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Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes

JOURNAL OF NEURAL ENGINEERING 2018年第2期15卷 026005-026005页

作者： Anderson, Daria Nesterovich Osting, Braxton Vorwerk, Johannes Dorval, Alan D. Butson, Christopher R. Univ Utah Dept Bioengn Salt Lake City UT 84112 USA Univ Utah Sci Comp & Imaging SCI Inst Salt Lake City UT 84112 USA Univ Utah Dept Math Salt Lake City UT 84112 USA Univ Utah Dept Neurol Salt Lake City UT 84112 USA Univ Utah Dept Neurosurg Salt Lake City UT 84112 USA

Objective. Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. Approach. Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). Main results. The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. Significance. This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and of

关键词： deep brain stimulation optimization algorithm hessian activating function Parkinson's disease finite element model directional electrodes

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Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期74卷 103450-103450页

作者： Harishvijey, A. Raja, J. Benadict Anna Univ Chennai Tamilnadu India PSNA Coll Engn & Technol Dindigul Tamilnadu India

Epileptic seizure in patients is detected from EEG signals with the use of automatic signal classification tech-niques. The accurate detection of epilepsy is essential to reduce the risk of seizure related complications. However the available automatic signal detection techniques give poor sensitivity and accuracy. In this work, an automatic signal classification method for detecting seizure from EEG signal is presented for obtaining good classification results. The proposed work improves the performance of detection using Variable Gaussian filter (VGF) with social spider algorithm (SSA) (SSA-VGF), Empirical Wavelet Transform (EWT) feature extraction method, K-Principal component analysis (K-PCA) based feature reduction and Fuzzy logic embedded RBF kernel based ELM algorithm (FRBFELM). The SSA-VGF method is used for removing noise artifacts from the given EEG signals. EWT is employed for feature extraction and the size of extracted features is reduced using K-PCA method. Finally the signals are classified as normal signals and epileptic signals using FRBFELM classifier. The perfor-mance of the proposed method is evaluated by measuring the metrics;PSNR, accuracy, sensitivity, and speci-ficity. The value of performance metrics obtained for the proposed work is 98.48%, 98.44% and 98.51%.

关键词： EEG signals Signal classification Noise removal Filter algorithm Feature extraction Feature reduction optimization algorithm

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Mechanical Behavior Monitoring and Load Inversion Analysis of Large-Diameter Underwater Shield Tunnel during Construction

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SENSORS 2024年第4期24卷 1310页

作者： Ma, Si-Yuan Ye, Xiao-Wei Liu, Zhi-Xiong Ding, Yang Zhang, Di Sun, Feng Zhejiang Univ Dept Civil Engn Hangzhou 310058 Peoples R China Hangzhou City Univ Dept Civil Engn Hangzhou 310015 Peoples R China China Railway Siyuan Survey & Design Grp Co Ltd Wuhan 430063 Peoples R China

The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.

关键词： Structural Health Monitoring shield tunnel Fiber Bragg grating inversion analysis Parameter estimation optimization algorithm

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Deep Learning Based Antenna Design and Beam-Steering Capabilities for Millimeter-Wave Applications

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IEEE ACCESS 2021年 9卷 145583-145591页

作者： Montaser, Ahmed M. Mahmoud, Korany R. Sohag Univ Fac Technol & Educ Elect Engn Dept Sohag 82524 Egypt Helwan Univ Fac Engn Dept Elect & Commun Cairo 11795 Egypt Minist Commun & Informat Technol Natl Telecommun Regulatory Author Giza 71515 Egypt

