Time Window Method Research Articles

Syncing the brain's networks: dynamic functional connectivity shifts from temporal interference.

Temporal interference (TI) stimulation, an innovative non-invasive brain stimulation approach, has the potential to activate neurons in deep brain regions. However, the dynamic mechanisms underlying its neuromodulatory effects are not fully understood. This study aims to investigate the effects of TI stimulation on dynamic functional connectivity (dFC) in the motor cortex. 40 healthy adults underwent both TI and tDCS in a double-blind, randomized crossover design, with sessions separated by at least 48 h. The total stimulation intensity of TI is 4 mA, with each channel's intensity set at 2 mA and a 20 Hz frequency difference (2 kHz and 2.02 kHz). The tDCS stimulation intensity is 2 mA. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before, during, and after stimulation. dFC was calculated using the left primary motor cortex (M1) as the region of interest (ROI) and analyzed using a sliding time-window method. A two-way repeated measures ANOVA (group × time) was conducted to evaluate the effects of TI and tDCS on changes in dFC. For CV of dFC, significant main effects of stimulation type (P = 0.004) and time (P < 0.001) were observed. TI showed lower CV of dFC than tDCS in the left postcentral gyrus (P < 0.001). TI-T2 displayed lower CV of dFC than TI-T1 in the left precentral gyrus (P < 0.001). For mean dFC, a significant main effect of time was found (P < 0.001). TI-T2 showed higher mean dFC than tDCS-T2 in the left postcentral gyrus (P = 0.018). Within-group comparisons revealed significant differences between time points in both TI and tDCS groups, primarily in the left precentral and postcentral gyri (all P < 0.001). Results were consistent across different window sizes. 20 Hz TI stimulation altered dFC in the primary motor cortex, leading to a significant decreasing variability and increasing mean connectivity strength in dFC. This outcome indicates that the 20 Hz TI frequency interacted with the motor cortex's natural resonance.

Estimate Suicidal Rate in Indonesian based on Time Window using Linear Regression

The global phenomenon of suicide should be a serious source of worry. Suicide rates are still relatively high in Indonesia. According to the research, there are many different reasons why people commit suicide. Anyone can commit suicide, whether they are young children, teenagers, or adults. Preventive action is one way to avoid this. The prevalence of suicide can be used to gauge the level of preventive action. You may gauge how active you are in implementing the most effective prevention by looking at the predicted suicide rate in the future. Linear regression is one technique for predicting suicide rates. The time window (tw) method is also used to prepare the data because it is in time series form. The best regression model was tw = 5 with MSE = 0.001147, RMSE = 0.033869, and R2 = 0.981643 obtained for all rates. The model with tw = 3, which has errors of MSE = 0.001547, RMSE = 0.039334, and R2 = 0.969458, is the most accurate one for the female rate. Finally, with errors MSE = 0.00318, RMSE = 0.056392, and R2 = 0.973341, we arrive at tw = 5 for the male rate

Anomaly detection of four-engine aircraft based on Parameter differences

Abstract In this paper, a real-time anomaly detection method combining GDN and Gaussian model is proposed for the real-time engine condition monitoring of four-engine aircraft. Firstly, the gas path parameters of the engine are selected and the difference of the gas path parameters is calculated to offset the influence of the external environment on the engine. Thirdly, the sliding time window method is used for real-time prediction, and the model prediction residuals are used for unsupervised anomaly detection. Finally, the Gaussian model is used to prune the anomalies detected by the GDN model to reduce the misjudgment of anomalies and improve the accuracy of anomaly detection. The experimental results show that the proposed method can detect 97.96% of the outliers in the test set and the accuracy reaches 89.47%, which is better than LSTM, AE, GDN, and Gaussian models alone.

