Arithmetic Model Research Articles

Strategies for improving Chinese language proficiency based on artificial intelligence technology

Abstract In recent years, the development of artificial intelligence technology and theory has been rapid, and the application in language science has been gradually comprehensive and diversified, especially the accuracy rate of artificial intelligence for Chinese language is up to 90%. In the era of artificial intelligence, the effect of different structures and parameters of arithmetic models on Chinese language recognition varies greatly. Language science is an important research area for realizing machine-human communication, and accurate comprehension of the meaning of linguistic expressions is the key to realize communication. In this paper, we construct a speech system that is different from the traditional stable time series for the irreplaceable characteristics of artificial intelligence technology to improve Chinese language ability. A dynamic Bayesian network (DBN) is used for modeling and analysis, and a DBN construction method is investigated to import a hidden Markov model in a speech recognition system to reveal the interactions between nodes within multiple time slices. The accuracy of dynamic Bayesian networks in Chinese dialect inference algorithms is demonstrated using Matlab simulations to characterize the reliability of speech features using a speech spectrogram. It proves that artificial intelligence technology and Chinese language science are complementary and mutually reinforcing, showing a good and rapid development trend.

Proyeksi Pertumbuhan Penduduk Kecamatan Cibinong Dengan Metode Matematik

This study examines the growth model with mathematical methods used to obtain information or predict future population growth in Cibinong. In this study, researchers used data we obtained from the Central Bureau of Statistics (BPS) of West Java Province. Data were taken by researchers from the results of the 2010 – 2020 population census. The method used is a mathematical method consisting of arithmetic models, geometric models and exponential models. . Based on the results of the analysis, that large population growth is known by using the Exponential Model. Meanwhile, for the Geometry, Arithmetic model, it shows that population growth continues to occur in the Cibinong area, although it is very small.

A constrained proper orthogonal decomposition model for upscaling permeability

AbstractReservoir modeling and simulation are vital components of modern reservoir management processes. Despite the advances in computing power and the advent of smart technologies, the implementation of model‐based operational/control strategies has been limited by the inherent complexity of reservoir models. Thus, reduce order models that not only reduce the cost of the implementation but also provide geological consistent prediction are essential. This article introduces reduced‐order models based on the proper orthogonal decomposition (POD) coupled with linear interpolation for upscaling. First, using POD‐based models, low rank approximate (LRA) are obtained by projecting the high dimensional permeability dataset to a low dimensional subspace spanned by its trajectories to decorrelate the dataset. Next, the LRA is integrated into the interpolation algorithm to generate upscaled values. This technique is highly scalable since low‐rank approximations are achieved by the variation in the number of modes used for reconstruction. To test the validity and reliability of the model, we show its application to the practical dataset from SPE10 benchmark case2. From statistics of the error analysis, the classical POD algorithm seems to be more preferred for LRA; however, since non‐negativity of the permeability data set is a priority, the constrained POD (non‐negative POD) algorithm described in this article is more appropriate. Finally, we compared the POD‐based models to a traditional industry‐standard upscaling technique (e.g., arithmetic mean) to highlight our model benefits/performance. Results show that the POD‐based models, particularly the non‐negative POD model, produce considerably less error than the arithmetic mean model in the upscaling process.

A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling.

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Wright’s Strict Finitistic Logic in the Classical Metatheory: The Propositional Case

Crispin Wright in his 1982 paper argues for strict finitism, a constructive standpoint that is more restrictive than intuitionism. In its appendix, he proposes models of strict finitistic arithmetic. They are tree-like structures, formed in his strict finitistic metatheory, of equations between numerals on which concrete arithmetical sentences are evaluated. As a first step towards classical formalisation of strict finitism, we propose their counterparts in the classical metatheory with one additional assumption, and then extract the propositional part of ‘strict finitistic logic’ from it and investigate. We will provide a sound and complete pair of a Kripke-style semantics and a sequent calculus, and compare with other logics. The logic lacks the law of excluded middle and Modus Ponens and is weaker than classical logic, but stronger than any proper intermediate logics in terms of theoremhood. In fact, all the other well-known classical theorems are found to be theorems. Finally, we will make an observation that models of this semantics can be seen as nodes of an intuitionistic model.

