Multi-objective optimization in wire-electro-discharge machining of TiC reinforced composite through Neuro-Genetic technique

This paper proposed a Neuro-Genetic technique to optimize the multi-response of wire electro-discharge machining (WEDM) process. The technique was developed through hybridization of a radial basis function network (RBFN) and non-dominated sorting genetic algorithm (NSGA-II). The machining was done on 5 vol% titanium carbide (TiC) reinforced austenitic manganese steel metal matrix composite (MMC). The proposed Neuro-Genetic technique was found to be potential in finding several optimal input machining conditions which can satisfy wide requirements of a process engineer and help in efficient utilization of WEDM in industry.     

Pseudo-bond graph modelling of temperature distribution in a through-process steel rolling

In a through-process hot steel rolling, the material moves from the reheat furnace to the rolling stands, and finally to the runout table. In this work, the pseudo-bond graph model has been developed to predict the temperature distribution in this process, which includes the study of essential temperature dynamics inside the material. For each section of the through-process rolling section, an individual pseudo-bond graph model has been developed, and finally those are connected to each other. By using this model one can predict the output temperature at the end of the runout table for a given set of input parameters at the entry of reheat furnace.     

An Architectural Framework for Accurate Characterization of Network Traffic

In networks carrying large volume of traffic, accurate traffic characterization is necessary for understanding the dynamics and patterns of network resource usage. Previous approaches to flow characterization are based on random sampling of the packets (e.g., Cisco's NetFlow) or inferring characteristics solely based on long lived flows (LLFs) or on lossy data structures (e.g., bloom filters, hash tables). However, none of these approaches takes into account the heavy-tailed nature of the Internet traffic and separates the estimation algorithm from the flow measurement architecture.In this paper, we propose an alternate approach to traffic characterization by closely linking the flow measurement architecture with the estimation algorithm. Our measurement framework stores complete information related to short lived flows (SLFs) while collecting partial information related to LLFs. For real-time separation of LLFs and SLFs, we propose a novel algorithm based on typical sequences from Information theory. The distribution (pdf) and sample space of the underlying traffic is estimated using the non-parametric Parzen window technique and likelihood function defined over the Coupon collector problem. We validate the accuracy and performance of our estimation technique using traffic traces from the internal LAN in our laboratory and from National Library for Applied Network Research (NLANR).     

An algorithm for recognizing palm polygons

A polygonP is said to be apalm polygon if there exists a pointxP such that the Euclidean shortest path fromx to any pointyP makes only left turns or only right turns. The set of all such pointsx is called thepalm kernel. In this paper we propose an O(E) time algorithm for recognizing a palm polygonP, whereE is the size of the visibility graph ofP. The algorithm recognizes the given polygonP as a palm polygon by computing the palm kernel ofP. If the palm kernel is not empty,P is a palm polygon.The extended abstract of this paper was reported at the Second Canadian Conference in Computational Geometry, pp. 246–251, 1990     

PsyCOP-a psychologically motivated connectionist system for objectperception

A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects (based an the concepts of conventional and structured connectionist computing) by integrating the psychological hypotheses with the generalized Hough transform technique. The psychological facts include the evidence of separation of two regions for identification ("what it is") and pose estimation ("where it is"). The system uses the mechanism of selective attention for initial hypotheses generation. A special two-stage training paradigm has been developed for learning the structural relationships between the features and objects and the importance values of the features with respect to the objects. The performance of the system has been demonstrated on real-life data both for single and mixed (overlapped) instances of object categories. The robustness of the system with respect to noise and false alarming has been theoretically investigated.     

Sink Mobility-Based Energy Efficient Routing Algorithm Variants in WSN

International Journal of Wireless Information Networks - In this work, energy efficient routing protocol variants considering different sink mobility in hierarchical cluster based wireless sensor...     

Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms

The Journal of Supercomputing - This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is...     

Topical delivery of paclitaxel for treatment of skin cancer

Context: Skin cancer represents the most growing types of cancer in human and ultraviolet radiation can be cited as one of the prime factor for its occurrence. Current therapy of skin cancer suffers from numerous side effects; for effective therapy, topical application of formulation of paclitaxel (PTX) can be considered as a novel approach.

Objective: The present study is an attempt to prepare formulation of solid lipid nanoparticles (SLN) of PTX for the effective treatment of various form of skin carcinoma.

Methods: The SLN were prepared by high-speed homogenization and ultrasonication method. The prepared SLN were characterized. The optimized PTX SLN were loaded in carbopol gel. The prepared gels were evaluated for its gelling properties and finally studied for in vivo anti-cancer efficacy and histopathological study.

Results: The particle size distribution was found to be in the range of 78.82–587.8?nm. The product yield (%) was found between 60% and 66% and showed a highest entrapment efficiency of 68.3%. The in vitro release of the drug from SLN dispersion was found to be biphasic with the initial burst effect, followed by slow release. SLN-loaded gel were subjected to permeability study and the results show steady-state flux (J_ss), permeability coefficient (K_p), and enhancement ratio were significantly increased in SLN-loaded gel formulation as compared with PTX-loaded gel. The histopathological study clearly reveals the efficacy of the SLN-F3 3G in the treatment of skin cancer.

Conclusion: The experimental formulations show controlled release of PTX and thus expected to show reduce dose-related side effects.     

On the effect of tool offset in hybrid-FSW of copper-aluminium alloy

Tool offset is one the most significant parameters in joining of dissimilar materials by friction stir welding (FSW) process. An investigation is carried out on the effect of tool offset toward thermal history, material flow pattern, mechanical properties, welding force, and weld joint morphology. It was found that offsetting toward aluminum side along with a plasma-assisted heat source is an efficient approach to address one of the most important apprehensions in aluminum-copper solid-state welding process. The offset influences the amount of intermetallic at the joint interface and in-effect impacts on final strength and material flow behavior. The optimum and continuous layer of intermetallic produces the maximum weld joint strength. The specimen welded with optimum tool offset shows the highest strength using 55 A plasma current in hybrid friction stir welding process.     

All‐Organic Dual Spin Valves with Well‐Resolved Four Resistive‐States

The formation of all‐organic dual spin valves (DSVs) with three organic spin‐selective layers, that is, spin‐injection, spin‐detection, and an additional spin‐filtering layer at the intermediate, is reported. As spin‐selective layers, manganese‐ and cobalt phthalocyanines, which are well‐known single‐molecule magnets, are used in their immobilized forms, so that all‐organic DSVs can be prefabricated for characterization. The three spin‐selective layers have provided four configurations with at most two spin‐flip interfaces enforcing spin‐flipping at the two nonmagnetic organic spacer layers, for which copper phthalocyanine is used. Since a couple of the four configurations have exhibited similar resistivities, the degeneracy in the resistive‐states is broken through asymmetric spin‐injection and spin‐detection layers and also through asymmetric thickness of the nonmagnetic spacer layers. When both the spin‐flip interfaces are made operative independently, a 2‐bit logic with four distinct resistive states can be achieved.