Random Generator Research Articles

Inverse Transformation to Generate Neutrosophic Random Variables Following Weibull and Geometric Distributions

In practical applications, we encounter many systems that cannot be studied directly, and the reason for this is due to the nature of the system or to the high cost. Therefore, we resort to the simulation process, which depends on conducting the study on systems similar to real systems, and then projecting these results, if they are appropriate, onto the system. The real system. The simulation process depends on generating a series of random numbers that follow a uniform probability distribution in the field [0, 1], then converting these random numbers into random variables that follow the probability distribution in which the system to be simulated operates. One of the most important conversion methods is the conversion method. The opposite. This method is used for probability distributions in which we can obtain a function inverse of its cumulative distribution function. In two previous researches, we used this method to generate neutrosophic random variables that follow a uniform distribution in the field [a, b] and the exponential distribution. In this research, we present a valuable study to clarify how to use this method. The method for generating neutrosophic random variables follows the Weibull distribution and the geometric distribution, based on what was presented in the classic study and in the research on neutrosophic random number generation.

Quantum randomness introduced through squeezing operations and random number generation

Quantum random numbers play a crucial role in diverse applications, including cryptography, simulation, and artificial intelligence. In contrast to predictable algorithm-based pseudo-random numbers, quantum physics provides new avenues for generating theoretically true random numbers by exploiting the inherent uncertainty contained in quantum phenomena. Here, we propose and demonstrate a quantum random number generator (QRNG) using a prepared broadband squeezed state of light, where the randomness of the generated numbers entirely originates from the quantum noise introduced by squeezing operation rather than vacuum noise. The relationship between entropy rate and squeezing level is analyzed. Furthermore, we employ a source-independent quantum random number protocol to enhance the security of the random number generator.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

Large-eddy simulation of wind pressures on elevated low-rise buildings

Residential buildings in coastal communities are often elevated to mitigate flooding and wave-surge impacts. However, the elevations change the local wind field characteristics and may increase the aerodynamic pressure on buildings. Post-event reconnaissance showed that elevated buildings experienced severe structural damage to the roofs, walls, and floors during major hurricanes. Currently, our understanding of aerodynamic pressure on elevated houses is limited. In this paper, a large eddy simulation (LES)-based method is developed to better understand the wind effects on elevated buildings. The inflow generation method, discretizing and synthesizing random flow generation, is adopted to generate inflow boundary conditions satisfying the target spectrum. Two sub-grid scale models, the Smagorinsky and wall-adapted local eddy-viscosity models, are employed to represent the unresolved small-scale eddies. It is found that the eddy structure sizes can strongly affect the pressure fluctuations at the eddy separation zones. The present study advances the understanding of aerodynamics on elevated buildings and provides a reference for future LES-based research on wind effect modeling.

Meta-Learning With Distributional Similarity Preference for Few-Shot Fault Diagnosis Under Varying Working Conditions.

Few-shot fault diagnosis is a challenging problem for complex engineering systems due to the shortage of enough annotated failure samples. This problem is increased by varying working conditions that are commonly encountered in real-world systems. Meta-learning is a promising strategy to solve this point, open issues remain unresolved in practical applications, such as domain adaptation, domain generalization, etc. This article attempts to improve domain adaptation and generalization by focusing on the distribution-shift robustness of meta-learning from the task generation perspective. In fact, few-shot fault diagnosis under varying working conditions allows to address the distribution shift problem in a natural way. An unsupervised across-tasks meta-learning strategy with distributional similarity preference is proposed, where the core is the distribution-distance-weighting mechanism. Differently from the naive random meta-train task generation strategy used in existing meta-learning methods, the source instances that present a more similar distribution with respect to the target instances gain larger weightings in the task generation. This strategy leads to a meta-task training set that is enough diverse, and at the same time can be easily learned due to the distribution similarity features of the source tasks. The proposed method introduces the concept of maximum mean discrepancy that is applied to derive the distribution distance of the measurements. Moreover, a model-agnostic meta-learning is applied to realize few-shot fault diagnosis under varying working conditions. The proposed solutions are verified and compared by considering two public datasets used for bearing fault diagnosis. The results show that the proposed strategy outperforms different related few-shot fault diagnosis methods under varying working conditions. Moreover, it is thus proved that, meta-learning with distribution similarity feature represents an effective approach for domain adaptation and generalization.

