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Annular micro-nano optic fiber sensor based on α-Fe2O3@SiO2@CS imprinting for Cu(II) ion detection

IF 2.6 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Optical Fiber Technology

Pub Date : 2025-02-21 DOI: 10.1016/j.yofte.2025.104174

Yue Feng , Weixiang Yuan , Wenbo Hao , Tao Shen

Sensors for the rapid and easy detection of copper ions (Cu²⁺) are crucial in drinking water and healthcare. However, this research is still challenging, such as more complicated sample pretreatment and longer detection time. In this paper, a highly sensitive optic fiber sensor for trace detection of Cu²⁺ in aqueous solution is proposed. Based on the principle of intermodal interference, the coating method of surface functionalization of functional groups is adopted, and the single-mode fiber (SMF) is bent to form an annular structure to enhance the interference, and the cone pulling machine is used to perform cone pulling treatment before coating a uniform layer of α-Fe₂O₃@SiO₂@CS nanocomposite imprinted material. The amino and hydroxyl groups have specific recognition functions for Cu²⁺ and can chelate with Cu²⁺, thus changing the refractive index of the material, which in turn makes the interference spectrum shifted. Furthermore, Cu²⁺ is chosen as the template ion to make ion-imprinted materials, and the trace detection of Cu²⁺. The results showed that the interference spectrum was red-shifted with the increase of Cu²⁺ concentration in the range of 0–1 μM with a concentration dependence coefficient of 3.35 nm/log·μM, and the maximum detection sensitivity of Cu²⁺ reached 1232.52 nm/μM with a good linearity of 0.994. The selectivity of Cu²⁺ was more than 70 times that of other ions. In addition, the sensor has the advantages of fast response time, high stability, good reproducibility, simple fabrication and low cost.

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引用次数: 0

High-sensitivity refractive index sensor based on ultrafine conical no-core optical fiber

IF 2.6 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Optical Fiber Technology

Pub Date : 2025-02-21 DOI: 10.1016/j.yofte.2025.104175

Yuxuan Qi , Hailong Liu , Ying Chen , Jiacheng Lv , Yubo Liu , Yanjie Zhao , Jiasheng Ni

A high-sensitivity refractive index (RI) sensor based on a Mach-Zehnder Interferometer (MZI) configuration utilizing ultrafine tapered no-core fiber (Tapered No-Core Fiber, TNCF) is proposed and fabricated. The sensor consists of a single-mode fiber (SMF) – no-core fiber (NCF) – single-mode fiber (SMF) structure, with a tapered configuration formed in the no-core fiber region via the fusion tapering process. Multi-mode interference (MMI) and the excitation of higher-order modes within the tapered region significantly enhance the evanescent field effect of the light beam, thereby improving the sensor’s sensitivity to refractive index variations. Three sensors with tapered waist diameters of 60.1 μm, 41.2 μm, and 22.1 μm were fabricated. Experimental results show that, within refractive index ranges of 1.338–1.358 and 1.347–1.405, the refractive index sensitivity of the sensor increases significantly as the waist diameter of the tapered region decreases. When the tapered waist diameter is 22.1 μm, the refractive index sensitivity reaches 617.60 nm/RIU and 643.36 nm/RIU, with linearity coefficients of R² = 0.9835 and R² = 0.9994, demonstrating excellent sensitivity and linear performance. The proposed sensor not only exhibits high sensitivity, structural stability, and a wide measurement range, but also demonstrates suitability for high-precision refractive index measurements in the fields of medicine, biological sensing, and energy.

