Search Results (4,244)

Wastewater Treatment Plants (WWTPs) rely on automatic control strategies to regulate pollutant concentrations and comply with environmental standards. Among them, Proportional Integral (PI) controllers are widely adopted for their simplicity and robustness, yet their effectiveness is limited by the nonlinear and time-varying dynamics of biological processes. In this work, Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN) PI controllers are proposed as data-driven replacements for conventional PIs in key WWTP feedback loops. Using the Benchmark Simulation Model No. 1 (BSM1), ANN controllers were trained to replicate the behavior of default nitrate and nitrite nitrogen ($S_{NO,2}$) and dissolved oxygen ($S_{O,5}$) loops, under both time-agnostic and time-aware strategies with three- and four-input configurations. The four-input time-aware model delivered the best results, reproducing PI behavior with high accuracy (coefficient of determination, $R^2\approx 0.99$) and considerably reducing control errors. For instance, under storm influent conditions, the $S_{O,5}$ controller reduced the Integral of Squared Error ($\mathrm{ISE}$) and Integral of Absolute Error ($\mathrm{IAE}$) by 84.7% and 68.4%, respectively, compared with the default PI. Beyond loop-level improvements, a Transfer Learning (TL) extension was explored: the trained $S_{O,5}$ controller was directly applied to additional aerated reactors ($S_{O,3}$ and $S_{O,4}$) without retraining, replacing fixed aeration and demonstrating adaptability while reducing design effort. Plant-wide evaluation with the $S_{NO,2}$ loop and three dissolved oxygen loops ($S_{O,3}$–$S_{O,5}$), all controlled by LSTM-based PI controllers, under storm influent conditions, showed further reductions in the Effluent Quality Index ($\mathrm{EQI}$) and the Overall Cost Index ($\mathrm{OCI}$) by 0.84% and 1.47%, respectively, highlighting simultaneous gains in effluent quality and operational economy. Additionally, the actuator and energy analyses showed that the LSTM-based controllers produced realistic and smooth control signals, maintained consistent energy use, and ensured stable overall operation, confirming the practical feasibility of the proposed approach. Full article

Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive temporal–spectral characteristics from vibration data. The methodology employs a hybrid deep learning architecture integrating convolutional neural networks (CNNs) with Transformers, where CNNs identify local features while Transformers capture extended dependencies. Meta-learning-enhanced Bayesian optimization and HyperBand (Meta-BOHB) is utilized for efficient hyperparameter selection. Evaluation on the Case Western Reserve University (CWRU) dataset using 5-fold cross-validation demonstrates a mean classification accuracy of 99.91% with exceptional stability (±0.08%). Comparative analysis reveals superior performance regarding precision, convergence rate, and loss metrics compared to existing approaches. Cross-dataset validation using Mechanical Fault Prevention Technology (MFPT) and Paderborn University (PU) datasets confirms robust generalization capabilities, achieving 100% and 98.75% accuracy within 5 and 7 iterations, respectively. Ablation studies validate the contribution of each component. Results demonstrate consistent performance across diverse experimental conditions, indicating significant potential for enhancing reliability and reducing operational costs in industrial fault diagnosis applications. The proposed method effectively addresses key challenges in bearing fault detection through advanced signal processing and optimized deep learning techniques. Full article

This study presents an initial feasibility concept paper for a proposed crosstie system, an innovative railroad crosstie reinforcement system designed to reduce the stresses transmitted to the underlying ballast. While not developed for a specific industry client, the proposed crosstie system lays the groundwork for patent application and potential commercialization, offering a novel alternative to conventional railroad construction. Finite Element Analysis demonstrated that this system can reduce effective stress on the ballast by up to 24%, effectively making train loads appear lighter to the substructure. The design of the proposed system focuses on mitigating the excessive stresses transmitted from crossties to the ballast layer in heavy axle load (HAL) freight rail operations. The goal was to create a reinforcement mechanism that is modular, compatible with existing track infrastructure, and capable of reducing maintenance costs by distributing loads more effectively across the ballast and subgrade. The findings indicate that this system is not only the most cost-effective and sustainable solution but also holds promise for reducing fixed stock investment, minimizing downtime for track maintenance, and enabling expanded rail network connectivity. These results support continued research and investment in the system’s development and deployment. Full article

