Accurate Path Research Articles

Clinical outcome of guided endodontics versus freehand drilling: A controlled clinical trial, single arm with external control group.

High-quality, prospective clinical studies are needed to increase evidence for guided endodontics. This study aims to assess the clinical outcome of guided endodontics for treatment of teeth presenting with pulp canal obliteration (PCO) in comparison with freehand treatment. This trial was registered in the ISRCTN.com registry (ISRCTN75277265) and designed as a controlled clinical trial: Single arm trial, prospective, nonrandomized, single-centre study (ethical approval number S64630). Inclusion criteria were; tooth presenting with PCO and symptoms and/or signs of apical periodontitis (AP). An external control group was selected from clinical records of patients presenting the same criteria but treated freehanded. Guided root canal treatments were performed by the same operator on all patients. Freehanded treatments were performed by a specialist in endodontics under microscope with pre-operative CBCT available. Primary outcome for both groups was evaluated as: canal found, canal not found, or perforation. As secondary outcome, the qualitative accuracy of the drill path was assessed as: optimal precision, acceptable precision or technical failure. Patients were followed up yearly. Descriptive statistics on the study patient's demographics and healing outcome were performed and specific statistical analysis was performed on each outcome variable. A total of 133 teeth were included (n = 60 guided, n = 73 freehanded) from 128 patients (n = 59 guided, n = 69 freehanded). The primary outcome for the guided group was: 59 teeth canals found and 1 tooth canal not found. No perforations were recorded. In the freehanded group, the root canal was successfully found in 59 teeth, seven were not found, and seven had a perforation. An analysis of all data showed that guided endodontics presented statistically significant better outcome than freehand treatment (p < .05). Guided endodontics showed a statistically significant better outcome than freehanded treatment resulting in less technical failures. However, it is a complex procedure which should be carried out by an experienced endodontist with the aid of a dental microscope.

Global Path Optimal Planning Method for Autonomous Vehicles Based on Image Feature Extraction

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference.

Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Drug-resistant epilepsy with frequent seizures are considered to undergo surgery to become seizure-free, but seizure-free rates have not dramatically improved, partly due to imprecise intervention locations. To address this clinical need, we construct effective connectivity to reveal epilepsy brain dynamics. Based on the propagation path captured by the high order effective connectivity, calculate the control centrality evaluation scheme of the excised area. We used three datasets: simulation dataset, clinical dataset, and public dataset. The epileptogenic propagation network was quantified by calculating high-order effective connection to obtain accurate propagation path, based on this, combined with the outdegree index for virtual resection. By removing electrodes and recalculating control centrality, we quantify each electrode or region’s control centrality to evaluate the virtual resection scheme. Three datasets obtained consistent results. We track the accurate propagation path and find the obvious inflection points occurring during the excision process. The minimum intervention targets were obtained by comparing different schemes without recurrence. The clinical data with multiple seizures found that after resection, the brain reaches a stable state and is less likely to continue spreading. By quantitative analysis of control centrality to evaluate the possible excision scheme, finally we obtain the best intervention area for epilepsy, which assist in developing surgical plans.

A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

Abstract Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly–Exploring Random Trees* (BI–RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub–regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

Improving neural-network-based prediction models for misalignment in off-axis three-mirror anastigmat telescopes

The performance of a telescope system heavily relies on the precise alignment of the mirrors. The off-axis three-mirror anastigmat (TMA) telescope presents unique challenges due to its complex optical design. Each optical element within the off-axis TMA telescope is inherently introduced with theoretical eccentricity and tilt. Furthermore, the incorporation of freeform surfaces and other optical elements with intricate surface features typically leads to low initial alignment accuracy of the optical path. With this low initial alignment accuracy and the noise of the measurements, the prediction of the misalignment of the telescope is getting harder. A fully connected neural network architecture is proposed as a misalignment calculation method for an off-axis TMA telescope system with a freeform surface. Random training data is created by using optical design software. The sensitivity of the mirrors to the wavefront error is quantified and incorporated into the loss function of the neural network to improve prediction accuracy. Adding the noisy measurement samples to the training data creates a noise-immune neural network model. Simulation results show that our model can successfully predict misalignment of the mirrors with noisy measurement values.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy

Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.

Obstacle Avoidance Path Planning Strategy for Autonomous Vehicles Based on Genetic Algorithm

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Stretch-steering of highly aligned discontinuous fiber tape with automated fiber placement