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引用次数: 0

Reinforcement learning assisted differential evolution with adaptive resource allocation strategy for multimodal optimization problems

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101888

Tao Ma , Hong Zhao , Xiangqian Li , Fang Yang , Chun-sheng Liu , Jing Liu

Multimodal optimization problems (MMOPs) present the challenge of identifying multiple optimal solutions within a search space, requiring algorithms to effectively balance exploration and exploitation. To enhance solution accuracy, the local search methods often focus on elite individuals, allocating additional fitness evaluations (FEs) to refine their solutions. However, once the optima near these elite individuals are identified, continued allocation of FEs becomes inefficient, leading to a waste of limited resources. This highlights the inherent difficulty of achieving a balance between exploration and exploitation within the population under constrained resources. To solve this problem, this paper proposes a new reinforcement learning-assisted differential evolution (RLDE) algorithm with adaptive resource allocation strategy. Firstly, the exploitation population is proposed, and the original population focuses on exploring undiscovered optimal regions and generating exploitation populations, while each exploitation population focuses on finding high-precision optima within its responsible optimal region. Secondly, a reinforcement learning-assisted adaptive resource allocation (RLRA) strategy is proposed to allocate FEs, which can reduce the waste of FEs and balance the exploration and exploitation ability among multiple populations. Finally, a local greedy mutation (LGM) strategy is proposed to help individuals evolve toward the neighborhood with better fitness values. Compared with 11 state-of-the-art multimodal algorithms, the RLDE achieves better or more competitive results in all accuracy levels. Besides, the results on the dielectric composite optimization problem verify the practicality of RLDE.

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引用次数: 0

Lattice-based sensor data acquisition strategy to solve sensor position drift in human gait phase recognition system with a single inertia measurement unit

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110286

Dianbiao Dong , Nannan Zhu , Jiehong Wang , Yuzhu Li

The inertia measurement unit (IMU) sensor units have garnered considerable utilization in gait phase recognition systems owing to their inherent data stability and compatibility with low-usage conditions. The extant scholarship concerning gait phase recognition predicated upon IMU sensors manifests a concentrated endeavor to augment the accuracy of recognition through the employment of diverse recognition algorithms and the fusion of multiple sensors. However, the impact of the drift of the IMU sensor position on the accuracy of the gait phase recognition algorithm during training and use is ignored, which is especially important for a single IMU gait recognition system. Therefore, taking convolutional neural network, deep neural network, support vector machine, and random forest algorithms as examples, this paper studies the impact of IMU sensor position drift on the accuracy of gait phase recognition algorithms. To address the quandary of position drift, this study proposes an innovative lattice-based data acquisition strategy for a single IMU sensor by selecting 9 uniformly distributed points on the posterior region of the calf. A treadmill walking test wearing an embedded IMU data acquisition system was organized to verify the practical performance of the lattice-based data acquisition strategy. By comparing the performance of different lattice combinations, a 5-point sensor acquisition strategy is proposed, which can effectively increase the accuracy of gait phase recognition with IMU sensor position drift by more than 10.29%.

{"title":"Lattice-based sensor data acquisition strategy to solve sensor position drift in human gait phase recognition system with a single inertia measurement unit","authors":"Dianbiao Dong , Nannan Zhu , Jiehong Wang , Yuzhu Li","doi":"10.1016/j.engappai.2025.110286","DOIUrl":"10.1016/j.engappai.2025.110286","url":null,"abstract":"<div><div>The inertia measurement unit (IMU) sensor units have garnered considerable utilization in gait phase recognition systems owing to their inherent data stability and compatibility with low-usage conditions. The extant scholarship concerning gait phase recognition predicated upon IMU sensors manifests a concentrated endeavor to augment the accuracy of recognition through the employment of diverse recognition algorithms and the fusion of multiple sensors. However, the impact of the drift of the IMU sensor position on the accuracy of the gait phase recognition algorithm during training and use is ignored, which is especially important for a single IMU gait recognition system. Therefore, taking convolutional neural network, deep neural network, support vector machine, and random forest algorithms as examples, this paper studies the impact of IMU sensor position drift on the accuracy of gait phase recognition algorithms. To address the quandary of position drift, this study proposes an innovative lattice-based data acquisition strategy for a single IMU sensor by selecting 9 uniformly distributed points on the posterior region of the calf. A treadmill walking test wearing an embedded IMU data acquisition system was organized to verify the practical performance of the lattice-based data acquisition strategy. By comparing the performance of different lattice combinations, a 5-point sensor acquisition strategy is proposed, which can effectively increase the accuracy of gait phase recognition with IMU sensor position drift by more than 10.29%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"147 ","pages":"Article 110286"},"PeriodicalIF":7.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics informed neural network with Fourier feature for natural convection problems