Plastic greenhouses, which account for the majority of protected horticulture facilities in East Asia, are highly susceptible to wind-induced uplift failures that can lead to severe structural and economic damage. To address this issue, this study developed a low-power and low-cost wireless monitoring system applying the concept of structural health monitoring (SHM) to greenhouse foundations. Each sensor node integrates a MEMS-based inertial measurement unit (IMU) for attitude estimation, a LoRa module for long-range alert transmission, and a microSD module for data logging, while a gateway relays anomaly alerts to users through an IP network. Uplift tests were conducted on standard steel-pipe foundations commonly used in plastic greenhouses, and the proposed sensor nodes were evaluated alongside a commercial IMU to validate attitude estimation accuracy and anomaly detection performance. Despite the approximately 30-fold cost difference, comparable attitude estimation results were achieved. The system demonstrated low power consumption, confirming its feasibility for long-term operation using batteries or small solar cells. These results demonstrate the applicability of low-cost IMUs for real-time structural monitoring of lightweight greenhouse foundations. Full article

Consumers and businesses are often connected online in today’s digitally connected world. Fast and barrier-free communication, easier and faster operation, and automation and networking of robots and production offer many competitive advantages. Recognizing the limiting factors of new technology, such as the significant dependency on technology and the vulnerability of IT devices, is crucial. As digitalization might increase the competitiveness of companies and have an impact on both the supply and value chains, we need to consider and assess their vulnerability from an information security perspective. Consequently, competitive advantage is not only about creating value more cost-efficiently and with higher quality but also about extracting the correct information from big data, interpreting and integrating it into business operations, and protecting it. This study proposes a fishbone model to help identify and overcome these challenges. It allows companies to identify the root cause of each information security incident. Full article

The aviation industry requires a series of actions that will transform its current status, aiming for sustainable operations. Aviation’s end-of-life stream is a pivotal lever for circularity, yet current dismantling and recycling practices leave significant value unrealized. Circular Economy could be considered as a transformational approach to the aviation industry and address its environmental and economic challenges, meeting sustainability principles. This study conducts a PRISMA-guided qualitative systematic review across academic and industry sources to synthesize regulations, technologies, and economics of aircraft decommissioning. It aims to quantify material recovery potential and environmental gains at the aircraft level and assess technology readiness and cost drivers for metals, polymers, and composites. Findings indicate that optimized decommissioning enables high-value part reuse and substantial material recovery (notably aluminum), with associated lifecycle greenhouse-gas avoidance at the aircraft scale. However, high costs, weak regulations, and limited recycling technologies hinder adoption. Results show that optimized dismantling and certified part-reuse pathways can recover up to 85–90% of total aircraft mass, with potential CO₂-emission avoidance of 25–35 t per narrow-body aircraft compared with landfill disposal. Metal recycling technologies (TRL 8–9) already achieve high yields, whereas polymer and composite recycling remain limited (TRL 5–6) by purity and certification barriers. A comparative assessment of EU, US, and Asia–Pacific regulations identifies enforcement and infrastructure gaps hindering implementation. The study introduces an integrated CE roadmap for aviation comprising (i) standards-aligned design-for-disassembly and digital traceability, (ii) accredited MRO-to-reuse networks, and (iii) performance-based policy incentives. Full article

Electrowetting display technology is increasingly prevalent in modern microfluidic and electronic paper applications, yet it remains susceptible to micro-scale defects such as screen burn-in, charge trapping, and pixel wall deformation. These defects often exhibit low contrast, irregular morphology, and scale diversity, posing significant challenges for conventional detection methods. To address these issues, we propose ASAF-Net, a novel lightweight network incorporating adaptive attention mechanisms for real-time electrowetting defect detection. Our approach integrates three key innovations: a Multi-scale Partial Convolution Fusion Attention module that enhances feature representation with reduced computational cost through channel-wise partitioning; an Adaptive Scale Attention Fusion Pyramid that introduces a dedicated P2 layer for micron-level defect detection across four hierarchical scales; and a Shape-IoU loss function that improves localization accuracy for irregular small targets. Evaluated on a custom electrowetting defect dataset comprising seven categories, ASAF-Net achieves a state-of-the-art mAP@0.5 of 0.982 with a miss detection rate of only 1.5%, while operating at 112 FPS with just 9.82 M parameters. Comparative experiments demonstrate its superiority over existing models such as YOLOv8 and RT-DETR, particularly in detecting challenging defects like charge trapping. This work provides an efficient and practical solution for high-precision real-time quality inspection in electrowetting display manufacturing. Full article

