Scale Principle Research Articles

Experimental study of air curtain thermal insulation efficiency and maximum temperature rise beneath the ceiling under longitudinal ventilation in bifurcated tunnels

With the rapid increase in the number of tunnels and the complexity of tunnel structures, tunnel safety has attracted widespread attention from all sectors of society. As a narrow and semi-enclosed space, a fire in a tunnel will cause serious accidental consequences. In this paper, a series of experiments were conducted in a 1:10 reduced-size bifurcation tunnel to investigate the temperature rise distribution pattern of the ceiling in the bifurcation tunnel and the cooling efficiency of the air curtain under the synergistic effect of longitudinal ventilation and air curtain. The reliability of the data was first confirmed by comparing it with the along-travel temperature decay model of Li. At the same time, by analyzing the temperature distribution in the center of the fire source at different air curtain wind speeds, the air curtain airflow could be obtained to contribute to the reduction of the temperature in the center of the fire source. And by analyzing the maximum temperature rise beneath the ceiling in the area on both sides of the air curtain, the effectiveness of the air curtain to insulate and block smoke was investigated. Finally, considering the influence of scale principle on the exit velocity of air curtain, the non-dimensional air curtain velocity was defined, and the prediction model of the maximum temperature rise of bifurcated tunnel ceiling under the synergistic effect of longitudinal ventilation and air curtain was obtained, which was in good agreement with the experimental data.

Distributed finite-time neural network observer-based consensus tracking control of heterogeneous underwater vehicles

This note scrutinizes the distributed finite-time tracking problem of heterogeneous multi-agent systems with uncertain dynamics and environmental disturbances. To estimate the exogenous disturbances, an adaptive neural network-based disturbance observer is developed in which the approximation error of the neural network is compensated using adaptation laws. Based on finite-time stability theory, a distributed finite-time observer is proposed for each follower in order to estimate the leader's position. Moreover, an adaptive dynamic sliding mode-based controller is also developed for each follower, which ensures finite-time convergence of the tracking error between the leader and the follower. Incorporating adaptive control into the developed composite control structure leads to reducing the chattering, as well as enhancing the robustness and accuracy. Stability analysis and finite-time convergence of the tracking error are performed using the Lyapunov theory and multiple time scale principle, respectively. Finally, simulations and comparisons are performed in order to verify the effectiveness of the developed control algorithm for a group of autonomous underwater vehicles (AUVs).

Robust adaptive control for formation-based cooperative transportation of a payload by multi quadrotors

This paper discusses the development of position control for a rigid body payload carried by a team of quadrotors in the presence of external disturbance and uncertainty in model parameters. The formation of quadrotors is controlled and maintained during payload transportation for uniform load distribution and collision avoidance. In the controller structure, an adaptive sliding mode hierarchical control with the constant plus proportional rate reaching law is designed to compensate for the uncertainties that have unknown bounds. The finite-time stability of the closed-loop system is performed using Lyapunov stability analysis and the multiple time scale principle. Finally, the simulation results are shown to confirm the performance of the proposed controller in payload trajectory tracking without knowing its mass while the formation of the quadrotors is maintained.

EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier

Emotions are strongly admitted as a main source to establish meaningful interactions between humans and computers. Thanks to the advancements in electroencephalography (EEG), especially in the usage of portable and cheap wearable EEG devices, the demand for identifying emotions has extremely increased. However, the overall scientific knowledge and works concerning EEG-based emotion recognition is still limited. To cover this issue, we introduce an EEG-based emotion recognition framework in this study. The proposed framework involves the following stages: preprocessing, feature extraction, feature selection and classification. For the preprocessing stage, multi scale principle component analysis and sysmlets-4 filter are used. A version of discrete wavelet transform (DWT), namely dual tree complex wavelet transform (DTCWT) is utilized for the feature extraction stage. To reduce the feature dimension size, a variety of statistical criteria are employed. For the final stage, we adopt ensemble classifiers due to their promising performance in classification problems. The proposed framework achieves nearly 96.8% accuracy by using random subspace ensemble classifier. It can therefore be resulted that the proposed EEG-based framework performs well in terms of identifying emotions.