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110327

Younes Bounnah , Mustapha Kamel Mihoubi , Salah Larbi

In this paper, we investigate the application of deep neural networks to solve the Navier-Stokes and heat equations within the framework of modeling the natural convection phenomenon. The main objective is to reconstruct the velocity and temperature fields in a differentially heated rectangular cavity while adhering to the imposed boundary conditions. Two main architectures are compared: the Fully Connected Neural Network and the Fourier Features Neural Network. A hyper-parameter tuning process was carried out to optimize the network performances. This tuning led to a final architecture composed of 6 layers, each with 128 neurons, and 64 Fourier frequencies, with the Mish activation function selected after testing several alternatives. Both architectures were trained on four cases, where the Rayleigh number ranges from 10⁴ to 10⁷, with quasi-randomly sampled points. The network predictions were then compared to the results obtained from numerical simulations of the Navier-Stokes and heat equations. The results show that for low Rayleigh numbers (10⁴ and 10⁵), both architectures converge quickly, producing smooth profiles dominated by low frequencies. However, for higher Rayleigh numbers (10⁶), the Fourier Features Neural Network outperforms the Fully Connected Neural Network by better capturing the complex and localized variations, thanks to its explicit integration of periodic components, which makes it particularly well-suited for multi-scale problems.

This study highlights the potential of deep neural networks to solve partial differential equations in complex configurations, offering a promising alternative to traditional methods. It also emphasizes the importance of choosing an architecture that fits the specific characteristics of the problem at hand, especially in cases where the solutions exhibit multi-scale variations or high frequencies.

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引用次数: 0

An indicator-based multi-objective evolutionary algorithm assisted by improved graph convolutional networks

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2025-02-21 DOI: 10.1016/j.swevo.2025.101892

Pengguo Yan , Ye Tian , Yu Liu

Recently, graph convolutional networks (GCN) have attracted significant attention due to their superior performance in handling non-Euclidean spaces, which enables GCN to model and analyze complex data structures that cannot be handled by traditional methods. Neural network-based multi-objective evolutionary algorithms (NNMOEAs) have made significant strides, predominantly focusing on mapping the decision space to the objective space, but may fail to focus on the interconnectedness of solutions within the decision space. To address this problem, this paper proposes a two-stage multi-objective optimization algorithm that utilizes graph convolutional networks to enhance population evolution. In the initial stage, the algorithm employs cosine similarity to represent the population as graph-structured data. A hypervolume-guided self-attention update mechanism is then introduced to balance exploration and exploitation, achieved by establishing an exploratory neighborhood population alongside an expanded neighborhood population. In the subsequent stage, a key node detection strategy is implemented, which considers both the global influence and local mediation roles of nodes. This strategy selects individuals with highly concentrated information to generate new solutions, thereby facilitating a thorough exploration of the solution space. The proposed algorithm is evaluated against five state-of-the-art MOEAs across five benchmark test suites and five real-world problems. The experimental results demonstrate its superior performance in addressing robust, variable linkages and imbalance mapping multi-objective optimization problems, as well as its feasibility in practical problems.

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引用次数: 0

RL-USRegi: Autonomous Ultrasound Registration for Radiation-Free Spinal Surgical Navigation using Reinforcement Learning

IF 5.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Automation Science and Engineering

Pub Date : 2025-02-21 DOI: 10.1109/tase.2025.3544413

Ang Li, Jiayi Han, Yongjian Zhao, Max Q.-H. Meng, Li Liu

引用次数: 0

Specific Torque Optimization of Control Moment Gyroscope Motor Based on Linear Regression Technique

IF 4.4 2区计算机科学 Q1 ENGINEERING, AEROSPACE

IEEE Transactions on Aerospace and Electronic Systems

Pub Date : 2025-02-21 DOI: 10.1109/taes.2025.3543801

Jin Ho Kim, Seun Guy Min