Search Results (1,111)

The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network’s learning capabilities for evaluating structural damage. Amplitude and time cycles of acceleration were analyzed as the structural response. These cycles underwent FFT analysis, leading to the identification of four diagnostic symptoms described by 20 features of the diagnostic signal, which in turn defined a condition vector. In the subsequent stage, the amplitude and frequency cycles served as input data for the neural network, and based on them, self-organizing maps (SOM) were generated. From these maps, a condition vector was defined for each of the four positions of the rim. Therefore, the technical condition of the wheel rim was determined based on the variance in condition parameter features, using reference frequencies of vibration spectra and SOM visualisations. The outcome of this work is a unique synergetic diagnostic system with innovative features, identifying the condition of a wheel rim through vibration and acoustic analysis along with neural network techniques in the form of Kohonen maps. Full article

The dimensional quality inspection of prefabricated concrete (PC) elements is crucial for ensuring overall assembly quality and enhancing on-site construction efficiency. However, current practices remain heavily reliant on manual inspection, which results in high operator dependency and low efficiency. Existing Light Detection and Ranging (LiDAR)-based methods also require skilled professionals for scanning and subsequent point cloud processing, thereby presenting technical challenges. This study developed a 3D LiDAR system for the automatic identification and measurement of the dimensional quality of PC elements. The system consists of (1) a hardware system integrated with camera and LiDAR components to acquire 3D point cloud data and (2) a user-friendly graphical user interface (GUI) software system incorporating a series of algorithms for automated point cloud processing using PyQt5. Field experiments comparing the system’s measurements with manual measurements on prefabricated bridge columns demonstrated that the system’s average measurement error was approximately 5 mm. The developed system can provide a quick, accurate, and automated inspection tool for dimensional quality assessment of PC elements, thereby enhancing on-site construction efficiency. Full article

In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS stability. To address the challenges of establishing consistent navigation and improving trajectory quality, this study introduces a novel method to directly identify AIS-matched trajectories from radar plots. This approach treats radar points as probability clouds, generating a multi-dimensional information layer by stacking these clouds after differential transformations based on AIS data. The resulting layer undergoes filtering and clustering to extract point sets that align with AIS data, effectively isolating matching trajectories. The algorithm, validated with simulated data, rapidly identifies target trajectories amid extensive interference without requiring strict parameter adjustments. In measured data, the algorithm rapidly provides matching trajectories, although further human judgment is still required due to the potential absence of true values in measured data. Full article

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. Full article

Improving productivity in industrial farming is crucial for precision agriculture, particularly in the broiler breeding sector, where swift identification of dead broilers is vital for preventing disease outbreaks and minimizing financial losses. Traditionally, the detection process relies on manual identification by farmers, which is both labor-intensive and inefficient. Recent advances in computer vision and deep learning have resulted in promising automatic dead broiler detection systems. In this study, we present an automatic detection and segmentation system for dead broilers that uses transformer-based dual-stream networks. The proposed dual-stream method comprises two streams that reflect the segmentation and detection networks. In our approach, the detection network supplies location-based features of dead broilers to the segmentation network, aiding in the prevention of live broiler mis-segmentation. This integration allows for more accurate identification and segmentation of dead broilers within the farm environment. Additionally, we utilized the self-attention mechanism of the transformer to uncover high-level relationships among the features, thereby enhancing the overall accuracy and robustness. Experiments indicated that the proposed approach achieved an average IoU of 88% on the test set, indicating its strong detection capabilities and precise segmentation of dead broilers. Full article

Understanding the dynamic spatial distribution and characteristics of fishing activities is crucial for fisheries management and sustainable development. In recent years, small pelagic fish and cephalopods in the Arabian Sea have become new targets for light purse seine fishing; however, there is a lack of publicly available reports. This study uses automatic identification system (AIS) data from January to May and October to December of 2021 to 2022 in the region between 58°–70° E and 10°–22° N to extract spatial distribution information through three methods. The results show that with a spatial resolution of 0.25° × 0.25°, the spatial similarity index between the fishing ground information extracted in 2022 and catch data was consistently above 0.60, reaching 0.76 in March 2021 and 0.79 in November 2022, while the spatial similarity index in March 2022 exceeded 0.71. The spatial distribution of fishing effort and kernel density was similar to that of the fishing grounds, and the fishing intensity information exhibited the highest spatiotemporal similarity with commercial catch data, making it more suitable as a substitute for fishery data. Therefore, effective international cooperation and efficient joint management mechanisms for fishing vessels are needed to enhance the regulatory oversight of fishing vessels in this region. Integrating AIS data with other technological methods is crucial for more effective monitoring and management of fishing vessels. The findings presented in this paper provide both quantitative and qualitative scientific support for resource conservation and sustainable development in the region. Full article