Analysis of the accuracy of EGNOS+SDCM positioning in aerial navigation

The article presents a modified scheme of determining the accuracy parameter of SBAS (Satellite Based Augmentation System) positioning with use of two supporting systems: EGNOS (European Geostationary Navigation Overlay Service) and SDCM (System of Differential Correction and Monitoring). The proposed scheme is based on the weighted mean model, which combines single solutions of EGNOS and SDCM positions in order to calculate the accuracy of position-ing of the aerial vehicle. The applied algorithm has been tested in a flight experiment conducted in 2020 in north-eastern Poland. The phase of approach to landing of a Diamond DA 20-C1 aircraft at the EPOD airport (European Poland Olsztyn Dajtki) was subjected to numerical analysis. The Septentrio AsterRx2i geodesic receiver was installed on board of the aircraft to collect and record GPS (Global Positioning System) observations to calculate the naviga-tion position of the aircraft. In addition, the EGNOS and SDCM corrections in the “*.ems” format were downloaded from the real time server data. The computations were realized in RTKPOST library of the RTKLIB v.2.4.3 software and also in Scilab application. Based on the conducted research, it was found that the accuracy of aircraft positioning from the EGNOS+SDCM solution ranged from -1.63 m to +3.35 m for the ellipsoidal coordinates BLh. Additionally, the accuracy of determination of the ellipsoidal height h was 1÷28% higher in the weighted mean model than in the arith-metic mean model. On the other hand, the accuracy of determination of the ellipsoidal height h was 1÷28% higher in the weighted mean model than for the single EGNOS solution. Additionally, the weighted mean model reduced the resultant error of the position RMS-3D by 1÷13% in comparison to the arithmetic mean model. The mathematical model used in this study proved to be effective in the analysis of the accuracy of SBAS positioning in aerial navigation

SELF-EMBEDDINGS OF MODELS OF ARITHMETIC; FIXED POINTS, SMALL SUBMODELS, AND EXTENDABILITY

Abstract In this paper we will show that for every cut I of any countable nonstandard model $\mathcal {M}$ of $\mathrm {I}\Sigma _{1}$ , each I-small $\Sigma _{1}$ -elementary submodel of $\mathcal {M}$ is of the form of the set of fixed points of some proper initial self-embedding of $\mathcal {M}$ iff I is a strong cut of $\mathcal {M}$ . Especially, this feature will provide us with some equivalent conditions with the strongness of the standard cut in a given countable model $\mathcal {M}$ of $ \mathrm {I}\Sigma _{1} $ . In addition, we will find some criteria for extendability of initial self-embeddings of countable nonstandard models of $ \mathrm {I}\Sigma _{1} $ to larger models.

ArithFusion: An Arithmetic Deep Model for Temporal Remote Sensing Image Fusion

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a future time t2 from the same location as inputs and fuses them to generate high-resolution images for the same location at t2. We propose an arithmetic operation applied to the low-resolution images at the two time points in feature space to take care of temporal changes. We evaluated the proposed model on three modality pairs for multimodal temporal image fusion, including downsampled WorldView-2/original WorldView-2, Landsat-8/Sentinel-2, and Sentinel-2/NAIP. Experimental results show that our model outperforms traditional algorithms and recent deep learning-based models by large margins in most scenarios, achieving sharp fused images while appropriately addressing temporal changes.

Deep Multimodal Learning for Traffic Speed Estimation Combining Dedicated Short-Range Communication and Vehicle Detection System Data

The estimation of traffic variables and provision of traffic information are the most important components of intelligent transportation systems. Advances in technology have led to the collection of various traffic sensor data, and nonlinear dependencies between traffic variables have enabled the development of models based on deep learning approaches. However, there is a missing data segment where data collection is not possible because of the non-installation of the sensor, malfunction of the sensor, or error in communication. In this study, a deep multimodal model is proposed for traffic speed estimation of the missing data segment. We implement the proposed model using two heterogeneous traffic sensors, that is, a vehicle detection system and dedicated short-range communication. The structure of the proposed model consists of three multilayer perceptron models, two of which receive each modality as input data and one fusion model that receives the concatenated outputs from each modality model as input data. To evaluate the estimation performance of the deep multimodal model, we use three performance measures to compare the multimodal model with the arithmetic average model and a single-modality model. The results show that the single-modality model and the proposed deep multimodal model outperform the arithmetic average model. In particular, the deep multimodal model shows the highest accuracies of 90.5% and 92.1% on weekends and peak hours, respectively, without reflecting the true value. The proposed deep multimodal model has three contributions, that is, high accuracy using two different sensors, robustness in various periods, and real-time application with fast computational time.

