Study on Aerodynamic Nonlinear Characteristics of Semiclosed Box Deck Based on Variation of Motion Parameters

In order to study the nonlinear characteristics of self-excited aerodynamic forces of bluff body bridge section with the change of motion parameters, a numerical wind tunnel is established by the dynamic mesh technique of computational fluid dynamics (CFD). A state-by-state forced vibration method is used to identify the self-excited aerodynamic forces of single degree-of-freedom (DOF) heaving and pitching motion. Fast Fourier transform (FFT) is adopted to obtain frequency-domain data for analysis. The reliability of the obtained aerodynamic results is verified by wind tunnel tests. The results show that the high-order harmonic components are found in the self-excited aerodynamic forces of semiclosed box deck section, which are more significant in aerodynamic lift than in aerodynamic moment. The proportion of aerodynamic nonlinear components increases with amplitude. The effect of amplitude on the nonlinear components of heaving motion is generally higher than that of pitching motion, and aerodynamic moment is highly sensitive to the increase of vertical amplitude. The variation of the nonlinear components of the deck section with frequency is not a simple monotonic relationship, and there is a stationary point at 10 Hz frequency. The existence of wind attack angle makes the proportion of nonlinear components reach more than 30% and greatly increases the proportion of second harmonic. In addition, the high-order harmonic components, which are not integer multiples, are found at large amplitude and positive angle of attack.

Electricity consumption forecasting using DFT decomposition based hybrid ARIMA-DLSTM model

Forecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long short-term memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy.

Control Techniques for a Class of Fractional Order Systems

The paper discusses several control techniques for a class of systems described by fractional order equations. The paper presents the unit frequency criteria that ensure the closed loop control for: Fractional Order Linear Systems, Fractional Order Linear Systems with nonlinear components, Time Delay Fractional Order Linear Systems, Time Delay Fractional Order Linear Systems with nonlinear components. The stability criterion is proposed for the systems composed of fractional order subsystems. These techniques are used in two applications: Soft Exoskeleton Glove Control, studied as a nonlinear model with time delay and Disabled Man-Wheelchair model, analysed as a fractional-order multi-system.

Optical soliton perturbation with Kudryashov’s law of refractive index by modified sub-ODE approach

This paper implements the sub-ODE method and a wide spectrum of solitons are recovered for Kudryashov’s law of refractive index. The self-phase modulation comprises of four nonlinear components of refractive index. The perturbation terms are all of Hamiltonian type and are considered with maximum intensity. The solutions are written in terms of Weierstrass’ elliptic functions and Jacobi’s elliptic function. With the modulus of ellipticity approaching zero or unity, soliton solutions emerge.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Modelling monthly influenza cases in Malaysia

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.

Novel Application of Selfish Herd Optimisation Based Fractional Order Cascaded Controllers for AGC Study

This article deals with Automatic Generation Control (AGC) of a three-area power system having five diversified sources of generation like thermal unit, hydro unit, wind unit, diesel unit and a gas unit are interconnected together. Area-1 of the power system consists of a thermal, a hydro and a wind unit, area-2 has a thermal, a hydro and a diesel unit and area-3 consists of a thermal, a hydro and a gas unit. To make system more realistic different nonlinear components like governor dead band (GDB), generation rate constraint (GRC), Boiler dynamics and communication delay are taken into account. A novel two degree of freedom fractional order PID with derivative filter and fractional order PD with derivative filter (2-DOF-FOPIDN-FOPDN) cascaded control strategy is adopted to improve the dynamic performance of the power system. Results obtained with the proposed cascaded controller are compared with that of PID, FOPID and 2-DOF-PIDN-PDN cascaded controller to prove its superiority. To enumerate the gains of different controllers optimally, a recently developed bio-inspired optimisation algorithm named Selfish Herd Optimisation (SHO) is capitalised. Further, the work is extended by taking a two area hydro thermal system to compare the result of the SHO tuned PID controller with that of modern hybrid firefly algorithm-pattern search (hFA-PS) technique. Transient analysis is carried out by applying a sudden load disturbance of 0.01 p.u in area-1 and the robustness of the controller is examined by varying both system parameters and applying a randomly varying load in area-1. From the investigation it is concluded that the 2-DOF-FOPIDN-FOPDN controller gives a flawless and a distinct performance.

Infradian rhythms of daily shoot increment in Salix viminalis (salicaceae) clones

The structure of seasonal dynamics of daily growth of shoots of basket willow (Salix viminalis) is described and analyzed. Object: model inbred-clone population of S. viminalis. Material: developing shoots on annual saplings from cuttings. Methods: comparative morphological, chronobiological, numerical analysis of time series. The formation of dimorphic root systems of one-year saplings from cuttings is described. It is established that the seasonal dynamics of daily increment of shoots is determined by the interaction of linear and nonlinear components. Linear components are approximated by regression equations, and nonlinear components are approximated by harmonic oscillation equations. The rhythmicity of seasonal dynamics of shoot growth is described. Four groups of biorhythms were identified: annual with a period of about 96 days, subannual with a period of 4064 days, and infradian with a period of 1924 days and infradian with a period of 1016 days. The alternation of peaks and dips in the seasonal dynamics of shoot increment is determined by infradian biorhythms with a period of 19...24 days. Infradian biorhythms with different periods are synchronized with each other. The probable reason is the existence of a pulse synchronizer of biorhythms. Interclonal differences in the seasonal dynamics of the daily growth of shoots were not revealed. The probable cause of intraclonal differences is the ontogenetic heterogeneity of vegetative buds, from which annual shoots have developed. To verify this hypothesis, we plan to observe the development of seedlings grown from cuttings harvested from different parts of the uterine shoots. The results obtained are recommended to be taken into account when planning agroforestry measures for crop of S. viminalis.