A state-of-the-art review on wind power deterministic prediction

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

Multivariable supertwisting fixed‐time approach for RLV re‐entry attitude control

SummaryThe attitude control problem of reusable launch vehicles (RLVs) is investigated based on multivariable supertwisting fixed‐time comprehensive control strategy. A novel multivariable supertwisting fixed‐time approach, which can guarantee that the states converge to zero within a fixed time, is proposed. According to the multiple time scale principle, the RLV attitude control system is divided into attitude angle subsystem and attitude angle rate subsystem. Based on multivariable supertwisting fixed‐time approach, comprehensive design of fixed‐time disturbance observer and controller is proposed to ensure that the attitude angle tracking error converges to zero within a fixed time. The features of the proposed control scheme contain that the settling time is independent of initial conditions and it has the better property of chattering reduction. Finally, the efficiency of the proposed method is verified through numerical simulation.

Solving large scale unit dilemma in electricity system by applying commutative law

The conventional system, pooling resources with large centralized power plant interconnected as a network. provides a lot of advantages compare to the isolated one include optimizing efficiency and reliability. However, such a large plant need a huge capital. In addition, more problems emerged to hinder the construction of big power plant as well as its associated transmission lines. By applying commutative law of math, ab = ba, for all a,b €-R, the problem associated with conventional system as depicted above, can be reduced. The idea of having small unit but many power plants, namely “Listrik Kerakyatan,” abbreviated as LK provides both social and environmental benefit that could be capitalized by using proper assumption. This study compares the cost and benefit of LK to those of conventional system, using simulation method to prove that LK offers alternative solution to answer many problems associated with the large system. Commutative Law of Algebra can be used as a simple mathematical model to analyze whether the LK system as an eco-friendly distributed generation can be applied to solve various problems associated with a large scale conventional system. The result of simulation shows that LK provides more value if its plants operate in less than 11 hours as peaker power plant or load follower power plant to improve load curve balance of the power system. The result of simulation indicates that the investment cost of LK plant should be optimized in order to minimize the plant investment cost. This study indicates that the benefit of economies of scale principle does not always apply to every condition, particularly if the portion of intangible cost and benefit is relatively high.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.

Combining geophysical techniques and multi-criteria GIS-based application modeling approach for groundwater potential assessment in southwestern Nigeria

A new approach of modeling very low frequency–electromagnetic (VLF–EM) and vertical electrical sounding (VES) data with a view of evaluating groundwater resources potential via application of GIS-based multi-criteria technique is investigated in this study. On eight VLF–EM traverses established in the site, 40 VES locations were combed. The acquired geophysical data (VLF–EM and VES) were processed applying Fraser/Karous–Hjelt filter and Win-Resist program geophysical software to determine the area subsurface geophysical parameters. Five hydrogeologic maps were produced based on the results of the interpreted geophysical parameters. The produced hydrogeologic maps were assigned suitable weights and different rankings to the individual classes boundary within the maps using the standard Saaty’s scale principle in the context of analytical hierarchy process (AHP) data mining technique. A raster-based empirical GIS model was developed for integrating the hydrogeologic maps to compute the groundwater potential index (GWPI) values in the range of 1.02–2.82 for the study area. Based on the estimated GWPI results, a final map zoning the area into low (0.0930–1.3922), medium (1.3922–1.9109) and high (1.9109–2.8173) groundwater potential classes was produced in GIS environment. The prediction accuracy of the produced potential map was established via cross-validation and in situ well correlation analysis. The results of the study established a new approach of modeling geophysical data for exploring groundwater productivity potential in the study area.

Positive Feedback in Climate: Stabilization or Runaway, Illustrated by a Simple Experiment

Abstract The response of the various climatic processes to climate change can amplify (positive feedback) or damp (negative feedback) the initial temperature perturbation. An example of a positive feedback is the surface albedo feedback: when the surface temperature rises, part of the ice and snow melts, leading to an increase in the solar radiation absorbed by the surface and to an enhanced surface warming. Positive feedbacks can lead to instability. On Venus, for example, a positive feedback is thought to have evolved into a runaway greenhouse effect. However, positive feedbacks can exist in stable systems. This paper presents a simple representation of a positive feedback in both a stable and an unstable system. A simple experimental device based on a scale principle is introduced to illustrate the positive feedback and its stabilization or runaway regimes. Stabilization can be achieved whether the amplitude of the positive feedback declines (e.g., “saturation” of the feedback) or remains constant. The device can also be used to illustrate the existence of tipping points, which are threshold values beyond which the amplification due to feedbacks or the stability of the system suddenly changes. The physical equations of the device are established in the framework of the feedback analysis. Key features to understand why a positive feedback does not necessarily lead to a runaway effect are described. The analogy between the different components of the device and those of the climate system is established. Finally, the contribution of individual feedbacks to the total climate response is addressed.

