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Robust Synthetic Air Data Estimation via Kalman-Aided Deep Learning Approach for Analytical Redundancy 基于卡尔曼辅助深度学习的分析冗余鲁棒综合大气数据估计

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal

Pub Date : 2026-02-02 DOI: 10.1109/JSEN.2026.3658151

Hyuntae Bang;Angelo Lerro;Wonkeun Youn;Hyojung Ahn

Reliable air data is essential for safe and stable flight operations. However, physical air data sensors are susceptible to failure due to environmental disturbances, especially in ultralight manned aircraft, where hardware redundancy is often impractical due to strict size, weight, and power constraints. Although model-based synthetic air data systems (SADS) have been proposed to reduce sensor reliance, they require precise aerodynamic coefficients and are sensitive to modeling errors. To address these limitations, this study proposes a lightweight, data-driven SADS framework based on a hybrid deep learning model that combines temporal and trend-based features. An unscented Kalman filter (UKF) is applied as a postprocessing step to enhance robustness against noise and anomalous inputs. The system is trained and validated on real-world flight data and demonstrates improved accuracy and stability over conventional deep learning baselines. These results suggest that the proposed method offers a robust and complementary alternative to model-based SADS, particularly in resource-constrained flight environments.

可靠的航空数据对安全稳定的飞行操作至关重要。然而，由于环境干扰，物理空气数据传感器容易发生故障，特别是在超轻型有人驾驶飞机上，由于严格的尺寸、重量和功率限制，硬件冗余通常是不切实际的。尽管基于模型的合成空气数据系统（SADS）已被提出以减少对传感器的依赖，但它们需要精确的空气动力学系数，并且对建模误差很敏感。为了解决这些限制，本研究提出了一个基于混合深度学习模型的轻量级数据驱动SADS框架，该模型结合了时间和基于趋势的特征。应用无气味卡尔曼滤波（UKF）作为后处理步骤，以增强对噪声和异常输入的鲁棒性。该系统在真实飞行数据上进行了训练和验证，并证明了比传统深度学习基线更高的准确性和稳定性。这些结果表明，所提出的方法为基于模型的SADS提供了一种鲁棒性和补充性的替代方案，特别是在资源受限的飞行环境中。

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引用次数: 0

Fabrication and Characterization of a 10 × 10 cm Cold Atmospheric Pressure Plasma Array. 10 × 10 cm低温大气压等离子体阵列的制备与表征。

IF 1.5 4区物理与天体物理 Q3 PHYSICS, FLUIDS & PLASMAS

IEEE Transactions on Plasma Science

Pub Date : 2026-02-01 Epub Date: 2025-12-30 DOI: 10.1109/tps.2025.3646554

Ranajoy Bhattacharya, Sumona Islam, Daniel Miller, Dylan Brown, Nuha Akhtar, Marcus Pearlman, Robert Hay, Kenneth A Cornell, Jim Browning

We report the development of a large area cold atmospheric plasma (CAP) array. The device consists of a parallel stack of 43 linear-discharge plasma elements that create a 10 cm × 10 cm treatment area. The CAP device is fabricated using low temperature co-fired ceramic (LTCC) layers to create 10 cm long linear discharge channels (1.1 mm discharge gap) with embedded opposing silver metal electrodes. A 21 kHz AC voltage of 1.55 kVrms applied to the electrodes generates an Ar plasma between the plates, with the gas flow directing the reactive species toward the intended biological sample (bacteria biofilms, etc.) to affect the antimicrobial treatment. Internal ballast resistors (20 kΩ) were used on each side of the two electrode elements to improve discharge uniformity and to prevent large filamentary discharges. Typical element discharge currents were 3.5-4 mA with the total array current tested at 178 mA (rms) to provide optimal device uniformity at a 1.55 kV (rms), and an argon flow rate of 130 lpm. Further, the gas flow system was optimized to obtain a uniform plasma. Treatment of Ps. fluorescence bacterial biofilms on stainless steel coupons demonstrated a 91% decrease in colony forming units after 150 s of treatment with a 1.5 cm gap.

