Variable Time Window Research Articles

The team orienteering problem with variable time windows

AbstractThe orienteering problem is well‐established within the domain of operational research. This paper introduces the team orienteering problem with variable time windows (TOPVTW) as a new variant of the team orienteering problem with time windows. The distinguishing feature of the TOPVTW is the adaptive time windows, which evolve dynamically as a consequence of the solution. This problem arises within the context of spread processes that must be contained. This propagation dictates the time windows, which should be observed by the teams. In turn, these teams must take actions influencing the spread and, consequently, the time windows. The time windows can affect both the nodes and the paths. Thus, two distinct versions of the problem are delineated, each supported by dedicated mixed integer programming models. An application is illustrated optimizing resources for wildfire suppression. This work serves as an initial stride toward further exploration and analysis within this domain.

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

Inventory Routing Problem With Split Delivery and Variable Time Windows for Customers With Small Capacity and Large Sales

Inventory Routing Problem With Split Delivery and Variable Time Windows for Customers With Small Capacity and Large Sales

Fall Detection Based on Graph Neural Networks with Variable Time Windows

Fall Detection Based on Graph Neural Networks with Variable Time Windows

Exploring variable observational time windows for patient–ventilator asynchrony during mechanical ventilation treatment

Background and Objective:Patient–ventilator asynchrony (PVA) is prevalent in mechanical ventilation (MV) for critically ill patients and has been associated with adverse patient outcomes. However, studies investigating the associations between PVA and patient outcomes employ differing time windows for PVA evaluation. In this study, machine learning methods are used to quantify the prevalence and magnitude of asynchrony at different time windows, as well as its temporal trends. The study aims to identify the optimal time window for assessing the temporal changes in the asynchrony index (AI) and magnitude of asynchrony (Masyn,avg). Methods:This study uses Convolutional Neural Networks (CNN) and Convolutional Autoencoders (CAE) to detect incidences of PVA and quantify its severity in 30 MV respiratory failure patients with 2722 h of respiratory data. The frequency of PVA and the breath-average magnitude were determined over different time periods, t, where t=0.5, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 45, 60 min and throughout MV. The AI for the patients was determined using the CNN model. Given an asynchronous breath, the CAEs were used to reconstruct asynchrony-free waveforms. The Masyn,avg was quantified as the difference between the two waveforms. The change in AI (ΔAI) and the change in Masyn,avg (ΔMasyn,avg) for all time windows, t were also calculated for each patient. Results:The median [interquartile range] overall AI and Masyn,avg for the patient cohort are 24.8 [12.9–46.1]% and 37.2 [33.4–45.3]% respectively. Analysis of the patient cohort also shows significant intra-patient variability in AI and Masyn,avg, while the inter-patient variation in AI is greater as compared to Masyn,avg. The cohort mean ΔAI and ΔMasyn,avg exhibit a converging trend with a minima at t=5 min and with values of 5.32 ± 2.37% and 2.80 ± 1.03%, respectively. A time window of t=5 min was preferred for AI and Masyn,avg evaluation as it can capture the granular changes in asynchrony while also being representative of longer temporal trends, thus preventing excessive variations in patient AI and Masyn,avg. Conclusion:Overall, this study provides new insight into both the short- and long-term trends of PVA in MV patients. By understanding these patterns, healthcare providers can enhance the monitoring of MV, leading to more informed and timely intervention. Ultimately, this could lead to improved patient care and outcomes.

Retracted: Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Retracted: Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Accurate modeling and performance evaluation of a total-body pet scanner using Monte Carlo simulations.

The limited axial field-of-view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total-body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose. The purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally. The total-body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA-NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated. Overall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivityfor the NEMA and extended phantoms at the center of the FOV were2.3% and 0.0%, respectively. The difference in peakNECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full-width-at-half maximum (FWHM) for six different positions and three directions was 0.12mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252-ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336-ring difference (2.04 Mcps). The developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner.

A Low Jitter, Wideband Clock Generator for Multi-Protocol Data Communications Applications

This paper presents a charge-pump phase-locked loop (PLL) frequency-synthesizer-based low-jitter wideband clock generator for multi-protocol data communications applications. Automatic frequency calibration (AFC) using linear variable time window technology and modified multi-modulus dividers (MMD) based on sub-multi-modulus dividers (SMMD) are developed for faster locking, lower jitter, and implementation of multi-protocol data communications applications. The clock generator is fabricated in 0.18 μm CMOS technology. The measured division ratio of the multi-modulus divider ranges from 1.875 to 25, and the output frequency is 46.875~625 MHz. The lock time does not exceed 30 μs, while jitter is less than 500 fs.

