Bidirectional interlinking converter(BIC)is the core equipment in a hybrid AC/DC microgrid connected between AC and DC ***,the variety of control modes and flexible bidirectional power flow complicate the influence of...
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Bidirectional interlinking converter(BIC)is the core equipment in a hybrid AC/DC microgrid connected between AC and DC ***,the variety of control modes and flexible bidirectional power flow complicate the influence of AC faults on BIC itself and on DC sub-grid,which potentially threaten both converter safety and system *** study first investigates AC fault influence on the BIC and DC bus voltage under different BIC control modes and different pre-fault operation states,by developing a mathematical model and equivalent sequence ***,based on the analysis results,a general accommodative current limiting strategy is proposed for BIC without limitations to specific mode or operation *** amplitude is predicted and constrained according to the critical requirements to protect the BIC and relieving the AC fault influence on the DC bus *** with conventional methods,potential current limit failure and distortions under asymmetric faults can also be ***,experiments verify feasibility of the proposed method.
Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastas...
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In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detectin...
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In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner's preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner's learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. Various approaches, such as color coding, audio scripts, mind maps, flashcards, etc., are implemented to adapt the content effectively for the detected categories of learners. The spaCy NLP-based named entity recognition (NER) model demonstrates a 94.16% F1 score and 0.92 exact match ratio
Traffic light recognition in autonomous driving is an essential but very challenging task because its performance is affected by unpredictable environmental conditions. Moreover, the shapes and installations of traffi...
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Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alte...
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Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.
The performance of camera-based place recognition has significantly improved with the rapid advancement of deep learning. However, RGB cameras still face challenges in handling variations in lighting conditions due to...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)a...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
One of the most popular techniques for changing the purpose of an image or resizing a digital image with content awareness is the seam-carving method. The performance of image resizing algorithms based on seam machini...
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In multilevel inverters, unused energies are created due to the asynchronous use of the input DC-sources. This means that when the input DC-sources are replaced by renewable systems such as photovoltaic arrays, some o...
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The integration of renewable energy sources (RESs) and energy storage (ES) systems into power grids has introduced significant challenges, particularly in terms of economic and environmental dispatch. The intermittent...
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The integration of renewable energy sources (RESs) and energy storage (ES) systems into power grids has introduced significant challenges, particularly in terms of economic and environmental dispatch. The intermittent nature of RESs and uncertainties related to their output make it difficult for traditional dynamic economic/emission dispatch (DEED) methods to ensure optimal power generation. The primary goal of DEED problems is to efficiently manage the production of electrical energy while satisfying various operational and system constraints, such as ramp rate (RR) limitations and the valve point effect (VPE). This optimization is achieved by minimizing both the overall fuel cost and emissions of power generation units. To address these challenges, this paper proposes a novel hybrid optimization approach that incorporates demand response programs (DRP) alongside ES and RES to enhance system flexibility. By leveraging DRP, consumers' participation in shifting or reducing their demand is considered, leading to a more balanced and cost-effective dispatch strategy. The proposed optimization framework employs a hybrid method that combines particle swarm optimization (PSO) and modified shuffled frog leaping algorithm (MSFLA) to effectively solve the DEED problem. Additionally, a fuzzy-based approach is utilized to achieve a trade-off between economic and environmental objectives, ensuring an optimal balance between fuel cost reduction and emission minimization. The effectiveness of the proposed methodology is validated on a test system with 10 generating units over a 24-h period. The numerical results indicate that, compared to conventional techniques such as grasshopper optimization (GO), PSO, shuffled frog leaping algorithm (SFLA), and other methods reported in the literature, the proposed strategy achieves significantly improved trade-off solutions. The incorporation of DRP further enhances the adaptability of the system by reducing peak demand and improving cost eff
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