Highly aligned discontinuous fiber (ADF) composites have been developed with mechanical properties comparable to continuous fiber composites. These materials can be stretched in the fiber direction during processing enabling the production of complex geometries. This study utilizes 57% fiber volume fraction ADF tape comprised of a thermoplastic Polyetherimide (PEI) matrix with 3 mm long IM7 carbon fibers. The Laser-Assisted Automated Fiber Placement (LA-AFP) process is used to study the limits of stretch-steering of ADF tape at small radii. An experimental technique based on photogrammetry was developed to quantify the effect of stretch parameters on the tape strain fields and path placement accuracy. The measurements captured the local in-plane strain tensor (longitudinal, transverse, and shear) across the width and along the length of steered tapes. The results demonstrate that placement path accuracy can be achieved when the tool center point (center of rotation) of the AFP head is located at the nip-point versus the roller centerline, which is typically used for steering continuous fiber tapes at large steering radii. Accuracy of the in-situ tape stretching was demonstrated up to 60% applied stretch strain. The measurements also confirm that when stretch-steering ADF tape, the resulting strain across the tape width is on average the superposition of the in-situ applied tensile strain and in-plane bending computed from the tape width and steering radius. A statistical-based methodology is presented to select the average tensile stretch levels to minimize the probability of defects forming during steering on the inner radius of the tape. The experiments show that a 12.5 mm wide tape can be accurately placed on a 50 mm radius of curvature without defects. This represents a two order of magnitude improvement in steerability over continuous fiber tapes of the same tape width found in peer-reviewed literature.

Optimizing flight path accuracy of an autonomous quadrotor in windy conditions: integrated control strategies for tracking under perturbations and uncertainties

Optimizing flight path accuracy of an autonomous quadrotor in windy conditions: integrated control strategies for tracking under perturbations and uncertainties

Enhancing students performance through dynamic personalized learning path using ant colony and item response theory (ACOIRT)

Generating personalized learning paths (PLP) tailored to each student's characteristics and abilities is challenging, especially when using multiple-choice test scores to predict knowledge levels, which can be affected by guessing. We combined the ant colony algorithm (ACO) with Item Response Theory (IRT) to address this issue. This method models the relationship between student responses and their latent abilities, to improve the prediction of knowledge levels resulting in more accurate personalized learning paths (PLP). The parameters used to create the PLP include the initial module, targeted module, knowledge level, pretest score, and difficulty level. The difficulty level is required to ensure the learning object aligns with the student's abilities. Additionally, the system retains a dynamic personalized learning path that accommodates changes in the student's abilities in each module. We divided 80 students into Group X (using ACOPLP) and Group Y (using ACOIRT), each with 40 students. We conducted a pretest and posttest on both groups in order to analyze the effects of implementing the PLP system. Group X outperformed Group Y on the pretest, however, Group Y surpassed Group X on the posttest, with score enhancements ranging from 60.8% to 127.8%. The statistical analysis revealed a noteworthy enhancement in performance for Group Y, with a p-value of 0.002. These results indicate that ACOIRT has the potential for improved student's performance.

Barrier Lyapunov Function-based Backstepping Controller Design for Path Tracking of Autonomous Vehicles

This research proposes a novel BLF-based backstepping controller for path tracking of Autonomous Vehicles (AVs) with unknown dynamics and unmeasurable states. The proposed framework includes: (1) forming geometric-dynamic model of the vehicle by combining the dynamics of the vehicle with the kinematics of the visual measurement system, (2) designing a fixed-time Extended-State Observer (ESO) to estimate the unknown dynamics and unmeasurable states, and (3) introducing a BLF-based controller for faster response and more accurate path tracking compared to previous BLF-based controllers. Besides the novelty of the BLF-based controller, by transforming the closed-loop error dynamics into a unified proportional-derivative (PD)-type structure, an intuitive criterion is proposed to provide a systematic procedure for comparing BLF-based controllers. A combined BLF is further proposed based on this performance criterion to eliminate the sensitivity of BLF-based controllers to the magnitude of the constraint. The stability analysis is performed for the fixed-time ESO and the closed-loop control system. MATLAB/CarSim co-simulation is conducted to evaluate the performance of the proposed control system. The outcomes of the work show that the closed-loop control system is exponentially stable. In addition, it can provide a faster response and result in more accurate path tracking compared to previous BLF-based control systems.

Controlling a Quadcopter with Static Loads and Dynamic Wind Disturbances using a Fuzzy Inference System

Maneuverability, hover, and simple mechanical design are the advantages of quadcopters. However, because quadcopters are smaller and lighter, they are more susceptible to wind than manned aircraft. The winds that cause air accidents are divided into several categories, namely downburst, turbulent wind, wind shear, and wind vortices. Disturbances and uncertainties, such as wind gusts, can result in difficulties in executing a mission on an accurate flight path. Quadcopter resilience is an important topic for UAV. Especially if the quadcopter is in terrain that is difficult for humans to reach. Hence, the system is susceptible and experiences reduced stability. Controlling a quadcopter with a cube-shaped static load to withstand turbulent wind gusts in this research uses robust Fuzzy Inference System control and trajectory control using LQR with Command-Generator Tracking. The results achieved through fuzzy control can fortify the quadcopter against half of the overall turbulent wind gusts with an RMSE of 0.0546. In contrast with the LQR-CGT control, which still exhibits an RMSE of 0.0795.

