Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.     

Randomized controlled trial comparing same-day discharge with hospital stay following haemorrhoidectomy

Neurological complications of 5-fluorouracil (5-FU) chemotherapy are uncommon events. The two patients presented demonstrate two manifestations of 5-FU neurotoxicity, namely a cerebellar syndrome in association with global motor weakness and bulbar palsy, and a bilateral third cranial (oculomotor) nerve palsy. Both highlight the rapid onset and severity of these unusual side effects but also emphasize that, unlike paraneoplastic syndromes or central nervous system involvement by tumour, complete spontaneous recovery is a potential outcome.     

EF-MPR,a new energy eFficient multi-point relay selection algorithm for MANET

MultiPoint Relay (MPR) selection algorithm is a flooding technique for propagating a broadcast message inside an ad-hoc network which reduces the number of unnecessary broadcast messages in order to save more energy in the network, minimize the number of packet collisions, and speed up the propagation time. In this paper, we demonstrate that MPR selection is an application of Set Covering Problem (SCP). A few optimization methods are developed in this work to find the optimum solution including Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and a new greedy algorithm. Extensive simulations are set up to evaluate the developed methods. The new algorithm is named Energy eFficient MPR or EF-MPR in short. The simulation results show that EF-MPR can reduce the number of MPR nodes up to 19%. Moreover, EF-MPR algorithm reduces the power-consumption of network up to 12% and speed up the propagation time by 9%.     

Regularized locality preserving discriminant embedding for face recognition

For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem and hence poor generalization. In this paper, we propose a new linear graph embedding technique based upon an adaptive locality preserving regulation model (ALPRM), known as Regularized Locality Preserving Discriminant Embedding (RLPDE). In RLPDE, the projection features are regulated based on ALPRM to approach population data locality, which can directly enhance the locality preserving capability of the projection features. This paper also presents the relation between locality preserving capability and class discrimination. Specifically, we show that the optimization of the locality preserving function minimizes the within-class variability. Experiments on three face datasets such as PIE, FRGC and FERET show the promising performance of the proposed technique.     

Application of Genetic Algorithm in unit selection for Malay speech synthesis system

Corpus based speech synthesis can produce high quality synthetic speech due to it high sensitivity to unit context. Large speech database is embedded in synthesis system and search algorithm (unit selection) is needed to search for the optimal unit sequence. Speech feature which served as target cost is estimated from the input text. The acoustic parameters which served as join cost are derived from mel frequency cepstral coefficients (MFCCs) and Euclidean distance. In this paper, a new method which is Genetic Algorithm is proposed to search for optimal unit sequence. Genetic Algorithm (GA) is a population based search algorithm that is based on the biological principles of selection, reproduction, crossover and mutation. It is a stochastic search algorithm for solving optimization problem. The speech unit sequence that has minimum join cost will be synthesized into complete waveform data.     

Fabrication of aluminium doped zinc oxide piezoelectric thin film on a silicon substrate for piezoelectric MEMS energy harvesters

Thin film piezoelectric materials play an essential role in micro electro mechanical system (MEMS) energy harvesting due to its low power requirement and high available energy densities. Non-ferroelectric piezoelectric materials such as ZnO and AlN are highly silicon compatible making it suitable for MEMS energy harvesters in self-powered microsystems. This work primarily describe the design, simulation and fabrication of aluminium doped zinc oxide (AZO) cantilever beam deposited on <100> silicon substrate. AZO was chosen due its high piezoelectric coupling coefficient, ease of deposition and excellent bonding with silicon substrate. Doping of ZnO with Al has improved the electrical properties, conductivity and thermal stability. The proposed design operates in transversal mode (d ₃₁ mode) which was structured as a parallel plated capacitor using Si/Al/AZO/Al layers. The highlight of this work is the successful design and fabrication of Al/AZO/Al on <100> silicon as the substrate to make the device CMOS compatible for electronic functionality integration. Design and finite element modeling was conducted using COMSOL? software to estimate the resonance frequency. RF Magnetron sputtering was chosen as the deposition method for aluminium and AZO. Material characterization was performed using X-ray diffraction and field emission scanning electron microscopy to evaluate the piezoelectric qualities, surface morphology and the cross section. The fabricated energy harvester generated 1.61?V open circuit output voltage at 7.77?MHz resonance frequency. The experimental results agreed with the simulation results. The measured output voltage is sufficient for low power wireless sensor nodes as an alternative power sources to traditional chemical batteries.     

