Real-time Algorithm Research Articles

Near real-time interpolative algorithm for modelling air quality in underground mines

As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest data reliability with the fewest possible sensors. Real-time sensors remain costly, and these costs are not exclusively financial; the time required to install, calibrate, and maintain real-time sensors poses a large barrier to widespread implementation. Current atmospheric monitoring systems typically rely on displaying point-specific values. This requires operators to infer real-time airborne contamination distributions. Monitoring and control software utilizing mine ventilation network (MVN) solvers has been implemented in limited cases because of their ability to simulate ventilation systems quickly, but these solvers use a one-dimensional representation of the mine, limiting spatial resolution of estimated distributions. Computational fluid dynamics (CFD) has likewise been considered as a means to improve spatial resolution, but processing times prevent its use as a basis for monitoring and control. For the real-time monitoring of airborne contamination distributions, we propose a spatial interpolation method that can estimate the distribution of airborne contaminants in near-real time. This method provides a middle ground between fast processing times and increased spatial resolution. With the use of a pathfinding algorithm and optimization through absolute percentage error minimization, this method outperforms spatial interpolation with a Euclidean distance. By providing contamination distribution information to operators, this method and its derivatives stand to outperform current atmospheric monitoring systems.

Research on Complementary Filtered Attitude Solution Method for Quadcopter Based on Double Filter Preprocessing

In the attitude calculation process of quadrotor, the traditional pre-filtering method has degraded the attitude solving accuracy due to incomplete denoising of accelerometer and gyroscope measurements. Therefore, a complementary filtered attitude solution method based on double filter pre-processing is proposed to address this problem. First, the signal decomposition, hard threshold denoising, and signal reconstruction of the accelerometer and gyroscope acquired data are performed using the Haar real-time wavelet filtering algorithm. The reconstructed signal is then filtered by the infinite impulse response ( IIR ) low-pass filtering algorithm to remove the residual high-frequency noise and complete the dual filtering preprocessing. Next, the gyroscope data is corrected with accelerometer data according to the complementary filtering algorithm. Finally, the corrected data are used to solve the quaternion and thus the attitude angle by combining the Longacurta method. The results show that the dual filtering preprocessing method can further reduce the noise in the measurements of accelerometer and gyroscope. The attitude angle results calculated by the proposed method in the static and dynamic hovering states of the four-rotor aircraft have a small degree of dispersion, which can effectively improve the accuracy of attitude calculation.

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

Comprehensive Travel Companion: A Hybrid Recommendation and Route Planning System for Globetrotting

This paper presents a comprehensive AI-powered travel companion system designed to enhance the travel experience for globetrotters. Leveraging cutting-edge artificial intelligence (AI) techniques, the system combines image recognition, recommendation algorithms, and route planning functionality to provide personalized travel recommendations and seamless navigation. The integration of collaborative filtering, content-based recommendation, and real-time route optimization algorithms ensures that users receive tailored suggestions and efficient travel routes based on their preferences, interests, and current context. The architecture, implementation details, and evaluation of the proposed system are discussed, highlighting its effectiveness in facilitating immersive and enjoyable travel experiences. Keywords: Travel Companion, Recommendation Systems, Route Planning, Image Recognition, Collaborative Filtering, Content-Based Recommendation, Dijkstra's Algorithm.

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s.

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

Efficiency Comparison CRAFT vs. SLP Methods in Production Optimization

The research conducted at PT. Carma Wira Jatim aimed to optimize the production area layout due to significant challenges stemming from machine delays and material handling inefficiencies. Through systematic layout planning (SLP) and computerized relative allocation of facilities technique (CRAFT), the study proposed improved layouts using Win QSB software. The results indicated differing efficiencies between the CRAFT and SLP methods, with CRAFT demonstrating a 4.5% reduction in total material handling moments compared to the initial layout, suggesting its superiority in this context. These findings underscore the importance of efficient layout design in mitigating production delays and enhancing operational effectiveness. Further research could explore real-time optimization algorithms to adapt layouts dynamically and investigate the impact of layout improvements on overall productivity and resource utilization. Highlight: Improved layout design enhances production efficiency. CRAFT demonstrates superior material handling optimization. SLP offers systematic planning for layout enhancement. Keywoard: Production Layout Optimization, SLP, CRAFT, Material Handling Efficiency, Operational Effectiveness

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

Impact of Audio Data Compression on Feature Extraction for Vocal Biomarker Detection: Validation Study.

