Optimization of multi-pass turning operations using hybrid teaching learning-based approach

This paper presents a novel hybrid optimization approach based on teaching–learning based optimization (TLBO) algorithm and Taguchi’s method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke–Jeeves patter search.     

Prediction of wear properties in A356 matrix composite reinforced with B4C particulates

In the present study, wear properties of A356 unreinforced alloy and composites with different vol.% of boron carbide particles were investigated. It is noted that composites exhibit better wear resistance compared to unreinforced alloy. According to the differences in wear rates of the composites, two separate wear rate were identified as low and high wear rate regimes. A combination of artificial neural network (ANN) and finite element technique (FEM) was implemented in order to predict the composites wear behavior. The FEM method is used for discretization and to calculate the transient temperature field of quenching. It is observed that predictions of ANN are consistent with experimental measurements for A356 composite and considerable savings in terms of cost and time could be obtained by using neural network model.     

Analysis of interaction between tunnels in soft ground by 3D numerical modeling

Tunnels are increasingly being excavated in soft ground conditions when services are required in urban environments, and it is now common to have tunnels crossing at various elevations. As the tunnel excavations are undertaken at different levels, there will be an interaction which can have a significant influence on stress distributions and consequently deformations within the tunnels and surface settlement. As multi-layer tunnelling is a three dimensional phenomenon in nature, 3D numerical solutions must be utilized for perpendicularly crossing tunnels at various levels. This paper reports the investigations into the changes in stress distribution, deformations and surface settlements which may be expected when the twin Tohid Tunnels pass beneath the Line 4 metro tunnel in Tehran.     

Experimental observation of speckle instability in a two-dimensional disordered medium

Temporal fluctuations of the speckle pattern formed upon backscattering of a laser beam from an interface between gold and nonlinear polymer film have been observed as a function of optical power. The instability can be explained by coupling of laser light to surface plasmons and other guided modes, which experience multiple scattering while propagating in the film along the interface. The speckle pattern produced in this process is extremely sensitive to fluctuations of the scattering potential near the interface.     

Accuracy improving guidelines for network anomaly detection systems

An unprecedented growth in computer and communication systems in the last two decades has resulted in a proportional increase in the number and sophistication of network attacks. In particular, the number of previously-unseen attacks has increased exponentially in the last few years. Due to the rapidly evolving nature of network attacks, a considerable paradigm shift has taken place in the intrusion detection community. The main focus is now on Network Anomaly Detection Systems (NADSs) which model and flag deviations from normal/benign behavior of a network and can hence detect previously-unseen attacks. Contemporary NADS borrow concepts from a variety of theoretical fields (e.g., Information theory, stochastic and machine learning, signal processing, etc.) to model benign behavior. These NADSs, however, fall short of achieving acceptable performance levels as therefore widespread commercial deployments. Thus, in this paper, we firstly evaluate the performance of eight prominent network-based anomaly detectors under malicious portscan attacks to identify which NADSs perform better than others and why. These NADSs are evaluated on three criteria: accuracy (ROC curves), scalability (with respect to varying normal and attack traffic rates, and deployment points) and detection delay. These criteria are evaluated using two independently collected datasets with complementary strengths. We then propose novel methods and promising guidelines to improve the accuracy and scalability of existing and future anomaly detectors. Experimental analysis of the proposed guidelines is also presented for the proof of concept.     

Adaptive nonlinear control of pH neutralization processes using fuzzy approximators

In this paper, an adaptive control scheme, based on fuzzy logic systems, for pH control is addressed. For implementation of the proposed scheme no composition measurement is required. Stability of the closed-loop system is established and it is shown that the solution of the closed-loop system is uniformly ultimately bounded and under a certain condition, asymptotical stability is achieved. Effectiveness of the proposed controller is tested through simulation and experimental studies. Results indicate that the proposed controller has good performances in set-point tracking and load rejection and much better than that of a tuned PI controller.     

Formal anonymity models for efficient privacy-preserving joins

Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, we present a secure multiparty computation protocol that enables record joins via consumers’ encrypted identifiers. Our solution is more practical than prior secure join models in that data holders need to interact with the third party one time per data submission. Though technically feasible, the speed of the basic protocol scales quadratically with the number of records. Thus, we introduce an extended version of our protocol in which data holders append k-anonymous features of their consumers to their encrypted submissions. These features facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations’ consumers. Beyond a theoretical treatment of the problem, we provide an extensive experimental investigation with data derived from the US Census to illustrate the significant gains in efficiency such an approach can achieve.