An integrated fuzzy-lp approach for a supplier selection problem in supply chain management

In supply chain management process, the firm select best supplier takes the competitive advantage to other companies. Thus, supplier selection is an important issue and with the multiple criteria decision-making approach, the supplier selection problem includes both tangible and intangible factors. This paper is aimed to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient. Finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. The integrated model is illustrated by an example in a textile firm.     

Graph partitioning active contours (GPAC) for image segmentation

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.     

Computational intelligence techniques for HVAC systems: A review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.     

An experimental study on performance and emission characteristics of a hydrogen fuelled spark ignition engine

In the present paper, the performance and emission characteristics of a conventional four cylinder spark ignition (SI) engine operated on hydrogen and gasoline are investigated experimentally. The compressed hydrogen at 20  MPa has been introduced to the engine adopted to operate on gaseous hydrogen by external mixing. Two regulators have been used to drop the pressure first to 300 kPa, then to atmospheric pressure. The variations of torque, power, brake thermal efficiency, brake mean effective pressure, exhaust gas temperature, and emissions of _NOx${\mathrm{NO}}_x$, CO, _CO2${\mathrm{CO}}_2$, HC, and _O2${\mathrm{O}}_2$ versus engine speed are compared for a carbureted SI engine operating on gasoline and hydrogen. Energy analysis also has studied for comparison purpose. The test results have been demonstrated that power loss occurs at low speed hydrogen operation whereas high speed characteristics compete well with gasoline operation. Fast burning characteristics of hydrogen have permitted high speed engine operation. Less heat loss has occurred for hydrogen than gasoline. _NOx${\mathrm{NO}}_x$ emission of hydrogen fuelled engine is about 10 times lower than gasoline fuelled engine. Finally, both first and second law efficiencies have improved with hydrogen fuelled engine compared to gasoline engine. It has been proved that hydrogen is a very good candidate as an engine fuel. The obtained data are also very useful for operational changes needed to optimize the hydrogen fueled SI engine design.     

Distributed audio sensing with homeostasis-inspired autonomous communication

Emerging applications of wireless sensor networks (WSN) requiring wide-band event signal communication such as multimedia surveillance sensor networks impose additional challenges including high communication bandwidth requirement and energy cost. Besides their partially or fully dependency on feedback messages from sink node, the existing protocols designed for WSN do not address the communication of wide-band event signals. Furthermore, the feedback messages may not reach in time to provide reliable communication of event information and save scarce network resources. Therefore, an autonomous communication protocol is imperative in order to provide wide-band event signal communication without any feedback from the sink. In nature, biological systems have self-organization capability, i.e., homeostasis, as they autonomously maintain a relatively stable equilibrium state for operation of vital functions. Hence, this natural phenomenon clearly gives promising inspirations in order to develop autonomous and efficient communication models and protocols for WSN domain. In this paper, the homeostasis-inspired autonomous communication (HAC) protocol is introduced for wireless audio sensor networks (WASN). Using the spectral properties of the wide-band event signal, i.e., audio signal, HAC enables WASN to maintain a relatively stable state in which sensor nodes reliably and energy-efficiently communicate the event signal to the sink node. Furthermore, with its self-organization capability, HAC does not rely on any feedback message from the sink node. Performance evaluations reveal that HAC successfully communicates wide-band event signal with minimum energy expenditure.     

Hydrogen sorption characteristics of Zonguldak region coal activated by physical and chemical methods

Hydrogen sorption characteristics of activated carbons (ACs) produced by physical and chemical activations from two coal mines (Kilimli and Armutcuk) in the Zonguldak region, Turkey were investigated by a volumetric technique at 77 K. H₂ adsorption isotherms were obtained on the samples exposed to pyrolytic thermal treatments in a temperature range of 600–900 °C under N₂ flow and chemical activation using different chemical agents such as KOH, NH₄Cl, ZnCl₂ from the two mines. Experimental hydrogen adsorption isotherm data at 77 K were used for the evaluation of the adsorption isotherm constants of the Brunauer-Emmett-Teller (BET) and the Langmuir models, and also the amount of hydrogen adsorbed on the various samples was evaluated by using the adsorption isotherm data. Higher hydrogen adsorption capacity values were obtained for all the heat and the chemically treated activated carbon samples from the Kilimli coal samples than Armutcuk. The amount of H₂ adsorbed on the original Kilimli coal samples was 0.020 wt%, and it was increased to 0.89 wt% on the samples pyrolyzed at 800 °C. The highest value of hydrogen adsorption obtained was 1.2 wt% for the samples treated with KOH+NH₄Cl mixture at 750 °C followed by oxidation with ZnCl₂. It was shown that chemical activations were much more effective than physical activations in increasing the surface area, pore volume and the hydrogen sorption capacities of the samples.     

Thermal stability of benzorods: molecular-dynamics simulations

Thermal stability of benzorods 2C6-20C6, which are obtained by stacking n (n=2-20) dehydrogenated benzene, have been investigated by molecular-dynamics simulations. It has been found that these structures assume a geometrical form depending on the number of dehydrogenated benzene layers, and they are stable under heat treatment up to elevated temperatures with a dependence on length.     

Investigation of Hydrophobic porous electrodes. I. Differential capacitance by a low frequency a.c. impedance technique

A simple a.c. technique has been developed for measuring the double layer capacitances of porous electrodes. A small alternating potential (±15 mV at 1 Hz) is applied potentiostatically to the test electrode and the phase and amplitude of the alternating current are monitored on an oscilloscope by the method of Lissajous figures. The value of the double layer capacitance for carbon and gold, together with the potential dependence for the gold capacitance,agree with published data for smooth electrodes. The technique can follow changes in the interfacial surface area between a porous electrode and the electrolyte as well as providing information about surface processes on finely divided materials.     

Adaptively learning local shape statistics for prostate segmentation in ultrasound

Automatic segmentation of the prostate from 2-D transrectal ultrasound (TRUS) is a highly desired tool in many clinical applications. However, it is a very challenging task, especially for segmenting the base and apex of the prostate due to the large shape variations in those areas compared to the midgland, which leads many existing segmentation methods to fail. To address the problem, this paper presents a novel TRUS video segmentation algorithm using both global population-based and patient-specific local shape statistics as shape constraint. By adaptively learning shape statistics in a local neighborhood during the segmentation process, the algorithm can effectively capture the patient-specific shape statistics and quickly adapt to the local shape changes in the base and apex areas. The learned shape statistics is then used as the shape constraint in a deformable model for TRUS video segmentation. The proposed method can robustly segment the entire gland of the prostate with significantly improved performance in the base and apex regions, compared to other previously reported methods. Our method was evaluated using 19 video sequences obtained from different patients and the average mean absolute distance error was 1.65 ± 0.47 mm.