Frequency Analysis Of Time Series Research Articles

Recent Trends in Oral Health Technology Patients: A Text Mining Analysis

Objectives: This study analyzed technological trends by examining patents related to oral health and dentistry, and discussed patent utilization and research directions. Methods: The patent data (n=6,865) related to oral health and dentistry were collected from KIPRIS on July 31, 2023. The collected data were analyzed by text mining using Python 3.10. Results: Among the keywords (n=37,155) extracted from the invention titles, ‘device’ showed the highest frequency. In the time series frequency analysis, keywords such as ‘recording’, ‘3-dimensional’, ‘scanner’, and ‘image’ were absent from 2003 to 2007. However, the frequency of these keywords has been steadily increasing since 2008. Furthermore, ‘implant’, ‘orthodontics’, and ‘3-dimensional’ demonstrated relatively high influence in the network analysis (Degree centrality) relating to clinical aspects. Conclusion: In this study, the keyword, ‘method’ was the most influential. Technologies related to ‘orthodontics’ and ‘implants’ have also been consistently researched. Since 2008, ‘3-dimensional’ has been increasing in importance. The emergence of new technological terms highlights the increasing importance of digitization and visualization. The oral health and dentistry-related technological trends identified in the study are expected to serve as valuable foundational data for future patent analysis in Korea.

Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry

Mixed convection of low and unitary Prandtl fluids in a vertical passage is fundamental to passive heat removal in liquid metal and gas-cooled advanced reactor designs. Capturing the influence of buoyancy in flow and heat transfer in engineering analysis is hence a cornerstone to the safety of the next-generation reactor. However, accurate prediction of the mixed convection phenomenon has eluded current turbulence and heat transfer modeling approaches, yet further development and validation of modeling methods is limited by a scarcity of high-fidelity data pertaining to reactor heat transfer. In this work, a series of direct numerical simulations was conducted to investigate the influence of buoyancy on descending flow of liquid sodium, lead, and unitary Prandtl fluid in a differentially heated channel that represents the reactor downcomer region. From time-averaged statistics, flow-opposing/aiding buoyant plumes near the heated/cooled wall distort the mean velocity distribution, which gives rise to promotion/suppression of turbulence intensity and modification of turbulent shear stress and heat flux distribution. Frequency analysis of time series also suggests the existence of large-scale convective and thermal structures rising from the heated wall. As a general trend, fluids of lower Prandtl number were found to be more susceptible to the buoyancy effect due to stronger differential buoyancy across the channel. On the other hand, the effectiveness of convective heat transfer of the three studied fluids showed a distinct trend against the influence of buoyancy. Physical reasoning on observation of the Nusselt number trend is also discussed.

Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental results proved the importance of the sEMG signals for extracting such reactions, in which, the segmentation and classification of the sEMG are vital tasks. The objective of the present work is to segment and classify the sEMG signals of patients to assist the design of clinical rehabilitation devices based on the classification of sEMG signals. In the pre-processing stage, a dual-tone multi-frequency signaling is designed for signal coding; subsequently, the pre-processed sEMG signal is transformed by the Fast Fourier Transfer. Afterward, a time-series frequency analysis is performed by applying Hidden Markov Models. A basic traditional long short-term memory (LSTM) model is addressed for waveform-based classification to be compared to the proposed improved deep BP (back-propagation)–LSTM for sEMG signal classification. Seventeen performance features are selected for evaluating the proposed multi-classification, deep learning model for classifying six actions, namely moving gesture of grip, slowly moving, flexor, straight lift, stretch, and up-high lift; which were proposed by rehabilitation physician. The experiment results indicated the superiority of the proposed method compared to other well-known classifiers, such as the neural network, support vector machine, decision trees, Bayes inference, and recurrent neural network. The proposed deep BP–LSTM network achieved 92% accuracy, 89% specificity, 91% precision, and 96% F1-score, in the multi-classification of the sEMG signals.

Statistical validity of water quality time series in urban watersheds

ABSTRACT The water resources quality continuous monitoring is a complex activity. It generates extensive databases with time series of many variables and monitoring points that require the application of statistical methods for the information extraction. The application of statistical methods for frequency analysis of time series is linked to attending of the basic assumptions of randomness, homogeneity, independence, and stationarity. However, despite its importance, the verification of these assumptions in water quality literature is unusual. Therefore, the present study tests the Upper Iguaçu basin water quality time series against the mentioned hypotheses. Rejection was observed in 15%, 26%, 51% e 31% for randomness, homogeneity, independence, and stationarity, respectively. The results evidenced the strong relation between monitoring strategy, data assessment and meeting of basic statistical assumptions for the analysis of water quality time series. Even with the existence of possible solutions for addressing those issues, the standard monitoring strategies, with irregular frequencies and lack of representativeness in relation to other periods, beyond commercial, act as an obstacle to their implementation.

Wavelet Characteristics of Hydrological and Dissolved Oxygen Time Series in a Lowland River

In this study, we investigated the temporal variability of dissolved oxygen and water temperature in conjunction with water level fluctuations and river discharge in the Narew lowland river reach. For this purpose, high resolution hydrologic and water quality time series have been used. Spectral analyses of time series using continuous wavelet transform scheme have been applied in order to identify characteristic scales, its duration, and localisation in time. The results of wavelet analysis have shown a great number of periodicities in time series at the inter-annual time scale when compared to the classical Fourier analysis. Additionally, wavelet coherence revealed the complex nature of the relationship between dissolved oxygen and hydrological variables dependent on the scale and localisation in time. Hence, the results presented in this paper may provide an alternative representation to a frequency analysis of time series.

Frequency analysis of time series by polynomials orthogonal on the grid

The problem of finding periodic components in a given time series is examined. Our approach to the harmonic analysis is based on the integrated method resting on discrete transforms that employ orthogonal polynomials and trigonometric systems.

Continuation methods for the computation of zeros of Szegö polynomials

Let { φ j } ∞ j = 0 be a family of monic polynomials that are orthogonal with respect to an inner product on the unit circle. The polynomials φ j arise in time series analysis and are often referred to as Szegö polynomials or Levinson polynomials. Knowledge about the location of their zeros is important for frequency analysis of time series and for filter implementation. We present fast algorithms for computing the zeros of the polynomials φ n based on the observation that the zeros are eigenvalues of a rank-one modification of a unitary upper Hessenberg matrix H n (0) of order n. The algorithms first determine the spectrum of H n (0) by one of several available schemes that require only O( n 2) arithmetic operations. The eigenvalues of the rank-one perturbation are then determined from the eigenvalues of H n (0) by a continuation method. The computation of the n zeros of φ n in this manner typically requires only O( n 2) arithmetic operations. The algorithms have a structure that lends itself well to parallel computation. The latter is of significance in real-time applications.