate the performance of the proposed RINN algorithm. The time-variance of the data transmission caused by signal fluctuations and the fuzzy loss ratio of data caused by equipment failures are considered in the network. Compared with the well investigated Ford-Fulkerson, Edmonds-Karp, and Cai-Sha algorithms, experimental results have proven the effectiveness of the RINN.

Neuronal branching is increasingly asymmetric near synapses, potentially enabling plasticity while minimizing energy dissipation and conduction time.

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time and materials. Moreover, we generally observe that higher degrees of asymmetric branching-potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity-occur closer to the tips than the soma (cell body).

A Fuzzy Logic Approach and Path Algorithm for Time and Energy Management of Smart Cleaning Robots

Smart home technologies (SHM) or devices provide some degree of digitally connected, automated, or enhanced services to building occupants in residential areas and have been becoming increasingly popular in recent years. SHM have the potential to improve home comfort, convenience, security, and energy management. Different technologies are used to equip household parts for smarter monitoring, movement, and remote control and to allow effective harmonic interaction between them. Especially, energy management and path-planning algorithms are some of the important problems for such technologies to get optimum efficiency and benefit. Smart vacuum cleaning robot is one of the applications of such devices with various functions. These cleaning robots have limited battery power and battery sizes, thus effective cleaning is critical. Additionally, the shortest / optimal path planning is essential for the efficient operation of effective cleaning based on the battery time. In this article, two distinct algorithms, which are Search algorithm and CSP algorithm are utilized to obtain distinct optimal minimum path lengths for keeping the home's total dirt level as low as possible. Depending on various types of linguistic, abstract, or perceptual variables, these algorithms are not enough for the energy management of the battery. Therefore, the fuzzy logic-based inference system is proposed for obtaining the charge durability of battery of the cleaning robot, in addition to these algorithms. The inputs affecting the charge durability are considered as floor type, dirt level and the width of area for the fuzzy approach.

Reliable Gamma-Interconnection Network for Data Analysis in Sensor Networks: Design and Performance Evaluation

In today’s era of high speed 5G internet all electronic sensor networks are connected through IoT. Bank transactions are digitized, people can access any data through their mobile phones, organizations and companies handle their projects through online meetings etc. Military and medical surveillance, navy navigation, weapon controlling, weather forecasting etc. involve big data analysis collected from sensors, that too at a very high speed with reliable results. This requires large number of parallel processors connected with huge Bank of memory modules to store big data. Reliable interconnection network is needed to connect these large number of parallel processors and memory modules efficiently hence Multistage Interconnection Networks (MINs) come into play, as they provide highly reliable communication for big data transfer between processors and memory modules whenever required. In this manuscript a new network named Reliable Gamma-interconnection Network (RGN) is introduced which possesses multiple paths between processors and memory modules with two totally disjoint path availability. It provides high reliability and minimum path distance between source node to destination node than other gamma networks known, with the minimum hardware complexity. Reliability estimation and evaluation of RGN has been presented in this paper and comparison of results achieved with other gamma networks has been done for validation purpose.

Distances in the face-centered cubic crystalline structure applying operational research.

The f.c.c. (face-centered cubic) grid is the structure of many crystals and minerals. It consists of four cubic lattices. It is supposed that there are two types of steps between two grid points. It is possible to step to one of the nearest neighbors of the same cubic lattice (type 1) or to step to one of the nearest neighbors of another cubic lattice (type 2). Steps belonging to the same type have the same length (weight). However, the two types have different lengths and thus may have different weights. This paper discusses the minimal path between any two points of the f.c.c. grid. The minimal paths are explicitly given, i.e. to obtain a minimal path one is required to perform only O(1) computations. The mathematical problem can be the model of different spreading phenomena in crystals having the f.c.c. structure.

The complex-formation dynamics of O + H(D)2+ (v = 0,1; j = 0) reactions on 12A'' and 12A′

The reactions O + H(D)2+ are studied based on the ground state potential energy surface (PES) 12A″ and the first excited PES 12A′. Both two potential energy surfaces comprise deep wells during the minimum energy reaction path. The average lifetimes of the complex-formation of the title reactions as a function of translational energy are calculated. The results indicated that the average lifetime of the complex-formation decreases with the increasing of collision energy for the two potential energy surfaces. There are two kinds of reaction mechanisms, one is direct reaction mechanism; another is indirect one. The indirect mechanism is dominated for all the collision energies studied. However, with the increasing of the collision energy, the direct reaction mechanism becomes more and more pronounced. Besides, the influences of reagent vibrational excitation and isotopic effect on the scalar properties and vector properties are studied in detail respectively in this work. The reagent vibrational excitation enhances the number of the direct reactive trajectories remarkably on 12A″, though it has nearly no influence on the average lifetime of the complex formation. In strong contrast, the reagent vibrational excitation suppresses the number of indirect reactive trajectories and reduces the average lifetime of the complex formation on the 12A′. The isotopic substitution has no influence on the scalar properties; however, it affects the vector properties strongly.