本文报道了大面积冷大气等离子体阵列的研制。该装置由43个线性放电等离子体元件组成的平行堆叠组成，形成一个10厘米× 10厘米的处理区域。CAP装置采用低温共烧陶瓷（LTCC）层制造，通过嵌入相对的银金属电极，形成10厘米长的线性放电通道（1.1毫米的放电间隙）。在电极上施加1.55 kVrms的21 kHz交流电压，在极板之间产生Ar等离子体，气流将反应物质导向预期的生物样品（细菌生物膜等），以影响抗菌处理。在两个电极元件的每侧都使用了内部镇流器电阻（20 kΩ），以提高放电均匀性并防止大丝状放电。典型元件放电电流为3.5-4 mA，测试总阵列电流为178 mA (rms)，在1.55 kV （rms）和130 lpm的氩气流速下提供最佳的器件均匀性。此外，对气流系统进行了优化，以获得均匀的等离子体。ps荧光细菌生物膜在不锈钢片上处理150 s后，菌落形成单位减少91%，间隙为1.5 cm。

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引用次数: 0

IEEE Sensors Council IEEE传感器委员会

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal

Pub Date : 2026-01-30 DOI: 10.1109/JSEN.2026.3653226

引用次数: 0

Lightweight Graph Transformers for Clutter and Target Classification in Automotive Radar 用于汽车雷达杂波和目标分类的轻型图转换器

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal

Pub Date : 2026-01-30 DOI: 10.1109/JSEN.2026.3657850

Adrian Gheorghiu;Tunc Alkanat;Ashish Pandharipande

Radar is a core sensor modality for scene perception to achieve higher levels of autonomy in automotive driving. A common occurrence in automotive radars is clutter—detections of nonexistent moving objects, that can adversely impact target detection and classification performance and subsequent driving actions. We propose lightweight tiny graph transformer network (TGTNet) models for classifying clutter from stationary and moving targets in the scene. Performance evaluation on the public RadarScenes show that our proposed TGTNet models achieve similar classification performance in precision and recall metrics in comparison to state-of-the-art models, with one to two orders of magnitude lower model size and significantly faster inference.

在汽车驾驶中，雷达是实现高水平自动驾驶的场景感知的核心传感器方式。在汽车雷达中常见的是对不存在的运动物体的杂波检测，这可能会对目标检测和分类性能以及随后的驾驶行为产生不利影响。提出了一种轻量级的微图变压器网络（TGTNet）模型，用于对场景中静止目标和运动目标的杂波进行分类。在公共雷达场景上的性能评估表明，与最先进的模型相比，我们提出的TGTNet模型在精度和召回指标方面取得了相似的分类性能，模型大小降低了一到两个数量级，推理速度明显加快。

引用次数: 0

Stagewise Optimization Framework for Fall Direction Recognition From Wearable Sensor Data Based on Machine Learning 基于机器学习的可穿戴传感器跌倒方向识别分阶段优化框架

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal

Pub Date : 2026-01-29 DOI: 10.1109/JSEN.2026.3656322

Minh Long Hoang

Accurate fall direction recognition is essential for developing effective fall prevention and intervention systems, yet it remains challenging due to the subtle differences in motion patterns across fall types. This research proposes a stagewise optimization framework for fall type recognition (SOFFDR), which systematically enhances classification performance through four sequential stages: 1) classifier selection via

$K$

-fold cross-validation over ten candidate algorithms; 2) superior filtering method determination; 3) optimal window time tracking for segmentbased feature extraction with Shapley additive explanations (SHAP) analysis; and 4) final classification using the best parameter combination from all previous stages. The framework was evaluated on wearable inertial measurement unit (IMU) data and compared against a traditional feature vector approach in which each recording is treated as a single instance. This feature vector method achieved an accuracy of 71% (macro

$F1$

-score = 0.72), with significant misclassifications between similar fall types. In contrast, the proposed SOFFDR system achieved 100% accuracy and perfect precision, recall, and F1-scores across all fall categories. These results highlight the critical role of systematic stagewise optimization, temporal segmentation, and filtering in enhancing fall type recognition performance from wearable sensor data. The proposed framework demonstrates its potential for high-precision fall monitoring applications in healthcare and assisted living environments.