Electric Vehicle Routing Problem With Variable Vehicle Speed and Soft Time Windows for Perishable Product Delivery

This work studies perishable products’ distribution using electric commercial vehicles (ECVs). Extra energy is consumed for refrigeration to keep such products from deteriorating, which shortens the limited driving range of ECVs. Besides charging at public recharging stations, their travel speed can be adjusted to improve their driving range and decrease distribution cost. We propose an Electric Vehicle Routing problem with variable vehicle speed and soft time windows for perishable products (EVRP-VS). An energy consumption rate function of refrigerated ECVs during driving is introduced into the problem. The function considers refrigeration and has a nonlinear relationship with ECV speed and weight. As long as the carriage is not empty, the vehicle needs refrigeration during driving and during its stay at customers and stations. A mathematical programming model is developed for EVRP-VS, to minimize total distribution cost, including vehicle cost, power cost, refrigeration cost, and penalty cost due to delayed delivery. An adaptive hybrid ant colony optimization (AHACO) with a two-stage speed optimization strategy is proposed to solve EVRP-VS. In the first stage, local speed optimization is used to optimize the speed of each ant (ECV) in each transfer step. In the second one, global speed optimization further optimizes the speed for each fixed route constructed by ants. AHACO is applied to many problem instances. Experimental results show that it can effectively solve EVRP-VS in comparison with CPLEX and other meta-heuristics.

Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.

Model-based simultaneous diagnosis of ammonia injection failure and catalyst ageing in [formula omitted] engine after-treatment systems

After-treatment systems (ATS) in vehicle powertrains are developed to achieve maximum pollutant abatement efficiency under design operating conditions. However, anomalies in the ATS may appear during real operation, such as reducing agent supply failure and catalyst ageing. As in the case of an ATS composed of a selective catalytic reduction (SCR) system plus an ammonia slip catalyst (ASC), injection failure leads to insufficient ammonia levels to reduce nitrogen oxide (NOx), and ageing decreases the ammonia storage capacity and NOx conversion efficiency, these drawbacks drive both pollutants to levels above those expected and allowed by governmental regulations. To address this problem, a novel methodology was proposed that simultaneously estimates the operational state of the ATS in terms of ammonia injection failure and ASC catalyst ageing. The approach through control-oriented models based on an artificial neural network (ANN) and sensor signal analysis (SSA), as well as an extended Kalman filter (EKF) observer, compares the current emission levels estimated by the observer in a two-dimensional probabilistic plan and a variable time-window, with those expected if the ATS were operating under normal conditions. The proposed methodology was evaluated in real driving cycles with the ATS subjected to several undesired operating conditions. As a result, the model was able to accurately estimate the levels of ammonia injection failure and catalyst ageing state. Therefore, enabling the control system to modify the ammonia injection strategy to achieve desired levels of NOx and NH3 emissions.

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

• A new R-DTFJSS idea was developed to realise high-efficiency production. • Edge computing-based R-DTFJSS framework can enhance the data processing ability. • A variable time window-based RS strategy was used to achieve operation allocation. • An IHA-based solve method was designed to reduce the computational complexity. Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.

Study of the impact of social learning and gamification methodologies on learning results in higher education

AbstractIn this study, as the last step of a longitudinal study of the impact of social learning and gamification methodologies on learning results in higher education, we have recorded the activity in a software platform based on Moodle, specially built for encouraging online participation of the students to design, carry out and evaluate a set of learning tasks and games, during two consecutive editions of an undergraduate course. Our aim is to confirm the relationships between the patterns of accomplishment of the gamified activities and the network structure of the social graphs associated with the online forums with knowledge acquisition and final outcomes. For this purpose, we have offered two learning paths, traditional and novel, to our students. We have identified course variables that quantitatively explain the improvements reported when using the innovative methodologies integrated into the course design, and we have applied techniques from the social network analysis (SNA) and the machine learning/deep learning (ML/DL) domains to conduct success/failure classification methods finding that, generally, very good results are obtained when an ensemble approach is used, that is, when we blend the predictions made by different classifiers. The proposed methodology can be used over reduced datasets and variable time windows for having early estimates that allow pedagogical interventions. Finally, we have applied other statistical tests to our datasets, that confirm the influence of the learning path on learning results.

A modern metric for acoustic clarity in critical listening environments

Characteristics of acoustics in small rooms have become less mysterious, more predictable and measurements more common than in past decades. Modern metrics for characterizing the acoustical performance of critical listening environments have not followed the recent predictive improvements. An Acoustic Clarity metric with a variable time window (Rear Reflection Clarity, CRR) is proposed for small (under approximately 200m3) critical listening environments such as recording studio control, mastering, film post, screening, audiophile listening, and similar rooms. The variable time window is defined by the arrival time difference between direct sound and the reflection from the rear wall, which is typically the strongest reflection and is opposite the on-axis response of the loudspeakers. Measured impulse response data collected in situ is presented and analyzed as case studies to characterize CRR. For simplicity, this presentation will focus on only the stereo components of surround-sound environments.

A High Throughput Lipidomics Method Using Scheduled Multiple Reaction Monitoring.

