Mohamed Salahuddin Habibullah

Discovering Unique, Low-Energy Pure Water Isomers: Memetic Exploration, Optimization, and Landscape Analysis

The discovery of low-energy stable and meta-stable molecular structures remains an important and unsolved problem in search and optimization. In this paper, we contribute two stochastic algorithms, the archiving molecular memetic algorithm (AMMA) and the archiving basin hopping algorithm (ABHA) for sampling low-energy isomers on the landscapes of pure water clusters (H2O)n. We applied our methods to two sophisticated empirical water cluster models, TTM2.1-F and OSS2, and generated archives of low-energy water isomers (H2O)n n=3-15. Our algorithms not only reproduced previously-found best minima, but also discovered new global minima candidates for sizes 9-15 on OSS2. Further numerical results show that AMMA and ABHA outperformed a baseline stochastic multistart local search algorithm in terms of convergence and isomer archival. Noting a performance differential between TTM2.1-F and OSS2, we analyzed both model landscapes to reveal that the global and local correlation properties of the empirical models differ significantly. In particular, the OSS2 landscape was less correlated and hence, more difficult to explore and optimize. Guided by our landscape analyses, we proposed and demonstrated the effectiveness of a hybrid local search algorithm, which significantly improved the sampling performance of AMMA on the larger OSS2 landscapes. Although applied to pure water clusters in this paper, AMMA and ABHA can be easily modified for subsequent studies in computational chemistry and biology. Moreover, the landscape analyses conducted in this paper can be replicated for other molecular systems to uncover landscape properties and provide insights to both physical chemists and evolutionary algorithmists.

Subjective operational reliability assessment of maritime transportation system

Abstract System reliability assessment is one of the major acts in the operation and maintenance of every industrial and service sector, which also holds true for maritime transportation system. The complexity of the maritime transportation system is a prime obstacle in the evaluation of the operational reliability of the system; mainly due to the fact that statistical data on the important parameters and variables is scarce. This makes the application of fuzzy sets and fuzzy logic a viable option to overcome the data problem with regards to imprecision or vagueness in parameters and variables values. In this paper, the different decisive factors, affecting maritime transportation systems, are modeled in the form of linguistic variables. Techniques such as aggregation, mapping of fuzzy sets using distance measure and fuzzy logic rule base are used to arrive at subjective operational reliability value. The complete procedure is demonstrated with an example.

Health Index-Based Prognostics for Remaining Useful Life Predictions in Electrical Machines

Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. Prognostics, a subject concerned with the prediction of the remaining useful life (RUL), has been increasingly studied and applied to practical systems, such as electrical systems, over the past few years. Here, with the adoption of a data-driven prognostics framework, this paper proposed a health index (HI)-based prognostics method to predict the RUL of electrical machines. By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).

Quantitative risk assessment through hybrid causal logic approach

In this paper, a hybrid causal logic (HCL) model is improved by mapping a fuzzy fault tree (FFT) into a Bayesian network (BN). The first step is to substitute an FFT for the traditional FT. The FFT is based on the Takagi–Sugeno model and the translation rules needed to convert the FFT into a BN are derived. The proposed model is demonstrated in a study of a fire hazard on an offshore oil production facility. It is clearly shown that the FFT can be directly converted into a BN and that the parameters of the FFT can be estimated more accurately using the basic inference techniques of a BN. The improved HCL approach is able to both accurately determine how failures cause an undesired problem using FFT and also model non-deterministic cause–effect relationships among system elements using the BN.

A Study of Lifetime Optimization of Transportation System

The operation process or environment usually has a significant influence on system lifetime. In this paper, a lifetime optimization approach based on linear programming (LP) is proposed to maximize the transportation system lifetime, in which a semi-Markov (SM) model is used to model the operation process. In the proposed method, we first formulate the optimization problem as an LP model that is used to find the optimal transient probability of each state. Then, an analytical method is performed to obtain the corresponding optimal sojourn-time distribution parameters of the SM process. Finally, the proposed approach is applied to a port oil transportation system to show that it can efficiently ensure that the transportation system has a long lifetime.

Application of the LP-ELM Model on Transportation System Lifetime Optimization

Considering factors such as economic costs and lives, an unreliable transportation system is more likely to cause severe consequences. Therefore, reliability optimization of transportation systems has attracted much attention over the past several decades. The traditional reliability optimization design is usually focused on redundancy allocation or reliability redundancy allocation. In practice, the operation process usually has a significant influence on the transportation system lifetime. By combining linear programming (LP) and extreme learning machine (ELM), a two-stage approach is proposed to optimize the transportation system lifetime, in which a semi-Markov model (SMM) is used to model the operation process. In the proposed method, we first formulate the optimization problem as an LP model, and the LP algorithm is utilized to search for the approximate optimal state probabilities. After data production and sample selection, ELM is trained with the produced training data and used to predict the optimal sojourn time distribution parameters. Applications on three different cases demonstrate that a higher lifetime can be ensured for the transportation system by using the proposed method.

Multi-objective optimization of large scale berth allocation and quay crane assignment problems

This paper describes the use of multi-objective optimization on a berth allocation and quay crane assignment problem (BACAP). The BACAP involves the simultaneous optimization of two highly-coupled container terminal operations, namely berth allocation and quay crane assignment, which have been traditionally solved as individual problems. The developed multi-objective evolutionary algorithm (MOEA) is validated on a large scale BACAP dataset, consisting of 23 berths and 87 quay cranes, generated based on the port conditions at the Pasir Panjang container terminal, which is the largest container terminal in Singapore. Optimization results show that the multi-objective optimization approach offers the port manager flexibility in selecting a desirable solution for implementation.

Comparative studies of multi-criteria decision making with application to condition monitoring of a ship turbine

Problems in multi-criteria decision making have always been of considerable interest to researchers over the decades, with various techniques and methodologies evolving continuously in this area. The use of fuzzy-set-based approaches has been widely documented in problems where the qualitative or subjective nature of the data, with information ambiguity or data imprecision, is inherent. In recent times, the approach of evidential reasoning, based on the Dempster–Shafer theory which considers uncertainty of a similar nature, has emerged as an alternative. As such, questions have arisen on the merits, and hence the accuracy, of these two kinds of method, i.e. the fuzzy-set-based approach and evidential reasoning, when applied to the considered problem. This paper aims to address some of these questions through a comparative study on the use of fuzzy-set-based methods and evidential reasoning methodology on the condition monitoring of a turbine on a transportation ship. Since the assessment of the ranking problem of the condition-monitoring technique has been well investigated and reported in the literature, methodical comparisons of the results obtained from the fuzzy-set-based methods and the evidential reasoning approach are studied. In the studies, it was concluded that the use of assessment with evidential reasoning, which considers incomplete information, provides identical results with those of fuzzy-set-based methods. The paper also revealed how the use of the evidential reasoning approach could replace fuzzy-set-based methods, which often demands complete subjective information.

A FLOW MODEL OF WEB SERVICES FOR THE GRID

The Grid seeks to provide a universal platform for collaborations among organizations to merge their resources together in a reliable and scalable manner for solving problems that are multidisciplinary in nature. This paper presents a model for representing a problem as a flow based on the semantics of WS–BPEL. In particular, the model permits a link to be declared inside not only a parallel task but also a sequential task. In addition, the concept of normal forms is presented to eliminate ambiguity in the model by regulating the use of links. Several comparisons are also made with a mix of literatures and standards to assess the efficacy of the model itself.