Search Results (3,613)

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. The MATLAB source code and the result files for the proposed algorithm are available in the supplementary materials file and a GitHub repository. Full article

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios. Full article

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. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP. Full article

Accurate forecasting of PV power not only enhances the utilization of solar energy but also assists power system operators in planning and executing efficient power management. The Temporal Convolutional Network (TCN) is utilized for feature extraction from the data, while the White Shark Optimization (WSO) algorithm optimizes the TCN parameters. Given the extensive dataset and the complex variables influencing PV output in this study, the maximal information coefficient (MIC) method is employed. Initially, mutual information values are computed for the base data, and less significant variables are eliminated. Subsequently, the refined data are fed into the TCN, which is fine-tuned using WSO. Finally, the model outputs the prediction results. For testing, one year of data from a dual-axis tracking PV system is used, and the robustness of the model is further confirmed using data from single-axis and stationary PV systems. The findings demonstrate that the MIC-WSO-TCN model outperforms several benchmark models in terms of accuracy and reliability for predicting PV power. Full article

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data. Full article

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness. Full article

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time. Full article

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms. Full article

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO₂, SO₂, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. Full article

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model’s overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness. Full article

Running machine learning algorithms for image classification locally on small, cheap, and low-power microcontroller units (MCUs) has advantages in terms of bandwidth, inference time, energy, reliability, and privacy for different applications. Therefore, TinyML focuses on deploying neural networks on MCUs with random access memory sizes between 2 KB and 512 KB and read-only memory storage capacities between 32 KB and 2 MB. Models designed for high-end devices are usually ported to MCUs using model scaling factors provided by the model architecture’s designers. However, our analysis shows that this naive approach of substantially scaling down convolutional neural networks (CNNs) for image classification using such default scaling factors results in suboptimal performance. Consequently, in this paper we present a systematic strategy for efficiently scaling down CNN model architectures to run on MCUs. Moreover, we present our CNN Analyzer, a dashboard-based tool for determining optimal CNN model architecture scaling factors for the downscaling strategy by gaining layer-wise insights into the model architecture scaling factors that drive model size, peak memory, and inference time. Using our strategy, we were able to introduce additional new model architecture scaling factors for MobileNet v1, MobileNet v2, MobileNet v3, and ShuffleNet v2 and to optimize these model architectures. Our best model variation outperforms the MobileNet v1 version provided in the MLPerf Tiny Benchmark on the Visual Wake Words image classification task, reducing the model size by 20.5% while increasing the accuracy by 4.0%. Full article

Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process. Full article

The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A dataset called unsafe actions of underground miners (UAUM) was constructed and included ten categories of such actions. Underground images were enhanced using spatial- and frequency-domain enhancement algorithms. A combination of the YOLOX object detection algorithm and the Lite-HRNet human key-point detection algorithm was utilized to obtain skeleton modal data. The CBAM-PoseC3D model, a skeleton modal action-recognition model incorporating the CBAM attention module, was proposed and combined with the RGB modal feature-extraction model CBAM-SlowOnly. Ultimately, this formed the Convolutional Block Attention Module–Multimodal Feature-Fusion Action Recognition (CBAM-MFFAR) model for recognizing unsafe actions of underground miners. The improved CBAM-MFFAR model achieved a recognition accuracy of 95.8% on the NTU60 RGB+D public dataset under the X-Sub benchmark. Compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, the recognition accuracy was improved by 2%, 2.7%, 7.3%, and 14.3%, respectively. On the UAUM dataset, the CBAM-MFFAR model achieved a recognition accuracy of 94.6%, with improvements of 2.6%, 4%, 12%, and 17.3% compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, respectively. In field validation at mining sites, the CBAM-MFFAR model accurately recognized similar and multiple unsafe actions among underground miners. Full article

Dam deformation is the most visual and relevant monitoring quantity that reflects the operational condition of a concrete dam. The seasonal variations in the external environment can induce seasonal fluctuations in the deformation of concrete dams. Hence, preprocessing the deformation monitoring series to identify seasonal fluctuations within the series can effectively enhance the accuracy of the predictive model. Firstly, the dam deformation time series are decomposed into the seasonal and non-seasonal components based on the seasonal decomposition technique. The advanced ensemble learning algorithm (Extreme Gradient Boosting model) is used to forecast the seasonal and non-seasonal components independently, as well as employing the Tree-structured Parzen Estimator (TPE) optimization algorithm to tune the model parameters, ensuring the optimal performance of the prediction model. The results of the case study indicate that the predictive performance of the proposed model is intuitively superior to the benchmark models, demonstrated by a higher fitting accuracy and smaller prediction residuals. In the comparison of the objective evaluation metrics RMSE, MAE, and R², the proposed model outperforms the benchmark models. Additionally, using feature importance measures, it is found that in predicting the seasonal component, the importance of the temperature component increases, while the importance of the water pressure component decreases compared to the prediction of the non-seasonal component. The proposed model, with its elevated predictive accuracy and interpretability, enhances the practicality of the model, offering an effective approach for predicting concrete dam deformation. Full article

The path planning problem is an important issue in maritime search and rescue. This paper models the path planning problem as a dynamic vehicle routing problem. It first designs a dynamic generator that transforms the existing benchmark sets for the static vehicle routing problem into dynamic scenarios. Subsequently, it proposes an effective Dynamic Ant Colony Optimization (DACO) algorithm, whose novelty lies in that it dynamically adjusts the pheromone matrix to efficiently handle customers’ changes. Moreover, DACO incorporates simulated annealing to increase population diversity and employs a local search operator that is dedicated to route modification for continuous performance maximization of the route. The experimental results demonstrated that the proposed DACO outperformed existing approaches in generating better routes across various benchmark sets. Specifically, DACO achieved significant improvements in the route cost, serviced customer quantity, and adherence to time window requirements. These results highlight the superiority of DACO in the dynamic vehicle routing problem, providing an effective solution for similar problems. Full article