Rolling discrete grey periodic power model with interaction effect under dual processing and its application

Time series characterized by periodic fluctuations are widespread in reality behavioural systems, and accurate forecasting of them has important practical application value. In this paper, based on the idea of discrete grey model, dual interaction effects and the nonlinear characteristics of the system are considered under the perspective of dual processing, and the fluctuating trend of the periodic sequence is portrayed through the introduction of the periodic dummy variable, and at the same time, combined with the extended periodic rolling time window method, the rolling discrete grey periodic power model with the interaction effect under dual processing is proposed, namely DIE-RDGM(1,1,T)α model. Differential evolution algorithm (DE) are employed to determine the optimum power index. Finally, the new model was compared with nine other forecasting models by three instances and applied to the problem of agricultural drought forecasting in Zhoukou City and Anyang City, Henan Province. The results show: (1) The new model has demonstrated strong competitiveness, confirming its rationality, validity, and superiority. (2) The DE is suitable for power index optimization search. (3) The new model can better describe the periodic change rule of seasonal-scale soil moisture in the two cities, and the forecast results are consistent with the actual drought conditions.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

ObjectiveIn this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.MethodsSpatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.ResultsThe proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.ConclusionThe proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Multi-Automated Guided Vehicles Conflict-Free Path Planning for Packaging Workshop Based on Grid Time Windows

In order to solve the problem of multi-AGV path planning in a packaging workshop, this paper proposes a multi-AGV path-planning method based on time windows. A grid method is selected to build a map model. A penalty function is added to the A* algorithm to reduce the number of invalid turns made by AGVs during transportation. The AGV priorities are set according to the differences in AGV transport states. At the same time, the raster time window method is used to describe the five types of conflict that may occur in the process of multi-AGV transport. Combined with the AGV priorities, a multi-AGV anti-collision strategy is provided to realize the conflict-free path planning of multiple AGVs. The algorithm’s effectiveness was verified by simulation, and a reasonable quantity of AGVs was proposed based on AGV sensitivity analysis. The improved A* algorithm combined with the time window method can realize multi-AGV collision-free transportation and improve the efficiency and reliability of multi-AGV transportation.

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.

An Eye Movement Classification Method based on Cascade Forest.

Eye tracking technology has become increasingly important in scientific research and practical applications. In the field of eye tracking research, analysis of eye movement data is crucial, particularly for classifying raw eye movement data into eye movement events. Current classification methods exhibit considerable variation in adaptability across different participants, and it is necessary to address the issues of class imbalance and data scarcity in eye movement classification. In the current study, we introduce a novel eye movement classification method based on cascade forest (EMCCF), which comprises two modules: (1) a feature extraction module that employs a multi-scale time window method to extract features from raw eye movement data; (2) a classification module that innovatively employs a layered ensemble architecture, integrating the cascade forest structure with ensemble learning principles, specifically for eye movement classification. Consequently, EMCCF not only enhanced the accuracy and efficiency of eye movement classification but also represents an advancement in applying ensemble learning techniques within this domain. Furthermore, experimental results indicated that EMCCF outperformed existing deep learning-based classification models in several metrics and demonstrated robust performance across different datasets and participants.

Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children

Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.

The time-window strategy in the online order batching problem

When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers.

Optimising the flight turnaround schedules: An improved sliding time windows approach based on MILP and CP models

This paper addresses the scheduling of turnaround operations for flights in civil airports. A series of complex issues, including the heterogeneity of flights and operators, resource constraints on turnaround operations, temporal restrictions and alternative service patterns of turnaround operations, multi-functional operators, multi-trip services and multi-to-one services, are jointedly considered. We propose two formulations for the problem: A mixed-integer linear programming (MILP) model, and a constraint programming (CP) model. For comparison purpose, we also present simplified versions of the models in which the multi-to-one services are prohibited. The uncertainty are incorporated by modifying the models based on robust optimisation principles. The numerical experiments are conducted using instances generated based on real flight data at Beijing Capital International Airport. It is shown that under current computer configurations, small-scale instances can be solved to the optimum by CPLEX within an acceptable period of time using the MILP model, while moderate-scale instances can be tackled using the CP formulation. To cope with large-scale instances, an improved sliding time window method is developed, and evaluated with different parameter combinations, showing that practical turnaround schedule would best be updated two to three times per hour. Besides, after a case study in both static and stochastic scenarios, we illustrate that multi-to-one services should be specifically considered and highlighted, for their existence could significantly improve the operator utilisation rate and reduce flight delay, especially when the uncertainties are taken into account.