准确的跌倒方向识别对于开发有效的跌倒预防和干预系统至关重要，但由于不同跌倒类型的运动模式存在细微差异，因此仍然具有挑战性。本研究提出了一种分阶段优化的跌落类型识别框架（SOFFDR），该框架通过四个连续的阶段系统地提高分类性能：1)通过10个候选算法的$K$ -fold交叉验证来选择分类器；2)优选滤波方法的确定；3)基于Shapley加性解释（SHAP）分析的最优窗时跟踪特征提取；4)利用各阶段的最佳参数组合进行最终分类。该框架在可穿戴惯性测量单元（IMU）数据上进行了评估，并与将每个记录视为单个实例的传统特征向量方法进行了比较。该特征向量方法的准确率为71%（宏观$F1$ -score = 0.72），相似的跌倒类型之间存在明显的错误分类。相比之下，所提出的SOFFDR系统在所有秋季类别中都达到了100%的准确率和完美的精度，召回率和f1分数。这些结果强调了系统的分阶段优化、时间分割和滤波在增强可穿戴传感器数据的跌倒类型识别性能方面的关键作用。提出的框架展示了其在医疗保健和辅助生活环境中高精度跌倒监测应用的潜力。

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引用次数: 0

Photovoltaic Module Backsheet Burns Attributed to Misaligned Busbar Wires 由于母线错位导致的光伏组件背板灼伤

IF 2.6 3区工程技术 Q3 ENERGY & FUELS

IEEE Journal of Photovoltaics

Pub Date : 2026-01-29 DOI: 10.1109/JPHOTOV.2026.3654082

Steve Johnston;Dana B. Kern;Rebecca B. Wai;Harvey Guthrey;Steven P. Harvey;Ingrid L. Repins

A photovoltaic module in an outdoor single-axis tracking, utility-scale field experienced a backsheet burn that exposed the rear of the cells and started a grass fire. The opening in the backsheet developed due to increased heating where the busbar wires stringing the cells together were offset from their original solder pad locations. Several other examples of similar backsheet burns were found at this installation, and some cases also show that thermal stress caused the front glass to crack and shatter. Photoluminescence, electroluminescence, and thermal imaging techniques document the module performance and cell damage. Ultraviolet fluorescence provides time-dependent information to distinguish an older cell crack that developed with the backsheet melting from a newer crack that formed during the arc flash fire event and a scrape likely from handling to remove and transport the module. Microscopy analysis identifies the metal composition of the solder as tin/lead and not a bismuth-containing low-temperature variety. Laser isolation of a cell contact area with misaligned wire shows intermittent contact quality, suggesting the module likely passed factory screening and showed no high contact resistance prior to aging in the field.

一个户外单轴跟踪的光伏组件，公用事业规模的领域经历了背板烧伤，暴露了电池的后部，并引发了草火。背板上的开口是由于加热增加而形成的，在这里，将电池串在一起的母线与原来的焊盘位置相偏移。在这个装置中发现了其他几个类似的背板烧伤的例子，一些案例也表明热应力导致前玻璃破裂和破碎。光致发光、电致发光和热成像技术记录了模块性能和细胞损伤。紫外线荧光提供了与时间相关的信息，以区分随着背板熔化而产生的旧电池裂纹，与弧闪光事件期间形成的新裂纹以及可能因搬运和运输模块而产生的刮伤。显微镜分析确定焊料的金属成分为锡/铅，而不是含铋的低温品种。用激光隔离有不对准导线的电池接触区域显示间歇性接触质量，表明该模块可能通过了工厂筛选，并且在现场老化之前没有显示高接触电阻。