NDSTRNG: Non-Deterministic Sampling-Based True Random Number Generator on SoC FPGA Systems

NDSTRNG: Non-Deterministic Sampling-Based True Random Number Generator on SoC FPGA Systems

Innovative continuum of care to promote exclusive breast feeding in Pakistan: protocol of a pilot randomised controlled trial

BackgroundBreastmilk being the ideal nutrition from birth to 2 years and beyond has many health benefits for both newborn and mothers. This study will assess the feasibility and acceptability of...

Impact of an imperfect sampling device on the security of a continuous-variable source-independent quantum random-number generator with a phase-randomized local oscillator

Impact of an imperfect sampling device on the security of a continuous-variable source-independent quantum random-number generator with a phase-randomized local oscillator

Optimization of Acoustic Entropy Source for Random Sequence Generation Using an Improved Grey Wolf Algorithm

Optimization of Acoustic Entropy Source for Random Sequence Generation Using an Improved Grey Wolf Algorithm

Improved Real‐Time Post‐Processing for Quantum Random Number Generators

AbstractRandomness extraction is a key problem in cryptography and theoretical computer science. With the recent rapid development of quantum cryptography, quantum‐proof randomness extraction has also been widely studied, addressing the security issues in the presence of a quantum adversary. In contrast with conventional quantum‐proof randomness extractors characterizing the input raw data as min‐entropy sources, it is found that the input raw data generated by a large class of trusted‐device quantum random number generators can be characterized as the so‐called reverse block source. This fact enables us to design improved extractors. Two novel quantum‐proof randomness extractors for reverse block sources that realize real‐time block‐wise extraction are proposed specifically. In comparison with the general min‐entropy randomness extractors, the designs achieve a significantly higher extraction speed and a longer output data length with the same seed length. In addition, they enjoy the property of online algorithms, which process the raw data on the fly without waiting for the entire input raw data to be available. These features make the design an adequate choice for the real‐time post‐processing of practical quantum random number generators. Applying the extractors to the raw data generated by a widely used quantum random number generator, a simulated extraction speed as high as 300 Gbps is achieved.

Reduced partial shading effect and enhancement of performance metrics using a Fibonacci based algorithm

AbstractPartial shading within arrays diminishes power output, induces hotspots, and compromises module integrity, thereby impacting system performance. The presence of bypass diodes further exacerbates these issues by introducing non‐convexities in power curves, leading to additional power losses. To solve this problem, a new reconfiguration technique named Fibonacci Random Number Generator is proposed in this work which minimizes the effects of shading on the panels. The proposed methodology swiftly reduces current discrepancies between PV array rows by reshuffles the panels in an array to disperse the shade better using a mathematical formula resulting in increased power output and smoother power curves during partial shading events. The effectiveness of the proposed method is measured in terms of GMPP, row current calculations, power loss (PL), mismatch losses (ML), execution ratio (ER), fill factor (FF), and capacity factor (CF) for four distinctive shading conditions. Validation of results in software and hardware platforms showcase the applicability of proposed approach in real‐time environments. Results indicate significant average power improvements of 25.49%, 15.47%, and 9.29% compared to existing popular reconfigurations like Skyscraper, Ken‐Ken, and Chaotic baker map. The proposed method stands out as a potent tool for optimizing PV arrays within real‐world systems grappling with partial shading issues.

The music box operad: Random generation of musical phrases from patterns

We introduce the notion of multi-pattern, a combinatorial abstraction of polyphonic musical phrases. The interest of this approach to encode musical phrases lies in the fact that it becomes possible to compose multi-patterns in order to produce new ones. This composition is parametrized by a monoid structure on the scale degrees. This dives the set of the musical phrases into an algebraic framework since the set of the multi-patterns is endowed with the structure of an operad. Operads are algebraic structures offering a formalization and an abstraction of the notion of operators and their compositions. Seeing musical phrases as operators allows us to perform computations on phrases and admits applications in generative music. Indeed, given a set of initial multi-patterns, we propose various algorithms to randomly generate a new and longer phrase emulating the style suggested by the inputted multi-patterns. The designed algorithms use sorts of grammars working with operads and colored operads, known as bud generating systems.