A Three-Step Heuristic Approach to the Electric Vehicle Path Planning Problem considering Charging

With the rapid development of the electric vehicle industry, the problem of electric vehicle mileage limitation still exists. Recent studies focus on the minimum energy consumption path planning method or the charging route planning method, with a lack of studies considering midway charging scenarios. In this study, we propose a graph processing method for the electric vehicle, given the energy consumption and road conditions, and establish a mixed integer planning model for the electric vehicle path planning problem. The objective is the shortest time, with energy consumption as a constraint, aiming at the problem of shortest path planning for electric vehicles with midway charging. Taking into account real-time traffic information and energy consumption information related to road conditions, a three-step heuristic algorithm based on preprocessing traffic network, charging path planning, and subpath planning is proposed for the electric vehicle path planning problem. The simulation results show that the proposed method can determine optimal paths including charging stations and effectively alleviate electric vehicles’ “range anxiety” problem during medium- and long-distance travel.

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment

The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this work, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

<p>In order to make the railroad inspection robot better adapt to its complex working environment, it is especially important to study the robot object avoidance algorithm. The WOA algorithm has simple and understandable structure and strong optimization ability but is prone to local convergence. IWOA-PSO is used for railway inspection robots. The performance of IWOA-PSO in the experimental results is better than that of WOA and PSO, and the average accuracy and standard deviation of the IWOA-PSO can better reach the theoretical optimal value in the function tests, and it has performance close to the theoretical value. In the simple environment object avoidance route planning, the minimum path length of IWOA-PSO is 850 mm, which is 53.6% less than that of the PSO algorithm, and the search time is 13.12 seconds, which is 5.11 seconds less than that of PSO algorithm; in the ordinary environment object avoidance route planning, the minimum path length of IWOA-PSO is 830 mm, while the path length of PSO algorithm is 1339 mm, the former is 38% less than the latter, and the search time of IWOA-PSO is 14.05 seconds less than PSO algorithm, so the method has better effect on object avoidance.</p> <p> </p>

Multibeam Array Antenna with Compact Size Butler Matrix for Millimeter-Wave Application

New radio wave technologies of millimeter-wave (mmWave), compact cell size, and multi beam base station are introduced with the recent development of the 5G mobile system. The Butler Matrix (BM) feed circuit is the most preferable candidate for the 5G mobile system since it can achieve multi beam radiationpatterns at the array antenna, provide structural compactness and produce good multi beams.The BM circuit is typically built on a single dielectric substrate. However, in this single-substrate structure, the micro strip line connecting several circuit elements in the BM spans over a large area, resulting in significant feeding loss in the millimeter frequency band.In this study, a compact size circuit configuration of BM is proposed, where the original single-substrate structure is modified into a two-substrate stacking structure. The via-hole is designed to connect the two substrates with minimal path loss. The BM is built for the 28 GHz band with four inputs and four outputs.The phase delay is optimized using via-hole to produce the phase difference of ±45º and ±135º. The coupling for the hybrid is -3 dB, while the transmission coefficient of -6 ± 3 is achieved from the BM structure and, the return loss (Sii) for both input and output ports are less than -10 dB. The two-substrate BM is combined with the rectangular patch antenna and the via-hole patch antenna in a planar configuration of 0.5 λ0spacing to obtain the radiation patterns. When the Port 1 through Port 4 of the BM are fed, four beams are created, with peak gains of 11.2 dBi, 9.87 dBi, 10.2 dBi, and 11.7 dBi, respectively, towards +16°, -35°, +39°, and -12°. The analysis includes the radiation performance from the ideal value and from the BM input. Three-dimensional representations of good multibeam radiation patterns are obtained after each input signal of the BM is fed

Criticality-based allocation of active redundancies for asymmetric components in coherent systems

The active redundancy technique is widely used in reliability engineering to enhance the performance of reliability systems. In this paper, we utilize the criticality ordering to study optimal allocations of active redundancies in coherent systems comprising independent components. Firstly, we establish optimal allocation strategies for two heterogeneous active redundancies for components ranked by the criticality ordering, which is characterized by the structure relationships induced from the associated minimal cut sets. Our findings demonstrate that, under certain conditions where the criticality ordering (and reliability performances) between any two original components is required, the stronger redundancy should be allocated to the more critical component. Secondly, we investigate the best allocation policies for a batch of homogeneous active spares for different components while preserving the criticality ordering. It is shown that allocating more active redundancies to the more critical component results in a more reliable coherent system. Additionally, we prove that under certain conditions, an imbalance in the numbers of allocated spares leads to even greater reliability in the system. Thirdly, we study optimal allocations for any two components in coherent systems when their locations are quantified by minimal path sets. The optimal allocation policies are further derived, most of which are similar to those obtained by applying minimal cut sets. Numerical examples are provided to illustrate the main findings, and we apply our findings to a real-world scenario in an airplane cockpit system to enhance its reliability.