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引用次数: 0

Effect of Laser Cutting Procedures on Overall Performance of N-Type TOPCon Silicon Solar Cells 激光切割工艺对n型TOPCon硅太阳能电池整体性能的影响

IF 2.6 3区工程技术 Q3 ENERGY & FUELS

IEEE Journal of Photovoltaics

Pub Date : 2026-01-28 DOI: 10.1109/JPHOTOV.2025.3646333

Huayun Ge;Xing Li;Chunlin Guo;Jiawang Chen;Deshuang Chen;Yong Ren;Rui Jia

Today, as the most well-developed photovoltaic (PV) technology, silicon-based solar cells still dominate the PV market. In order to further maximize the power output of the PV modules, it is vital to cut the full solar cells into half-size cells. An increase of 3% in overall power output can often be seen for the half-cell PV modules. However, the solar cell splitting process, which is usually conducted by laser cutting, inevitably causes structural damage and power conversion efficiency loss to the cut cells. The overall performance of the cut solar cell is greatly dependent on the laser cutting scenario. How the laser cutting parameters, such as cutting technique and cutting direction influence the performance of the solar cell is still under debate. In this work, commercial efficient N-type TOPCon solar cells were subjected to laser scribe and mechanical cleaving and thermal laser separation from different cutting side, respectively, to comprehensively investigate the effect of laser cutting procedures on the overall performance of the cells.

今天，作为最发达的光伏（PV）技术，硅基太阳能电池仍然主导着光伏市场。为了进一步最大化光伏组件的输出功率，将完整的太阳能电池切成一半大小的电池是至关重要的。对于半电池光伏组件，总功率输出通常可以增加3%。然而，通常采用激光切割进行的太阳能电池分裂过程不可避免地会对被切割的电池造成结构损伤和功率转换效率损失。切割太阳能电池的整体性能在很大程度上取决于激光切割场景。激光切割工艺、切割方向等参数对太阳能电池性能的影响一直存在争议。本研究以商用高效n型TOPCon太阳能电池为研究对象，分别从不同切割侧进行激光刻划、机械切割和热激光分离，全面考察激光切割工艺对电池整体性能的影响。

引用次数: 0

Vision–Tactile Sensor Fusion System for Fabric Sorting and Robotic Grasping in Textile Recycling 纺织回收中织物分拣与机器人抓取的视觉触觉融合系统

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal

Pub Date : 2026-01-27 DOI: 10.1109/JSEN.2026.3656234

Jiayao Li;Yu Gao;Yijia Yan;Zhenke Li;Xin Wu;Jipeng Huang

Sorting discarded fabrics is a critical yet challenging task in textile recycling due to the diversity of material types and surface textures. We present a vision–tactile robotic system leveraging multimodal sensing to enable accurate fabric recognition and adaptive grasping. The system employs a stereo RGB camera with MobileNet-SSD on the Myriad X chip for coarse object detection and 3-D localization, achieving a mean average precision (mAP50) of 93.50% at 23 FPS. For fine-grained texture classification, tactile images are processed by a lightweight MobileNetv3- Textile model on NVIDIA Jetson Orin, achieving 27.3 FPS with 8.5-ms inference latency. Two complementary datasets were constructed: a visual dataset with 20 fabric categories for appearance-based classification and a tactile dataset with 191 categories capturing weaving patterns for precise texture discrimination. Sensor fusion is performed in real time, integrating visual and tactile modalities to enhance recognition accuracy and grasp reliability. A resource-constrained control unit manages tactile processing, gripper force modulation via optical flow, and sensor coordination. Experimental evaluation demonstrates that the proposed multimodal sensing approach significantly improves perception robustness and operational efficiency, providing a scalable solution for automated fabric handling in recycling. We release the dataset in https://github.com/AumnceLi/Visual-tactile-fabricdataset.git

