Optimizing the tilt angle of solar collectors

Solar collectors need to be tilted at the correct angle to maximize the performance of the system. In this paper, the annual solar fraction of the system (the fraction of energy that is supplied by solar energy) is used as an indicator to find the optimum inclination angles for a thermosyphon solar water heater installed in northern and southern parts of Jordan. Calculations are carried out using the powerful computer program TRNSYS (Transient System Simulation). The system is assumed to operate with a daily hot water load of 150 l at 55°C flowing during the day according to the widely used Rand consumption profile. The results show that the optimum inclination angle for the maximum solar fraction is about φ+(0→10°) for the northern region (represented by Amman) and about φ+(0→20°) for the southern region (represented by the town of Aqaba). These values are greater than those for maximum solar radiation (which is commonly used as an indicator) at the top of the collector by about 5 to 8°.     

Thermal analysis of multigeneration system using geothermal energy as its main power source

The study presented in this paper examines the operation of an integrated system. The study aims to present a method for utilizing geothermal energy in a way that minimizes energy waste and delivers maximum efficiency. A high-temperature geothermal well with a temperature of 300 °C is used as its primary source of energy. The system produces space heating, space cooling, electric power, hot water, freshwater and hydrogen as its outputs. These outputs utilize the excess energy that is obtained from the geothermal well, and by doing so, reduces waste, and increases the overall efficiency of the system. Among these outputs, freshwater and hydrogen are considered the most valuable, as water is an essential life resource and hydrogen is a prized form of energy. The novelty of this system compared to other geothermal sources is that it does not rely on any other source of input energy. It produces both freshwater, hydrogen and considerable amounts of electric power for commercial, industrial and/or residential use. Electric power is produced by two power cycles; the first one is a double flash steam cycle in the geothermal system and the second one is an organic Rankine cycle. 40% of the total electric power produced is sent to an electrolyzer to produce hydrogen gas. Freshwater is produced by single flash desalination. The system produces 22.1 MW of power as net electricity output. The system is assessed energetically and exergetically; it is found that the energy efficiency is 49.1%, while the exergy efficiency is 67.9%. Further parametric studies are carried out using Engineering Equation Solver (EES) to investigate the influence of operating conditions on the energy and exergy of the system. Moreover, major exergy destruction areas in the system are also identified.     

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

COMPUTING PERFECT AND STABLE MODELS USING ORDERED MODEL TREES

Ordered model trees were introduced as a normal form for disjunctive deductive databases. They were also used to facilitate the computation of minimal models for disjunctive theories by exploiting the order imposed on the Herbrand base of the theory. In this work we show how the order on the Herbrand base can be used to compute perfect models of a disjunctive stratified finite theory. We are able to compute the stable models of a general finite theory by combining the order on the elements of the Herbrand base with previous results that had shown that the stable models of a theory T can be computed as the perfect models of a corresponding disjunctive theory ɛ T resulting from applying the so called evidential transformation to T. While other methods consider many models that are rejected at the end, the use of atom ordering allows us to guarantee that every model generated belongs to the class of models being computed. As for negation-free databases, the ordered tree serves as the canonical representation of the database.     

Velocity-based modeling of physical interactions in dense crowds

We present an interactive algorithm to model physics-based interactions in dense crowds. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We combine velocity-based collision-avoidance algorithms with external physical forces. The overall formulation produces various effects of forces acting on agents and crowds, including balance recovery motion and force propagation through the crowd. We further extend our method to model more complex behaviors involving social and cultural rules. We use finite-state machines to specify a series of behaviors and demonstrate our approach on many complex scenarios. Our algorithm can simulate a few thousand agents at interactive rates and can generate many emergent behaviors.     

Speaker identification investigation and analysis in unbiased and biased emotional talking environments

This work aims at investigating and analyzing speaker identification in each unbiased and biased emotional talking environments based on a classifier called Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is unbiased towards any emotion, while the second talking environment is biased towards different emotions. Each of these talking environments is made up of six distinct emotions. These emotions are neutral, angry, sad, happy, disgust and fear. The investigation and analysis of this work show that speaker identification performance in the biased talking environment is superior to that in the unbiased talking environment. The obtained results in this work are close to those achieved in subjective assessment by human judges.     

Deduction in non-Horn databases

The class of non-Horn, function-free databases is investigated and several aspects of the problem of using theorem proving techniques for such databases are considered. This includes exploring the treatment of negative information and extending the existing method, suggested by Minker, to accept non-unit negative clauses. It is shown that the algorithms based on the existing methods for the treatment of negative information can be highly inefficient. An alternative approach is suggested and a simpler algorithm based on it is given. The problems associated with query answering in non-Horn databases are addressed and compared with those for the Horn case. It is shown that the query evaluation process can be computationaly difficult in the general case. Conditions under which the process is simplified are discussed. The topic of non-Horn general laws is considered and some guidelines are suggested to divide such laws into derivation rules and integrity constraints. The effect of such a division on the query evaluation process is discussed.     

Fingerprint classification: a review

Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.     

Thermal radiation effects on flow of Jeffery fluid in converging and diverging stretchable channels

Neural Computing and Applications - This paper investigates the flow of Jeffery fluid between converging and diverging channels. The walls of the channel are assumed to be capable for stretching or...     

An energy-efficient 3D-stacked STT-RAM cache architecture for cloud processors: the effect on emerging scale-out workloads

This paper focuses on energy consumption which is a major problem in the dark silicon era. As energy consumption becomes a key issue for operation and maintenance of cloud data centers, cloud computing providers are becoming significantly concerned. Here, we show how spin-transfer torque random access memory (STT-RAM) can be used as an on-chip L2 cache to obtain lower energy compared to conventional L2 caches, like SRAM. High density, fast read access and non-volatility make STT-RAM a significant technology for on-chip memories. Previous studies have mainly studied specific schemes based on common applications and do not provide a thorough analysis of emerging scale-out applications with multiple design options. Here, we discuss different outlooks consisting of performance and energy efficiency in cloud processors by running emerging scale-out workloads. Experiment results on the CloudSuite benchmarks show that the proposed method reduces energy by 51% (on average) and improves energy delay product by 37% (on average) where instruction per cycle degradation is only 22% (on average) compared to the SRAM method.