Behavior Of Appliances Research Articles

How patient-selected colors for removable appliances are reflected in electronically tracked compliance (wear times and wear behavior).

A broad spectrum of colors for removable appliances, intended to optimize acceptance of treatment and patient cooperation, have been available on the dental market for years. This is the first study to analyze how patient-selected colors are reflected in wear times and wear behavior of removable appliances. The study included 117 children (55 girls and 62 boys) who were treated with active removable plate or functional appliances. All patients were offered to choose from 11 different colors, which were pooled into six groups (black, blue, green, yellow, pink, red) for analysis, or to combine any two to four colors ("multicolored" group) for their appliances. All appliances featured a built-in microsensor (TheraMon; MC Technology, Hargelsberg, Austria) for objective wear-time tracking. Differences between wear times were analyzed using pairwise t tests and Tukey correction. The longest median wear times were recorded in the blue and green groups (≈11 h/d) and the shortest ones in the red and pink groups (≈9 h/d), but they were not significantly influenced by the patient-selected colors. The median wear times involved an age-related decrease by 0.56 h/y that was statistically significant ( P = .00005). No gender-specific patterns of wear behavior were observed. Patient-selected colors for removable appliances can presumably improve acceptance of treatment, but they are not associated with statistically significant improvements in wear time or wear behavior.

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

The present study uses power data from panel meter connected to the micro-grid to identify electrical energy consumption of the school appliances and their behavior in both warm-up, standby and active operation states. Before the conduction of experiments a load auditing of the school appliances was carried out by reading the rated power of each device (e.g. photocopy machine, printer, and fridge). The captures of this kind of information were essential as it provides a starting point to determine energy use of each appliance and hence simplify the process of identification. The electric parameters such as active power, reactive power and current were used to analyze the behavior of electrical appliances in all states. Experimental results show that, both active and reactive power were found to be high for old Canon photocopy machine as compared to new Canon photocopy machine. Another experiment reveals that printing one copy by using HP laser printer consumes about 700 W, while photocopying one copy with new Canon machine utilizes approximately 1100 W. This study concludes that new photocopy machine consumes more electric energy in warm-up state as compared to other states (standby and active operation states). Future work is to develop an algorithm for demand side management strategies which will enable efficient utilization of the electric energy from the micro-grid and hence bring the intended energy impact to the school.

Short-term residential electric load forecasting: A compressive spatio-temporal approach

Load forecasting is an essential step in power systems operations with important technical and economical impacts. Forecasting can be done both at aggregated and stand-alone levels. While forecasting at the aggregated level is a relatively easier task due to smoother load profiles, residential forecasting (stand-alone level) is a more challenging task due to existing diurnal, weekly, and annual cycles effects in the corresponding time series data and fluctuations caused by the random usage of appliances by end-users. Exploring the available historical load data, it has been discovered that there usually exists an interesting trend between the data from a target house and the data from its surrounding houses. This trend can be exploited for improving the forecast accuracy. One can define several different features for each house, including house size, occupancy level, and usage behavior of appliances. While the number of such features can be large, the main challenge is how to determine the best candidates (features) for an input set without increasing the forecasting computational costs. With this objective in mind, we present a forecasting approach which combines ideas from Compressive Sensing (CS) and data decomposition. The idea is to provide a framework which facilitates exploiting the existing low-dimensional structures governing the interactions among residential houses. The effectiveness of the proposed algorithm is evaluated using real data collected from residential houses in TX, USA. The comparisons against benchmark methods show that the proposed approach significantly improves the short-term forecasts.

An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is a set of techniques used to estimate the electricity consumed by individual appliances in a building from measurements of the total electrical consumption. Most commonly, NILM works by first attributing any significant change in the total power consumption (also known as an event) to a specific load and subsequently using these attributions (i.e. the labels for the events) to estimate energy for each load. For this last step, most published work in the field makes simplifying assumptions to make the problem more tractable. In this paper, we present a framework for creating appliance models based on classification labels and aggregate power measurements that can help to relax many of these assumptions. Our framework automatically builds models for appliances to perform energy estimation. The model relies on feature extraction, clustering via affinity propagation, perturbation of extracted states to ensure that they mimic appliance behavior, creation of finite state models, correction of any errors in classification that might violate the model, and estimation of energy based on corrected labels. We evaluate our framework on 3 houses from standard datasets in the field and show that the framework can learn data-driven models based on event labels and use that to estimate energy with lower error margins (e.g., 1.1–42.3%) than when using the heuristic models used by others.

