Search Results (14)

Recently, the maritime logistics industry has been transitioning to smart logistics by leveraging such technologies as AI and IoT. In particular, maritime big data plays a significant role in providing various services, including ship operation monitoring and greenhouse gas emissions assessment, and is considered essential for delivering maritime logistics services. Marine big data comprise real-world data collected during ship operations, but it is susceptible to loss due to temporal and environmental constraints. To address this issue, an Emulator is proposed to generate supplemental data, including location data, data count, and average distance, using accumulated maritime transport data. This study proposes an Emulator that repetitively generates new data such as location data, data count, and average distance using maritime transport data accumulated up to now. The location data is generated using the cumulative distance and trigonometric ratios based on the location information of standard routes. The data count and average distance are calculated based on user-input parameters such as voyage time and data interval. The generated data is inserted into a database and monitored on a map in real time. Experiments were conducted using maritime transport route data, and the results validated the effectiveness of the Emulator. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article

This study is intended as a note and provides an extension to a much-used and established test for portfolio efficiency, the Gibbons, Ross, and Shanken GRS-Wald test. Tests devised to measure portfolio efficiency are crucial to the theoretical issues related to CAPM (Capital Asset Pricing Model) testing and have applications for the fund manager who seeks to rank portfolio performance. This study looks at the GRS-Wald test for portfolio efficiency and extends it to make it visually more interpretive without any loss of generality in its structure. The geometrically recast statistic draws upon the trigonometric properties of a portfolio in the mean-variance space and a mathematical proof of the equivalence of the two statistics is provided. The GRS-Wald test is a widely used statistic in studies addressing the issue of portfolio efficiency and CAPM deviations. A simulation demonstrates the use of the recast GRS-Wald test in testing for the mean-variance efficiency of a test portfolio. The study also provides a table of the GRS-Wald test, based on a range of mean-variance locations (cosine of portfolio angles) at which the test portfolio and the efficient market portfolio can be placed. Full article

Water inrush in karst tunnels will cause casualties and economic losses. Thus, it is significant to objectively assess the water inrush risk level and adopt valid preventive measures to reduce losses from this disaster. The relationship between the factors affecting water inrush in the dynamic coupling system is strong nonlinear, so the attribute recognition model, which lessens the mutation points and error and causes the evaluation results to be more reasonable and accurate, is improved nonlinearly in this paper. Firstly, the assessment system was established by selecting seven factors: formation lithology, unfavorable geological conditions, attitude of rock formation, landform and physiognomy, contact zones of dissolvable and insoluble rock, layer and interlayer fissures, and groundwater level. Secondly, the multi-factor interaction matrix, C-OWA operator, and variable weight theory are used to calculate the constant weight and variable weight of each evaluation index. In addition, the linear attribute measurement function of the attribute identification model is optimized by using the non-linear trigonometric function to distinguish the risk level of the water inrush. Finally, the proposed model was successfully used in Qiyueshan Tunnel. The evaluation results of the risk level are more accurate than other methods, and they are in agreement with the excavation results. The proposed model provides a valuable reference for the risk assessment and project management of tunnel construction. Full article

The choice of an appropriate regression model for econometric modeling minimizes information loss and also leads to sound inferences. In this study, we develop four quantile regression models based on trigonometric extensions of the unit generalized half-normal distributions for the modeling of a bounded response variable defined on the unit interval. The desirable shapes of these distributions, such as left-skewed, right-skewed, reversed-J, approximately symmetric, and bathtub shapes, make them competitive models for bounded responses with such traits. The maximum likelihood method is used to estimate the parameters of the regression models, and Monte Carlo simulation results confirm the efficiency of the method. We demonstrate the utility of our models by investigating the relationship between OECD countries’ educational attainment levels, labor market insecurity, and homicide rates. The diagnostics reveal that all our models provide a good fit to the data because the residuals are well behaved. A comparative analysis of the trigonometric quantile regression models with the unit generalized half-normal quantile regression model shows that the trigonometric models are the best. However, the sine unit generalized half-normal (SUGHN) quantile regression model is the best overall. It is observed that labor market insecurity and the homicide rate have significant negative effects on the educational attainment values of the OECD countries. Full article

