Feature-based detection using Bayesian data fusion

Current cocaine detection techniques used at borders have their challenges, which include cost of training specialised operators, the high chance of operator error and the dangers involved in exposure of both operators and container contents to radioactive material. This paper describes a technique which utilises the benefits of data fusion to develop a non-invasive system which relies less on the expertise of the operator, whilst improving false positive rates. To improve the capabilities of the cocaine-detecting fibre-optic sensor, the raw data was pre-processed and features were identified and extracted. The output of each feature is a decision on the classification and the conditional probability that it belongs to the chosen class based on the observed data, which serve as input into a Bayesian data fusion module and outputs the probability that a sample belongs to a class based on the observed features and makes a decision based on the class with the higher probability. The results show that the Bayesian fusion module greatly improves the detection rates of individual feature

Investigating Heat Loss through Vestibule Doors for a Non-Residential Building

The aim of this study was to investigate the effects of air flow movement through high use front entrance doors of a college building with large flows of people. The objectives were to visualize and quantify the resultant energy losses through the entrance doors, coupled with investigating any potential improvements that can be obtained through improved design. The findings of the study suggest that the heat loss from the front entrance design can contribute to up to 2.8% of the buildings’ energy loads. It was also seen that a vestibule creates a tunnel effect for cold ambient air to enter the building without hot air escaping from the vestibule. Rather hot stale air exits through openings at the ceiling height. Potential solutions with entrance design are investigated and their results compared to the outcomes of a similar model designed using Computational Fluid Dynamics (CFD)

Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang

Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang

Mechanical cooling energy reduction for commercial buildings in hot climates: effective use of external solar shading incorporating effects on daylight contribution

This paper investigates the effectiveness of multiple external shading devices and identifies the most effective fixed external shading configurations for commercial building types in hot climates. Daylight contribution is also analysed in detail in order to monitor the daylighting factor reduction including uniformity for each shading configuration. Existing dynamic thermal modeling software is used to completing analysis on a theoretical open office plan building. Simulation results indicate that multiple angled external shading is the most effective solution for commercial buildings in hot climates. The calculated diurnal cooling load reduction for East, South and West elevations are 46.20%, 41.16% and 46.53% respectively. Furthermore, daytime cooling load (kW) reduction is reduced by 17.80% using the optimum solution. All dynamic thermal simulations are compared against a base case to clearly show possible cooling energy reduction (MWh) and carbon dioxide emissions (CO2) associated with cooling system for single storey open office building

Predicting the impact of upgrading Chinese building design

In order to reduce building energy consumption, Chinese government has revised the Chinese building design standard. In the new guide the use of individual room temperature control is highly recommended for new and refurbishment buildings. However, critical evidence on how this improvement can have an impact on the building energy consumption is not clear. This paper is aiming to explore how the upgrading residential standards affect the building energy performance. In order to evaluate its impact on the building energy consumption, two buildings were chosen: one complying with the old Chinese building design standard, while the other complies with the new standard. Additionally, the real time measured data from a typical residential building in China are used to validate a dynamic building performance simulation

Investigating the effect of tightening residential envelopes in the Mediterranean region

Nowadays, buildings are responsible for the 40% of energy consumption (36% of greenhouse gas emissions) in the European Union. The European Council pointed out the need to refurbish a large amount of the existing building inventory, as new buildings are related to the 1-2% of the total energy consumption. Air-infiltration and tightness of buildings are usually neglected parameters during retrofitting or building design, especially in the Southern European counterparts, where air-tightness standards are absent from the national building regulations. To this effect, this study investigates the impact of tightening existing residential envelopes, focusing on the impact to the default construction and synergies arisen between air-tightness and other interventions (i.e. thermal insulation). The study was undertaken in the Mediterranean climate conditions, examining detached houses located in Cyprus. This is the first study in national level, presenting the air-tightness characteristics of buildings as these were collected by a blower door test. In general, the outcome shows that the improvement of air-tightness primarily reduces the energy associated with winter thermal loads. Apart from that the tightness of building envelopes beneficially contributes on the performance of other energy saving measures. In particular, the reduction by thermal insulation can be enhanced up to 12%, while the synergy with a glazing system may reduce heating demand up to 7%

Mechanical ventilation & cooling energy versus thermal comfort: A study of mixed mode office building performance in Abu Dhabi

In hot climates, office building ventilation and cooling dual operation can cause high energy consumption in order to maintain thermal comfort limits. Using mixed mode ventilation and cooling operation, incorporation of natural ventilation strategies can offer significant reductions in annual energy consumption. Natural ventilation operation can be used with an external air temperature ranging from 24 to 28oC. Within this paper, a literature on thermal comfort is completed to understand temperature limits for hot climates. This work details theoretical model analysis of a simple mixed mode office building located in a hot climate, Abu Dhabi, United Arab Emirates. This is completed using dynamic thermal simulation. The aim of this work is to evaluate the impacts on mechanical ventilation and cooling energy when raising internal comfort temperatures beyond 24oC; to a maximum of 28oC. Time/temperature analysis is completed for different months of the year to ascertain when thermal comfort temperatures are exceeded and full mechanical operation is required. Results from this analysis show yearly ventilation and cooling energy savings ranging between 21‐39% and demonstrate that higher mechanical cooling set point operations can be achieved when human occupants have access to openable windows

Investigating heat loss through vestibule doors for a non-residential building

The aim of this study was to investigate the effects of air flow movement through high use front entrance doors of a college building with large flows of people. The objectives were to visualize and quantify the resultant energy losses through the entrance doors, coupled with investigating any potential improvements that can be obtained through improved design. The findings of the study suggest that the heat loss from the front entrance design can contribute to up to 2.8% of the buildings’ energy loads. It was also seen that a vestibule creates a tunnel effect for cold ambient air to enter the building without hot air escaping from the vestibule. Rather hot stale air exits through openings at the ceiling height. Potential solutions with entrance design are investigated and their results compared to the outcomes of a similar model designed using Computational Fluid Dynamics (CFD)

Adaptive comfort model incorporating temperature gradient for a UK residential building

Thermal comfort field experiments were conducted to acquire thermal comfort data of 119 participants in a test house representative of a typical UK house. This paper compares the performance of popular PMV-based thermal comfort index vs neutral temperature based on Actual Mean Vote. The aim of this research was to incorporate vertical thermal gradient, which is usually a neglected yet highly influential parameter in a residential setting and propose a new adaptive thermal comfort model. The new adaptive model (LPMV) has been developed using a polynomial curve fit method. This method was chosen as it has the capability to correlate indoor environmental parameters with AMV and incorporated them in the generated mathematical model. The model requires temperature gradient and SET* only to determine neutral temperatures which makes it the first of its kind. The LMPV model was rigorously tested against thermal comfort data compiled in this study and against independent/unbiased data (the ASHRAE RP-884 database). LPMV showed up to 0.7°C improvement in predicting neutral temperature of occupants compared to the famous Fanger’s PMV model. This can result in better prediction of a suitable heating setpoint temperature which has great implications on annual energy demand