Search Results (597)

To address the issues of bulky structure and complex transmission systems in current transplanters, a compact, electric-driven automatic transplanter was designed. Using pepper plug seedlings as the test subject, this study investigated plug tray dimensions and planting patterns. According to the design requirement that the width of the single-row transplanter must be less than 62.5 cm, a three-dimensional transplanter model was constructed. The transplanter comprises a coaxially installed dual-layer seedling conveying device and a sector-expanding automatic seedling picking and depositing device. The structural dimensions, drive configurations, and driving forces of the transplanter were also determined. Finally, the circuit and pneumatic system were designed, and the transplanter was assembled. Both bench and field tests were conducted to select the optimal working parameters. The test results demonstrated that the seedling picking and depositing mechanism met the required operational efficiency. In static seedling picking and depositing tests, at three transplanting speeds of 120 plants/min, 160 plants/min, and 200 plants/min, the success rates of seedling picking and depositing were 100%, 100%, and 97.5%, respectively. In the field test, at three transplanting speeds of 80 plants/min, 100 plants/min, and 120 plants/min, the transplanting success rates were 94.17%, 90.83%, and 88.33%, respectively. These results illustrate that the compact, electric-driven seedling conveying and picking and depositing devices meet the operational demands of automatic transplanting, providing a reference for the miniaturization and electrification of transplanters. Full article

Bipolar power supply can effectively reduce line losses and optimize power transmission. This paper proposes a low-power bipolar DC–DC converter with voltage self-balancing, which not only achieves bipolar output but also automatically balances the inter-pole voltage under load imbalance conditions without requiring additional voltage balancing control. This paper first elaborates on the derivation process of the proposed converter, then analyzes its working principles and performance characteristics. A 400 W experimental prototype is built to validate the correctness of the theoretical analysis and the voltage self-balancing capability. Finally, loss analysis and conclusions are presented. Full article

This article first constructs a multi-layer deep learning neural network to help understand the structural characteristics of communication data, thereby learning complex functions and obtaining the predicted network values. At the same time, signal transmission is achieved through the interconnection of neurons, the representation performance of which is enhanced through activation functions; this completes the modeling of IoT communication models. Then, we use the analytic hierarchy process to construct a deep learning autoencoder and extract the feature elements of network communication reliability parameters. Finally, we use the obtained total reliability indicators as features for automatic coding and evaluate the mapping relationship between indicators. The results show that the success rates of handovers in deep leaning-based IoT communication based are all greater than 99.6%. The predicted transmission rate can reach a maximum of 99.5%, achieving error free communication output and improving fidelity. Full article

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management. Full article

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article

Because of their potential “smart” applications, multifunctional stimuli-responsive polymers are gaining increasing scientific interest. The present work explores the possibility of developing such materials based on the hydrolytically stable N-3-dimethylamino propyl methacrylamide), DMAPMA. To this end, the properties in aqueous solution of the homopolymer PDMAPMA and copolymers P(DMAPMA-co-MMA_x) of DMAPMA with the hydrophobic monomer methyl methacrylate, MMA, were explored. Two copolymers were prepared with a molar content x = 20% and 35%, as determined by Proton Nuclear Magnetic Resonance (¹H NMR). Turbidimetry studies revealed that, in contrast to the homopolymer exhibiting a lower critical solution temperature (LCST) behavior only at pH 14 in the absence of salt, the LCST of the copolymers covers a wider pH range (pH > 8.5) and can be tuned within the whole temperature range studied (from room temperature up to ~70 °C) through the use of salt. The copolymers self-assemble in water above a critical aggregation Concentration (CAC), as determined by Nile Red probing, and form nanostructures with a size of ~15 nm (for P(DMAPMA-co-MMA₃₅)), as revealed by transmission electron microscopy (TEM) and dynamic light scattering (DLS). The combination of turbidimetry with ¹H NMR and automatic total organic carbon/total nitrogen (TOC/TN) results revealed the potential of the copolymers as visual CO₂ sensors. Finally, the alkylation of the copolymers with dodecyl groups lead to cationic amphiphilic materials with an order of magnitude lower CAC (as compared to the unmodified precursor), effectively stabilized in water as larger aggregates (~200 nm) over a wide temperature range, due to their increased ζ potential (+15 mV). Such alkylated products show promising biocidal properties against microorganisms such as Escherichia coli and Staphylococcus aureus. Full article

This study analyzed the frequency control challenges within the West Africa Power Pool Interconnected Transmission System (WAPPITS) as it plans to incorporate variable renewable energy (VRE) resources, such as wind and solar energy. Concerns center on the ability of WAPPITS primary frequency control reserves to adapt to high VRE penetration given the synchronization and frequency control problems experienced by the three separate synchronous blocks of WAPPITS. Optimizing solutions requires a better understanding of WAPPITS’ current frequency control approach. This study used questionnaires to understand operators’ practical experience with frequency control and compared these observations to field tests at power plants and frequency response metrics during system events. Eight (8) of ten (10) Transmission System Operators (TSOs) indicated that primary frequency control service was implemented in the TSO, but nine (9) of ten TSOs indicated that the reserves provided were inadequate to meet system needs. Five (5) of ten (10) respondents answered “yes” to the provision of secondary frequency control service, while only one (1) indicated that secondary reserves were adequate. Three (3) TSOs indicated they have AGC (Automatic Generation Control) installed in the control room, but none have implemented it for secondary frequency control. The results indicate a significant deficiency in primary control reserves, resulting in a reliance on under-frequency load shedding for primary frequency control. Additionally, the absence of an AGC system for secondary frequency regulation required manual intervention to restore frequency after events. To ensure the effectiveness of battery energy storage systems (BESSs) and the reliable operation of the WAPPITS with a higher penetration of inverter-based VRE, this paper recommends (a) implementing and enforcing basic primary frequency control structures through regional regulation and (b) establishing an ancillary services market to mobilize secondary frequency control resources. Full article

