Search Results (553)

To solve the issue of environmental noise pollution and promote the resource recycling of waste pomelo peel, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM) are used to systematically characterize the microstructure and chemical composition of waste pomelo peel. It was found that waste pomelo peel has a porous network structure, which is conducive to the improvement of sound absorption performance. Waste pomelo peel/polycaprolactone (PCL) sound-absorbing composites are prepared by the hot-pressing molding process, and the single-factor analysis method is adopted to explore the effects of seven factors (waste pomelo peel mass fraction, composite density, composite thickness, hot-pressing time, hot-pressing pressure, hot-pressing temperature, and thickness of rear air layer) on the sound absorption performance. Through process optimization, under the optimal conditions, the average sound absorption coefficient (SAC) of the composites reaches 0.54, the noise reduction coefficient (NRC) reaches 0.57, and the maximum SAC reaches 0.99, with the sound absorption performance reaching Grade III. This study not only provides a new idea for the preparation of porous sound-absorbing composites but also opens a new path for the high-value utilization of waste pomelo peel resources. Full article

The fast-changing scenario of the transportation industry due to the rapid adoption of electric vehicles (EVs) imposes significant challenges on power distribution networks. Challenges such as dynamic and concentrated charging loads necessitate intelligent demand-side management (DSM) strategies to ensure grid stability and cost efficiency. This study proposes a novel two-stage framework integrating Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and linear programming (LP) to optimize EV charging loads across four operational scenarios: Summer Weekday, Summer Weekend, Winter Weekday, and Winter Weekend. Utilizing a dataset of 72,856 real-world charging sessions, the first stage employs HDBSCAN to segment charging behaviors into nine distinct clusters (Davies-Bouldin score: 0.355, noise fraction: 1.62%), capturing temporal, seasonal, and behavioral variability. The second stage applies linear programming optimization to redistribute loads under real-time pricing (RTP), minimizing operational costs and peak demand while adhering to grid constraints. Results demonstrate the load optimization by total peak reductions of 321.87–555.15 kWh (23.10–25.41%) and cost savings of $27.35–$50.71 (2.87–5.31%), with load factors improving by 14.29–17.14%. The framework’s scalability and adaptability make it a robust solution for smart grid integration, offering precise load management and economic benefits. Full article

Real-time industrial inspection is a crucial component of production automation, with key challenges lying in enhancing detection accuracy for specific tasks and effectively mitigating the adverse impacts of complex production environments. Addressing these issues, this paper proposes an innovative solution. We introduce the Head_DySnake module in the detection head to significantly bolster the capture and recognition capabilities of defect texture features. Concurrently, at the initial stage of the backbone network, we integrate the Attention Denoising Module(ADConv) module, which employs an attention-guided mechanism for effective noise reduction in production environments, thereby eliminating high-level noise caused by high background similarity. Through these optimizations, our research achieves a 6.3% mAP improvement on the NEU-DET dataset with a computational demand of merely 9.8 GFLOPs, and a 5.9% mAP improvement on the GC10-DET dataset. This study thoroughly explores the impact of steel defect-specific textures on recognition performance and validates the positive role of attention-guided environmental denoising strategies in enhancing model robustness. These findings offer new perspectives for lightweight model design and performance optimization in industrial production inspection and are expected to provide valuable insights for the detection of other types of defects in related research fields. Full article

Land observation multispectral satellites (e.g., Landsat-8/9 and Sentinel-2) offer high spatial resolution but have limited spectral bands for water color observation and insufficient spectral resolution. This study proposes a spectral reconstruction model based on a residual neural network (Deep Spectral Reconstruction Learning Network, DSR-Net) to provide additional spectral bands support for nearshore water observations. The model is trained on 60 million pairs of quasi-synchronous reflectance data, and achieves stable reconstruction of 15 water color channels of the surface level reflectance for water pixels (ρ_w) from visible to near-infrared bands, considering sensor noise and atmospheric correction errors. Validation results based on AERONET-OC data show that the root mean square error of reconstructed ρ_w by DSR-Net ranges from 4.09 to 5.18 × 10⁻³, representing a reduction of 25% to 43% compared to original atmospheric correction results. The reconstruction accuracy reaches the observation level of the Sentinel-3/OLCI water color sensor and is universally applicable to different water categories, effectively supporting nearshore water color observation tasks such as colored dissolved organic matter inversion and cyanobacteria monitoring. The errors in the multispectral reflectance-based ρ_w primarily arise from sensor noise and atmospheric correction errors. After DSR-Net reconstruction, approximately 59% of the uncertainty caused by sensor noise and 38% of that caused by atmospheric correction errors are reduced. In summary, the spectral reconstruction products generated by DSR-Net not only significantly enhance the water color observation capabilities of current satellite sensors but also provide critical technical support for marine environmental monitoring and the design of next-generation sensors. Full article

Adaptive Linear Neuron (ADALINE) is a well-known neural network method that has been utilized for generating a reference current intended to regulate the operation of shunt-typed active harmonic filters (SAHFs). These filters are essential for improving power quality by mitigating harmonic disturbances and restoring current waveform symmetry in power systems. While the latest variant, Simplified ADALINE, offers notable advantages over its predecessors, such as a reduced complexity and faster learning speed, its performance has primarily been evaluated under stable grid conditions, leaving its performance under distorted environments largely unexplored. To address this gap, this work introduces two key modifications to the Simplified ADALINE framework: (1) the integration of a new phase-tracking algorithm based on the concept of orthogonality and selective filtering, and (2) transitioning from the direct current control (DCC) to an indirect current control (ICC) mechanism. Test environments featuring distorted grids and nonlinear rectifier loads are simulated in MATLAB/Simulink software to evaluate the performance of the proposed method against the existing Simplified ADALINE method. The key findings demonstrate that the proposed method effectively handled harmonic distortion and noise disturbance. As a result, the associated SAHF achieved an additional reduction in %THD (by 10.77–13.78%), a decrease in reactive power (by 58.3 VAR–67 VAR), and improved grid synchronization with a smaller phase shift (by 0.9–1.2°), while also maintaining proper waveform symmetry even in challenging grid conditions. Full article