Lipid compositions of cells, tissues, and bio-fluids are complex, with varying concentrations and structural diversity making their identification challenging. Newer methods for comprehensive analysis of lipids are thus necessary. Herein, we propose a targeted-mass spectrometry based lipidomics screening method using a combination of variable retention time window and relative dwell time weightage. Using this method, we identified more than 1000 lipid species within 24-min. The limit of detection varied from the femtomolar to the nanomolar range. About 883 lipid species were detected with a coefficient of variance <30%. We used this method to identify plasma lipids altered due to vitamin B12 deficiency and found a total of 18 lipid species to be altered. Some of the lipid species with ω-6 fatty acid chains were found to be significantly increased while ω-3 decreased in vitamin B12 deficient samples. This method enables rapid screening of a large number of lipid species in a single experiment and would substantially advance our understanding of the role of lipids in biological processes.

Q-factor estimation from surface seismic data in the time-frequency domain: A comparative analysis

The quality factor Q is generally used to describe seismic attenuation that leads to amplitude decay (AD) and wavelet distortion. Time-frequency transforms are commonly used to measure quality factor Q on surface seismic data. These methods capture frequency changes over time using a fixed or variable sliding time window. Other adaptive transforms also can provide time localization, and they often are superior for Q estimation. In this study, we compare three time-frequency transforms and indicate how the choice of a fixed- or variable-time window or an adaptive transform affects the accuracy and robustness of Q-factor estimation. We use the short-time Fourier and continuous wavelet transforms as fixed- and variable-window transforms, respectively. The synchrosqueezed wavelet transform is used as an adaptive transform. We compare four Q-factor estimation methods in the time-frequency domain, such as the AD, spectral ratio, centroid frequency shift, and compound time-frequency variable methods. Furthermore, we study some of the difficulties associated with these estimation methods, such as quantitative attenuation sensitivity, noise robustness, regression bandwidth influence, and key parameter selection for each time-frequency transform. Real data examples are used to investigate the robustness of Q-factor estimation with different methods using different time-frequency transforms and the statistics of how well the attenuation measurements match the expected seismic attenuation behavior. Furthermore, in these real data examples, we are able to use the Q estimates to compensate for attenuation through inverse Q filtering.

Determining the Near‐Instantaneous Curvature of Earth's Bow Shock Using Simultaneous IBEX and MMS Observations

AbstractThe Interstellar Boundary Explorer (IBEX) mission provides a unique opportunity to study Earth's magnetosphere on a global scale via energetic neutral atom imaging. Due to the orbital configuration, IBEX is in a side‐viewing vantage point relative to the magnetosphere for a few months every year. This allows the Earth's magnetosphere to be imaged in the X‐Z geocentric solar ecliptic (GSE) plane. Using IBEX observations and near‐simultaneous in‐situ observations of Earth's bow shock (BS) from the Magnetospheric Multiscale mission, we determine the curvature of Earth's BS in the X‐Z GSE plane for 22 instances. We then compare the curvature to different upstream solar wind parameters in order to identify correlations with the contemporary physical conditions. We find that the solar wind dynamic pressure, as well as the interplanetary magnetic field (IMF) strength, X‐Y, and Y‐Z components are all linearly related to the curvature. We then investigate the time scales over which these correlations persist using variable time windows over which the upstream parameters are averaged. Lastly, we investigate the asymmetry of the BS in the X‐Z plane by comparing an asymmetry measure with different solar wind parameters. We find that the magnetic tension force from the IMF deflects the BS, and thus the underlying magnetopause obstacle. The results presented here provide the first near‐instantaneous observations of Earth's BS on a global scale, show the importance of IMF on generating asymmetries, and provide a basis for future modeling and observational studies of the BS.

A Search for Correlated Low-energy Electron Antineutrinos in KamLAND with Gamma-Ray Bursts

Abstract We present the results of a time-coincident event search for low-energy electron antineutrinos in the KamLAND detector with gamma-ray bursts (GRBs) from the Gamma-ray Coordinates Network and Fermi Gamma-ray Burst Monitor. Using a variable coincidence time window of ±500 s plus the duration of each GRB, no statistically significant excess above the background is observed. We place the world’s most stringent 90% confidence level upper limit on the electron antineutrino fluence below 17.5 MeV. Assuming a Fermi–Dirac neutrino energy spectrum from the GRB source, we use the available redshift data to constrain the electron antineutrino luminosity and effective temperature.

A dynamic control strategy of district heating substations based on online prediction and indoor temperature feedback

Refined control is significant to ensure on-demand heating and efficient operation in district heating system (DHS). This paper proposes a dynamic control strategy for substations based on online prediction and indoor temperature measurement. Firstly, cross-correlation function method coupled with variable time window, which is a dynamic time lag analysis method, is introduced to analyze the delay time between indoor and comprehensive outdoor temperature. This time lag is used to decide control period. Then, an online multiple linear regression (MLR) model is introduced to predict the supply temperature. The prediction value is adjusted according to the deviation of the consumers’ set point indoor temperature and the actual indoor temperature before sent to the substation controller. The proposed strategy was applied in a practical DHS engineering, and the results showed that the indoor temperature non-uniformity coefficient was reduced from 0.05 to 0.04, the overall heating season heat consumption index was reduced, and the energy saving rate was about 6%.

Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.