Vocal biomarkers, derived from acoustic analysis of vocal characteristics, offer noninvasive avenues for medical screening, diagnostics, and monitoring. Previous research demonstrated the feasibility of predicting type 2 diabetes mellitus through acoustic analysis of smartphone-recorded speech. Building upon this work, this study explores the impact of audio data compression on acoustic vocal biomarker development, which is critical for broader applicability in health care. The objective of this research is to analyze how common audio compression algorithms (MP3, M4A, and WMA) applied by 3 different conversion tools at 2 bitrates affect features crucial for vocal biomarker detection. The impact of audio data compression on acoustic vocal biomarker development was investigated using uncompressed voice samples converted into MP3, M4A, and WMA formats at 2 bitrates (320 and 128 kbps) with MediaHuman (MH) Audio Converter, WonderShare (WS) UniConverter, and Fast Forward Moving Picture Experts Group (FFmpeg). The data set comprised recordings from 505 participants, totaling 17,298 audio files, collected using a smartphone. Participants recorded a fixed English sentence up to 6 times daily for up to 14 days. Feature extraction, including pitch, jitter, intensity, and Mel-frequency cepstral coefficients (MFCCs), was conducted using Python and Parselmouth. The Wilcoxon signed rank test and the Bonferroni correction for multiple comparisons were used for statistical analysis. In this study, 36,970 audio files were initially recorded from 505 participants, with 17,298 recordings meeting the fixed sentence criteria after screening. Differences between the audio conversion software, MH, WS, and FFmpeg, were notable, impacting compression outcomes such as constant or variable bitrates. Analysis encompassed diverse data compression formats and a wide array of voice features and MFCCs. Wilcoxon signed rank tests yielded P values, with those below the Bonferroni-corrected significance level indicating significant alterations due to compression. The results indicated feature-specific impacts of compression across formats and bitrates. MH-converted files exhibited greater resilience compared to WS-converted files. Bitrate also influenced feature stability, with 38 cases affected uniquely by a single bitrate. Notably, voice features showed greater stability than MFCCs across conversion methods. Compression effects were found to be feature specific, with MH and FFmpeg showing greater resilience. Some features were consistently affected, emphasizing the importance of understanding feature resilience for diagnostic applications. Considering the implementation of vocal biomarkers in health care, finding features that remain consistent through compression for data storage or transmission purposes is valuable. Focused on specific features and formats, future research could broaden the scope to include diverse features, real-time compression algorithms, and various recording methods. This study enhances our understanding of audio compression's influence on voice features and MFCCs, providing insights for developing applications across fields. The research underscores the significance of feature stability in working with compressed audio data, laying a foundation for informed voice data use in evolving technological landscapes.

Adapting support vector optimisation algorithms to textual gender classification

In this paper, we focus on the problem of determining the gender of the person described in a biographical text. Since support vector machine classifiers are well suited for text classification tasks, we present a new stopping criterion for support vector optimisation algorithms tailored to this problem. This new approach exploits the geometric properties of the vector representation of such content. An experiment on a set of English and Spanish biographical articles retrieved from Wikipedia illustrates this approach and compares it to other machine learning classification algorithms. The proposed method allows real-time classification algorithm training. Moreover, these results confirm the advantage of leveraging additional gender information in strongly inflected languages, like Spanish, for this task.

A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism.

Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model's training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices.