Sensor Configuration Problem: Application to a Membrane Separation Unit

For high performance model based control applications the state and parameter estimation algorithms are essential. Furthermore the accuracy of the resulting estimates highly depends on what is being measured. In this work we address the output channel selection problem by making use of the degree of observability measures defined by the observability gramians of the associated system and possible different sensor configurations. The observability gramians for large scale and nonlinear first principle models are difficult to compute. Instead the data based approximations, the empirical observability gramians, are constructed. The degree of observability measures are used for comparing the spectral properties of the observability gramians to decide the sensor configuration. The output channel selection procedure is applied to an industrial membrane filtration system.

KEY TECHNIQUE OF RP&M NETWORKED SERVICE SYSTEM

The structure of the networked integrated service system established on the basis of distributed component structure is expounded, and corresponding functional modules are designed according to each component feature. An index system of RPM process decision including goal layer, criterion layer and scheme layer is proposed. A new method for RPM process selection based on expert system and fuzzy synthesis evaluation is presented and the support system for RPM manufacture process selection is set up. Based on the analysis of process planning of RPM collaborative manufacture, different types of RPM process monitoring points are determined according to the time scale principle,which provides the theoretical basis for the process management and monitoring. The implementation of the networked service system verifies its capacity of supporting enterprises with the convenient and rapid remote development of new products.

Commitment to Health Scale

The Commitment to Health Scale (CHS) was developed to predict likelihood of clients being able to permanently adopt new health-promoting behaviors. Commitment is based on the association between starting new health behaviors and long-term performance of those behaviors. The CHS evolved from an examination of Prochaska and DiClemente's Stages of Change Algorithm, Decisional Balance Scale, and Strong and Weak Principle (Velicer, Rossi, Prochaska, & DiClemente, 1996). Scale items were assessed by classical and Rasch measurement methods. The research was performed in three separate studies at various locations in the United States and included approximately 1100 subjects. A new unidimensional variable was identified called Commitment to Health. Internal consistency reliability of the scale was .94 (Cronbach's alpha). External validity and reliability were assessed based on expected and observed ordering and between known groups. Scale scores predicted self-reported health behaviors and body mass index.

Reduced-scale model tests for cut and cover tunnels subjected to eccentric load

Cut and cover tunnel has been considered to be an environment-friendly construction technique to reduce the anticipating destruction of ecosystem. This paper describes the results of reduced-scale model tests for cut and cover tunnels, which are especially subjected to eccentric load. Experimental tests were carried out carefully under control and according to designed procedures. The scale principle that has strong influence on model test results was characterized by flexibility theory. All the model tests were performed using sand under plane strain two-dimensional condition without water pressure and earth pressures, radial displacements, and internal and external strains of model lining were measured. Direct pressure on the top of model cell was applied by air pressure to simulate the overburden pressure induced by the construction of embankment. Two main conditions were set to observe the influences of eccentric load on cut and cover tunnels: (a) location of embankment slope; and (b) angle of embankment slope. The model cell, measuring 150 cm wide by 30 cm long, was manufactured on steel base as shown in Fig. (a). To simulate the in situ high pressure in field condition, the pressure apparatus was designed as shown in Fig. (b). Model ground was formed with dry sand by raining method and the construction sequence was simulated by pressure apparatus. Model tunnel lining was fabricated with aluminum and had a diameter of 30 cm in a half circle shape Fig. (c). Model lining thickness was determined to 8 mm based on scale principle with flexibility ratio (EI). Based on the results of experimental tests, the measurements were analyzed and interpreted: (a) The earth pressure on the side of eccentric load increases rapidly when the locations of embankment slope are within 1.0 D (D: equivalent diameter of tunnel model) from the center of model lining. (b) The earth pressure on the side of eccentric load increases in proportion to the angle of embankment slope. (c) The measurements of displacement and strain show that the lining on the side of eccentric load, deforms into the tunnel, however, contrary behavior of lining is found on opposite side. (A). Reprinted with permission from Elsevier. For the covering abstract see ITRD E124500.