Quasi-Infinite Horizon Model Predictive Control with Fixed-Time Disturbance Observer for Underactuated Surface Vessel Path Following

As a flexible, autonomous and intelligent motion platform, underactuated surface vessels (USVs) are expected to be an ideal means of transport in dangerous and complex marine environments. The success and efficiency of maritime missions performed by USVs depend on their ability to accurately follow paths and remain robust against wind and wave disturbances. To this end, this paper focuses on accurate and robust path following control for USVs under wave disturbances. Model predictive control with a quasi-infinite horizon is proposed which converts the objective function from an infinite horizon to an approximate finite horizon, providing the convergence performance in long prediction horizons and reducing the computation load explicitly. To enhance robustness against disturbances, a fixed-time disturbance observer is applied to estimate the time-varying and bounded disturbances. The estimated value is provided to the controller input to form a robust control framework with disturbance feedforward compensation and predictive control feedback correction, which is substantially different from existing works. The convergence and optimality of the proposed algorithm are presented mathematically. Finally, we demonstrate the advantages of the algorithm in both theory and simulation.

Electron Energy-Loss Spectroscopy Method for Thin-Film Thickness Calculations with a Low Incident Energy Electron Beam

In this study, the thickness of a thin film (tc) at a low primary electron energy of less than or equal to 10 keV was calculated using electron energy-loss spectroscopy. This method uses the ratio of the intensity of the transmitted background spectrum to the intensity of the transmission electrons with zero-loss energy (elastic) in the presence of an accurate average inelastic free path length (λ). The Monte Carlo model was used to simulate the interaction between the electron beam and the tested thin films. The total background of the transmitted electrons is considered to be the electron transmitting the film with an energy above 50 eV to eliminate the effect of the secondary electrons. The method was used at low primary electron energy to measure the thickness (t) of C, Si, Cr, Cu, Ag, and Au films below 12 nm. For the C and Si films, the accuracy of the thickness calculation increased as the energy of the primary electrons and thickness of the film increased. However, for heavy elements, the accuracy of the film thickness calculations increased as the primary electron energy increased and the film thickness decreased. High accuracy (with 2% uncertainty) in the measurement of C and Si thin films was observed at large thicknesses and 10 keV, where tλ≈1. However, in the case of heavy-element films, the highest accuracy (with an uncertainty below 8%) was found for thin thicknesses and 10 keV, where tλ≤0.29. The present results show that an accurate film thickness measurement can be obtained at primary electron energy equal to or less than 10 keV and a ratio of tλ≤2. This method demonstrates the potential of low-loss electron energy-loss spectroscopy in transmission electron microscopy as a fast and straightforward method for determining the thin-film thickness of the material under investigation at low primary electron energies.

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks.

This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.

86‐2: Spatial Reality Display System Based on Eye Tracking and Pixel Interleaving Technology

With the development of naked‐eye display technology, users can experience real stereoscopic effects more freely without glasses. This paper introduces the BOE's second generation 31.5‐inch 8K naked‐eye 3D display system, which improves optical path accuracy and reduces system delay through eyetracking and synchronization technology. Special optimizations have been made for mobile and wide‐angle viewing scenes, significantly improving the display effect.

Frost heave of subgrade soil under complex traffic loads: Test system and experiments

Soil moisture freezing in cold climates leads to frost heave, a phenomenon influenced by soil texture, temperature, moisture levels, and applied loads. This investigation explores frost heave characteristics of subgrade soil under traffic-induced cyclic stresses, including cyclic compressive stress and alternating horizontal cyclic shear stress. A new frost heave test system was developed, featuring advanced temperature control and accurate loading path reproduction. Comprehensive frost heave experiments were performed to examine the frost heave process under various cyclic stress circumstances. Results indicate that cyclic stresses intensify in-situ frost heave of soil, with horizontal cyclic shear stress having a more significant promoting effect than vertical cyclic stress. The combination of vertical cyclic stress and horizontal cyclic shear stress leads to an increase in segregated frost heave. Moreover, vertical cyclic stress amplifies water absorption during soil frost heave. Total vertical deformation encompasses frost deformation in the frozen zone and consolidation in the unfrozen zone. Vertical cyclic stress may inhibit segregated ice lens formation and encourage consolidation in the unfrozen zone, thereby impeding vertical deformation. The simultaneous application of vertical cyclic stress and horizontal cyclic shear stress results in more intense ice segregation and moisture accumulation near the stable frost front.

Preventing The Impact of Unequal Thought Social Media on LGBT and HIV-AIDS In Mindset of Gen Z

The present study reports the comprehensive study after pandemic era in early 2024, aims to determine an effective methodical approach in exploiting the impact of thought inequality when using social media as an effort to prevent the transmission of HIV/AIDS (LGBT) infections. The preparation of this literature review uses the narrative literature review method by entering keywords into the PubMed, ProQuest, Wiley, CINAHL and databases of ScienceDirect. The results of the research show that the effective method used in utilizing social media in efforts to prevent the transmission of HIV/AIDS (LGBT) infections including Network Monitoring Systems and direct intervention. The Network Monitoring System method can analyze the scientific role of health sociology in statistical data or social change trends which are used as material considerations in formulating health promotion policies. Meanwhile, direct intervention with a comprehensive educational approach in empowering individuals, communities or groups becomes a stimulant for one is the potential to oversee health problems independently. In conclusion, both produce outputs in the form of increasing awareness in the prevention and transmission of HIV/AIDS (LGBT), healthy sexual knowledge, minimizing stigma towards HIV/AIDS sufferers, getting accurate target tracing paths, education will be provided.