Load Management in Power Grids

Decentralized renewable energy sources become more and more common. This leads to stability problems in power grids. Conventional energy sources are easy to control. In contrast, wind and solar power are much more difficult to forecast. Forecasts are only possible short term and are more imprecise. Producers and consumers of energy can try to help reducing stability problems. Contributions towards a decision support system are proposed and recommend how to alter the behavior of producers and consumers. On the producer side centrally controlled heat and power plants are able to shift load in a virtual power plant. The plant operator offers a load curve based on forecasts. The centrally controlled heat and power plants help to mitigate the effect of revised forecasts. An incentive based control on the consumer side is also proposed. Smart appliances react to pricing information. They alter their execution window towards the cheapest time slot, if possible. The exact behavior of appliances in the expected field experiment is still partially unknown. It is necessary to simulate the behavior of these appliances and to train an artificial neural network. The artificial neural network allows computing the pricing signal leading to a desired load shift.

Orthodontic Applications of a Superelastic Shape-Memory Alloy Model

Duning orthodontic treatment, dental appliances (braces) made of shape memory alloys have the ponearly uniform low level stresses to dentitions during tooth movement over a large range of tooth displacement. In this paper we model superelastic behaviour of dental appliances using the finite element method developed by F. Auricchio et al. Results of the mathematical model For 3-point bending and several promising 'closing loop' designs are compared with laboratory results for the saine coufigurations.

On the effects of fuel leakage on CO production from household burners as revealed by lif and cars

Measurements of the distributions of CO, OH, and temperature in flames from two commonly used, commercially available household burners are presented. The local mole fractions of CO and relative distribution of OH have been obtained using laser-induced fluorescence, while the local temperatures have been determined by coherent anti-Stokes Raman scattering (CARS). For both burners, burning in the open air, CO formation outside the main flames has been observed and attributed to the leakage of fuel-air mixture at the edges of the flame, where the fuel is subsequently converted to CO in the boundary layer between the flame and the surroundings. For a rich-premixed, multiblade burner, which gives Bunsen-like flames, the CO produced by the leaking fuel appears to be oxidized by OH arising from the outer cones of adjacent flames, and burns out to low concentrations. In the case of a lean-premixed burner, the CO produced by fuel leakage remains in the cool boundary layer without adequate burnout. Possible consequences for appliance behavior are discussed.

Orthodontisches Meß- und Simulationssystem (OMSS) für die statische und dynamische Analyse der Zahnbewegung

An orthodontic measurement and simulation system (OMSS) is presented. The major components of this system are two measuring tables each comprising a force/torque sensor and a motor driven, fully three-dimensionally adjustable positioning stage. The force/torque sensors are capable of measuring simultaneously all forces and torques acting on a tooth. Using the computer program "OMSS" which runs on a personal computer, several different measurements can be conducted. On the one hand, the system supports absolute measurements such as the registration of a force/deflection diagram. Furthermore, even multidimensional force/deflection- or torque/distance curves can be measured. On the other hand, simulations of orthodontic tooth movement can be conducted. In such simulations, the force system acting on a tooth is measured and the resulting tooth movements are calculated. By using this "computer typodont", not only the static but the dynamic behaviour of orthodontic appliances can be studied. The application of this system is demonstrated by the analysis of several orthodontic problems.

A device for determining the mechanical behavior of orthodontic appliances.

A device for measuring uniplanar force systems from orthodontic appliances is described. The device can be used to rapidly determine forces and moments from appliances which are statically indeterminate.

A Method of Estimating Demand

A method is presented for the estimation of a "probable" annual maximum collective demand in kva (kilovolt-amperes) for a set of heterogeneous residential electric appliances. The method considers diversity between appliances of different types as well as diversity among those of the same type. It is based on a probable model of appliance behavior using parameters obtained from observed operation. The accuracy of the method and its application are examined, and representative parameter values are presented but not derived. A sample calculation is included.