Voice Recognition Based Vital Parameter Monitoring Patient BED

In healthcare protocols continuous monitoring of vital parameters plays a crucial role in patient care. Integrating advanced technologies for both monitoring and patient comfort enhances the efficiency of healthcare delivery. This paper presents the design and development of a novel patient bed system equipped with vital parameter monitoring capabilities and adjustable head position controlled through voice commands. The proposed system leverages state-of-the-art sensors to monitor vital parameters such as heart rate, blood pressure, oxygen saturation, and temperature. These sensors are seamlessly integrated into the bed, providing non-intrusive and continuous monitoring of the patient's health status. Real-time data acquisition and processing algorithms ensure accurate and timely assessment of the patient's condition. Furthermore, the patient bed is designed with an adjustable head position feature, allowing for optimal comfort and support. Unlike conventional manual or automated adjustment mechanisms, our system employs voice control technology for intuitive and hands-free operation. Patients can simply issue verbal commands to adjust the head position according to their preferences, enhancing overall comfort and facilitating better rest.

Decision Support System for Optimizing Tactics and Strategies of Sports Competition Using Reinforcement Learning Algorithm

This paper presented an augmented reality secure edge machine learning dynamic optimization model for detecting volleyball movement recognition using a decision support system. The proposed Edge Augmented Decision Support System Blockchain (EdgeAu-DSSBC) model aims to address the limitations of centralized machine learning models, such as high latency, network congestion, and data privacy concerns, by utilizing edge computing and dynamic optimization techniques. The proposed EdgeAu-DSSBC system consists of two main components: an augmented reality interface that allows users to interact with the system and an edge machine learning algorithm that performs the recognition task. The system uses dynamic optimization techniques to optimize the parameters of the machine learning algorithm in real time based on the feedback received from the users. The decision support system provides additional guidance to the users and helps them to make informed decisions based on the recognition results. The EdgeAu-DSSBC model was evaluated using a dataset of volleyball movement videos and compared to existing centralized and edge-based machine-learning models. The experimental results demonstrate that the EdgeAu-DSSBC exhibits improved performance with an accuracy of 99%.

Real-time statistical algorithm for cherry tomatoes with different ripeness based on depth information mapping

Rapid and non-destructive automatic statistics of cherry tomatoes at different ripeness stages help better manage resources during harvesting, storage, and transportation processes. Currently, the inspection of cherry tomatoes (ripeness assessment and counting) still faces challenges, such as excluding background cherry tomatoes, detecting heavily obscured ones, and tracking similar feature extraction across frames. This study presented a statistical algorithm for cherry tomatoes with different ripeness. Firstly, a complete field of view was achieved by stitching images from dual cameras. Then, during the detection phase, the proposed depth information mapping and morphological operations were employed to filter out background cherry tomatoes effectively. Secondly, the SimAM attention module was introduced to enhance the focus of the YOLO v7-tiny model on small and occluded targets. The ReID feature extraction model was replaced with the lighter and more powerful MobileNeXt model, with the input resolution adapted to 64 × 64 based on the morphological characteristics of cherry tomatoes. Finally, the statistics of cherry tomatoes at different ripeness levels were conducted using the improved DeepSORT algorithm. The ablation experimental results prove the effectiveness of the proposed algorithm. The improved YOLO v7-tiny has a mAP of 87.3 % on the dataset, combining depth information mapping with morphological operations. Compared with the original DeepSORT algorithm, the improved DeepSORT algorithm has RMSE decreased by 15.46 to 3.11, and R2 increased by 0.071 to 0.998. The statistical algorithm enables real-time statistical of the number of cherry tomatoes at different ripeness levels during inspection.

Interferometer-based chemical sensor on chip with enhanced responsivity and low-cost interrogation

We report experimental results of an interferometric chemical sensor integrated on a silicon chip. The sensor measures refractive index variations of the liquid that contacts exposed spiraled silicon waveguides on one branch of a Mach-Zehnder interferometer. The system requires neither laser tuning nor spectral analysis, but a laser at a fixed wavelength, and a demodulation architecture that includes an internal phase modulator and a real-time processing algorithm based on multitone mixing. Two devices are compared in terms of sensitivity and noise, one at 1550 nm wavelength and TE polarization, and an optimized device at 1310 nm and TM polarization, which shows 3 times higher sensitivity and a limit of detection of 2.24·10−7 RIU.