Restricted Boltzmann Machines Implemented by Spin-Orbit Torque Magnetic Tunnel Junctions.

Artificial intelligence has surged forward with the advent of generative models, which rely heavily on stochastic computing architectures enhanced by true random number generators with adjustable sampling probabilities. In this study, we develop spin-orbit torque magnetic tunnel junctions (SOT-MTJs), investigating their sigmoid-style switching probability as a function of the driving voltage. This feature proves to be ideally suited for stochastic computing algorithms such as the restricted Boltzmann machines (RBM) prevalent in pretraining processes. We exploit SOT-MTJs as both stochastic samplers and network nodes for RBMs, enabling the implementation of RBM-based neural networks to achieve recognition tasks for both handwritten and spoken digits. Moreover, we further harness the weights derived from the preceding image and speech training processes to facilitate cross-modal learning from speech to image generation. Our results clearly demonstrate that these SOT-MTJs are promising candidates for the development of hardware accelerators tailored for Boltzmann neural networks and other stochastic computing architectures.

A non-degenerate n-dimensional integer domain chaotic map model with application to PRNG

To address the limitations of existing chaotic maps, we proposed a non-degenerate n-dimensional (n ≥ 2) integer domain chaotic map (nD-IDCM) model that can construct any non-degenerate n-dimensional integer domain chaotic maps. Moreover, we analyzed its chaotic behavior through Lyapunov exponent, and found that the nD-IDCM generates chaotic sequences in the integer domain, which effectively resolves the issue of finite precision effect when implementing existing chaotic maps on computers or digital devices. To verify the effectiveness of nD-IDCM, we presented two instances to demonstrate how the positive Lyapunov exponents can be regulated by manipulating the parameter matrix. Subsequently, we have scrutinized their dynamical behavior using Kolmogorov entropy, sample entropy, correlation dimension and randomness testing via TestU01. Finally, to assess the feasibility of nD-IDCM, we devised a keyed pseudo random number generator (PRNG) based on a 3D-IDCM that can ensure superior randomness and unpredictability. Experimental results indicated that integer domain chaotic maps constructed using nD-IDCM have desirable Lyapunov exponents and exhibit ergodicity within a sufficient larger chaotic range.

Effects of source space resolution, randomization and averaging in focal EEG source localization: Estimation of forward and inverse errors

In this article, we study randomized multiresolution forward modeling and source localization in electroencephalography (EEG). Our goal is to determine numerically the effects of variable source space resolution on both forward modeling and source localization perspectives and to examine how it affects the detection of the sources at different depths inside the brain. Our concentration is in the main principles of multiresolution methods: random source space generation and the reconstruction obtained via averaging over multiple maximum a posteriori (MAP) estimations obtained at a finite set of different sub source spaces, and their suggested enhancement in distinguishability of weak components. The motivation of averaging is to reduce the discretization and optimization error and marginalize other variable factors. Due to the unguided randomized source space generation, multiple samples of the same size source spaces are needed. For this very reason, a large number of source spaces of multiple source counts are used to obtain statistically feasible results in this paper. This study verifies the described postulates of the multiresolution technique by showing averaging to be crucial in order to obtain a robust projection to a lower resolution, where the lead field matrix is overdetermined. From the viewpoint of source localization, the multiresolution approach was found to improve the reconstruction accuracy obtained with each resolution level below the highest and observed to be necessary to achieve an accuracy comparable to that of forward modeling.

Comparative effects of shockwave therapy and Maitland lumbar mobilization on pain, disability, and range of motion in patients with mechanical low back pain: A pilot study

Objectives: Low back pain (LBP) has been considered a neuromusculoskeletal issue that affects a wide variety of populations around the globe at some stage in their life. This paper aimed to see the comparative effects of shockwave therapy and Maitland lumbar mobilizations on pain severity, disability, and range of motion (ROM) values in subjects with mechanical LBP. Methods: A randomized clinical trial was carried out involving 26 subjects divided into two groups using a random number generator table. After baseline therapy (lumbar stretches and core strengthening exercises), shockwave therapy was administered to Group A (2000 shocks/session, pressure of 2 bars, frequency 10 Hz), while Group B received Maitland lumbar mobilizations (starting from Grade 1, with 3–4 sets of oscillations with 40 counts/set, two sets of oscillation to one level below and above the affected vertebras). Both therapeutic interventions were administered for 30 days, twice a week (total of eight sessions) for individual groups. Results: Numeric pain rating scale, Oswestry LBP disability index, lumbar flexion, and extension ROM pre- and post-treatment values showed significant differences with P < 0.05. A greater difference in mean values was observed in group B receiving Maitland mobilization as compared to group A receiving shockwave therapy. Between-group analysis showed a statistically significant difference in both groups with P < 0.005. Conclusion: Both groups showed significant effects in reducing pain levels, functional disability scores, and improving flexion and extension ROM values. However, participants receiving Maitland mobilization showed more significant improvement in all variables than shockwave therapy.

