Stochastic Modelling as a Design Technique for Predicting Internal Heat Gains in Buildings

Building internal heat gains from people, lighting and equipment have been identified as a source of uncertainty in estimating building heating and cooling loads and energy requirements. The common practice for estimating the internal heat gains relies on a single worst case design value which is usually assumed constant over the occupation period (constant profiles). In reality the building occupancy and the use of lighting and equipment are characterized by being partially time dependent and partially random. A preliminary study using simple variable profiles has shown that the consideration of the time variation of internal heat gains can lead to significant effects on the estimates of the heating and cooling loads and the total energy requirements when compared with the constant profiles. This study justified the development of more accurate methods of estimation of the internal heat gains. One suitable approach for modelling the random variation of internal heat gains is the use of stochastic techniques. Hence a stochastic model has been developed using real-life data to predict the likely building occupancy patterns of office buildings and the likely use of the lighting and equipment in different time steps. Arrival and departure information for staff and visitors was collected from two large office buildings. Statistical analysis of this information provided the input data on occupancy for the stochastic model. Good agreement between predicted and observed patterns was obtained within the available data. The lighting use has been related to the occupancy patterns, daylight levels and type of control. A stochastic model has been developed for predicting the use of manually controlled lighting for a probabilistic model described by BRE which was based on field studies of artificial lighting . Models were also developed for localised and photoelectric lighting control The use of equipment has been predicted based on the simulated occupancy patterns and the average probability of use. The model of equipment offers the possibility of simulating the use of equipment according to its function and type (personal or general). The results of the predictions of internal heat gains for each time step of the day (the stochastic profiles) have been used as an input to Strathclyde University's building thermal modelling program ESP to predict the overall heating and cooling loads and the total energy requirements for a well defined building. The results were compared with those obtained from using the constant profiles. The summarized results have shown that the use of the constant profiles of internal heat gains leads to inaccurate estimation in the building total heating and cooling energy requirement. The difference depends on the type of light control and is in the range of 12 to 42%. Differences of this order may be important when the thermal modelling programs are being used to compare design alternatives

Asymptotics of Transmit Antenna Selection: Impact of Multiple Receive Antennas

Consider a fading Gaussian MIMO channel with $N_\mathrm{t}$ transmit and $N_\mathrm{r}$ receive antennas. The transmitter selects $L_\mathrm{t}$ antennas corresponding to the strongest channels. For this setup, we study the distribution of the input-output mutual information when $N_\mathrm{t}$ grows large. We show that, for any $N_\mathrm{r}$ and $L_\mathrm{t}$, the distribution of the input-output mutual information is accurately approximated by a Gaussian distribution whose mean grows large and whose variance converges to zero. Our analysis depicts that, in the large limit, the gap between the expectation of the mutual information and its corresponding upper bound, derived by applying Jensen's inequality, converges to a constant which only depends on $N_\mathrm{r}$ and $L_\mathrm{t}$. The result extends the scope of channel hardening to the general case of antenna selection with multiple receive and selected transmit antennas. Although the analyses are given for the large-system limit, our numerical investigations indicate the robustness of the approximated distribution even when the number of antennas is not large.Comment: 6 pages, 4 figures, ICC 201

On Robustness of Massive MIMO Systems Against Passive Eavesdropping under Antenna Selection

In massive MIMO wiretap settings, the base station can significantly suppress eavesdroppers by narrow beamforming toward legitimate terminals. Numerical investigations show that by this approach, secrecy is obtained at no significant cost. We call this property of massive MIMO systems `secrecy for free' and show that it not only holds when all the transmit antennas at the base station are employed, but also when only a single antenna is set active. Using linear precoding, the information leakage to the eavesdroppers can be sufficiently diminished, when the total number of available transmit antennas at the base station grows large, even when only a fixed number of them are selected. This result indicates that passive eavesdropping has no significant impact on massive MIMO systems, regardless of the number of active transmit antennas.Comment: 7 pages, 2 figures; To be presented in IEEE Global Communications Conference (Globecom) 2018 in Abu Dhabi, UA

Predicting tensile strength of spliced and non-spliced steel bars using machine learning- and regression-based methods

Mechanical properties of steel reinforcement bars, which have a critical effect in the overall performance of reinforced concrete (RC) structures, should be reported and assessed before being used in structural elements. Determining bars’ properties could be time-consuming and expensive specifically in the case of incorporating splices. Therefore, this study aims to predict tensile strength of bars using machine learning-based methods including nonlinear regression, ridge regression and artificial neural network. To this end, a comprehensive database including over 200 tests on non-spliced and spliced steel bars by mechanical couplers was collected from the available peer-reviewed international publications. Bar size, splice method, steel grade, temperature and splice characteristics (length and outer diameter of couplers) were the input parameters considered for predicting tensile strength. The efficiency of the models was evaluated through Taylor diagram and common performance metrics namely coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results demonstrated that the predicted values agreed well with the actual values reported in the experimental studies used for collecting the database. A parametric study was also conducted in order to examine the influence of coupler length, coupler outer diameter and temperature on the tensile strength of spliced bars. Based on the parametric study results, three different equations were suggested for calculating tensile strength of spliced bars using the mentioned parameters. The outcomes of this study can assist practitioners to effectively and accurately estimate tensile strength of spliced and non-spliced steel bars in reinforced concrete structures without the need to carry out expensive and timely physical tests

A New Scheme for Removing Duplicate Files from Smart Mobile Devices

The continuous development of the information technology and mobile communication world and the potentials available in the smart devices make these devices widely used in daily life. The mobile applications with the internet are distinguished simple, essay to use in any time/anywhere, communication between relatives and friends in different places in the world. The social application networks make these devices received several of the duplicate files daily which lead to many drawbacks such inefficient use of storage, low performance of CPU, RAM, and increasing consumption battery. In this paper, we present a good scheme to remove from the duplicate files, and we focus on image files as a common case in social apps. Our work overcomes on the above-mentioned issues and focuses to use hash function and Huffman code to build unique code for each image. Our experiments improve the performance from 1046770, 1995808 ns to 950000, and 1981154 ns in Galaxy and HUAWEI, respectively. In the storage side, the proposed scheme saves storage space from 1.9 GB, 1.24 GB to 2 GB, and 1.54 GB, respectively