The Effect of Snake Muscular System on Actuators’ Torque

Most of the research conducted on snake robots has been on movement, control or dynamics. There is only some research dealing with the reduction of actuators’ sizes. Actuator size usually depends on the force/torque it can provide. Small actuators imply a more efficient, long lasting, lighter and more flexible robot. The required force/torque and energy consumption consequently is directly affected by the mechanism design. Mother nature has always presented optimum systems and has inspired engineers. In this paper, we have adopted the snake anatomy to design a snake robot. The results show a reduction in torque demand. This robot is an extension of our previous research on building a snake without including the anatomy. The new robot weighs about only one-third of the previous version.     

Keyword-based patent citation prediction via information theory

ABSTRACT

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from the abstracts of selected patents. After applying three classes of RA (variable-based analysis without and with loops and state-based analysis), nine specific IV states of a predicting model are extracted. These states involve the four keywords of “chamber”, “hous”, “main”, and “return”. Lastly, the abstracts of the patents are examined to identify the technical subjects relevant to smart building technologies for which these keywords are proxies.     

Video frame rate up conversion under inconsistent camera motion

In this paper, we address the problem of video frame rate up-conversion (FRC) in the compressed domain. FRC is often recognized as video temporal interpolation. This problem is very challenging when targeted for video sequences with inconsistent camera and object motion, such as sports videos. A novel compressed domain motion compensation scheme is presented and applied in this paper, aiming at up-sampling frame rates in sports videos. MPEG-2 encoded motion vectors (MVs) are utilized as inputs in the proposed algorithm. The decoded MVs undergo a cumulative spatiotemporal interpolation. An iterative rejection scheme based on the dense motion vector field (MVF) and the generalized affine motion model is exploited to detect global camera motion. Subsequently, the foreground object separation is performed by additionally examining the temporal consistency of the output of iterative rejections. This consistency check process helps coalesce the resulting foreground blocks and weed out the unqualified blocks. Finally, different compensation strategies for the camera and object motions are applied to interpolate the new frames. Illustrative examples are provided to demonstrate the efficacy of the proposed approach. Experimental results are compared with the popular block and non-block based frame interpolation approaches.

Resolution of Semantic Heterogeneity in Database Schema Integration Using Formal Ontologies

This paper addresses the problem of handling semantic heterogeneity during database schema integration. We focus on the semantics of terms used as identifiers in schema definitions. Our solution does not rely on the names of the schema elements or the structure of the schemas. Instead, we utilize formal ontologies consisting of intensional definitions of terms represented in a logical language. The approach is based on similarity relations between intensional definitions in different ontologies. We present the definitions of similarity relations based on intensional definitions in formal ontologies. The extensional consequences of intensional relations are addressed. The paper shows how similarity relations are discovered by a reasoning system using a higher-level ontology. These similarity relations are then used to derive an integrated schema in two steps. First, we show how to use similarity relations to generate the class hierarchy of the global schema. Second, we explain how to enhance the class definitions with attributes. This approach reduces the cost of generating or re-generating global schemas for tightly-coupled federated databases.     

Free convection of hybrid Al2O3-Cu water nanofluid in a differentially heated porous cavity

Hybrid nanofluids are a new type of enhanced working fluids, engineered with enhanced thermo-physical properties. The hybrid nanofluids profit from the thermo-physical properties of more than one type of nanoparticles. The present study aims to address the free convective heat transfer of the Al₂O₃-Cu water hybrid nanofluid in a cavity filled with a porous medium. Two types of important porous media, glass ball and aluminum metal foam, are considered for the porous matrix. The experimental data show dramatic enhancement in the thermal conductivity and dynamic viscosity of the synthesized hybrid nanofluids, and hence, these thermophysical properties could not be modeled using available models of nanofluids. Thus, the actual available experimental data for the thermal conductivity and the dynamic viscosity of hybrid nanofluids are directly utilized in the present theoretical study. Various comparison with results published previously in the literature are performed and the results are found to be in excellent agreement. In most cases, the average Nusselt number Nu_l is decreasing function of the volume fraction of nanoparticles. The results show the reduction of heat transfer using nanoparticles in porous media. The observed reduction of the heat transfer rate is much higher for hybrid nanofluid compared to the single nanofluid.     

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.     

Semantics preserving SPARQL-to-SQL translation

Most existing RDF stores, which serve as metadata repositories on the Semantic Web, use an RDBMS as a backend to manage RDF data. This motivates us to study the problem of translating SPARQL queries into equivalent SQL queries, which further can be optimized and evaluated by the relational query engine and their results can be returned as SPARQL query solutions. The main contributions of our research are: (i) We formalize a relational algebra based semantics of SPARQL, which bridges the gap between SPARQL and SQL query languages, and prove that our semantics is equivalent to the mapping-based semantics of SPARQL; (ii) Based on this semantics, we propose the first provably semantics preserving SPARQL-to-SQL translation for SPARQL triple patterns, basic graph patterns, optional graph patterns, alternative graph patterns, and value constraints; (iii) Our translation algorithm is generic and can be directly applied to existing RDBMS-based RDF stores; and (iv) We outline a number of simplifications for the SPARQL-to-SQL translation to generate simpler and more efficient SQL queries and extend our defined semantics and translation to support the bag semantics of a SPARQL query solution. The experimental study showed that our proposed generic translation can serve as a good alternative to existing schema dependent translations in terms of efficient query evaluation and/or ensured query result correctness.     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Memristive fuzzy edge detector

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our memristive fuzzy edge detector (implemented in analog form) compared to other commonly used edge detectors is it can be implemented in parallel form, which makes it a powerful device for real-time applications.     

The release of cefazolin from chitosan/polyvinyl alcohol/sepiolite nanocomposite hydrogel films

Films of chitosan/polyvinyl alcohol (PVA)/sepiolite nanocomposite were prepared by a simple and “green” route through solution mixing; followed by freezing–thawing cycles. The structures of nanocomposites were characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis, X-ray diffractometry, and Fourier transform infrared spectroscopy. The SEM and TEM micrographs confirmed a needle-type dispersion of sepiolite nanoclay in the hydrogel nanocomposites. The effects of sepiolite and chitosan/PVA weight ratio on the swelling of nanocomposites were investigated. The water absorbency of nanocomposites was decreased by introducing sepiolite nanoclay. The nanocomposites with high content of chitosan showed high swelling capacity. The nanocomposite films showed pH-dependent swelling behavior with a maximum water absorbency under acidic pH. The cefazolin with a broad-spectrum activity toward gram-positive and gram-negative bacteria was loaded in hydrogels. The release of cefazolin from nanocomposites was evaluated at pH 7.4. The content of released drug was affected by both sepiolite amount and chitosan/PVA weight ratio. The nanocomposites films released more cefazolin than the neat hydrogel film. Cefazolin-loaded nanocomposites showed the antibacterial activity with a large zone of inhibition against gram-positive Bacillus cereus bacterium.