Rhetorical questions, relevance and scales

Rhetorical questions, and other varieties of pragmatically conditioned questions, present a challenge for a purely truth-conditional theory of the interpretation of interrogatives. In this paper, it is argued that relevance-theoretic principles account for the conditions of use of rhetorical questions. Concretely, it is proposed that a pragmatic principle, the "Bottom of Scale Principle", critically interacts with the Principle of Relevance and derives the dynamic meaning of the rhetorical use of a question. The Bottom of Scale Principle is also associated with the particular entailment and monotonicity properties of interrogatives, which explain the ability of rhetorical questions to license negative polarity items.

The Behaviour of the Firm under Alternative Regulatory Constraints

We review the case for intermediate power incentive regulation such as sliding scale when the regulator is badly informed and the firm's profits have a shadow resource cost. We then evaluate a number of different regulatory regimes including sliding scale in terms of productive and allocative efficiency. We find that the sliding scale principle can be applied quite generally—to dividends, profits or rate of return and that it has attractive economic properties.

Environmentally induced changes in sensory sensitivities.

For studying changes in the sensory sensitivities, psychophysical methods offer a variety of models. The psychophysical approach also holds for applications on indoor air quality. The human senses can be used for characterizing environments in terms of effects on health and comfort, and have been used by the WHO for determining recommended exposure limits. The use of psychophysical methods for air pollution applications is illustrated by two studies on effects on sensory functions in smokers and passive smokers. Olfactory as well as auditory effects were found. Furthermore, methodological findings are presented, such as obtaining information on detectability and perceived intensity in the very same estimation procedure. The results also demonstrate the need for calibrating scales with regard to individual scaling behavior in perceived intensity measurements as well as the capability of the master scale principle for performing such a calibration.

A new sliding scale principle and the spectroscopic ADC based on this principle

A new sliding scale averaging scheme is proposed. For a N-bit ADC the number of bits M for a sliding scale may be increased up to N−2. At the same time the full number of channels can be used, which is impossible for the conventional sliding scale method. Experiments have been carried out to verify the principle.

Formaldehyde odor and its interaction with the air of a sick building

Apparently the most typical effects of formaldehyde on humans in a nonindustrial indoor environment are the sensory reactions. This paper reports on the absolute odor threshold and the perceived odor strength of formaldehyde as well as on the interaction effects between formaldehyde and the indoor air of a “sick building” (a detached preschool). The absolute odor threshold of formaldehyde was determined by a modified method of limits with forced choice responses. The odor strength of formaldehyde and the mixtures of formaldehyde and the indoor air was determined by magnitude estimation according to a master scale principle, using pyridine as master stimuli. The individual odor thresholds for formaldehyde were shown to range over two powers of ten and the distribution to be extremely positively skewed. The median value of the individual odor thresholds (ED 50s) was 0.06 mg/m 3 (0.05 ppm). The psychophysical relationship for formaldehyde odor strength is a power function (exponent = 0.76). the results from mixing formaldehyde with indoor air is mainly a relative increase in the perceived odor strength of the stimulus mixture at low concentrations while the perceived odor strength at high concentrations remains largely unchanged.

Olfactory and chemical characterization of indoor air. Towards a psychophysical model for air quality

The relationship between the odor strength of total air samples and the odor strengths of the constituents was investigated in three field experiments in an office building and a new preschool. The odor strength was scaled by magnitude estimation according to a master scale principle which results in comparable values for the total and the constituent odors. Between 60 and 120 chemical components were detected by GC/FID in the indoor air samples ( N = 66). Most (81%) of the detected components in an air sample were odorous, even though most of them were of the low concentrations. By a method of pattern analysis, chemical as well as odor patterns of indoor air were found to be characteristics of different buildings. From the odor patterns (POG), the “odor print” of the outdoor air associated with the buildings was also recognized in the indoor air. Thus, the “odor print” of an air sample is different from its “chemical print”. A model was found that predicts the overall odor strength of an air sample from the number of FID-detected components most frequently reported to have a strong odor.