This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging the Hilbert transform to map current signals into 2D Generalized Lissajous Trajectories. These trajectories amplify key SAF features (e.g., zero-break distortion and random pulses). A ResNet50-based image recognition model achieves high-precision fault detection under specific load types, with a validation accuracy of up to 99.91% for linear loads and 98.93% for nonlinear loads. The algorithm operates within 1.6 ms, enabling real-time circuit breaker tripping. The proposed method achieves higher recognition accuracy with lower computational cost compared to other image-based methods. In this paper, an adjustable load signal modeling approach is proposed to visualize the current signal using GLT and complete the lightweight identification based on ResNet network, which provides new ideas and methods for series arc fault detection. Full article

Crack segmentation in cluttered scenes with slender and irregular patterns remains difficult, and practical systems must balance accuracy and efficiency. We present CONTI-CrackNet, which is a lightweight visual state-space network that integrates a Multi-Directional Selective Scanning Strategy (MD3S). MD3S performs bidirectional scanning along the horizontal, vertical, and diagonal directions, and it fuses the complementary paths with a Bidirectional Gated Fusion (BiGF) module to strengthen global continuity. To preserve fine details while completing global texture, we propose a Dual-Branch Pixel-Level Global–Local Fusion (DBPGL) module that incorporates a Pixel-Adaptive Pooling (PAP) mechanism to dynamically weight max-pooled responses and average-pooled responses. Evaluated on two public benchmarks, the proposed method achieves an F1 score (F1) of 0.8332 and a mean Intersection over Union (mIoU) of 0.8436 on the TUT dataset, and it achieves an mIoU of 0.7760 on the CRACK500 dataset, surpassing competitive Convolutional Neural Network (CNN), Transformer, and Mamba baselines. With 512 × 512 input, the model requires 24.22 G floating point operations (GFLOPs), 6.01 M parameters (Params), and operates at 42 frames per second (FPS) on an RTX 3090 GPU, delivering a favorable accuracy–efficiency balance. These results show that CONTI-CrackNet improves continuity and edge recovery for thin cracks while keeping computational cost low, and it is lightweight in terms of parameter count and computational cost. Full article

As demand for heat and power continues to grow, production planning of a combined heat and power (CHP) system becomes one of the most crucial optimization problems. Due to the fluctuations in demand and production costs of heat and power, it is necessary to quickly solve the production planning problem of the contemporary CHP system. In this paper, we propose a Hybrid Time series Informed neural Network (HYTIN) in which, a deep learning-based planner for CHP production planning predicts production levels for heat and power for each time step. Specifically, HYTIN supports inventory-aware decisions by utilizing a long short-term memory network for heat production and a convolutional neural network for power production. To verify the effectiveness of the proposed method, we build ten independent test datasets of 1200 h each with feasible initial states and common limits. Experimentation results demonstrate that HYTIN achieves lower operation cost than the other baseline methods considered in this paper while maintaining quick inference time, suggesting the viability of HYTIN when constructing production plans under dynamic variations in demand in CHP systems. Full article