A Wavelet Transform-Based Real-Time Filtering Algorithm for Fusion Magnet Power Signals and Its Implementation

With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.

A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI.

At present, fMRI studies mainly focus on the entire low-frequency band (0. 01-0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency-based dynamic functional connectivity (dFC) analysis method was proposed in this study, which was then applied to a schizophrenia study. First, three frequency bands (Conventional: 0.01-0.08 Hz, Slow-5: 0.0111-0.0302 Hz, and Slow-4: 0.0302-0.0820 Hz) were obtained using Fast Fourier Transform. Next, the fractional amplitude of low-frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by the sliding time window method at four window-widths. Finally, recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of patients with schizophrenia and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with the conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.

Energy‐compatible modulating functions for the stochastic generation of fully non‐stationary artificial accelerograms and their effects on seismic site response analysis

AbstractThe reliability of non‐linear dynamic analysis aimed to predict the seismic performance of structural and geotechnical systems, as well as their dynamic interaction, is significantly affected by the selection of suitable input motions. Several procedures for selecting actual earthquake records compatible with the seismic hazard at the site of interest and for evaluating synthetic accelerograms consistent with a seismological framework are available in the literature. However, the degree of uncertainty affecting these approaches might lead to unreliable performance predictions especially for strongly non‐linear problems, such as those involving the response of soils to cyclic and dynamic loading. In this vein, the paper presents a procedure for generating fully non‐stationary ground motions ensuring energy compatibility with a target motion. Two different approaches have been introduced: i) the time window method based on the central finite difference approach and ii) the simplified intensity‐compatible approach in which a closed form expression is provided to evaluate a modulating function that depends on the Arias intensity and on the strong motion duration of a target motion. To highlight the reliability and the accuracy of the proposed procedure, several sets of spectrum‐compatible artificial accelerograms, generated starting from a rock outcropping motion, were used as input motions in a series of one‐dimensional site response analyses. The analysis results are presented and discussed in the paper highlighting the influence of the main features of the proposed generation procedure on the variability of the computed site response.

An accurate fault location method for wireless sensor network based on random matrix theory

In order to improve the effect of fault location, this paper proposes an accurate fault location method for wireless sensor networks based on random matrix theory. The standard non Hermite matrix is used to extract accurate fault location data. Considering the volatility of the original data, the original random matrix is preprocessed. Based on the real-time sliding time window method, the space-time characteristic data of network faults are determined, and the precise fault location of wireless sensor networks based on random matrix theory is realized.Experimental results show that the false positive rate of the proposed method is only 2%. The average fault location accuracy is as high as 96.4% and the fault location time is only 15.1 s, which shows that the proposed method has a good location effect.

State‐of‐Charge Prediction of Lithium‐Ion Batteries Based on Sparse Autoencoder and Gated Recurrent Unit Neural Network

Lithium‐ion batteries are widely used in daily life because of their fast charging and high energy density. The accurate prediction of state‐of‐charge (SOC), critical for the quality evaluation and long‐term planning of lithium‐ion batteries, has still been challenging owing to sophisticated battery dynamics and ever‐changing ambient conditions. Herein, a joint sparse autoencoder (SAE) and gated recurrent unit (GRU) model are developed to improve SOC prediction performance. The sliding time window method is first utilized for data reconstruction to explore the continuous time trend and discrete state characteristics in multi‐cycle data of the lithium‐ion batteries. The SAE is then used to extract features from the reconstruction data. The GRU is finally applied to realize an accurate SOC prediction. The performance of proposed method is tested on a benchmark lithium‐ion battery degradation dataset and actual battery degradation experiments. Experimental tests show that the present model has the lowest root mean square error of 0.176 and 0.271, the lowest mean absolute error of 0.1396 and 0.2412, and the lowest mean square error of 0.03098 and 0.07355 on two datasets, respectively, demonstrating that the proposed method outperforms the state‐of‐the‐art methods in terms of accuracy and robustness.