In this study, a deep neural network (DNN) is implemented to soft computation of the dual-band circularly polarized bone-shaped patch antenna (BSPA) at 28 GHz and 38 GHz for 5G applications. Via a simulated database of 150 BSPAs, a DNN model is constructed on a 5-layer system using an adaptive learning rate algorithm. The framework and hyper-parameters of the DNN model are optimized in the training phase of a hybrid algorithm combining strengths of both particle swarm optimization (PSO) and a modified version of the gravitational search algorithm (MGSA-PSO). To generate the database for training and testing the model, 150 BSPAs with different geometrical are simulated in terms of the resonant frequency using a precise electromagnetic analysis platform. A fabricated BSPA operating at 28 GHz and 38 GHz is used to test and verify the DNN model. Then, the application of DNN with back-propagation algorithm and weighted MGSA-PSO algorithm is used for beam-steering the main beam pattern of the designed uniform circular antenna array with side-lobe level <= -30 dB by estimating the appropriate feeding phases of the 16 elements. Several illustrative examples are placed to beam-steer the pattern in the desired direction to check the validity of the technique.

关键词： Antenna arrays Antenna radiation patterns Computational modeling Antennas Indexes Signal processing algorithms Phased arrays Deep learning artificial neural network optimization algorithm mm-wave antennas circular polarization beamforming technique

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Novel Approach for Partial Discharge Localization Based on Fiber-Optic F-P Sensing Array and Modified TDOA in a 110-kV Transformer

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhang, Zhixian Chen, Weigen Wu, Kejie Liu, Hong Chen, Xingang Jiang, Tianyan Ma, Zhipeng Feng, Wenlin Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China State Grid Tianfu New Area Elect Power Supply Co Chengdu 610000 Peoples R China

Accurately and sensitively detecting and locating the partial discharge (PD) inside transformers is of great significance for ensuring the safe operation of transformers. Due to the obstruction of the ultrasonic signal by the transformer casing and electromagnetic interference in the field, the PD detection accuracy and sensitivity of piezoelectric ultrasonic sensors are limited. This study, based on a real 110-kV transformer, explored the installation method of the fiber-optic Fabry-Perot (F-P) sensor array and the localization method for PD and compared the detection performance of fiber-optic F-P sensors and piezoelectric sensors. In response to optical and acoustic noise present in the field, an adaptive Wiener filter-based noise reduction method was proposed to enhance the accuracy of arrival time extraction. Based on the time-difference-of-arrival (TDOA) principle, a localization equation considering ultrasonic time delay increment was constructed. A PD localization method based on the SOGWO was introduced, and the localization accuracy and its fluctuations were analyzed under conditions considering and not considering the ultrasonic time delay increment. The research results indicate that fiber-optic F-P sensors have higher sensitivity and better resistance to electromagnetic noise and external acoustic noise interference. The solid structure inside the transformer has a significant impact on ultrasonic localization accuracy. Considering the ultrasonic time delay increment can improve localization accuracy, but it may lead to greater fluctuations in the accuracy. The research findings of this article highlight the advantages of fiber-optic F-P sensors in detecting PD in transformers.

关键词： Acoustic signal denoising optical fiber sensors optimization algorithm partial discharge (PD) ultrasonic localization

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Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Fooladi, Mahmood Golmohammadi, Mohammad Hossein Rahimi, Iman Safavi, Hamid Reza Nikoo, Mohammad Reza Isfahan Univ Technol Dept Civil Engn Esfahan Iran Sultan Qaboos Univ Coll Engn Dept Civil & Architectural Engn Muscat Oman