RETRACTED ARTICLE: There are Infinitely Many Mersenne Prime Numbers. Applications of Rasiowa–Sikorski Lemma in Arithmetic (II)

AbstractThe paper is concerned with the old conjecture that there are infinitely many Mersenne primes. It is shown in the work that this conjecture is true in the standard model of arithmetic. The proof refers to the general approach to first–order logic based on Rasiowa-Sikorski Lemma and the derived notion of a Rasiowa–Sikorski set. This approach was developed in the papers [2–4]. This work is a companion piece to [4].

RETRACTED ARTICLE: The Twin Primes Conjecture is True in the Standard Model of Peano Arithmetic: Applications of Rasiowa–Sikorski Lemma in Arithmetic (I)

AbstractThe paper is concerned with the old conjecture that there are infinitely many twin primes. In the paper we show that this conjecture is true, that is, it is true in the standard model of arithmetic. The proof is based on Rasiowa–Sikorski Lemma. The key role are played by the derived notion of a Rasiowa–Sikorski set and the method of forcing adjusted to arbitrary first–order languages. This approach was developed in the papers Czelakowski [4, 5]. The central idea consists in constructing an appropriate countable model $$\textbf{A}$$ A of Peano arithmetic by means of a Rasiowa–Sikorski set. This model validates the twin prime conjecture. Since $$\textbf{A}$$ A is elementarily equivalent to the standard model, the conjecture follows. Thus the standard model validates the twin primes conjecture. More generally, it is shown that de Polignac’s conjecture has a positive solution. The paper employs methods borrowed from the contemporary mathematical logic. Such a ’logical’ approach may be viewed as a useful addition to the dominant methodology in number theory based on mathematical analysis.

On some stochastic comparisons of arithmetic and geometric mixture models

On some stochastic comparisons of arithmetic and geometric mixture models

ELEMENTARY SCHOOL STUDENTS' ERRORS IN SOLVING WORD PROBLEMS BASED ON NEWMAN ERROR ANALYSIS

Based on previous studies, elementary school students often experience errors in solving word problems on cube and block material. Confirmed Newman Error Analysis (NEA) can analyze the location of student errors in solving word problems. Hence, the purpose of this study was to analyze the errors made by students in solving word problems based on NEA. The type of research used is qualitative research with case studies. The research subjects were 34 students of grade five elementary students with data collection techniques, including tests, interview sheets, and documentation. Data analysis using NEA consists of five stages: reading, comprehension, transformation, process skills, and encoding. From the study results, reading errors were 12,74%, comprehension errors were 51,96%, transformation errors were 9,8%, process skills errors were 52,94%, and encoding errors were 67,64% of students. These errors are caused because students do not read and understand the questions, do not know the formulas and arithmetic operations model used, and count errors.

Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion

The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.

Fast computation of the N-th term of a q-holonomic sequence and applications

In 1977, Strassen invented a famous baby-step/giant-step algorithm that computes the factorial N! in arithmetic complexity quasi-linear in N. In 1988, the Chudnovsky brothers generalized Strassen's algorithm to the computation of the N-th term of any holonomic sequence in essentially the same arithmetic complexity. We design q-analogues of these algorithms. We first extend Strassen's algorithm to the computation of the q-factorial of N, then Chudnovskys' algorithm to the computation of the N-th term of any q-holonomic sequence. Both algorithms work in arithmetic complexity quasi-linear in N; surprisingly, they are simpler than their analogues in the holonomic case. We provide a detailed cost analysis, in both arithmetic and bit complexity models. Moreover, we describe various algorithmic consequences, including the acceleration of polynomial and rational solving of linear q-differential equations, and the fast evaluation of large classes of polynomials, including a family recently considered by Nogneng and Schost.

Does Set Theory Really Ground Arithmetic Truth?

