Window Span Research Articles

Bayesian Hyperparameter Optimization of stacked Bidirectional Long Short-Term Memory neural network for the State of Charge estimation

The battery industry has recently grown as a result of electric power adoption in many applications showing less reliance on fossil fuels. Accurate estimation of the State of Charge (SOC) is vital for optimal Battery Management System (BMS) operation to maintain the use of the battery within the safety limits. Data-driven methods require a careful hyperparameter selection as their results strongly depend on their choice. Heuristic methods involve manual tuning or exhaustive ways, which can be computationally demanding. Here a Bayesian Hyperparameter Optimization (BHO) with a Gaussian process is proposed, exploiting the benefits of the probabilistic approach to hyperparameter tuning to reduce the computational effort. For the accurate prediction of SOC, the BHO technique is implemented on a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network, incorporating a novel set of hyperparameters. The presented method is validated using two well-documented public datasets. Results are compared using datasets with different time granularity, revealing accuracy and computational differences while exhibiting superior yield capabilities compared to other state-of-the-art methods. To assess the computational effort associated with data size and model creation, a time granularity analysis was performed. The analysis demonstrated that decreasing the timestep resulted in reduced network creation time while maintaining similar error levels. Moreover, in order to ensure the comparability of the method, the metric of Floating-point Operations (FLOPs) was integrated to quantify the network’s computational volume. Finally, a sensitivity analysis performed on the time-series window span showed accuracy and computational load differences highlighting the need for caution and careful consideration.

Study on Multi-Objective Optimization of Logistics Distribution Paths in Smart Manufacturing Workshops Based on Time Tolerance and Low Carbon Emissions

In the Industry 4.0 environment, an ideal smart factory should be intelligent, green, and humanized, and the logistics transportation from raw materials to final products in the factory should be completed by smart logistics. In order to address the problems of low efficiency, poor workstation service satisfaction, high distribution costs, and non-greening during the logistics distribution processes in discrete smart manufacturing workshops are required. A mathematical model of optimized multi-objective green logistics distribution paths in a smart manufacturing workshop has been constructed in this study, with low costs, high efficiency, and workstation service satisfaction taken into consideration. Then, this mathematical model was solved with an improved ant colony optimization algorithm. A “time window span” was introduced in the basic ant colony optimization algorithm to prioritize the services to workstations with a relatively high urgency in material demand, with the aim of improving workstation service satisfaction. Lastly, in order to verify the effectiveness of the model and algorithm proposed in this study, a simulation experiment has been conducted on the workstation logistics distribution system in a smart manufacturing workshop to provide convincing evidence for optimizing workstation logistics distribution paths in workshops of discrete manufacturing enterprises.

Polarization bases compensation towards advantages in satellite-based QKD without active feedback

Long-distance photonic implementations of quantum key distribution protocols have gained increased interest due to the promise of information-theoretic security against unauthorized eavesdropping. However, a significant challenge in this endeavor is photon-polarization getting affected due to the birefringence of fibers in fiber-based implementations, or variation of reference frames due to satellite movement in long-haul demonstrations. Conventionally, active feedback-based mechanisms are employed for real-time polarization tracking. Here, we propose and demonstrate an alternative approach via a proof-of-principle experiment over an in-lab entanglement-based (BBM92) protocol. In this approach, we perform a quantum state tomography to arrive at optimal measurement bases for any one party resulting in maximal (anti-)correlation in measurement outcomes of both parties. Our polarization-entangled bi-photons have 94% fidelity with a singlet state and a Concurrence of 0.92. By considering a representative 1 ns coincidence window span, we achieve a quantum-bit-error-rate (QBER) of ≈5%, and a key rate of ≈35 Kbps. The performance of our implementation is independent of any local polarization rotation. Finally, using optimization methods we achieve the best trade-off between the key rate, QBER, and balanced key symmetry. Our approach obviates the need for active polarization tracking. It is also applicable to such demonstrations with non-maximally entangled states and prepare-and-measure-based protocols with partially polarized single-photon sources.

