Thermal analysis of a ceramic coating diesel engine piston using 3-D finite element method

In this study, firstly, thermal analyses are investigated on a conventional (uncoated) diesel piston, made of aluminum silicon alloy and steel. Secondly, thermal analyses are performed on pistons, coated with MgO-ZrO₂ material by means of using a commercial code, namely ANSYS. Finally, the results of four different pistons are compared with each other. The effects of coatings on the thermal behaviors of the pistons are investigated. It has been shown that the maximum surface temperature of the coated piston with material which has low thermal conductivity is improved approximately 48% for the AlSi alloy and 35% for the steel.     

A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser–based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains. The aspects are also transferred from the source domains to a target domain. Moreover, aspect pruning is applied to further improve the performance of aspect extraction. In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction. The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature. The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.     

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Developing robust vision modules for microsystems applications

In this work, several robust vision modules are developed and implemented for fully automated micromanipulation. These are autofocusing, object and end-effector detection, real-time tracking and optical system calibration modules. An image based visual servoing architecture and a path planning algorithm are also proposed based on the developed vision modules. Experimental results are provided to assess the performance of the proposed visual servoing approach in positioning and trajectory tracking tasks. Proposed path planning algorithm in conjunction with visual servoing imply successful micromanipulation tasks.     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

A Novel Approach to Steganography: Enhanced Least Significant Bit Substitution Algorithm Integrated with Self-Determining Encryption Feature

One of the most well-known and used algorithms for Steganography is Least Significant Bit (LSB) substitution. Although LSB has several advantages such as simplicity, efficiency, and easy-to-do implementation, it has some distinct disadvantages such as it openness to miscellaneous attacks. In this study, we aim to improve the traditional LSB algorithm by eliminating its main disadvantage, being easy to detect, and this way propose an enhanced LSB algorithm called E-LSB. We mainly aim to minimize differences which are due to encryption and image hiding steps in LSB algorithm and make it more difficult to notice that some text has been hidden in the original cover image.
As most of the researchers and practitioners in security field argue, stenographic techniques alone are not sufficient for protecting sensitive information and thus must be used together with encryption algorithms. Therefore, the proposed approach integrates E-LSB with an encryption algorithm. E-LSB does not modify the file size and allows the flexibility of choosing one of well-known encryption algorithms including RSA, AES and CAESAR, but others can be implemented in it. With a set of experiments, the proposed approach is compared with the traditional LSB based embedding approach, and its efficiency and usability is analyzed. A set of performance evaluations realized with the developed software tool based on E-LSB algorithm show that E-LSB is better than the traditional LSB algorithm from security point of view.     

Preparation and Study of Titanium Coating Deposited on a Magnesium Substrate by High-Velocity Oxy-Fuel Spraying

Metal Science and Heat Treatment - Methods of electron microscopy and x-ray diffractometry are used to study the structure of a titanium coating deposited on a magnesium substrate by high-velocity...     

Effect of Alumina on the Structure and Mechanical Properties of Spark Plasma Sintered Boron Carbide

Uniform densification of relatively thick (~7 mm) consolidated boron carbide plates at relatively low temperatures (e.g. 1800°C) and low facture toughness are two of the primary challenges for further development of boron carbide applications. This work reports that these two challenges can be overcome simultaneously by adding 5 wt% alumina as a sintering aid. Nearly fully dense (97%), fine grained boron carbide (B₄C) samples were produced using spark plasma sintering at 1700°C and above in the B₄C‐5 wt% Al₂O₃ system. The alumina and boron carbide matrix reacted to form an Al₅O₆BO₃ (a mullite‐like phase) during sintering. The Al₅O₆BO₃ phase facilitated uniform densification via liquid phase sintering. This secondary phase is dispersed throughout the intergranular pores, providing obstacles for crack propagation and resulting in tougher boron carbide ceramics.     

Bond strength and adaptation of pulp capping materials to dentin

This study evaluated the shear bond strength (SBS) and internal marginal adaptation of pulp‐capping materials to dentin. Flat occlusal deep dentin surfaces were produced and randomly assigned to two groups (sound or artificial caries‐affected dentin). The specimens in each group were assigned to one of seven subgroups according to the materials used: Biodentine, Theracal LC, Ultra‐Blend plus, Calcimol LC, ApaCal ART, EQUIA Forte, and Ionoseal. Buildups (3‐mm inner diameter and 2‐mm deep) were made over the dentin surfaces. The bonded specimens were tested under shear forces at a crosshead speed of 0.8 mm/min and fracture modes were determined using a stereomicroscope at 25× magnification. The materials were applied to the pulp floor of prepared Class I cavities and then the cavities were restored with composite resin. Restored molar teeth were subjected to 5,000 thermocycles and sectioned in a bucco–lingual direction. Resin replicas were made to determine the adaptation at the pulp floor with scanning electron microscopy. Significant differences were determined among both bond strengths and gap formations of the materials. EQUIA Forte applied to both dentin substrates had a significantly higher SBS than the other materials. The bond strength of each material was not influenced by the dentin condition. Biodentine (3.03%), EQUIA Forte (7.83%), and Theracal LC (13.37%) had lower gap formations compared to other materials but were not significantly different from each other.