Research on schedling optimization of four-way shuttle-based storage and retrieval systems

In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the four-way shuttle system. An improved genetic algorithm is used to solve the task planning, and an improved A* algorithm is used to solve the path optimization within the shelf level. The conflicts generated by the parallel operation of the four-way shuttle system are classified, and the improved A* algorithm based on the time window method is constructed for path optimization through the dynamic graph theory method to seek safe conflict-free paths. Through simulation example analysis, it is verified that the improved A* algorithm proposed in this paper has obvious optimization effect on the model of this paper.

Research on Topic Discovery and Evolution Trend Based on Temporal Keyword Characteristics Analysis

In order to mine the research topics in massive articles, sort out the evolution context and correlation relationship of research topics, enhance the scientificity and vividness of evolution results, this paper proposes the concept of temporal influence factor as an important feature in keyword extraction. Using the time window method, the topic model is used to mine and identify topics and perform visual analysis. It is verified that keyword extraction with time series features can improve the effect of topic model. Through visualization, we can not only observe the overall trend of topic popularity, but also analyze the evolution of topic content in each time period and observe the trend of splitting and merging.

Research on Stock Index Prediction Based on Stock Correlation Network and Deep Learning

Aiming at the problem of stock index prediction, constructing a time series stock correlation network based on the fundamentals and technology of stock index components, then using the depth map neural network to learn the hierarchical representation of stock correlation network, and obtaining the candidate prediction signal in an end-to-end way. The architecture composed of depth map neural network method and end-to-end strategy is called DIFFPOOL architecture. Taking the CSI 300 index as the research object, combining the DIFFPOOL architecture with softmax classifier, long-term and short-term memory neural network (LSTM), linear regression, and logical regression, respectively, and uses the sliding time window method to obtain the corresponding prediction accuracy of stock index. The accuracy of the combined model under the optimal parameters fluctuates in the interval [0.56, 0.62]. Ultimately, the first mock exam is based on the mean absolute error (MAE) and the root mean square error (RMSE). The regression models of DIFFPOOL and regression models are compared with LSTM, recurrent neural network (RNN), and back propagation neural network (BP). Compared with the single model, the MAE and RMSE of the combined model are smaller, 0.0061 and 0.0081, respectively. Experiments show that by aggregating the node attribute information of the stock association network hierarchically, we can dynamically capture the impact of different industry sectors on stock index price fluctuations and further improve prediction accuracy.

A Gray Target Calculation–Cloud Gravity Center Health Assessment Method for Gas Turbine Engine

Abstract Since the health status of gas turbine engine is difficult to quantify, which brings great challenges to health assessment and remaining useful life predictions. To solve this problem, a gray target calculation and cloud gravity center (GTC–CGC) health assessment method is proposed. The three-dimensional data normalization process is to characterize the different initial states between samples. The empirical signal-to-noise ratio and entropy weight are used to calculate the subjective and objective weights of health indicators, and convex optimization is used to realize the fusion assessment. The sliding time window method is used to calculate the real-time health state of the gas turbine engine. Finally, health assessment and remaining useful life prediction tests were conducted on turbofan engines using the NASA C-MAPSS dataset. The experimental results show that compared with the self-organizing neural network, the monotonicity, robustness, and trend of the health assessment results were improved by 0.3216, 0.0843, and 0.0355, respectively. The third-order linear regression algorithm was used for remaining useful life prediction, the prediction score of this model is 265, and root mean squared error is 26.1542, which is equivalent to the prediction accuracy of mainstream intelligent prediction methods such as neural network.