This paper introduces a preprocessing and feature selection technique for maritime target estimation. Given the distinct challenges of the maritime environment and the use of multiple sensors, we propose a target estimation model designed to achieve high accuracy while minimizing computational costs through suitable data preprocessing and feature selection. The experimental results demonstrate excellent performance, with the mean square error (MSE) reduced by about 99%. This approach is expected to enhance vessel tracking in situations where vessel estimation sensors, such as the automatic identification system (AIS), are disabled. By enabling reliable vessel tracking, this technique can aid in the detection of illegal vessels. Full article

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. Full article

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. Full article

Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system’s ability to identify speakers even in noisy environments accurately. Using softmax, we combine voice activity detection (VAD) with a multilayer perceptron (MLP). The VAD component aims to remove noisy frames from the recording. The softmax function addresses these residual traces by assigning a higher probability to the speaker’s voice compared to the noise. We tested our proposed solution on the Kaggle speaker recognition dataset and compared it to two baseline systems. Experimental results show that our approach outperforms the baseline systems, achieving a 3.6% and 5.8% increase in test accuracy. Additionally, we compared the proposed MLP system with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) classifiers. The results demonstrate that the MLP with VAD and softmax outperforms the LSTM by 23.2% and the BiLSTM by 6.6% in test accuracy. Full article

This research paper presents an effective approach to reducing marine pollution and costs by determining the optimal marine alternative fuels framework for short-sea shipping vessels, with a focus on energy efficiency. Employing mathematical models in a Python environment, the analyses are tailored specifically for conventional and fully autonomous high-speed passenger ferries (HSPFs) and tugboats, utilizing bottom-up methodologies, ship operating phases, and the global warming potential approach. The study aims to identify the optimal marine fuel that offers the highest Net Present Value (NPV) and minimal emissions, aligning with International Maritime Organization (IMO) regulations and environmental objectives. Data from the ship’s Automatic Identification System (AIS), along with specifications and port information, were integrated to assess power, energy, and fuel consumption, incorporating parameters of proposed marine alternative fuels. This study examines key performance indicators (KPIs) for marine alternative fuels used in both conventional and autonomous vessels, specifically analyzing total mass emission rate (TMER), total global warming potential (TGWP), total environmental impact (TEI), total environmental damage cost (TEDC), and NPV. The results show that hydrogen (H2-Ren, H2-F) fuels and electric options produce zero emissions, while traditional fuels like HFO and MDO exhibit the highest TMER. Sensitivity and stochastic analyses identify critical input variables affecting NPV, such as fuel costs, emission costs, and vessel speed. Findings indicate that LNG consistently yields the highest NPV, particularly for autonomous vessels, suggesting economic advantages and reduced emissions. These insights are crucial for optimizing fuel selection and operational strategies in marine transportation and offer valuable guidance for decision-making and investment in the marine sector, ensuring regulatory compliance and environmental sustainability. Full article

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency. Full article

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least $6\times {10}^{-4}$ compared with the baseline model. Full article

Property-driven hardware verification provides a promising way to uncover design vulnerabilities. However, developing security properties that check for highly concealed security vulnerabilities remains a significant challenge. In this paper, we propose a scheme, called HT-PGFV, to implement hardware Trojan security property assertion automatic generation and formal security verification for Trojan-infected designs. In our scheme, we develop a hardware Trojan security property assertion generation method for automated hardware which can extract hardware Trojan security properties from Trojan-infected designs by performing the three main steps of Trojan-infected signal identification based on feature matching, influence-cone-analysis-based Trojan path identification, and information flow trace mining, and formulate them as SystemVerilog assertions. In addition, we develop a formal security verification method based on information flow analysis which can formally verify hardware Trojan security properties and detect hardware Trojans violating information flow security policies by checking the security of information flows via our developed RT-level hardware information flow security models. The proposed method is demonstrated on several Trojan benchmarks from Trust-Hub. Experimental results show that our scheme can generate hardware Trojan security property assertions for Trojan-infected designs and detect information leakage and functionality change hardware Trojans activated by external inputs or internal conditions. Full article

Ecosystem-based fisheries management requires high-precision fisheries information to provide relevant data for natural resource management, assessment, and marine spatial planning. This study utilizes Automatic Identification System (AIS) data from light purse seine vessels from the Chinese mainland that were collected from May to November between 2020 and 2022, along with the corresponding environmental data. By applying boosted regression trees (BRTs) and generalized additive models (GAMs), this study establishes nonlinear relationships between fishing intensity and predictor variables and explores the ecological and environmental drivers behind the spatial distribution of light purse seine vessels from the Chinese mainland in the Northwest Pacific. This research identifies the key influencing factors and reveals significant seasonal preferences for different marine environments in various months, with chlorophyll-a being the primary influencing factor. The predicted fishing effort closely resembles observed data, providing valuable information to support fisheries resource management and planning. Full article