As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article

Severe acute respiratory syndrome coronavirus 2 has undergone several mutations since 2020, and novel variants continue to emerge to this day. The immune escape ability of the emerging mutants is enhanced and results in robust transmissibility. The neutralizing ability of the antibodies produced in the human body during previous infections is decreased against some of these mutants, which poses a severe challenge to the preventive and therapeutic effectiveness of vaccines and antibody drugs. The nucleocapsid protein is one of the main structural proteins of the coronavirus and plays an important role in the life cycle of the novel coronavirus. This protein is one of the key targets for drug development, and the first major step in drug development is to obtain pure nucleocapsid proteins. However, since nucleocapsid proteins have a nucleic acid-binding function and automatically undergo liquid–liquid phase separation and agglomeration, the purification of full-length nucleocapsids is challenging. In this context, a set of easy-to-operate processes was developed in this study for the purification of nucleocapsid proteins. Finally, a pure full-length nucleocapsid protein without nucleic acid contamination was obtained, which exhibited significantly enhanced accessibility for structural and functional virological studies, vaccine development, and related research applications. Further, the nucleic acid-binding domain of the nucleocapsid protein was targeted, and potential severe acute respiratory syndrome coronavirus 2 inhibitors were identified using virtual screening and biolayer interferometry technology. Notably, the eukaryotically expressed nucleocapsid protein demonstrated a significantly greater binding affinity for Light Green SF Yellowish (K_D = 119.7 nM) compared to that demonstrated by its prokaryotic counterpart (K_D = 19.9 × 10³ nM). The findings of this study suggest the importance of considering both protein source and post-translational modifications of the target proteins to be used in drug screening workflows. Therefore, this compound not only represents a novel therapeutic candidate for COVID-19 but also a critical tool for elucidating antiviral mechanisms. Full article

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article

Multi-level coded modulation (MLCM) enhances data transmission by allocating error correction more effectively to bits with higher error probabilities, thus optimizing redundancy and improving performance. Despite MLCM’s advantages over traditional bit-interleaved coded modulation (BICM) systems in certain scenarios, its integration with hybrid automatic repeat request (HARQ) systems remains underexplored. HARQ, which combines the benefits of forward error correction (FEC) and automatic repeat request (ARQ), significantly increases resilience to interference and fading, enhancing overall system reliability. This paper bridges the gap by integrating HARQ techniques into the MLCM framework, which was specifically adapted to the layered nature of MLCM. We present tailored hybrid retransmission strategies for each layer of MLCM, demonstrating substantial gains in retransmission efficiency and overall transmission performance. Full article

A diode-based rectifier (DR) is an attractive transmission technology for offshore wind farms, which reduces the volume of large bulk platforms. A novel parallel–series DC wind farm based on a distributed DR is proposed, which meets the requirements of high voltage and high power with an isolation capability from other units. The coupling mechanism between a modular multilevel converter (MMC) and a DR has been built, and the coordinate control strategy for the whole system has been proposed based on the MMC triple control targets with intermediate variables. Under the proposed control strategy, the system automatically operates at maximum power point tracking (MPPT). The feasibility of topology and the effectiveness of the control strategy are verified under start-up, power fluctuation, onshore alternating current (AC) fault, and direct current (DC) fault based on the power systems computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulation. Full article

Micro-terrain recognition plays a crucial role in the planning, design, and safe operation of transmission lines. To achieve intelligent and automatic recognition of micro-terrain surrounding transmission lines, this paper proposes an improved semantic segmentation model based on UNet++. This model expands the single encoder into multiple encoders to accommodate the input of multi-source geographic features and introduces a gated fusion module (GFM) to effectively integrate the data from diverse sources. Additionally, the model incorporates a dual attention network (DA-Net) and a deep supervision strategy to enhance performance and robustness. The multi-source dataset used for the experiment includes the Digital Elevation Model (DEM), Elevation Coefficient of Variation (ECV), and profile curvature. The experimental results of the model comparison indicate that the improved model outperforms common semantic segmentation models in terms of multiple evaluation metrics, with pixel accuracy (PA) and intersection over union (IoU) reaching 92.26% and 85.63%, respectively. Notably, the performance in identifying the saddle and alpine watershed types has been enhanced significantly by the improved model. The ablation experiment results confirm that the introduced modules contribute to enhancing the model’s segmentation performance. Compared to the baseline network, the improved model enhances PA and IoU by 1.75% and 2.96%, respectively. Full article

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, laser marking, key tooth cutting, deburring, defects inspection, and a discharge station. IoT technology enables real-time monitoring and data transmission through a visual panel that displays the operational status of each station and provides immediate alerts in case of abnormalities for quick intervention. The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. Chronological data are used to support predictive maintenance, production parameter optimization, and energy efficiency improvements. Overall, the system effectively integrates automation, real-time monitoring, and quality control to ensure stable production and high product quality. Full article