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems. Full article

With regard to low-voltage (LV) distribution networks, the quality of distributed electricity can be compromised by the level of phase load imbalance. Consequently, numerous phase load balancing (PLB) algorithms have been proposed in the specialized literature. However, those models have been focused on the quality of the solution obtained rather than performance, which leads to reduced practical applicability for the distribution network (reduced scalability, slow convergence, and a higher computational cost). Furthermore, certain constraints regarding the electrical network and the switching operations of consumers must be integrated into the mathematical model. In this context, the proposed PLB algorithm represents an improved constrained greedy optimization (ICGO), capable of achieving fast convergence even on large datasets, with a lower computational cost. Three scenarios (30, 250, and 500 consumers), each with 20 distinct initial non-symmetries, were simulated. The results support the practical effectiveness and scalability of the ICGO: an absolute value of the neutral current below 0.63 A (99.53% relatively reduction), a current unbalance index below 0.1%, a small number of iterations (between 4 and 11 iterations), and an execution time between 0.00051 and 0.01149 s). Therefore, this research proposes an efficient PLB algorithm, with the possibility for its improvement in future work. Full article

By analyzing all types of risks in the investment process of a municipal railroad construction project, 16 investment risk factors are extracted, and a network of investment risk factors and a comprehensive impact matrix of the project are constructed by comprehensively applying social network analysis (SNA) and the decision-making test and evaluation laboratory (DEMATEL) method. By analyzing the point centrality, proximity centrality and intermediate centrality of the SNA network, core risk factors such as insufficient operation and management level (degree centrality: 51.111) and cost overruns (in-closeness centrality: 93.75) are identified; through the correlation strength analysis of risk factors via the DEMATEL method, “policy–approval–schedule–cost” is clearly identified. Moreover, through the DEMATEL method, correlation intensity analysis between risk factors was clarified, and six key risk transmission paths were identified, such as “policy–approval–duration–cost”, “market–cost–operation”, etc., among which the cumulative impact coefficient of the “market–cost–operation” path reached 0.664. According to the results of the analysis of core risk factors and key risk transmission paths, targeted investment risk response proposals for municipal railroad construction projects are put forward with regard to four aspects: strengthening the control of core driving factors, curbing the deterioration of key results factors, blocking the risk of intermediate conduction factors, and resisting the impact of marginal risk factors. Full article

Detecting corroded metal fittings in UAV-based transmission line inspections is challenging due to the small object size and environmental interference, causing high false and missed detection rates. To address these, this paper proposes a novel knowledge-distilled lightweight YOLO model, integrating a densely-connected convolutional network and spatial pixel-aware self-attention mechanism in the teacher model training stage to enhance feature transfer and structured feature utilization for reducing environmental interference, while employing the lightweight MobileNet as the feature extractor in the student model training stage and optimizing candidate box migration via the teacher model’s efficient intersection-over-union non-maximum suppression (EIoU-NMS). This model overcomes the challenges of small-object fitting detection in complex environments, improving fault identification accuracy and reducing manual inspection costs and missed detection risks, while its lightweight design enables rapid deployment and real-time detection on UAV terminals, providing a reliable technical solution for unmanned smart grid operation. Experimental results on actual UAV inspection images demonstrate that the model significantly enhances detection accuracy, reduces false and missed detections, and achieves faster speeds with substantially fewer parameters, highlighting its outstanding effectiveness and practicality in power system maintenance scenarios. Full article

Monitoring bottom-hole pressure (BHP) is critical for reservoir management and flow assurance, especially in offshore fields where challenging conditions and production losses are more impactful. However, reliability issues and high installation costs of Permanent Downhole Gauges (PDGs) often limit access to this vital data. Soft sensors offer a cost-effective and reliable alternative, serving as backups or replacements for physical sensors. This study proposes a novel data-driven methodology for estimating flowing BHP using wellhead and topside measurements from plant monitoring systems. The framework employs ensemble methods combined with clustering techniques to partition datasets, enabling tailored supervised training for diverse production conditions. Aggregating results from sub-models enhances performance, even with simpler machine learning algorithms. We evaluated Linear Regression, Neural Networks, and Gradient Boosting (XGBoost and LightGBM) as base models. A case study of a Brazilian Pre-Salt offshore oilfield, using data from 60 wells across nine platforms, demonstrated the methodology’s effectiveness. Error metrics remained consistently below 2% across varying production conditions and reservoir lifecycle stages, confirming its reliability. This solution provides a practical, economical alternative for studies and monitoring in wells lacking PDG data, improving operational efficiency and supporting reservoir management decisions. Full article