Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

Intrusion detection and prevention system for an IoT environment

Internet of Things (IoT) security is the act of securing IoT devices and networks. IoT devices, including industrial machines, smart energy grids, and building automation, are extremely vulnerable. With the goal of shielding network systems from illegal access in cloud servers and IoT systems, Intrusion Detection Systems (IDSs) and Network-based Intrusion Prevention Systems (NBIPSs) are proposed in this study. An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering. The proposed NBIPS inspects network activity streams to identify and counteract misuse instances. The NBIPS is usually located specifically behind a firewall, and it provides a reciprocal layer of investigation that adversely chooses unsafe substances. Network-based IPS sensors can be installed either in an inline or a passive model. An inline sensor is installed to monitor the traffic passing through it. The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.     

A comparative study on the metaheuristic-based optimization of skew composite laminates

Engineering with Computers - Plate structures are the integral parts of any maritime engineering platform. With the recent focus on composite structures, the need for optimizing their design and...     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

     

Aluminum phosphate sealing to improve insulation resistance of plasma-sprayed alumina coating

The insulation resistance of conventional atmospheric plasma-sprayed alumina coatings with 10–15% porosity is ～10¹¹ Ω. The presence of pores, lamellae boundaries, and other non-fillings dampens the insulation resistance of the coating. In the present study, aluminum phosphate was used to seal the surface of plasma-sprayed alumina coating and evaluate the effect of sealing on the insulation resistance and its thermal cycling response. Sealing was carried out with three concentrations of sealant (P/Al molar ratio of 3, 10, and 15). Characterization by X-ray diffraction and scanning electron microscopy revealed the primary sealing phase as aluminum metaphosphate and effective sealing of the pores by the aluminum phosphate phases. Insulation resistance is improved by two orders of magnitude after sealing the coated samples. Sealing with P/Al molar ratio 3 exhibited maximum insulation resistance of ～10¹³ Ω at room temperature. Thermal cycling studies between 650°C and 200°C on the sealed samples showed deterioration in thermal cycling life after sealing.     

Biocompatible antimicrobial cotton fibres for healthcare industries: a biogenic approach for synthesis of bio‐organic‐coated silver nanoparticles

Cotton fibres coated with biogenically fabricated silver nanoparticles (SNPs) are most sought material because of their enhanced activity and biocompatibility. After successful synthesis of SNPs on cotton fibres using leaf extract of Vitex negundo Linn, the fibres were studied using diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray, and inductively coupled plasma atomic emission spectrometry. The characterisation revealed uniformly distributed spherical agglomerates of SNPs having individual particle size around 50 nm with the deposition load of 423 μg of silver per gram of cotton. Antimicrobial assay of cotton–SNPs fibres showed effective performance against pathogenic bacteria and fungi. The method is biogenic, environmentally benign, rapid, and cost‐effective, producing highly biocompatible antimicrobial coating required for the healthcare industry.Inspec keywords: cotton, health care, nanoparticles, coatings, silver, fibres, nanofabrication, scanning electron microscopy, X‐ray chemical analysis, atomic emission spectroscopy, plasma applications, microorganisms, biotechnologyOther keywords: biocompatible antimicrobial cotton fibre coating, healthcare industry, bioorganic‐coated silver nanoparticle synthesis, biogenically fabricated silver nanoparticle, SNP, leaf extraction, Vitex negundo Linn, diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray spectrometry, inductively coupled plasma atomic emission spectrometry, uniformly distributed spherical agglomerate, antimicrobial assay, pathogenic bacteria, fungi, Ag     

VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

A vendor-managed inventory (VMI) relationship between a downstream retailer and an upstream vendor consists of two distinct components: (i) information sharing (IS) and (ii) a shift in decision-making responsibility. This study compares these two components of VMI in a two-stage serial supply chain based on the ‘static uncertainty’ strategy under dynamic and random demand with fill rate constraints. Numerical experiments are conducted using analytical models to identify the conditions where the incremental value of VMI over IS is significant. The results provide guidelines relevant to academia and supply chain practitioners in taking VMI adoption decision above and beyond IS according to their specific business environment.     

Investigation of TiAlN coated roller burnishing on Al-(B4C)p MMC workpiece material

Burnishing avoids the need for super finishing operations after the conventional turning process, to enhance the surface quality. This paper deals with the surface modifications of Al(B₄C)_p Metal Matrix Composites (MMC) workpiece material after burnishing with a TiAlN coated WC roller. The burnishing speed, lubrication type, burnishing passes, and coating were the input parameters. Surface hardness and roughness after the burnishing were studied. It was found that the coating on the WC roller had enhanced the hardness in the workpiece after burnishing in the case of Al-5?wt.% (B₄C)_p, under all conditions. The effect of the coating on the work piece surface hardness was not significant with Al-10?wt.% (B₄C)_p. While burnishing Al-5?wt.% (B₄C)_p, the minimum surface roughness combined with maximum surface hardness was obtained, during the third pass under dry condition using uncoated rollers. The number of passes to achieve the desired surface conditions reduced, on using coated rollers with kerosene as the lubricant.