Siloxene,Germanane, and Methylgermanane: Functionalized 2D Materials of Group 14 for Electrochemical Applications

2D monoelemental group 14 materials beyond graphene, such as silicene and germanene, have recently gained a lot of attention. Covalent functionalization of group 14 layered materials can lead to significant tuning of their properties. While optical and electronic properties of germanene, silicene, and their derivatives have been studied in detail previously, there is no information on their electrochemistry and toxicity. Herein, electrochemical applications of 2D siloxene, germanane, and methylgermanane, specifically for detection of an important biomarker, dopamine, as well as catalyzation of oxygen reduction and hydrogen evolution reactions, which are important in energy applications, are explored. Among the three materials, germanane portrays most superior properties for the electrochemical applications mentioned. All three materials possess fast heterogeneous electron transfer rates, relative to bare glassy carbon electrodes. In addition, toxicity studies of these materials are conducted to gain insights on their possible harmful effects toward human health. The results of this study show siloxene nontoxic while germanane and methylgermanane impose dose‐dependent toxicity. Interestingly, methylation successfully reduce the toxicity of methylgermanane at lower concentrations. These studies provide fundamental insights into electrochemical and toxic properties of functionalized group 14 layered materials for future electrochemical applications.     

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.     

A low frequency tunable hybrid generator

A serious of experimental tests on a commercial brass reinforced PZ bimorph cantilever of the type 5H4E were carried out in a vibration laboratory using a Labview of NI make as instrumentation and control system. Originally the bender acted as a tunable PZ generator, using perforated steel shims as a proof mass. The generator was converted to a hybrid generator by adding ring magnets to its other surface and making them working as part of an electromagnetic generator in addition of being a proof mass. Two arrangement of the electromagnetic generator were investigated at a frequency of (33) Hz which is the resonance frequency at max power of the original PZ generator. By using a shaker the cantilever was vibrated and set into acceleration of (0.25) g rms during all stages as this value was considered to be available and acceptable in balanced large rotating machines in industry. The hybrid generator produced (187) μW for max power, (14.8) V_rms for max OCV, and (8.26) V_rms for max on load voltage for the PZ part, while the electromagnetic part produced max (70) V_rms, (490) μW for arrangement (1) and (34) V_rms, (116) μW for arrangement (2) on no load condition and at the same frequency. The experimental results obtained from the electromagnetic generator are acceptable compared with theory.     

Design, simulation and fabrication of piezoelectric micro generators for aero acoustic applications

Energy harvesters based on acoustic vibration sources can generate electrical power through piezoelectric transduction. Significant miniaturization of electro mechanical devices using MEMS fabrication technology has encouraged the creation of portable, miniature energy harvesters. A niche application is aero acoustics, where wasted, high dB and high frequency sound generated by aircrafts are transformed into useful energy. Having self-powered, miniature acoustic sensors allows noise detection monitoring systems to be self-sustaining. This paper illustrates an Aluminium doped Zinc Oxide (AZO) cantilever beam on stainless steel substrate with a top copper electrode. Design and finite element modelling of the design was conducted using Coventorware™. The AZO piezoelectric thin film was RF-sputtered on the stainless steel substrate. Characterizations were performed using X-ray diffraction (XRD) and scanning electron microscopy (SEM) to evaluate the piezoelectric qualities and surface morphology, respectively. Experimental measurements indicate approximately 345.4 mV AC output voltage (open circuit voltage) is produced at vibration frequencies of 30 kHz. This is in accordance with the Coventorware™ simulation results. This measured power level is sufficient to power a miniature wireless acoustic sensor nodes to monitor noise generated by aircrafts.