We consider the foundational relation between arithmetic and set theory. Our goal is to criticize the construction of standard arithmetic models as providing grounds for arithmetic truth. Our method is to emphasize the incomplete picture of both theories and to treat models as their syntactical counterparts. Insisting on the incomplete picture will allow us to argue in favor of the revisability of the standard-model interpretation. We start briefly characterizing the expansion of arithmetic ‘truth’ provided by the interpretation in a set theory. Interpreted versions of an arithmetic theory into set theories generally have more theorems than the original. This theorem expansion is not complete however. Using this, the set theoretic multiversalist concludes that there are multiple legitimate standard models of arithmetic. We suggest a different multiversalist conclusion: while there is a single arithmetic structure, its interpretation in each universe may vary or even not be possible. We continue by defining the coordination problem. We consider two independent communities of mathematicians responsible for deciding over new axioms for ZF and PA. How likely are they to be coordinated regarding PA’s interpretation in ZF? We prove that it is possible to have extensions of PA not interpretable in a given set theory ST. We further show that the number of extensions of arithmetic is uncountable, while interpretable extensions in ST are countable. We finally argue that this fact suggests that coordination can only work if it is assumed from the start.

Statistical Analysis of Baseline Load Models for Residential Buildings in the Context of Winter Demand Response

By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic models achieve similar performances to the more complex regression model without the need for weather data. These simpler models were also found to be less sensitive to the number of events called during the season. Performing individual adjustments for each of the two daily peak periods also provides better accuracy.

Photovoltaic parameter estimation using honey badger algorithm and African vulture optimization algorithm

The accurate arithmetical models of solar photovoltaic (PV) modules plays a vital role in the performance, operation, and control of the PV systems as a part of renewable generation. Therefore, the proposed paper develops a novel robust Honey Badger Algorithm (HBA) and African Vulture Optimization algorithm (AVOA) that have been utilized for deducing the unknown parameters of various solar PV units accurately and efficiently. The HBA and AVOA techniques are developed to identify the approximate undefined parameters of different PV system models, namely, single diode model (SDM), double diode model (DDM), and three diode model (TDM). The attained results from the application of HBA and AVOA are to optimize the objective function of the current errors between the measure and the evaluation of the PV modules. Moreover, statistical tests have been presented to demonstrate the effectiveness of HBA and AVOA. The numerical results of the developed HBA and AVOA prove them as competitor methods for the parameters’ extraction of solar PV systems.

Model and simulation of bilayered tantalum-oxide (Pt/Ta2O5/TaOx/Pt) memristor

As a new type of microelectronic device with a special resistance change mechanism, the memerists based on the novel resistive materials have been an emerging hot-spot in the area of electronics. Among tremendous memerists, the bilayered tantalum-oxide (Pt/Ta2O5/TaOx/Pt) devices are believed to one of the typical device structures, its peculiar current-voltage (I-V) feature makes it distinct from other traditional devices, like resistor, capacitor or metal-oxide-semiconductor (MOS) transistor. It has been accepted as an alternative candidate of the traditional silicon-based memories. There have been lots of experimental works in this field, however, the theoretical works about its physical model and simulation are relatively few. In this work, its working principle is illustrated firstly, according to the thermionic emission theory of Schottky barrier in semiconductor physics. Then a quantitative physical model is presented and simulated, the calculated results are compared with the reported data to evaluate the effectiveness of the proposed model. The established physical model is constructed and calculated mathematically by using MATLAB, the computed results are compared with the experimental data to verify the proposed arithmetic model.

Forecasting an electricity demand threshold to proactively trigger cost saving demand response actions

This paper presents a novel methodology that empowers virtually any electricity consumer paying for peak demand charges to proactively execute demand response actions even without receiving signals or information coming from the utility, and only when necessary to effectively reduce demand charges and user inconvenience. The proposed methodology employs different arithmetic models and tree-based machine learning models to determine an efficient electricity demand threshold value before the start of a billing period. This methodology is completely model agnostic so additional models can be integrated without changing the proposed process. The threshold value produced can be used to proactively trigger peak demand shaving and other demand response actions in order to reduce demand charges. The results obtained using real data showed that regression random decision forest based models outperformed different arithmetic models and other tree-based machine learning models at determining this threshold value for an industrial, an educational with solar photovoltaic electricity generation, and a residential consumer. The results also showed that the consumers evaluated could potentially achieve between 63% to 75% of total potential demand charge reductions during a year. These results translate to US$ 159.00, US$ 23,290.00, and US$ 107,389.00 in savings for the residential, industrial, and educational consumer respectively.