This paper proposes a novel mathematical theory of adaptation to convexity of loss functions based on the definition of the condense-discrete convexity (CDC) method. The developed theory is considered to be of immense value to stochastic settings and is used for developing the well-known stochastic gradient-descent (SGD) method. The successful contribution of change of the convexity definition impacts the exploration of the learning-rate scheduler used in the SGD method and therefore impacts the convergence rate of the solution that is used for measuring the effectiveness of deep networks. In our development of methodology, the convexity method CDC and learning rate are directly related to each other through the difference operator. In addition, we have incorporated the developed theory of adaptation with trigonometric simplex (TS) designs to explore different learning rate schedules for the weight and bias parameters within the network. Experiments confirm that by using the new definition of convexity to explore learning rate schedules, the optimization is more effective in practice and has a strong effect on the training of the deep neural network. Full article

A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss function is applied in a multi-scale residual convolutional neural network (named MR-CNN-A network) embedded with an attention mechanism for the recognition task. Firstly, a multi-scale convolution kernel is used to obtain multi-scale features. Then, an attention mechanism is used to fuse these multi-scale feature maps. Furthermore, a cosx-function-weighted cross-entropy loss function is used to deal with the class imbalance in underwater acoustic data. This function adjusts the loss ratio of each sample by adjusting the loss interval of every mini-batch based on cosx term to achieve a balanced total loss for each class. Two imbalanced underwater acoustic data sets, ShipsEar and autonomous underwater vehicle (self-collected data) are used to evaluate the proposed network. The experimental results show that the proposed network outperforms the support vector machine and a simple convolutional neural network. Compared with the other three loss functions, the proposed loss function achieves better stability and adaptability. The results strongly demonstrate the validity of the proposed loss function and the network. Full article

Auditory steady-state response (ASSR) is a translational biomarker for several neurological and psychiatric disorders, such as hearing loss, schizophrenia, bipolar disorder, autism, etc. The ASSR is sinusoidal electroencephalography (EEG)/magnetoencephalography (MEG) responses induced by periodically presented auditory stimuli. Traditional frequency analysis assumes ASSR is a stationary response, which can be analyzed using linear analysis approaches, such as Fourier analysis or Wavelet. However, recent studies have reported that the human steady-state responses are dynamic and can be modulated by the subject’s attention, wakefulness state, mental load, and mental fatigue. The amplitude modulations on the measured oscillatory responses can result in the spectral broadening or frequency splitting on the Fourier spectrum, owing to the trigonometric product-to-sum formula. Accordingly, in this study, we analyzed the human ASSR by the combination of canonical correlation analysis (CCA) and Holo-Hilbert spectral analysis (HHSA). The CCA was used to extract ASSR-related signal features, and the HHSA was used to decompose the extracted ASSR responses into amplitude modulation (AM) components and frequency modulation (FM) components, in which the FM frequency represents the fast-changing intra-mode frequency and the AM frequency represents the slow-changing inter-mode frequency. In this paper, we aimed to study the AM and FM spectra of ASSR responses in a 37 Hz steady-state auditory stimulation. Twenty-five healthy subjects were recruited for this study, and each subject was requested to participate in two auditory stimulation sessions, including one right-ear and one left-ear monaural steady-state auditory stimulation. With the HHSA, both the 37 Hz (fundamental frequency) and the 74 Hz (first harmonic frequency) auditory responses were successfully extracted. Examining the AM spectra, the 37 Hz and the 74 Hz auditory responses were modulated by distinct AM spectra, each with at least three composite frequencies. In contrast to the results of traditional Fourier spectra, frequency splitting was seen at 37 Hz, and a spectral peak was obscured at 74 Hz in Fourier spectra. The proposed method effectively corrects the frequency splitting problem resulting from time-varying amplitude changes. Our results have validated the HHSA as a useful tool for steady-state response (SSR) studies so that the misleading or wrong interpretation caused by amplitude modulation in the traditional Fourier spectrum can be avoided. Full article

Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state–space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including $SARIMA\left(1,0,1\right){\left(0,1,1\right)}_{12}$, $NNAR{\left(3,1,2\right)}_{12}$,$ETS\left(A,N,A\right)$, and $TBATS(1,\left\{0,0\right\},0.8,\left\{<12,5>\right\}$. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries. Full article

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images. Full article

With the rapid development of urbanization, substantial land areas and houses are expropriated, which can cause huge numbers of disputes related to expropriation compensation. The root of the disputes is that the associated subjects are affected by various behavioral preferences and make different cognitive fairness judgments based on the same compensation price. However, the existing expropriation compensation strategies based on the market value under the assumption of “the economic man” hypothesis cannot meet the fairness preference demands of the expropriated. Therefore, finding a compensation price that satisfies subjects’ multidimensional fairness preferences, including profit-seeking, loss aversion, and interactive fairness preferences, is necessary. Only in this way can the subjects reach an agreement regarding fair compensation and resolve their disputes. Because of the fuzziness of subjects’ expected revenues, this paper innovatively introduces trigonometric intuitional fuzzy numbers to construct one-dimensional and multidimensional fair fuzzy equilibrium evaluation models. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is adopted to convert a multidimensional problem into a multiattribute group decision problem, which simplifies the problem of finding multidimensional equilibrium when considering the multidimensional fairness preferences of the two subjects. Real case data are introduced to verify the validity of this method. The research results show that upward revision of the multidimensional fairness preferences based on the market value assists in achieving a fair compensation agreement. Consideration of the influence of the subjects’ multidimensional fairness preferences on the fairness equilibrium is conducive to resolving the disputes, and provides a reference for the settlement of expropriation compensation disputes in developing countries. Full article

Prestressed concrete cylinder pipes (PCCPs) can suffer from prestress loss caused by wire-breakage, leading to a reduction in load-carrying capacity or a rupture accident. Reinforcement of PCCPs with external prestressed steel strands is an effective way to enhance a deteriorating pipe’s ability to withstand the design load. One of the principal advantages of this reinforcement is that there is no need to drain the pipeline. A theoretical derivation is performed, and this tentative design method could be used to determine the area of prestressed steel strands and the corresponding center spacing in terms of prestress loss. The prestress losses of strands are refined and the normal stress between the strands and the pipe wall are assumed to be distributed as a trigonometric function instead of uniformly. This derivation configures the prestress of steel strands to meet the requirements of ultimate limit states, serviceability limit states, and quasi-permanent limit states, considering the tensile strength of the concrete core and the mortar coating, respectively. This theory was applied to the reinforcement design of a PCCP with broken wires (with a diameter of 2000 mm), and a prototype test is carried out to verify the effect of the reinforcement. The load-carrying capacity of the deteriorating PCCPs after reinforcement reached that of the original design level. The research presented in this paper could provide technical recommendations for the application of the reinforcement of PCCPs with external prestressed steel strands. Full article

In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation ${\sin }^{-1}\left(x\right)\approx x$ and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector. Full article

This paper presents an improved VLSI (Very Large Scale of Integration) architecture for real-time and high-accuracy computation of trigonometric functions with fixed-point arithmetic, particularly arctangent using CORDIC (Coordinate Rotation Digital Computer) and fast magnitude estimation. The standard CORDIC implementation suffers of a loss of accuracy when the magnitude of the input vector becomes small. Using a fast magnitude estimator before running the standard algorithm, a pre-processing magnification is implemented, shifting the input coordinates by a proper factor. The entire architecture does not use a multiplier, it uses only shift and add primitives as the original CORDIC, and it does not change the data path precision of the CORDIC core. A bit-true case study is presented showing a reduction of the maximum phase error from 414 LSB (angle error of 0.6355 rad) to 4 LSB (angle error of 0.0061 rad), with small overheads of complexity and speed. Implementation of the new architecture in 0.18 µm CMOS technology allows for real-time and low-power processing of CORDIC and arctangent, which are key functions in many embedded DSP systems. The proposed macrocell has been verified by integration in a system-on-chip, called SENSASIP (Sensor Application Specific Instruction-set Processor), for position sensor signal processing in automotive measurement applications. Full article