With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent C...
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The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind *** types of wind-farm modelling aim to identify the accuracy and simulation time ...
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The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind *** types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power *** this study,dynamic simulation of equivalent models of a sample wind farm,including single-turbine representation,multiple-turbine representation,quasi-multiple-turbine representation and full-turbine representation models,are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT *** developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate *** wake effects between wind turbines for the fullturbine representation and multiple-turbine representation models have been considered using the Jensen *** developed model improves the extraction power of the turbine according to the layout of the wind *** accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm,including active and reactive powers,voltage and instantaneous flicker *** study was carried out on a sample wind farm,which included 39 wind *** simulation results confirm that the computational loads of the single-turbine representation(STR),the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39,1/8 and 1/8 times the full-turbine representation model,*** the other hand,the error of active power(voltage)with respect to the full-turbine representation model is 74.59%(1.31%),43.29%(0.31%)and 7.19%(0.11%)for the STR,the multiple-turbine representation and the quasi-multiple representation,respectively.
Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwate...
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Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b
In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuse...
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In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile *** study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public *** proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger *** pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of ***,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual *** acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public *** implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.
Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achi...
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Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achieving reliable semantic scene understanding. Current approaches for RGB-Thermal semantic segmentation often overlook the unique characteristics exhibited by each modality at different encoding layers and underutilize the complementary information between the two modalities during decoding. To acquire complementary cross-modality encoding and decoding features, we propose a multi-branch differential bidirectional fusion network known as MDBFNet. Firstly, it models the dependencies between the modality-specific characteristics and the different encoding layers, and designs a TIR-led detail enhancement module (TDE) and an RGB-led semantic enhancement module (RSE) to guide distinguishable fusion for different layer features. Secondly, a three-branch fusion decoder with three supervision (TFDS) is proposed to thoroughly explore the complementary decoding features between two modalities. Experiments on MFNet and PST900 datasets show that our method surpasses state-of-the-art methods by a clear margin. IEEE
The partial differential equation(PDE)solution of the telegrapher is a promising fault location method among time-domain and model-based *** research works have shown that the leap-frog process is superior to other ex...
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The partial differential equation(PDE)solution of the telegrapher is a promising fault location method among time-domain and model-based *** research works have shown that the leap-frog process is superior to other explicit methods for the PDE ***,its implementation is challenged by determining the initial conditions in time and the boundary conditions in *** letter proposes two implicit solution methods for determining the initial conditions and an analytical way to obtain the boundary conditions founded on the signal *** results show that the proposal gives fault location accuracy superior to the existing leap-frog scheme,particularly in the presence of harmonics.
For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...
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For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
Securing low-power Internet-of-Things (IoT) sensor nodes is a critical challenge for the widespread adoption of IoT technology due to their limited energy, computation, and storage resources. As an alternative to the ...
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Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to...
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Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless *** this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous *** Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)*** adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)*** conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT *** developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio *** formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide *** MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to *** address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced *** simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.
Serverless computing has shifted cloud server management responsibilities away from end users and towards service providers. Serverless computing offers greater scalability, flexibility, ease of deployment, and cost-e...
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