Applied Numerical Methods Using Matlab®

Yang, Won Young; Cao, Wenwu; Kim, Jaekwon; Park, Kyung W.; Park, Ho‐Hyun; Joung, Jingon; Ro, Jong‐Suk; Lee, Han L.; Hong, Cheol‐Ho; Im, Tae-Ho

doi:10.1002/9781119626879

Cited by 46 publications

(17 citation statements)

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“…Thus, we estimated the Joint PDFs of the following pairs of dimensions (factors): BSSA-10 SP-PANSI PNI, BSSA-10 SP-PANSI PSI, BSSA-10 AE-PANSI PNI, BSSA-10 AE-PANSI PSI, BSSA-10 IP-PANSI PNI, BSSA-10 IP-PANSI PSI, BQREB BP-PANSI PNI, BQREB BP-PANSI PSI, BQREB CM-PANSI PNI, BQREB CM-PANSI PSI, BQREB R-PANSI PNI, BQREB R-PANSI PSI, APGAR-PANSI PNI, and APGAR-PANSI PSI. We implemented our proposed method by using MATLAB ® 2020a version [32]. For each pair of factors, we approximated their Joint PDF by employing the Kernel Smoothing Function Estimate mvksdensity.…”

Section: Proposed Method: a Monte Carlo Based Methods To Assess Suicimentioning

confidence: 99%

A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

Niño-Ruiz

Herrera

Expósito-Concepción

et al. 2020

IJERPH

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It is very common to perform statistical tests to obtain insights about populations based on samples. For instance, in the context of psychology, when a set of instruments are applied to individuals, psychologists typically look for an explanation of particular psychological constructs (variables), such as personality, intelligence, or emotional functioning. It is common to cross statistical information from the results of different psychological tests to measure certain variables or to confirm prior beliefs. Here, we estimate the Joint Probability Density Function of suicide-related vulnerability and protective factors to assess suicide risk in adolescents. A Markov Chain Monte Carlo Method is employed to move away from the typical Gaussian assumption on data. This allows us to estimate probabilities of the development of suicidal ideation based on samples (which form a Markov chain). We employ our proposed statistical method at a high school in Colombia. The results reveal that adolescents can develop suicidal ideation as a consequence of the following factors, together with their corresponding probabilities: poor school performance 52%, low academic expectations 27%, school integration problems 68%, risky eating behaviors (binge-purge) 42%, risky eating behaviors (compensatory measurements) 21%, risky eating habits (restriction) 22%, and low family functionality 16%.

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Section: Proposed Method: a Monte Carlo Based Methods To Assess Suicimentioning

confidence: 99%

A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

Niño-Ruiz

Herrera

Expósito-Concepción

et al. 2020

IJERPH

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“…Even if there exist some computationally efficient solvers to find roots of the polynomial equation, such as [20], we will introduce a simple yet efficient roots solving algorithm to reduce the computation cost further.…”

Section: B Efficient Methods To Solve Polynomial Inequalitiesmentioning

confidence: 99%

“…where dEP(e j , V is ) d can be reformulated as polynomial inequalities by (20) as well. For the last four conditions in (8), it is straightforward to transfer them in polynomial inequality forms, e.g., the sixth condition is transformed as…”

Section: (19)mentioning

confidence: 99%

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A Generalized Continuous Collision Detection Framework of Polynomial Trajectory for Mobile Robots in Cluttered Environments

Zhang

Han

et al. 2022

IEEE Robot. Autom. Lett.

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In this paper, we introduce a generalized continuous collision detection (CCD) framework for the mobile robot along the polynomial trajectory in cluttered environments including various static obstacle models. Specifically, we find that the collision conditions between robots and obstacles could be transformed into a set of polynomial inequalities, whose roots can be efficiently solved by the proposed solver. In addition, we test different types of mobile robots with various kinematic and dynamic constraints in our generalized CCD framework and validate that it allows the provable collision checking and can compute the exact time of impact. Furthermore, we combine our architecture with the path planner in the navigation system. Benefiting from our CCD method, the mobile robot is able to work safely in some challenging scenarios.

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“…(n) = 1, 2, ::::, 16: In matrix laboratory (MATLAB) version 2020b, [18][19][20] i.e., MATLAB 2020b, the deep neural network toolbox is used, which enables UEs to construct deep learning models and used to follow the training process.…”

Section: Model Trainingmentioning

confidence: 99%

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Pandya

Wakchaure²,

Shankar³

et al. 2021

Journal of Defense Modeling & Simulation

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In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.

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Applied Numerical Methods Using Matlab®

Cited by 46 publications

References 0 publications

A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

A Generalized Continuous Collision Detection Framework of Polynomial Trajectory for Mobile Robots in Cluttered Environments

Analysis of NOMA-OFDM 5G wireless system using deep neural network

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