Verb-Noun Collocations In Newspaper Editorials In Ghana: A Corpus-Based Analysis

This paper is a corpus-based study which aims at profiling the most frequent verb-noun collocations and their communicative functions in newspaper editorials in Ghana. In all, a total of 92,927 running words were culled from 220 newspaper editorials from The Ghanaian Times and The Daily Graphic, which were published in the 2016 and 2017 news years, for compilation of a specialised corpus for the study. From the collocation tab of the AntConc corpus software, sixty-seven Verb-Noun Collocations were found to constitute the most frequently occurring collocations in the newspaper editorials under study. The corpus revealed that both predictive and open Verb-Noun Collocations which alternate at the left and right sides on the collocation window span are mostly used by newspaper editorial writers in Ghana. Again, it was observed that phrasal patterns of a noun collocate differed according to its position on either the left or right side of a verb node. The semantic prosodies of the profiled verb-noun collocations revealed five major discourses which constituted the most discussed issues in the newspaper editorials published in 2016 and 2017 news years.These issues were governance, politics and elections, peace and security, law and order, and corruption.

Static and Dynamic Characteristics of Functional Network Connectivity in Neurologically Asymptomatic Patients Undergoing Maintenance Hemodialysis: A Resting-State Functional MRI Study.

The characteristics of static functional network connectivity (sFNC) and dynamic FNC (dFNC) in neurologically asymptomatic patients undergoing maintenance hemodialysis are unknown. Elucidating these characteristics may improve our understanding of the mechanisms of neuropathological damage in these patients. To explore the static and dynamic characteristics of FNC in neurologically asymptomatic patients undergoing maintenance hemodialysis and the relationship between FNC-related parameters with the neuropsychological scores and blood biomarkers. Retrospective. A total of 23 neurologically asymptomatic patients undergoing maintenance hemodialysis and 25 healthy controls matched for age, sex, and years of education. A 3.0 T MRI/functional MRI and three-dimensional-T1 structural imaging ASSESSMENT: Independent components; spatial map intensity; sFNC and dFNC strengths; and time attribute parameters (mean dwell time, fractional window, and number of transitions) were determined. Neuropsychological tests were performed. Blood biochemical tests were performed for the patients but not healthy controls. Chi-squared test, one-sample t-test, two-sample t-test, partial correlation analysis, and family-wise error and false discovery rate correction. P < 0.05 denoted statistical significance. Significant group differences in the strengths of sFNC and dFNC between networks were found. The sFNC strength between the visual and sensorimotor networks was significantly associated with the global cognitive function score (i.e. the Montreal Cognitive Assessment [MoCA]) (r=0.606). The sFNC strength between the salience and default mode networks was significantly associated with anxiety scores (r=0.458). In state 1, positive correlations were found between the mean dwell time and backward digital span task score (r =0.562), fractional window and MoCA score (r=0.576), and fractional window and backward digital span task score (r=0.592). Neurologically asymptomatic patients undergoing maintenance hemodialysis had defective sFNC and dFNC. Our results provide a new perspective on the mechanism of neuropathological damage in patients undergoing maintenance hemodialysis. 1 TECHNICAL EFFICACY: Stage 1.

Current Progress and Future Perspectives of Electrolytes for Rechargeable Aluminum‐Ion Batteries

Aluminum‐ion batteries (AIBs) with Al metal anode are attracting increasing research interest on account of their high safety, low cost, large volumetric energy density (≈8046 mA h cm−3), and environmental friendliness. Specifically, the reversible Al electrostripping/deposition is achieved with the rapid development of room temperature ionic liquids, and rapid progress has been made in fabricating high‐performance and durable AIBs during the past decade. This review provides an integrated comprehension of the evolution of AIBs and highlights the development of various non‐aqueous and aqueous electrolytes including high‐temperature molten salts, room temperature ionic liquids, and gel–polymer electrolytes. The critical issues on the interplay of electrolytes are outlined in terms of the voltage window span, the effective ion species during charge storage (Al3+ or ) and their underlying charge transfer (e.g., interfacial transfer and diffusion), and the solid electrolyte interface formation and its role. Following the critical insight, future perspectives on how to practically design feasible AIBs are given.

Decoupled Right Invariant Error States for Consistent Visual-Inertial Navigation

The invariant extended Kalman filter (IEKF) is proven to preserve the observability property of visual-inertial navigation systems (VINS) and suitable for consistent estimator design. However, if features are maintained in the state vector, the propagation of IEKF will become more computationally expensive because these features are involved in the covariance propagation. To address this issue, we propose two novel algorithms which preserve the system consistency by leveraging the invariant state representation and ensure efficiency by decoupling features from covariance propagation. The first algorithm combines right invariant error states with first-estimates Jacobian (FEJ) technique, by decoupling the features from the Lie group representation and utilizing FEJ for consistent estimation. The second algorithm is designed specifically for sliding-window filter-based VINS as it associates the features to an active cloned pose, instead of the current IMU state, for Lie group representation. A new pseudo-anchor change algorithm is also proposed to maintain the features in the state vector longer than the window span. Both decoupled right- and left-invariant error based VINS methods are implemented for a complete comparison. Extensive Monte-Carlo simulations on three simulated trajectories and real world evaluations on the TUM-VI datasets are provided to verify our analysis and demonstrate that the proposed algorithms can achieve improved accuracy than a state-of-art filter-based VINS algorithm using FEJ.

Flexible in-plane zinc-ion hybrid capacitors with synergistic electrochemical behaviors for self-powered energy systems

Flexible in-plane zinc-ion hybrid micro-capacitors (ZIHCs) are built up with matchable device capacitance, operating voltage window and life span for advanced self-powered energy systems.

Determining of Climatic Parameters Using CFD in Different Window Span in Naturally Ventilated Greenhouses

The aim of this study was to compare with the measured inner air temperature and relative humidity values and the simulated values determined with Computational Fluid Dynamics (CFD) in the naturally ventilated gable-roofed single glass greenhouse located North-South direction, having 45o and 90° window spans and under no cultivation.&#x0D; The measured values were recorded every 2 hours from 8 am to 18 pm using the relative humidity and air temperature sensors placed in 7 different locations. Measurements were made in case of the 45° and 90° window span openness. For CFD simulations, the SolidWorks 2011 software was used. The values of air temperature and relative humidity inside the greenhouse were simulated depending on the outside ambient conditions and structural and physical properties of greenhouse. Then, the measured values were compared with the simulated values and error rate for each sensors were determined.&#x0D; As a result, it was determined minimum error rates of the measured and simulated air temperature and relative humidity in greenhouses is 4.8% and 4.7%, respectively. The study showed that the CFD can be used as a powerful tool for determining inner climatic factors in naturally ventilated greenhouses.

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery.

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

Attentional limits in visual search with and without dorsal parietal dysfunction: space-based window or object-based span?

Attentional resource and distribution are specifically impaired in simultanagnosia, and also in the visuo-attentional form of developmental dyslexia. Both clinical conditions are conceived as a limitation of simultaneous visual processing after superior parietal lobule (SPL) dysfunction (review in Valdois et al., 2019). However, a reduced space-based attentional window (i.e. a limited visual eccentricity at which the target object can be identified, Khan et al. 2016) has been demonstrated in simultanagnosia versus a reduced object-based span (i.e. a limited number of objects processed at each fixation, Bosse et al., 2007) in developmental dyslexia. In healthy individuals, the cost in reaction times per item in serial search tasks suggests that a group of objects is processed simultaneously at a time, but this group is also undefined and depends on the visual complexity of the task.Healthy individuals and a patient with simultanagnosia performed serial search tasks involving either symbols (made of separable features) or objects made of non-separable features, and with distractors that were either all identical or all dissimilar. We used a moving window paradigm to determine whether the task was performed with a “working space” versus a “working span” limitation in control group and in patient with bilateral SPL damage.We found that healthy individuals performed search in a color task comprising non-separable feature objects and dissimilar distractors with a limited space-based attentional window; this attentional window, as well as the mean saccade amplitude used to displace it across the visual display, were independent of set size, thus inconsistent with an object-based attentional span. In the symbol task comprising a feature-absent search in which all feature-present distractors were dissimilar, we observed that mean saccade amplitude decreased with set size and that search performance could not be mimicked by a moving window of a single diameter; instead participants seemed to process a fixed number of symbols at a time (object-based span).Following bilateral SPL lesions, patient IG demonstrated a similar space-based search process in the color search task with a normal attentional window. In contrast, her cost-per-item in the symbol task increased dramatically, demonstrating a clear deficit of simultaneous object perception. These results confirmed the specific contribution of the SPL to the visual processing of multiple objects made of separable features (like letters), and more dramatically when they are all different, which explains the specific difficulty for a reading beginner in case of SPL dysfunction.

Measuring grammatical status in Chinese through quantitative corpus analysis

This paper applies a quantitative model developed for measuring grammatical status, using data from the Lancaster Corpus of Mandarin Chinese (lcmc). The model takes into account four quantitative factors (token frequency, collocate diversity, colligate diversity and deviation of proportions) and uses them as predictors in a binary logistic regression in order to compute a score of grammatical status between ‘0’ (lexical/non-grammatical) and ‘1’ (highly grammatical) for each given element. The results of the lcmc model are then compared to those of a similar study of the British National Corpus (bnc). The comparison suggests that token frequency emerges as one of the most relevant parameters for quantifying degrees of grammatical status in both language models, together with the collocate diversity measure when using a broad window span. On the other hand, the colligational measures (left- or right-based) and the other collocate diversity measures using small spans (left- or right-based) contribute very differently to the two languages due to their typologically distinctive structures.

Monitoring and sensing COVID-19 symptoms as a precaution using electronic wearable devices

PurposeThe purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.Design/methodology/approachThe function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.FindingsExperiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.Originality/valueThe presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

Proposing A New Variable Window for Better Side Lobe Reduction

Digital signal processing is most widely used to process the signal. In digital signal processing filters are used to remove some unwanted constituents from aspired signal. Windowing is a scheme of finite impulse response filters. Present paper proposes a new versatile window function. It has two variable parameters first one is window span N and another changeable parameter is r. when the value of variable parameter r increases width of major lobe of window also increases with better side lobe reduction and vice versa. Gaussian window and Kaiser window are the well-known variable windows. This paper shows that the proposed window has more desirable results in comparison of Gaussian and Kaiser window with low power loss and better side lobe reduction. To achieve minimum power loss peak side lobe level should have to minimum. Proposed window has low peak side lobe level (-17.681dB) in comparison of Gaussian (-11.836dB) and Kaiser window (-6.9704dB). Proposed work shows that the proposed window has finer spectral characteristic then Gaussian and Kaiser window. FIR filter formed by applying proposed window has narrow -3dB bandwidth (2π×0.320 rad/sample) corresponding to FIR filter formed by using Gaussian and Kaiser window. Ripple ratio of FIR filter plotted by applying proposed window (-144.321dB) is less corresponding to FIR filter delineated by using Gaussian and Kaiser which indicates that the proposed window will give better side lobe rejection and reduce the aliasing problem. In the biomedical field noise present in ECG signal can also reduce by using proposed window.

Improving traffic noise transmission loss of plenum windows by using sound scatterer arrays

Plenum windows have been used recently in residential buildings erected next to busy trunk roads. These windows are able to provide an additional 8 dB traffic noise reduction than the conventional casement windows, but at the same time they can allow for natural ventilation of the residential units they protect acoustically. The window design consists of two parallel glass panes—one at the building façade (outer) and the other in the indoor side (inner). The outer and inner window openings are located on opposite window of the window span so that they, together with the cavity between the two window panes, form a passage for air movement, while the window’s structure acts as an acoustic filter to attenuate noise. To improve the acoustical performance of this window design, it is proposed in this study to install sound scatterer arrays into the window cavity. It is found from finite-element simulation that such proposal, under appropriate settings, can result in about 4–5 dB improvement of traffic noise transmission loss across a plenum window without significant reduction of air movement across the window. The acoustical performance will further be improved without the increase in air flow resistance when the scatterer rows are staggered.

A Meta-Assembly of Selection Signatures in Cattle.

Since domestication, significant genetic improvement has been achieved for many traits of commercial importance in cattle, including adaptation, appearance and production. In response to such intense selection pressures, the bovine genome has undergone changes at the underlying regions of functional genetic variants, which are termed “selection signatures”. This article reviews 64 recent (2009–2015) investigations testing genomic diversity for departure from neutrality in worldwide cattle populations. In particular, we constructed a meta-assembly of 16,158 selection signatures for individual breeds and their archetype groups (European, African, Zebu and composite) from 56 genome-wide scans representing 70,743 animals of 90 pure and crossbred cattle breeds. Meta-selection-scores (MSS) were computed by combining published results at every given locus, within a sliding window span. MSS were adjusted for common samples across studies and were weighted for significance thresholds across and within studies. Published selection signatures show extensive coverage across the bovine genome, however, the meta-assembly provides a consensus profile of 263 genomic regions of which 141 were unique (113 were breed-specific) and 122 were shared across cattle archetypes. The most prominent peaks of MSS represent regions under selection across multiple populations and harboured genes of known major effects (coat color, polledness and muscle hypertrophy) and genes known to influence polygenic traits (stature, adaptation, feed efficiency, immunity, behaviour, reproduction, beef and dairy production). As the first meta-assembly of selection signatures, it offers novel insights about the hotspots of selective sweeps in the bovine genome, and this method could equally be applied to other species.

ECG 기반의 운전자별 인지 부하 평가 방법 개발

Objective: The present study is to develop an effective evaluation method for a driver’s cognitive workload using electrocardiography (ECG) signal. Background: ECG measures such as heart rate (HR) and heart rate variability (HRV) have been used for cognitive workload evaluation. Since ECG changes by cognitive workload vary largely depending on personal characteristics, an optimal analysis protocol of ECG needs to be tailored to each individual accordingly; however, existing studies have not considered personal characteristics in ECG analysis for cognitive workload evaluation. Method: The proposed evaluation method uses the area under the receiver operating characteristic (ROC) curve (AUC). A preliminary analysis was conducted with ECG data collected in a driving simulator while an n-back task was conducted. AUC analysis was performed for four ECG metrics (mean IBI, SDNN, RMSSD, and RMSE), three window spans (20, 30, and 40 seconds), and three update rates (1, 2, and 3 seconds). Results: It was identified that the optimal ECG analysis parameters of metric, window span, and update rate maximizing the discriminability of cognitive workload evaluation varied between individual drivers. Conclusion: The finding of the present study supports the use of an individually customized ECG analysis protocol for better evaluation accuracy of a drivers’ cognitive workload. Application: The proposed ECG analysis method for cognitive workload evaluation can be applied to development of a safe driving support system.

Robustness of different regression modelling strategies in epidemiology: a time-series analysis of hospital admissions and air pollutants in Lisbon (1999-2004)

Studies of the acute health effects of air pollution have used exposure windows of different spans and related them to single-day responses. Little is known about whether an increased response window span might be a viable alternative to single-day responses. Our aim is to compare a new model specification where both the exposure and response variables are represented as 7 day moving averages (CMA&CMA model) with the most widely used model specifications in the literature, where the response variable is usually a single-day, in terms of coefficients and their precision and robustness. To this end, daily series of 12 emergency-related hospital admissions and 6 air pollutants spanning 5.5 years in Lisbon were analysed through single-pollutant linear regression and, when necessary M-estimation. With our data, the CMA&CMA model yields coefficients that are very close to models where only the exposure variable is specified as a moving average whether the latter are estimated by OLS or robust M-estimation. In addition, the CMA&CMA model leads to more precise and robust estimates than other model specifications. The new model specification is a straightforward tool for adjusting weekend effects and errors. It is also analogous to robust estimation, with the added advantages of being sensitive to extreme values that are clustered in time, and leading to more precise and robust estimates without loss of high-frequency information. One drawback is the induction of autocorrelation in the residuals. Copyright © 2009 John Wiley & Sons, Ltd.

Do body condition and plumage during fuelling predict northwards departure dates of Great Knots Calidris tenuirostris from north‐west Australia?

It is often assumed that strong selection pressures give rise to trade‐offs between body condition and time in long‐distance migrating birds. Birds that are ‘behind schedule’ in fuel deposition or moult should delay departure, and this should result in a negative correlation between initial condition and departure date. We tested this hypothesis in the Great Knot Calidris tenuirostris migrating from north‐west Australia to eastern Asia en route to Siberia. Great Knot gain mass and moult into breeding plumage before leaving northern Australia in late March and early April, and fly 5400–6000 km to eastern China and Korea. We radiotracked 27 individuals (17 males and ten females) to determine departure dates; 23 migrated and four remained in Australia. We characterized body condition at capture using body mass, predicted pectoral muscle mass (based on ultrasound estimates of the size of the pectoral muscles) and breeding plumage scores. Residual condition indices were uncorrelated, indicating that at the individual level, variation in one fuelling component was not strongly associated with variation in the other components. Birds that did not depart had lower residual body mass and breeding plumage indices than those that did migrate; these four birds may have been subadults. Neither sex, size nor the condition indices explained variation in departure date of migrants. Reasons for this are explored. Departure dates for northward migrating waders indicate that the migration window (span over which birds depart) decreases with proximity to the northern breeding grounds. We suggest that migration schedules become tighter as birds get nearer to the breeding grounds. Thus the lack of a relationship between condition and departure date in Great Knots may reflect the fact that the departure episode under study is the first one in sequence and is still 4–8 weeks before breeding.

Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation

Local likelihood estimation has proven to be an effective method for obtaining estimates of parameters that vary with a covariate. To obtain useful estimates of such parameters, approximating models are used. In such cases it is useful to consider window based estimates. We may need to choose between competing approximating models. In this article, we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also made useful for window span selection. The usefulness of the weighted version of these criteria is demonstrated through a simulation study and an application to three datasets.