This paper presents a novel approach of establishing a multichannel optical communication link, combining optical fiber cable (OFC) and free space optics (FSO) technology. By leveraging multiple lengths of optical fib...
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Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...
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Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance *** studies have mostly concentrate...
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Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance *** studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power ***-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as *** research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power *** dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for *** classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted *** simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.
This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodol...
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In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-c...
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Fruit safety is a critical component of the global economy, particularly within the agricultural sector. There has been a recent surge in the incidence of diseases affecting fruits, leading to economic setbacks in agr...
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In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communicati...
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In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communication through open networks is insecure and has many vulnerabilities, making it susceptible to unauthorized access and misuse. Encryption models are used to secure medical data from unauthorized access. In this work, we propose a bit-level encryption model having three phases: preprocessing, confusion, and diffusion. This model is designed for different types of medical data including patient information, clinical data, medical signals, and images of different modalities. Also, the proposed model is effectively implemented for grayscale and color images with varying aspect ratios. Preprocessing has been applied based on the type of medical data. A random permutation has been used to scramble the data values to remove the correlation, and multilevel chaotic maps are fused with the cyclic redundancy check method. A circular shift is used in the diffusion phase to increase randomness and security, providing protection against potential attacks. The CRC method is further used at the receiver side for error detection. The performance efficiency of the proposed encryption model is proved in terms of histogram analysis, information entropy, correlation analysis, signal-to-noise ratio, peak signal-to-noise ratio, number of pixels changing rate, and unified average changing intensity. The proposed bit-level encryption model therefore achieves information entropy values ranging from 7.9669 to 8.000, which is close to the desired value of 8. Correlation coefficient values of the encrypted data approach to zero or are negative, indicating minimal correlation in encrypted data. Resistance against differential attacks is demonstrated by NPCR and UACI values exceeding 0.9960 and 0.3340, respectively. The key space of the proposed model is 1096, which is substantially mor
Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with d...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signa...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals,such as vocal vibrations in wearable speaking assistants for those with speaking ***,high-performance polymeric piezoelectric accelerometers suitable for such applications are *** organic compounds such as PVDF have inferior properties to their inorganic counterparts such as ***,most existing polymeric piezoelectric accelerometers have very unbalanced performance *** often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers,leading to increased noise density and limited application *** this study,a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is *** new design aims to simultaneously achieve high sensitivity,broad bandwidth,and low *** samples were manufactured and characterized,demonstrating an average sensitivity of 29.45 pC/g within a±10 g input range,a 5%flat band of 160 Hz,and an in-band noise density of 1.4μg/√*** results surpass those of many PZT-based piezoelectric accelerometers,showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.
As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capabili...
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As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capability of a transmission network for climate-impacted power *** this paper,we propose a systematic TEP proce-dure for renewable-energy-dominated power grids considering climate impact(CI).Particularly,this paper develops an im-proved model for TEP considering climate impact(TEP-CI)and evaluates the reliability of power grid with the obtained transmission investment ***,we create climate-impact-ed spatio-temporal future power grid data to facilitate the study of TEP-CI,which include the future climate-dependent re-newable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission(TX-123BT)***,the TEP-CI model is proposed,which considers the variation in renewable power generation and dy-namic line rating,and the investment plan for future TX-123BT system is ***,a customized security-con-strained unit commitment(SCUC)is presented specifically for climate-impacted power *** reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC *** whole procedure presented in this paper enables numerical studies on power grid planning considering climate *** can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.
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