Development of a vision system integrated with industrial robots for online weld seam tracking

This paper develops a laser-structured light vision system for the industrial welding manipulator. The designed structured light vision system consisting of a line laser projector and an industrial camera is a decent 3D object data acquisition platform. By applying the principle of triangulation, the depth value of the object can be obtained via the laser plane in the captured image. According to hand-eye calibration and the principle of triangulation, the 3D position of the objects in the robot coordinate system is calculated. Based on the discriminated convolution tracker and deep reinforcement learning that increases the accuracy and processing time of the tracker, allowing simultaneous scanning for seam data acquisition and welding along the weld path, a real-time seam tracking algorithm is developed to handle the square-groove butt joint with small gaps (i.e., <1 mm). Experiments are undertaken using a Yaskawa industrial welding robot integrating the designed laser-structured light vision system. Experiment results show the significant performance of the developed system in welding square-groove butt joints with curved profiles and small gaps, i.e., the welding absolute mean error is 0.31 mm, whereas in the case of using convolution tracker only, the welding absolute mean error 1.7 mm.

Real-Time Point Cloud Clustering Algorithm Based on Roadside LiDAR

Real-Time Point Cloud Clustering Algorithm Based on Roadside LiDAR

Structurally Aware 3D Gas Distribution Mapping Using Belief Propagation: A Real-Time Algorithm for Robotic Deployment

This paper proposes a new 3D gas distribution mapping technique based on the local message passing of Gaussian belief propagation that is capable of resolving in real time, concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. The gas mapping problem is formulated as a 3D factor graph of Gaussian potentials, the connections of which are conditioned on local occupancy values. The Gaussian belief propagation framework is introduced as the solver and a new hybrid message scheduler is introduced to increase the rate of convergence. The factor graph problem is then redesigned as a dynamically expanding inference task, coupling the information of consecutive gas measurements with local spatial structure obtained by the robot. The proposed algorithm is compared to the state of the art methods in 2D and 3D simulations and is found to resolve distribution maps orders of magnitude quicker than typical direct solvers. The proposed framework is then deployed for the first time onboard a ground robot in a 3D mapping and exploration task. The system is shown to be able to resolve multiple sensor inputs and output high resolution 3D gas distribution maps in a GPS denied cluttered scenario in real time. This online inference of complicated plume structures provides a new layer of contextual information over its 2D counterparts and enables autonomous systems to take advantage of real time estimates to inform potential next best sampling locations.

Optimal and Quasi-Optimal Automatic Tuning of Vibration Neutralizers

Vibration neutralizers are single-degree-of-freedom devices affixed to vibrating structures in order to reduce the response at a specific troublesome harmonic excitation frequency. As this frequency may vary over time, it becomes imperative to track and adjust the neutralizer to maintain the optimal performance. Recent years have witnessed the emergence of adaptive tunable vibration neutralizers, offering real-time adjustment capabilities through external actions. Thanks to real-time control algorithms, these devices enable the automatic mitigation of vibration levels in mechanical structures. A particularly successful algorithm for the automatic tuning of these devices leverages the phase angle between the base acceleration and the neutralizer’s mass. This study critically examines the justification for employing such an algorithm and scrutinizes its optimal applicability limits, particularly in the context of viscous and structurally damped systems. The findings reveal that this algorithm accurately approximates optimum tuning for systems with low damping. Moreover, from an engineering perspective, the algorithm remains acceptable even for heavily damped structures. Through a focused and comprehensive analysis, this paper provides valuable insights into the efficacy and limitations of the phase-angle-based tuning algorithm, contributing to the advancement of adaptive vibration control strategies in smart structures.

Vision SLAM algorithm for wheeled robots integrating multiple sensors.

Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios.