Moving Time Intervals Research Articles

Beneficial Effects of Self-Motion for the Continuous Phase Analysis of Ac-Coupled Doppler Radars

This paper analyzes the beneficial effects on phase detection arising from the motion of an ac-coupled Doppler radar. Indeed, although the presence of an ac coupling stage suppresses the dc offset after the receiver RF output, due to the coupling capacitor, a high-pass behavior is introduced; the presence of a high-pass behavior leads to signal distortion, particularly for low Doppler frequencies, which are typical in many biomedical or industrial applications. Since the target displacement is usually extracted from the phase history, this effect might, in turn, worsen the overall accuracy of the system. Moreover, if the target alternates stationary and moving time intervals, the phase detection step becomes challenging. Indeed, during the stationary time, the output of the RF front-end shows only noise fluctuations that, in turn, result in uncorrelated phases which might be confused with the real target displacement. This negative effect might be avoided by keeping the radar continuously moving, thus exploiting what is usually considered a state that is negative and worthy of attention. In this contribution, this effect is addressed from a different perspective, and ad hoc experimental case studies are shown to demonstrate the effectiveness of the proposed system. This task has been accomplished through theoretical analysis and related experimental activity.

Control of electricity consumption regimes of producers consumers

The article investigates models and methods of complex control of power consumption modes. It is shown that the power shortage during peak hours makes it difficult to maintain a balance between generation and consumption capacity, which led to the need to impose restrictions on the electrical load of industrial consumers and the development of methods and tools to control power consumption. Moreover, the tasks of controlling the modes of active power consumption and reactive power compensation are closely related. It is shown that the following methods of power consumption management are currently known: instantaneous rate; at the ideal rate; management on the forecast value; control with the use of average power on a moving time interval ("moving window" method). Optimal control of power consumption system modes and reactive power modes for the relevant control period is based on the use of operational forecasting methods. Studies have shown that a positive role in improving the efficiency of optimization of power consumption modes can play and influencing its level of voltage control in the power supply system. In this regard, it is advisable to move to a comprehensive solution to the problem of power management, both through the management of consumers - regulators, and the impact on the voltage regimes of the power supply system. Static characteristics of changes in active and reactive powers from voltage changes in the power supply system are given. Solving the problem of obtaining static characteristics is possible as a result of active experiments; or based on the integration of static characteristics of individual electrical receivers. Focus on the second way is advisable with a limited number of different types of electrical receivers. However, in any case, it is necessary to determine the static characteristics of individual large consumers - regulators, disabling or enabling which can significantly affect the integrated static characteristics of the power supply. An approach to integrated management of power consumption modes is proposed, which consists in the fact that from the beginning of the controlled interval periodically formed real values of active and reactive electricity consumption, as well as voltage values at the controlled point of the network. Then the forecast of active and reactive energy consumption to the end of the controlled interval and the forecast of the average voltage level is determined, the deviation between the forecast and set power consumption is determined and, if the forecast value of power consumption exceeds the set. At the same time determine the possible changes in reactive power and, if necessary, control the capacitor banks. Experimental studies of the considered models are carried out. The structural scheme of the system of complex control and management of power consumption modes is given.

ADAPTIVE MODELS OF ELECTRIC LOAD FORECASTING OF PRODUCTION CONSUMERS

The article investigates models and methods of electric load forecasting. It is shown that the following methods of power consumption control are currently known: instantaneous norm; at the ideal rate; management on the forecast value; control with the use of average power on a moving time interval ("moving window" method). It is shown that it is better to focus on those methods that are based on the study of forecast estimates, which are the source information for management decisions. The main requirements for real-time systems are: high accuracy of operational forecasting and simplicity of algorithms, which provides a minimum solution time; work in conditions of uncertain and insufficient information, ensuring the stability of management. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that automated control systems have specific requirements for mathematical forecasting methods due to little study of the nature of the forecast parameter, small amount of reporting statistics and insufficient reliability of source information and most accurately meets such requirements, adaptive approach to method design. The adaptive approach allows to solve the problem of adequacy of the method of the object of forecasting and from the point of view of simplicity of realization and time of calculations in the first place it is necessary to put adaptive methods of forecasting and, first of all, the method of exponential smoothing. Exponential smoothing, considered in this paper as a predictive model, allows to identify the inadequacy of the model to the real process and to bring the estimate of the determined basis of the process closer to the real one, ie to reduce the prediction error. However, this requires time, which increases with increasing changes in the coefficients of the model. In this regard, there is a problem of regulating the reaction rate of the predicted model to changes in its coefficients. A number of methods of automatic adjustment of the smoothing parameter are considered and analyzed: the evolutionary method of adaptation, methods using the tracking signal, methods of adapting the parameter by, optimization using gradient smoothing. It is shown that the method of adaptation using the tracking signal is simple and especially valuable for modeling series with a short history. Due to its simplicity, this method is especially convenient where predictions are made using computer technology. Studies of this model of forecasting on statistical data obtained at various enterprises have shown that the model adapts to real data at step 4-6 of forecasting and then the forecast error does not exceed 2%. Analysis of adaptive forecasting models based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. The greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of the previously existing qualitative relationships of the parameters of the projected system. If there is a jump, it is very important to assess whether the deviation is caused by an obstacle or whether it is due to a change in the predicted process. If the changes are caused by an obstacle, it must be filtered out. If the deviations are caused by a change in the model, then the current process data are of the greatest value. From the point of view of fast working off of abrupt change the model of exponential smoothing with high value of the smoothing parameter is rather effective. However, this model is highly susceptible to interference. To eliminate this circumstance, a modified procedure for correcting the parameters of the forecasting model is proposed. The procedure is based on the introduction of a logical operator, which is based on the analysis of inconsistencies in the forecasts and imposes additional restrictions on changes in the smoothing parameter and the values of the original statistics. Experimental studies of the considered models are carried out.

COMBINED METHOD OF ELECTRIC ENERGY CONSUMPTION MANAGEMENT OF PRODUCTION FACILITIES

The article investigates the methods of power consumption control. It is shown that the power shortage during peak hours makes it difficult to maintain a balance between generation and consumption capacity, which leads to the need to introduce restrictions on the electrical load of industrial consumers and the development of methods and tools to control power consumption. The need to manage energy consumption is due to a number of reasons, including: 1) the significant difference between peak load and night failure in power systems; 2) insufficient regulatory capacity of thermal power plants and nuclear power plants to cover the variable part of load schedules; 3) unfavorable tendency to reduce the share of shunting capacity in power systems caused by the consolidation of power units; 4) significant capital and energy costs associated with the construction and operation of peak units; 5) technical feasibility and economic feasibility of artificial alignment of load schedules. It is shown that the following methods of power consumption control are currently known: instantaneous rate; at the ideal norm; management on the forecast value; control with the use of average power on a moving time interval (moving window method) and combined methods that use different combinations, as well as the distribution of control influence between consumers - regulators (resource allocation method). It is better to consider such a control method that provides for a larger number of managed consumers random time distribution of possible short-term emissions, their minimum absolute value, as well as more complete use by the consumer of the declared capacity or allocated power and energy limits. According to these criteria, the listed management methods are compared. Moreover, the method of instantaneous rate is implemented in control or automatic control using a signal proportional to the average power for 1, 2 or 3 minutes, the method of the ideal norm involves measuring the average power of the consumer for short periods of time (up to three minutes) and comparing this value with the declared ( or limit) power, control with prediction is that the control signal is formed as the difference between the amount of possible power and predicted at the end of the control cycle. To increase the efficiency of power consumption management, a combined control method is proposed, which is a synthesis of two known methods - control at the ideal norm and control over the predicted value of power consumption, which combines the advantages of both methods. The condition of management is maintenance of such mode of power consumption at which the set capacity is not exceeded and its set value is used as much as possible. Fulfillment of this condition is provided by working off of the restrictions set to each step of management. Due to the fact that different control steps have different control reactions to the real process, appropriate control methods are used for different steps. In the first steps, when there is enough time to adopt the optimal control effect and it can be adjusted, the control is based on the forecast value of electricity consumption, in the last steps, which have special responsibility for the control process; control is carried out by the ideal norm. Thus, this method combines the advantages of forecast management (control effect is justified) and the method of management at the ideal norm (in the last step of management, which allows optimal use of the remaining power limit and not exceed the specified value) with a priori justified order of consumers-regulators.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Long–Term Variations in Solar Differential Rotation and Sunspot Activity, II: Differential Rotation Around the Maxima and Minima of Solar Cycles 12 – 24

Studies of variations in the solar differential rotation are important for understanding the underlying mechanism of solar cycle and other variations of solar activity. We analyzed the sunspot-group daily data that were reported by Greenwich Photoheliographic Results (GPR) during the period 1874 – 1976 and Debrecen Photoheliographic Data (DPD) during the period 1977 – 2017. We determined the equatorial rotation rate [ $A$ ] and the latitude gradient [ $B$ ] components of the solar differential rotation by fitting the data in each of the 3-year moving time intervals (3-year MTIs) successively shifted by one year during the period 1874 – 2017 to the standard law of differential rotation. The values of $A$ and $B$ around the years of maxima and minima of Solar Cycles 12 – 24 are obtained from the 3-year MTIs series of $A$ and $B$ and studied the long-term cycle-to-cycle modulations in these coefficients. Here we have used the epochs of the maxima and minima of the Solar Cycles 12 – 24 that were recently determined from the revised Version-2 international sunspot-number series. We find that there exits a considerably significant secular decreasing-trend in $A$ around the maxima of solar cycles. There exist no secular trends in both $A$ and $B$ around the minima of solar cycles. The secular trend in $B$ around the maxima of solar cycles is also found to be statistically insignificant. We fitted a cosine function to the values of $A$ , and also to those of $B$ , after removing the corresponding linear trends. The cosine-fits suggest that there exist ≈54-year (≈94-year) and ≈82-year (≈79-year) periodicities in $A$ ( $B$ ) around the maxima and minima of solar cycles, respectively. The amplitude of the cosine-profile of $A$ ( $B$ ) around the minima of solar cycles is about 41% (65%) larger than that of $A$ ( $B$ ) around the maxima. In addition, the cosine profiles of $A$ and $B$ suggest a large (up to $180^{\circ }$ ) phase difference between the long-term variations of $A$ , and also between those of $B$ , around maxima and minima of solar cycles. Implications of all these results are discussed briefly.

WebObs: The Volcano Observatories Missing Link Between Research and Real-Time Monitoring

Volcanological observatories have common needs and often common practical issues for multi-disciplinary data monitoring applications. Real-time access to integrated data, technical metadata, modeling and estimation of uncertainties are fundamental for an efficient interpretation. But in fact, the heterogeneity of instruments or acquisition systems and the inherent problems to produce rapid models using real-time data lead to difficulties that may hinder crisis management. In an attempt to globally address these questions, the French volcanological and seismological observatories have developed a specific operational software system over the past 19 years. Based on GNU/Linux open source tools and a Web interface, the WebObs system mainly offers: (1) a modular database for equipment network management; (2) a dozen of evolving dedicated periodic tasks for each monitoring technique like seismology, deformations and geochemistry that use standard data formats with automated execution of periodic tasks that produce high-quality graphs on preset moving time intervals, data exports, optional event notifications including e-mail alerting, instruments status controls based on their data validity; (3) web-form interfaces for manual data input/editing and export; (4) a user request form to adjust the tasks parameters for a single execution and to produce customized graphs and data exports. This system hence constitute a web-based tool that perform integrated, centralized and automated real-time volcano monitoring. It has therefore become a strong support for data analysis and exchange between researchers, engineers, and technicians during periods of unrest as well as periods of long-term quiescence. WebObs is also widely open for development of interdisciplinary modeling and enhanced data processing. This allows scientists to test new methods with real-time data flux and to instantaneously share their results in the community.

Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California

Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large wildfires greater than 5000 acres in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for the comparison. The standard AF products from the moderate resolution imaging spectroradiometer (MODIS), the visible infrared imaging radiometer suite (VIIRS), and the combined products were used as inputs. We found that the estimated fire progression areas generated by the natural neighbor method with the combined MODIS and VIIRS AF input layers performed the best, with R 2 of 0.7 ± 0.31 and RMSE of 1.25 ± 1.21 (103 acres) at a daily time scale; the accuracy was higher when assessed at a two-day rolling window, e.g., R 2 of 0.83 ± 0.20 and RMSE of 0.74 ± 0.94 (103 acres). A relatively higher spatial accuracy was found using the 375 m VIIRS AF product as inputs, with a kappa score of 0.55 and an overall accuracy score of 0.59, when interpolated with the natural neighbor method. Furthermore, the locational pixel-based comparison showed 61% matched to a single day and an additional 25% explained within ±1 day of the estimation, revealing greater confidence in fire progression estimation at a two-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.

Validity and reliability of the Cold Discomfort Scale: a subjective judgement scale for the assessment of patient thermal state in a cold environment

Complementary measures for the assessment of patient thermoregulatory state, such as subjective judgement scales, might be of considerable importance in field rescue scenarios where objective measures such as body core temperature, skin temperature, and oxygen consumption are difficult to obtain. The objective of this study was to evaluate, in healthy subjects, the reliability of the Cold Discomfort Scale (CDS), a subjective judgement scale for the assessment of patient thermal state in cold environments, defined as test–retest stability, and criterion validity, defined as the ability to detect a difference in cumulative cold stress over time. Twenty-two healthy subjects performed two consecutive trials (test–retest). Dressed in light clothing, the subjects remained in a climatic chamber set to −20 °C for 60 min. CDS ratings were obtained every 5 min. Reliability was analysed by test–retest stability using weighted kappa coefficient that was 0.84 including all the 5-min interval measurements. When analysed separately at each 5-min interval the weighted kappa coefficients were was 0.48–0.86. Criterion validity was analysed by comparing median CDS ratings of a moving time interval. The comparison revealed that CDS ratings were significantly increased for every interval of 10, 15, and 30 min (p < 0.001) but not for every interval of 5 min. In conclusion, in a prehospital scenario, subjective judgement scales might be a valuable measure for the assessment of patient thermal state. The results of this study indicated that, in concious patients, the CDS may be both reliable and valid for such purpose.

CLIMTREG: Detecting temporal changes in climate–growth reactions – A computer program using intra-annual daily and yearly moving time intervals of variable width

Analysing climate–growth relationships is one of the key areas in dendrochronological research. One problem however remained unsolved – the discrepancy between the trees continuous growth over the vegetation period and the mean climate variables stretching over much longer periods. Here we present the possibility to calculate climate–growth correlations based on daily climate data using variable temporal width together with moving correlations to accommodate for short term as well as long term influences on tree growth. For the first time this offers the opportunity to acknowledge annual changes in the growing seasonal length and effects of short extreme events. Numerous outputs in data- and graphic-files allows a comparison of varying periods with significant correlations between climate and tree growth. Furthermore the use of climate scenarios is an optional tool for growth predictions.

Time-dependent vector stabilization

The paper presents necessary and sufficient conditions for a single input system defined by a time-dependent vector to be stabilized by a linear feedback. The paper employs a new technique based on averaging along a moving time interval.

DEVELOPMENT OF INTELLIGENT DECISION MAKING MODEL FOR STOCK MARKETS

This paper is focused on the development of intelligent decision making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions, concerning the purchase of the stocks. Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the „global best” ANNs for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different number of neural networks, moving time intervals and commission fees. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market.

PERIODICITIES IN THE NORTH–SOUTH ASYMMETRY OF THE SOLAR DIFFERENTIAL ROTATION AND SURFACE MAGNETIC FIELD

We have studied the temporal variations in the north–south asymmetries of the differential rotation parameters A, B, and the ‘mean’ rotation rate Ā, by determining their values from Greenwich data for sunspot groups (1879–1976) in the northern and southern hemispheres, during moving time intervals of lengths 3 yr and 5 yr, successively displaced by 1 yr. The variation in the north–south asymmetry (Ā\(a \)) of Ā is similar to the variation in the asymmetry (B\(a \)) of B but with opposite sign. These variations of Ā\(a \) and B\(a \) may represent components of an anti-symmetric torsional oscillation which are in opposite phase with each other.

Periodicities in the solar differential rotation, surface magnetic field and planetary configurations

Using Greenwich data on sunspot groups during 1874–1976, we have studied the temporal variations in the differential rotation parametersA andB by determining their values during moving time intervals of lengths 1–5 yr successively displaced by 1 yr. FFT analysis of the temporal variations ofB (orB/A) shows periodicities 18.3 ± 3 yr, 8.5 ± 1 yr, 3.9 ± 0.5 yr, 3.1 ± 0.2 yr, and 2.6 ± 0.2 yr at levels ≥ 2σ. This analysis also shows five more periodicities at levels 1–2σ. The maximum entropy method is used to set narrower limits on the values of these periods. The reality of the existence of all these periodicities ofB (orB/A ) except the one at 2.8 yr is confirmed by analyzing the simulated time series ofB andB/A with values ofA andB randomly distributed within the limits of their respective uncertainties. Four of the prominent periods ofB agree, within their uncertainties, with the known periods in the the large-scale photospheric magnetic field. The deviations from the average differential rotation are larger near the sunspot minima. On longer time scales, the variations in the amount of sunspot activity per unit time are well correlated to the variations in the amplitudes of the ‘torsional oscillation’ represented by the 22-yr periodicity inB. All the periods inB found here are in good agreement with the synodic periods of two or more consecutive planets. The possibility of planetary configurations providing perturbations needed for the Sun's MHD torsional oscillations is speculated upon and briefly discussed.

Computer analysis of mechanical and electrical uterine activity.

To facilitate the study of myometrial activity two algorithms were developed for continuous analysis of both electrical and mechanical (intrauterine pressure) activity. A combination of maxima- and level detection was used to define cycles in the intrauterine pressure signal, in which frequency, period time, area of the cycle, amplitude, overall- and maximum rate of rise were calculated. The electrical signal was assessed by calculating the root-mean-square (RMS) value in a moving time interval. A threshold value was applied to the obtained RMS curve to detect bursts of electrical activity present in the original signal. Burst frequency, burst period time and mean burst RMS value were calculated from the RMS curve. The algorithms were applied to recordings obtained from chronically instrumented ewes in various experiments.