A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.     

Time is of the essence: a conjecture that hemispheric specialization arises from interhemispheric conduction delay

Tomasch (1954) and Aboitiz et al. (1992) found the majority of the fibers of the human corpus callosum are under 1 micron in diameter. Electron microscopic studies of Swadlow et al. (1980) and the detailed study of LaMantia and Rakic (1990a) on macaques show the average size of the myelinated callosal axons also to be less than 1 micron. In man, the average-sized myelinated fiber interconnecting the temporal lobes would have a one-way, interhemispheric delay of over 25 msec. Thus, finely detailed, time-critical neuronal computations (i.e., tasks that strain the capacity of the callosum and hence could not be handled by just the larger fibers) would be performed more quickly via shorter and faster intrahemispheric circuits. While one transit across the commissural system might yield tolerable delays, multiple passes as in a system involving "setting" would seem prohibitively slow. We suggest that these temporal limits will be avoided if the neural apparatus necessary to perform each high-resolution, time-critical task is gathered in one hemisphere. If the, presumably overlapping, neural assemblies needed to handle overlapping tasks are clustered together, this would lead to hemispheric specialization. The prediction follows that the large brains of mammals such as elephants and cetaceans will also manifest a high degree of hemispheric specialization.     

Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub‐shaped failure rate data of newly marketed consumer electronics. The time‐dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average‐based and exponential smoothing‐based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique. Copyright © 2015 John Wiley & Sons, Ltd.     

Therapeutic strategies in acute myocardial infarction. Results of STIM 93 registry

A registry was set up by the national college of cardiologists practicing in general hospitals in February 1993. The data concerned mode of admission, demographic details, initial clinical and haemodynamic evaluation and hospital outcome. Special attention was given to the electrocardiographic changes before and, in patients receiving thrombolytic therapy, after treatment. An analysis of predictive factors for mortality was performed both in the group of patients "revascularised" and in the group treated conventionally. One thousand and twenty three cases from 327 centres were analysed. There were 1292 men and 531 women, with an average age of 67.9 years. The average time interval from onset of symptoms to hospital admission was 5 h 30 min, 56.8% of patients arriving within 6 hours. 36.4% of the population underwent thrombolysis or angioplasty, 75% of patients under 75 years of age admitted before the 5th hours underwent a procedure of myocardial revascularisation. The hospital mortality was 14%, 8.7% in those revascularised and 17% in patients treated conventionally. Factors predictive of mortality were age, female gender, Killip Classes III or IV, systolic blood pressure of less than 100 mmHg, heart rate of more than 100/min and contraindications of thrombolysis. The maximum ST depression, the sum of ST depression, the sum of ST elevation and depression, were also significant predictive factors of a fatal hospital outcome in the whole population group and in patients treated conventionally. In the reperfused group, only the initial sum of ST elevation and depression was predictive of mortality: 120 minutes after the beginning of thrombolysis, the sum of ST elevations and of elevations and depressions was predictive of twice the mortality when the values exceeded 0.6 mv and 1.4 mv respectively.     

Quart-parametric interpolations for intersecting paths

The intersecting path is an important tool path generation method. This paper proposes an approach for the quart-parametric interpolation of intersecting paths. The objective of our approach is that the intersecting paths for surface machining can be directly interpolated within the computer numerical control (CNC) system. This enables the CNC interpolator to process the intersecting paths without geometric approximation as in existing approaches and take into consideration any specific feedrate profiles and further machining dynamical issues along the path.The interpolation of the intersection of two general parametric surfaces is transferred into interpolation of its projection curves and the time trajectories of four parameters along the intersecting curves are obtained. Our strategy is to carry out the quart-parametric interpolation based on the projection interpolation. The feedrate control method is developed, and then the interpolation algorithms for two projection curves are proposed. An error reduction scheme is presented to alleviate point deviation from the drive parametric surface. Simulations of quart-parametric interpolation have been carried out to verify the effectiveness of the proposed algorithm.