Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven ...
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This paper proposes an approach for improving performance of unimodal models with multimodal training. Our approach involves a multi-branch architecture that incorporates unimodal models with a multimodal transformer-...
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The paper discusses the most current advancements in image analysis and computer vision systems and how they are being used to assess the grade of food products. computer vision is a quick, reliable, and objective exa...
The paper discusses the most current advancements in image analysis and computer vision systems and how they are being used to assess the grade of food products. computer vision is a quick, reliable, and objective examination method that has spread throughout many different sectors. Its efficiency and precision enable the creation of fully automated systems by meeting the growing needs for quality and output. With a focus on monochrome image processing, colour images, and multispectral imaging for contemporary sorting and grading systems, the needs and most current advancements in hardware and software for machine vision systems are reviewed. The suggested method utilises the SVM algorithm to learn and identify fruit quality while using processing of images to extract features. Research experiments show how accurate the system is. Also included are demonstrations of uses for spotting faults, pollution, and illness on vegetables and fruits.
Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into ...
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Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. T...
Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models.
A deep learning (DL) based approach for near-field (NF) source localization is introduced to enhance the accuracy and robustness in both direction of arrival (DOA) and range estimation. The received data is first prep...
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For outlier extinguish investigating population, especially on the impulsive outlier, the outlier extinguish approach is a prevalent process, which is first executed before other progress operations therefore the iter...
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Modal dispersion is well recognized as multimode fiber's primary limitation, leading to connection failures when transmitting huge volumes of data. Numerous central launch conditions have been employed to overcome...
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In recent years, electric vehicles (EVs) have been a countermeasure to the serious carbon emission problem in the transportation sector. However, despite being one of the essential infrastructures in the EV ecosystem,...
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This paper proposes a hysteresis current control (HCC) method for a single-inductor buck-boost non-isolated onboard charger for electric vehicles. The charger is capable of working both in the boost and buck modes. Th...
This paper proposes a hysteresis current control (HCC) method for a single-inductor buck-boost non-isolated onboard charger for electric vehicles. The charger is capable of working both in the boost and buck modes. The proposed HCC relies on the buck-boost inductor current and its reference which is generated using a proportional-resonant (PR) controller using grid current error. The reference current generated by PR controller is modified to suppress the oscillations in the inductor currents. An active damping by using a virtual resistor connected in series to filter inductor is used which does not require an additional sensor. A proportional-integral (PI) controller is used to generate the amplitude of grid current reference, which is utilized in constant current (CC) and constant voltage (CV) modes. The effectiveness of the proposed control strategy as well as the control method, is investigated by simulation studies by considering two different battery voltage levels (48V and 350V). The results show that the proposed method is able to charge the battery in CV and CC modes. Moreover, the grid current is maintained in unity power factor at a reasonably low total harmonic distortion (THD) which is smaller than the limits recognized by international standards.
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