The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single p...
Freight rail transport plays key role in the transition to sustainable development. However, on European mainlines, freight trains have to cope with busy passenger operation—mostly in the form of (integrated) periodic timetables. Freight trains are c...
21 March 2018
Cities that support cycling for transportation reap many public health benefits. However, the prevalence of this mode of transportation is low in Latin American countries and the association with facilities such as bike paths and train/subway station...
Each loop in a multi-loop rail network consists of two segments, both of which have roughly the same conditions and mileage and are approximately symmetrical. This paper is devoted to optimizing the paths of trains formed at the loading area in a mul...
The virtual track train (VTT) is a new urban public transportation system that adopts all-axle steering and distributed drive. The Super autonomous Rail rapid Transit (SRT), as one of them, adopts a four-module six-axle structure. In response to its...
The reduction of train-induced ground vibrations by different railway lines and by mitigation measures in the propagation path is analysed in a unified approach by two-dimensional finite element calculations where the reduction is expressed as the am...
This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algor...
This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to...
Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we tracked the paths of 46 autumn cold surges affecting North China from 1961 to 2014, and classified them by clustering analysis, thereby investigating their changes...
To enhance the navigation efficiency and obstacle avoidance capability of omnidirectional mobile robots in unstructured and complex environments, this paper conducts research on intelligent path planning and management for X-type Mecanum-wheeled mobi...
Modern mining engineering has become a huge system project with the increased intensification and complexity of mining engineering, which interwinds, involving many factors. Ethical issues in the main body of mining engineering have become more and m...
Adding inspection trains to existing railway timetables is a complex task that must balance operational efficiency and service reliability, which are essential for the sustainable operation and maintenance of high-speed railway infrastructure. To add...
Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. These triples can then be used to build a knowledge graph. Recently, pre-training models that have learned prior semantic a...
13 July 2016
Stop-skipping is a key method for alleviating congestion in rail transit, where schedules are sometimes difficult to implement. Several mechanisms have been proposed and analyzed in the literature, but very few performance comparisons are available....
Electric Multiple Unit (EMU) high-level maintenance planning is a typical discrete system. EMU high-level maintenance (HM) planning determines when to undergo HM or execute transportation task for train-sets, based on practical requirements such as p...
Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through wind...
19 June 2021
This study aims to synthesize and understand the qualitative and empirical evidence previously published on adapted sports career development. This systematic literature review also aimed at developing a conceptual model of the training and career pa...
The occurrence of injuries due to unintentional falls becomes particularly dangerous in people with neurological disorders such as Parkinson’s disease (PD). The present study aimed to evaluate the influence of the Rock Steady Boxing (RSB) progr...
Railway communications are closely impacted by the evolution and availability of new wireless communication technologies. Traditionally, the critical nature of railway services, the long lifecycle of rolling stock, and their certification processes c...
The Dujiangyan–Siguniangshan mountain rack railway project is China’s first mountain rail transit. Most of its lines are located in mountainous areas and close to natural ecological protection areas, which have strict restrictions on the...
This study seeks to overcome the limitations of traditional gas well production peak forecasting approaches, particularly in the context of handling multi-dimensional features, complex dynamic behaviors, and non-linear relationships. Accordingly, a d...
(1) Introduction: This study aimed to investigate the effects of curved-path stride gait training on the gait ability of patients with stroke. (2) Materials and Methods: Thirty patients with stroke were randomly assigned to curved-path stride gait tr...
In this paper, we propose a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (DRL) to complete the multi-agent path-finding simulation. In the training of physical robots, collisions...
AI provides a new method for massive simulated data calculations in molecular dynamics, materials, and other scientific computing fields. However, the complex structures and large-scale parameters of neural network models make them difficult to devel...
Maize is one of the world’s major food crops, and its yields are closely related to the sustenance of people. However, its cultivation is hampered by various diseases. Meanwhile, maize diseases are characterized by spots of varying and irregula...
Conservation of energy usage is essential in chemical process plants due to the expanded energy users and demands alongside the carry-on hike of energy prices. This study analyzed the performance of energy savings in a heat exchanger network (HEN). I...
The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destina...
Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration...
This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwate...
The absence of career planning is closely linked to low income, severe work environments, lack of health facilities, low life expectancy, and unemployment risks. (1) Background: career planning is the basis of individual career development. The aim o...
5 February 2022
Background: Learning to cycle is an important milestone in a child’s life, so it is important to allow them to explore cycling as soon as possible. The use of a bicycle with training wheels (BTW) for learning to cycling is an old approach pract...
There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a mode...
The human body posture and trajectory are important parameters of the optimal path in speed climbing, and current researchers are focused on them. However, the performance of the newly developed analysis tools for synchronously and accurately analyzi...
A transparent, end-to-end pathway from learning-level training to deployable fixed-point hardware is presented and framed as gradients to gates. A didactic XOR convolutional network is first employed so that backpropagation, post-training quantizatio...
This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized t...
For path following of snake robots, many model-based controllers have demonstrated strong tracking abilities. However, a satisfactory performance often relies on precise modelling and simplified assumptions. In addition, visual perception is also ess...
Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a traini...
Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. T...
This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-ti...
This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of ob...
Reinforcement learning techniques have been widely applied to multi-UAV cooperative path-planning tasks. However, existing multi-agent reinforcement learning methods are still affected by environmental non-stationarity, cooperation difficulties among...
In outbound China–Europe Railway Express (CR Express) operations, dispersed cargo access, transshipment-node activation, order consolidation path assignment, block train line selection, and departure-time matching are strongly coupled, directly...
China’s railway network is one of the largest railway networks in the world. By the end of 2016, the total length of railway in operation reached 124,000 km and the annual freight volume exceeded 3.3 billion tons. However, the structure of network do...
This study investigates the dual-task control challenge of path following and obstacle avoidance for deep-sea mining robots operating in complex, unstructured environments. To address the limitations of traditional training strategies, we propose an...
The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a de...
Background: Esophagogastroduodenoscopy (EGD) is essential for diagnosing upper gastrointestinal disorders. Traditional training for novice endoscopists is often inefficient and inconsistent. This study evaluates the effectiveness of an AI-assisted sy...
To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conserv...
Deep reinforcement learning (DRL) has shown great potential in path planning tasks. However, in sparse reward environments, DRL still faces significant challenges such as low training efficiency and a tendency to converge to suboptimal policies. Trad...
Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of...
Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, ins...
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