This work presents an 80 Gbps photonics-aided millimeter-wave (mm Wave) wireless communication system employing 16-Quadrature Amplitude Modulation (16-QAM) and a 1 × 2 single-input multiple-output (SIMO) architecture with maximum ratio combining (MRC) to achieve robust 87.5 GHz transmission over 4.6 km. By utilizing polarization-diverse optical heterodyne generation and spatial diversity reception, the system enhances spectral efficiency while addressing the low signal-to-noise ratio (SNR) and channel distortions inherent in long-haul links. A blind equalization scheme combining the constant modulus algorithm (CMA) and decision-directed least mean squares (DD-LMS) filtering enables rapid convergence and suppresses residual inter-symbol interference, effectively mitigating polarization drift and phase noise. The experimental results demonstrate an SNR gain of approximately 3 dB and a significant bit error rate (BER) reduction with MRC compared to single-antenna reception, along with improved SNR performance in multi-antenna configurations. The synergy of photonic mm Wave generation, adaptive spatial diversity, and pilot-free digital signal processing (DSP) establishes a robust framework for high-capacity wireless fronthaul, overcoming atmospheric attenuation and dynamic impairments. This approach highlights the viability of 16-QAM in next-generation ultra-high-speed networks (6G/7G), balancing high data rates with resilient performance under channel degradation. Full article

The growing demand for efficient energy management has become essential for achieving sustainable development across social, economic, and environmental sectors. Accurate energy demand forecasting plays a pivotal role in energy management. However, energy demand data present unique challenges due to their complex characteristics, such as multi-seasonality, hidden structures, long-range dependency, irregularities, volatilities, and nonlinear patterns, making energy demand forecasting challenging. We propose a hybrid dimension reduction deep learning algorithm, Temporal Variational Residual Network (TVRN), to address these challenges and enhance forecasting performance. This model integrates variational autoencoders (VAEs), Residual Neural Networks (ResNets), and Bidirectional Long Short-Term Memory (BiLSTM) networks. TVRN employs VAEs for dimensionality reduction and noise filtering, ResNets to capture local, mid-level, and global features while tackling gradient vanishing issues in deeper networks, and BiLSTM to leverage past and future contexts for dynamic and accurate predictions. The performance of the proposed model is evaluated using energy consumption data, showing a significant improvement over traditional deep learning and hybrid models. For hourly forecasting, TVRN reduces root mean square error and mean absolute error, ranging from 19% to 86% compared to other models. Similarly, for daily energy consumption forecasting, this method outperforms existing models with an improvement in root mean square error and mean absolute error ranging from 30% to 95%. The proposed model significantly enhances the accuracy of energy demand forecasting by effectively addressing the complexities of multi-seasonality, hidden structures, and nonlinearity. Full article

Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge–cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming. Full article

This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model—with conventional convolutional layers replacing the separable convolutional layers—can be adopted as a more precise alternative. Full article

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications. Full article

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article

Multi-organ medical image segmentation is essential for accurate clinical diagnosis, effective treatment planning, and reliable prognosis, yet it remains challenging due to complex backgrounds, irrelevant noise, unclear organ boundaries, and wide variations in organ size. To address these challenges, the boundary feature learning and enhancement network is proposed. This model integrates a dedicated boundary learning module combined with an auxiliary loss function to strengthen the semantic correlations between boundary pixels and regional features, thus reducing category mis-segmentation. Additionally, channel and positional compound attention mechanisms are employed to selectively filter features and minimize background interference. To further enhance multi-scale representation capabilities, the dynamic scale-aware context module dynamically selects and fuses multi-scale features, significantly improving the model’s adaptability. The model achieves average Dice similarity coefficients of 81.67% on synapse and 90.55% on ACDC datasets, outperforming state-of-the-art methods. This network significantly improves segmentation by emphasizing boundary accuracy, noise reduction, and multi-scale adaptability, enhancing clinical diagnostics and treatment planning. Full article

Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

Background/Objectives: Traditional hearing aid noise reduction algorithms offer no additional benefit in noisy situations for bimodal cochlear implant (CI) users with a CI in one ear and a hearing aid (HA) in the other. Recent breakthroughs in deep neural network (DNN)-based noise reduction have improved speech understanding for hearing aid users in noisy environments. These advancements could also boost speech perception in noise for bimodal CI users. This study investigated the effectiveness of DNN-based noise reduction in the HAs used by bimodal CI patients. Methods: Eleven bimodal CI patients, aged 71–89 years old, were fit with a Phonak Audéo Sphere Infinio 90 HA in their non-implanted ear and were provided with a Calm Situation program and Spheric Speech in Loud Noise program that uses DNN-based noise reduction. Sentence recognition scores were measured using AzBio sentences in quiet and in noise with the CI alone, hearing aid alone, and bimodally with both the Calm Situation and DNN HA programs. Results: The DNN program in the hearing aid significantly improved bimodal performance in noise, with sentence recognition scores reaching 79% compared to 60% with Calm Situation (a 19% average benefit, p < 0.001). When compared to the CI-alone condition in multi-talker babble, the DNN HA program offered a 40% bimodal benefit, significantly higher than the 21% score seen with the Calm Situation program. Conclusions: DNN-based noise reduction in HA significantly improves speech understanding in noise for bimodal CI users. Utilization of this technology is a promising option to address patients’ common complaint of speech understanding in noise. Full article