Investigation of the multi-pass drawing process for steel thin-walled ribbed tube using Taguchi-grey relational analysis optimisation by Xi Wang, Haojie Zhang, Wei Li, Chaoyang Sun

“…However, due to the extensive workload involved in determining the optimal geometries of drawing tool and billet size, the efficiency of MPD design largely depends on trial and error as well as experimental experience. To address this challenge, this study presents an optimisation method that combines the Taguchi-grey relational analysis and numerical simulation for MPD of 316L stainless steel thin-walled ribbed tubes. …”
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RUL Prediction Based on MBGD-WGAN-GRU for Lithium-Ion Batteries by Zhiguo Zhao, Ke Li, Yibo Dai, Biao Chen, Yeqin Wang, Qian Zhao

“…Experimental results demonstrate that the mean absolute error of the proposed method on B0005, B0006, and B0007 batteries is 0.0023 Ah, 0.0030 Ah, and 0.0014 Ah, respectively, confirming its effectiveness and practical applicability. …”
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Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks by Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Quan Pan, Min Yang

“…This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem, where each bandit process is associated with a target and its TEC states evolve with different transition rules for different actions, i.e., either the target is tracked or not. …”
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Fast and efficient Sb-based type-II phototransistors integrated on silicon by Lining Liu, Simone Bianconi, Skyler Wheaton, Nathaniel Coirier, Farah Fahim, Hooman Mohseni

“…Their energy consumption is about 5 fJ/bit at 3.2 Gbps, with an error rate below 10−9 at −25 dBm optical power at 1550 nm. …”
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Hardware Implementation for Triaxial Contact-Force Estimation from Stress Tactile Sensor Arrays: An Efficient Design Approach by María-Luisa Pinto-Salamanca, Wilson-Javier Pérez-Holguín, José A. Hidalgo-López

“…The calculation of force vectors on the proposed hardware required an average time of 58.68 ms, with an estimation error of 12.6% for normal forces and 7.7% for tangential forces on a 10 × 10 taxel tactile sensor array. …”
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A Deep Variational Approach to Multiterminal Joint Source-Channel Coding Based on Information Bottleneck Principle by Shayan Hassanpour, Matthias Hummert, Dirk Wubben, Armin Dekorsy

“…In this article, we concentrate on a generic multiterminal joint source-channel coding scenario, appearing in a wide variety of real-world applications. Specifically, several noisy observations from a source signal must be compressed at some intermediate nodes, before getting forwarded over multiple error-prone and rate-limited channels towards a (remote) processing unit. …”
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Wide-Range Tunable Narrow Band-Stop Filter Based on Bilayer Graphene in the Mid-Infrared Region by Yindi Wang, Hongxia Liu, Shulong Wang, Ming Cai

“…In addition, the fabrication processing and fabrication error tolerance are investigated, and it has proved that the filter is quite tolerant to fabrication deviations. …”
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A review of IoT network management: Current status and perspectives by Moussa Aboubakar, Mounir Kellil, Pierre Roux

“…However, due to the presence of an important number of heterogeneous and resources constrained devices (in terms of memory, CPU and bandwidth) communicating over error-prone and lossy radio channels and often deployed in hostile environments (e.g. war zone), IoT networks are experiencing various network performance problems (e.g. excessive energy consumption resulting from network device failure). …”
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Visible Light Communication Using Receivers of Camera Image Sensor and Solar Cell by Yang Liu, Hung-Yu Chen, Kevin Liang, Chin-Wei Hsu, Chi-Wai Chow, Chien-Hung Yeh

“…Experimental results show that by using the proposed SOP ER enhancement scheme, the bit error rate (BER) improvement can be up to two orders of magnitude. …”
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Leveraging Edge Intelligence for Solar Energy Management in Smart Grids by Trong-Minh Hoang, Tuan-Anh Pham, van-Nhan Nguyen, Duc-Thang Doan, Nhu-Ngoc Dao

“…The model has been validated using two benchmark datasets (Alibaba Competition and GEFCom), achieving an impressive mean squared error of 0.0051 on long-term forecasts. The results demonstrate that our approach ensures high forecasting accuracy while maintaining computational efficiency, making it well-suited for real-time, IoT-enabled solar energy applications in smart grids.…”
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Development of a method for multielemental determination in water by EDXRF with radioisotopic source of 238Pu. by Camilo Fuentes Serrano, Juan Reinaldo Estevez Alvares, Alfredo Montero Alvarez, Ivan Pupo Gonzales, Zahily Herrero Fernandez, Dennys Leyva Bombuse, Piet Van Espen, Jossue Arteche Diaz, Lino Valcarcel Rojas, Jose Araujo Dos Santos Júnior

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An improved Harris corner detection method for honeycomb structures by Pengfei Zheng, Wilson Byiringiro, Weiwei Xie, Zhengkai Jiang, Can Cui, Yongjun Wu

“…Test results confirm that the proposed method accurately determines the side lengths and angles with an error margin of less than ±2% compared with manual measurements, effectively evaluating the regularity of the honeycomb. …”
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Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning by Bahar Uddin Mahmud, Guanyue Hong, Virinchi Ravindrakumar Lalwani, Nicholas Brown, Zachary D. Asher

“…Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliability issues under varying lighting conditions. …”
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Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach by Jing Luo, Guangxin Tian, Chen Xu, Shijie Zhang, Zhen Liu

“…The extracted temperature field data was used to train a DeepONet neural network, which achieved accurate temperature predictions (with a L2 relative error of 1.5689% and an R² score of 0.9991) while operating faster than conventional finite element analysis. …”
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Batch gradient based smoothing L 2/3 regularization for training pi-sigma higher-order networks by Khidir Shaib Mohamed, Raed Muhammad Albadrani, Ekram Adam, Yan Xiong

“…Training a PSNN requires modifying the weights and coefficients of the polynomial functions to reduce the error between the expected and actual outputs. It is a generalization of the conventional feedforward neural network and is especially helpful for function approximation applications. …”
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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. …”
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Evapotranspiration Estimation with the Budyko Framework for Canadian Watersheds by Zehao Yan, Zhong Li, Brian Baetz

“…Results show that the developed Budyko models performed well, with the best model achieving a Nash-Sutcliffe Efficiency (NSE) value of 0.74 and a Root Mean Square Error (RMSE) value of 55.5 mm/year. The attribution analysis reveals that climate factors have a greater influence on <i>AET</i> changes compared to vegetation factors. …”
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Real-Time Vision-Based Wheel-Rail Interface Monitoring for Tramway Running Performance by Sangyup Lee, Jin Choi, Naekyung Kim, Janghun Hyeon

“…Field trials on straight and curved tracks at speeds from 10 to 50 km/h demonstrate submillimeter accuracy (mean error<inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula>0.46 mm) and consistent robustness. …”
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Estimation of the Number of Active Sweat Glands Through Discrete Sweat Sensing by Jelte R. Haakma, Elisabetta Peri, Simona Turco, Eduard Pelssers, Jaap M. J. den Toonder, Massimo Mischi

“…The results show that device layout significantly affects accuracy, with error rates below 0.2% for low and medium sweat rates (below 0.2 nL min⁻¹ per gland). …”
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Deep learning based localisation and classification of gamma photon interactions in thick nanocomposite and ceramic monolithic scintillators by Mushen Shen, Ragy Abraham, Elise Cribbin, Harrison Gregor, Mitra Safavi-Naeini, Daniel Franklin

“…Across the evaluated materials, median total localisation error ranged from 0.58 mm to 2.91 mm with the CNN and 0.59 mm to 2.10 mm with InceptionNet, assuming 50% detector quantum efficiency. …”
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