由于材料类型和表面纹理的多样性，对废弃织物进行分类是纺织品回收中一项关键但具有挑战性的任务。我们提出了一个视觉触觉机器人系统，利用多模态传感来实现准确的织物识别和自适应抓取。该系统在Myriad X芯片上采用了带有MobileNet-SSD的立体RGB相机，用于粗目标检测和3d定位，在23 FPS下实现了93.50%的平均精度（mAP50）。对于细粒度纹理分类，触觉图像由NVIDIA Jetson Orin上的轻量级MobileNetv3- Textile模型处理，实现27.3 FPS和8.5 ms推理延迟。构建了两个互补的数据集：包含20个织物类别的视觉数据集用于基于外观的分类，以及包含191个织物类别的触觉数据集用于精确的纹理识别。传感器融合实时进行，整合视觉和触觉模式，提高识别精度和把握可靠性。一个资源受限的控制单元管理触觉处理，通过光流的抓手力调制和传感器协调。实验评估表明，多模态感知方法显著提高了感知鲁棒性和操作效率，为织物回收自动化处理提供了可扩展的解决方案。我们在https://github.com/AumnceLi/Visual-tactile-fabricdataset.git中发布数据集

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引用次数: 0

An Approach for Modeling, Simulation, and Parameter Estimation of Half-Cell Bifacial PV Modules 半电池双面光伏组件的建模、仿真和参数估计方法

IF 2.6 3区工程技术 Q3 ENERGY & FUELS

IEEE Journal of Photovoltaics

Pub Date : 2026-01-26 DOI: 10.1109/JPHOTOV.2026.3653064

Gustavo H. P. Santos;Samuel Silva;Givaldo Reis;André R. Mocelin;Roberto Zilles;Henry P. Viveros;Elmer P. T. Cari

Bifacial modules have gained prominence due to their ability to capture additional energy from rear-side irradiance. However, models and parameter estimation methods for bifacial modules remain limited largely due to varying interpretations of rear irradiance effects. An approach to address that issue is the standardization of models in line with the IEC TS 60904-1-2 standard. This study proposes a bifacial module model based on that standard featuring two current sources, each representing front and rear irradiances, and introduces bifaciality coefficient as a new parameter to be estimated. A conventional half-cell bifacial photovoltaic module with 655 W was tested in a solar simulator, and the resulting data were used for simulations and parameter estimation of the proposed model by a mean–variance mapping optimization algorithm. According to the results, the model can effectively estimate the parameters and represent the bifacial photovoltaic module comprehensively due to its low error rates and a strong fit to

$I$

–

$V$

and

$P$

–

$V$

curves. Furthermore, a sensitivity analysis revealed the incorporation of the new parameter does not affect the identifiability of the model.

双面模组因其从背面辐射中捕获额外能量的能力而获得突出地位。然而，双面模块的模型和参数估计方法仍然有限，主要是由于对背面辐照效应的不同解释。解决这个问题的方法是根据IEC TS 60904-1-2标准对模型进行标准化。本研究基于该标准提出了一个双面模块模型，该模型具有两个电流源，每个电流源分别代表前后辐照度，并引入了双面系数作为需要估计的新参数。在太阳能模拟器上对655 W的传统半电池双面光伏组件进行了测试，并利用结果数据对所提出的模型进行了仿真和参数估计，采用均值方差映射优化算法。结果表明，该模型错误率低，对$I$ - $V$曲线和$P$ - $V$曲线具有较强的拟合性，能够有效估计参数，全面表征双面光伏组件。此外，灵敏度分析表明，新参数的纳入并不影响模型的可识别性。

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引用次数: 0

2025 Index IEEE Open Journal of Nanotechnology 2025年IEEE纳米技术开放期刊

IF 1.9 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

IEEE Open Journal of Nanotechnology

Pub Date : 2026-01-23 DOI: 10.1109/OJNANO.2026.3656032

引用次数: 0

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