While the rain gauge measurements are unevenly distributed in many world regions, it is necessary to use remote sensing-based precipitation data at high spatial and temporal resolutions. On the other hand, quantifying the uncertainty of precipitation is a vital issue for hydrometeorological applications globally. For this purpose, applying a new fusion framework using high-resolution remote sensing datasets can provide accurate precipi-tation evaluation against local observations with low uncertainty. This study examines three weighted fusion -based models containing the Ordered-Weighted-Averaged (OWA) family approach based on the ORLIKE method (OWA-ORLIKE) and ORNESS method (OWA-ORNESS) as well as the Entropy-weight (EW) method to combine different remote sensing precipitation products over different climate zones of Iran. In this case, mul-tiple monthly remotely sensed datasets, including ERA5, ERA5-Land, TerraClimate, GPM, PERSIANN-CDR, TRMM, and CHIRPS, are utilized to assess precipitation patterns versus local measurements, which were gath-ered by Google Earth Engine (GEE) platform. Furthermore, the K-means algorithm is employed to cluster ground -based precipitation stations based on the climate zones category across Iran. Additionally, the Genetic Optimi-zation algorithm (GOA) is applied to specify optimal values of weights in weighting-based models. The per-formance of single and combination datasets are evaluated using statistical error metrics, including Pearson correlation coefficient (PCC), root mean square error (RMSE), Kling Gupta efficiency (KGE), and bias. The Thiessen polygon method has been applied to calculate each cluster's mean precipitation to obtain the optimal weights of stations. Finally, as an efficient uncertainty approach, Cross Wavelet Transform (XWT) method has been used for uncertainty assessment of monthly and seasonal precipitation series. Results indicated that the OWA family as the best fusion model had almost the lowest uncertaint

关键词： Precipitation patterns Remotely sensed datasets K -means algorithm Weighted fusion -based models optimization algorithm Uncertainty assessment

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How to achieve energy efficiency and sustainability of large ships: a new tool to optimize the operation of on-board diesel generators

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ENERGY 2023年第1期282卷

作者： Barone, Giovanni Buonomano, Annamaria Del Papa, Gianluca Maka, Robert Palombo, Adolfo Univ Naples Federico II Dept Ind Engn Ple Tecchio80 I-80125 Naples Italy Concordia Univ Dept Bldg Civil & Environm Engn 1455 Maisonneuve Blvd W Montreal PQ Canada

In this paper a novel dynamic simulation model for enhancing the sustainability of transportation systems equipped with diesel co-generators is presented. In each simulation time step of the carried out energy perfor-mance analyses, the best operation/combination of generators as well as their optimal part load ratio are determined with the aim to achieve the minimum fuel consumption. Input to the model are: size of each installed diesel generator;number of engines;required power profile;route scheduling and hourly weather data. The developed simulation model is implemented in TRNSYS environment. Here, a new in-house TRNSYS type written in Fortran is also included. With the presented approach -especially helpful for cruise ship designers, manu-facturers and owners -new design criteria and useful technical results can be *** show the capability of the tool a novel case study is presented. It concerns on an existing cruise ship to be refurbished from the energy point of view. Here, four diesel generators are installed on-board. The ship energy system includes five electric chillers, two auxiliary boilers, two reverse osmosis and two multi-stage flash desalination devices (driven by the diesel generators waste heat recoveries). Simulation results show remarkable energy savings obtained through the proposed optimization approach. Specifically, a maximum primary energy saving of about 15 GWh/y is obtained compared to the initial reference scenario. It corresponds to a fuel con-sumption reduction of 2.5 kt/y (-1.6 M$/y) and to an avoided CO2 emission of 8.0 kt/y.

关键词： Cruise ships Ship energy efficiency Dynamic simulation optimization algorithm Energy saving

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A novel reinforcement learning based Heap-based optimizer

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Ma, Xuesen Zhong, Zhineng Li, Yangyu Li, Dacheng Qiao, Yan Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Intelligent Mfg Inst HFUT Hefei Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei Peoples R China

The Heap-Based optimization (HBO), a novel meta-heuristic algorithm, constructs a hierarchical heap resembling a company structure to facilitate interaction among search agents. This enables the search agent to learn various levels of information from the population to obtain improved solutions, but it has drawbacks such as limited search capability and slow convergence speed when tackling complex optimization problems. To address these challenges, we propose an enhanced heap optimizer based on reinforcement learning(RLHBO), which demonstrates robust stability, rapid convergence, and high solution accuracy. Firstly, fixed operators are replaced with a reinforcement learning dynamic control search strategy in RLHBO to regulate the search range of agents. This prevents agents from being restricted in their search when encountering local or global optima, thus enhancing their search capability. Additionally, RLHBO introduces a dimensional selflearning strategy and a convergence strategy based on search direction similarity to address issues such as invalid and discrete solutions, accelerating convergence. Furthermore, an initialization strategy based on quasi-oppositional learning is employed to increase the diversity of the algorithm-generated population, surpassing the limitations of random initialization and further boosting convergence speed. Extensive testing on various complex functions from CEC2017 and CEC2022 demonstrates that RLHBO, as presented in this paper, outperforms HBO and numerous other advanced MAs in terms of search capability and convergence speed.