A new probability-distribution scale synthetic eddy method for large eddy simulation of wind loads

The accurate generation of inflow turbulence in large eddy simulation (LES) plays a crucial role in assessing the wind loads on buildings. To faithfully recreate turbulent flow fields, the inflow turbulence must accurately replicate vortex structures. In this study, we propose a novel method for generating turbulent inflow called the probability-distribution scale synthetic eddy method (PDSSEM). PDSSEM utilizes a random generation of eddy positions, with the length of each eddy scale determined by its probability density function (PDF). This approach ensures that PDSSEM maintains consistency in the mean wind speed profile, turbulence integral length, turbulence intensity profile, and wind speed spectra of the atmospheric boundary layer (ABL). The PDSSEM method can be more easily applied compared with multi-scale synthetic eddy method (MSSEM), making it highly versatile and adaptable. The self-sustaining characteristics of the flow field generated by PDSSEM are validated, revealing abundant vortex structures and good agreement with targeted values for turbulence flow characteristics. Furthermore, comparing the wind pressures of a tall building with the experimental data, the accuracy and feasibility of PDSSEM are comprehensively demonstrated. Thus, it indicates the great potential of the proposed method for broader computational wind engineering applications, including wind load evaluation and flow pattern investigation.

Random Numbers Generated Based on Dual-Channel Chaotic Light

This paper presents a chaotic system based on novel semiconductor nanolasers (NLs), systematically analyzing its chaotic region and investigating the influence of key parameters on the unpredictability of chaotic output. This study found that under optical feedback conditions, NLs generate chaos across a wide range of feedback parameters, with the highly unpredictable region completely overlapping with the chaotic region. Further injection into the slave lasers enhances the chaotic output, expanding the range of unpredictability. Additionally, we analyzed the impact of internal parameter mismatch on the complexity of chaotic signals and found it to be similar to the scenario when parameters are matched. Using this chaotic system as an entropy source, we constructed a random number generator (RNG) and investigated the effects of internal parameters mismatch and differences in the injection parameters on the generator’s performance. The simulation results show that the RNG performs well under different parameter settings, and the generated random sequences pass all random number tests successfully. Therefore, this chaotic system can yield a high-complexity chaotic light source with appropriate parameter selection, and when combined with effective post-processing, it can generate high-quality random numbers. This is crucial for advancing the realization of small-sized, high-randomness RNGs.

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p-value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

Further Enhancement of Planned Random Algorithm Applied in Character Creation for Video Games

This paper introduces the Further Enhanced Planned Random Algorithm (FEPR Algorithm) applied in character creation for video games, addressing limitations of the previous Enhanced Planned Random Algorithm by employing various enhancements. The study's purpose was to further increase the apparent randomness by reducing the repetitions of data results. Employing an experimental quantitative research design, the study used the C++ programming language and libraries such as the “random” library which includes the Mersenne Twister PRNG (Pseudocode Random Number Generator). The effectiveness of the FEPR Algorithm was tested against the 2022 Enhanced Planned Random Algorithm through a series of 1000 iterations in 5 test cases, evaluating the randomness of the generated sequences. Results indicated an 84.47% reduction in pattern repetitions in both the total and average repetitions with the FEPR Algorithm compared to the control. This study's conclusion posits that the FEPR Algorithm successfully further increases the apparent randomness compared to the EPR algorithm. Recommendations include extending the research to other gaming elements and fields where randomness is crucial. The research implications discuss the advancement of randomization processes in algorithmic design. Practical implications highlight the potential for improved game design and player experience, while social implications consider the broader impact on digital entertainment and diversity.