Hybrid UWB-Inertial TDoA-Based Target Tracking With Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

Dynamic Reconfiguration to Optimize Energy Production on Moving Photovoltaic Panels

Urban transport systems play a major role in the development of today’s societies, but they require technological changes to reduce their environmental impact. The problem lies in their level of autonomy, which is why electrical energy production systems are proposed for self-consumption, efficiently feeding their accumulators. As the energy provided by photovoltaic installations has lower recharge speeds, conventional systems with high transfer amperage and higher voltage are required. For this reason, solar installations are used for additional services and to support their autonomy. The present work tries to find the best solution for both constant voltage and peak current systems. Once found, these solutions will be applied in real time for the dynamic recharging of battery packs, trying to achieve vehicles that are progressively more energetically autonomous. To solve these situations, a new computational method for calculating voltage and amperage has been developed in this work, based on Dijkstra’s minimum path search algorithm on graph theory, adapted to electrical circuits. Once this algorithm has been established, the panel performance analysis sensors, developed at the University of Malaga, are combined with different electronic solutions described in this article (Wi-Fi relay devices using esp8266 chips or feeding these relays through panels and establishing the voltage drop to switch the connection), achieving precise and sufficiently fast solutions at very low cost. Both series and parallel transitions are possible, depending on the type of energy generation required. The theoretical solutions using Minkowski paths, analyzed in the past, have been simulated and subsequently constructed in this paper, indicating the diagrams necessary for their realization.

Minimum path deviation rapid online path planning of unmanned aerial vehicles with avoidance zones constraints

This paper investigates a minimum path deviation online path planning problem for UAV with constrainted avoidance zones. The nonlinear and nonconvex of this problem make the path planning is very time consuming and not acceptable for using onboard. Three new state variables and two control variables are redefined to convert the nonconvex problem into a second-order cone convex programming problem, which can be solved in polynomial time by primal-dual interior-point method, theoretical proof demonstrates the equivalence of the relaxed convex problem and the original problem. We propose a sequential second-order cone programming algorithm in order to get the optimal solution of the convexified problem. Numerical simulations show the effectiveness and less time-consuming of the proposed algorithm, no initial guess required and efficient solving process indicate that the algorithm has potential future for online path planning applications.

Development and Performance Evaluation of Integrated Hybrid Power Module for Three-Phase Servo Motor Applications.

This study aims to develop a 30 kHz/12 kW silicon carbide (SiC)/Si integrated hybrid power module (iHPM) for variable frequency drive applications, particularly industrial servo motor control, and, additionally, to theoretically and experimentally assess its dynamic characteristics and efficiency during operation. This iHPM integrates a brake circuit, a three-phase Si rectifier, and a three-phase SiC inverter within a single package to achieve a minimal current path. A space-vector pulse width modulation (SVPWM) scheme is used to control the inverter power switches. In order to reduce parasitic inductance and power loss, an inductance cancellation design is implemented in the Si rectifier and SiC inverter. The switching transients and their parasitic effects during a three-phase operation are assessed through an electromagnetic-circuit co-simulation model, by which the power loss and efficiency of the iHPM are estimated. The modeled parasitic inductance of the inverter is validated through inductance measurement, and the effectiveness of the simulated results in terms of switching transients and efficiency is verified using the experimental results of the double pulse test and open-loop inverter operation, respectively. In addition, the power loss and efficiency of the SiC MOSFET inverter are experimentally compared against those of a commercial Si IGBT inverter.

The usefulness of (d,b)-MCs and (d,b)-MPs in network reliability evaluation under delivery or maintenance cost constraints

Reliability and delivery cost or maintenance cost are important indicators showing the stability and the quality of operation of a multi-state flow network. Many authors developed (d,b)-minimal cut based or (d,b)-minimal path based algorithms to compute the network reliability, i.e., the probability that a demand d can be delivered from the source node to the sink node subject to the constraint that the total delivery cost (maintenance cost) is not greater than b. It is proven that contrary to prevalent methods in existing literature, both (d,b)-minimal cuts and (d,b)-minimal paths are insufficient to compute the exact value of the reliability of a multi-state flow network under budget or maintenance budget constraints. Moreover, it is also shown how this value can be determined.