关键词： Meta-heuristic algorithm optimization algorithm Heap-based optimizer Reinforcement learning

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Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls

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BUILDINGS 2023年第9期13卷 2358页

作者： Mirrashid, Masoomeh Naderpour, Hosein Kontoni, Denise-Penelope N. Jakubczyk-Galczynska, Anna Jankowski, Robert Nguyen, Tan N. Semnan Univ Fac Civil Engn Semnan *** Iran Univ Peloponnese Sch Engn Dept Civil Engn GR-26334 Patras Greece Hellenic Open Univ Sch Sci & Technol GR-26335 Patras Greece Gdansk Univ Technol Fac Civil & Environm Engn PL-80233 Gdansk Poland Sejong Univ Dept Architectural Engn 209 Neungdong Ro Seoul 05006 South Korea

This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls.

关键词： composite shear wall reinforced concrete flexural behavior flexural capacity optimization algorithm soft computing computational intelligence

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An effective model for network selection and resource allocation in 5G heterogeneous network using hybrid heuristic-assisted multi-objective function

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Urooj, Shabana Arunachalam, Rajesh Alawad, Mohamad A. Tripathi, Kuldeep Narayan Sukumaran, Damodaran Ilango, Poonguzhali Princess Nourah bint Abdulrahman Univ Coll Engn Dept Elect Engn POB 84428 Riyadh 84428 Saudi Arabia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamilnadu India Imam Mohammad Ibn Saud Islamic Univ Dept Elect Engn Riyadh 11432 Saudi Arabia Madhav Inst Technol & Sci MITS Dept Comp Sci & Engn Gwalior Madhya Pradesh India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Eng Dept Math Chennai 600062 Tamilnadu India Panimalar Engn Coll Dept Elect & Commun Engn Chennai 600123 Tamilnadu India

Multiple service requirements in various communication environments are fulfilled by the fifth-generation (5G) mobile communication network. Heterogeneous Networks (HetNet) have been introduced as a newly developed network structure. Conventional cellular networks have prominent limitations in terms of frequency utilization, coverage, proliferation of transmission services, and power consumption. The previously used approaches provide precise results but are time-consuming, making them tricky to apply for huge networks, and makes them complicated to solve multifaceted problems. In order to decrease complementary interference and accomplish spectrum sharing, an effective resource allocation technique is necessary. Likewise, interference produced by the potential Secondary Users (SUs) must be kept at a minimum value within the authorized network. Considering the perspective of the supplementary networks, it is vital to delegate networks to SUs in order to lower the gross contamination and manage the overall network's quantity. An efficient method is developed on a heuristic basis in order to solve the issues regarding resource allocation in 5G networks. With the aid of every user device's network properties, network selection, and resource allocation are considered the two major concerns that need to be attained in 5G heterogeneous network systems. To meet this requirement, the multi-objective constraints are considered with the help of the Integrated African Vultures with Grasshopper optimization algorithm (IAVGOA). The constraints that are taken into consideration are communication cost, interference between all the SU to the primary user, Quality of Service (QoS), and congestion control. The performance of the executed model is examined using various metrics. When compared with the existing models, the proposed method achieves impressive results in appropriately selecting the network and allocating the resources over 5G networks.

关键词： Network selection Resource allocation 5G heterogeneous network Integrated African vultures with grasshopper optimization algorithm Multi -objective constraints

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