Rituparna Datta

• PhD
• PostDoc Position at Korea Advanced Institute of Science and Technology

Drones are technically denoted as unmanned aerial vehicles (UAVs) or unmanned aerial systems (UAS). These are remotely or autonomously piloted aircraft and can fly without an onboard human pilot while being controlled from the ground. The need for UAVs is commonly found in wireless communication beyond human trajectory. Recently, drones have been u...

The requirement to include optimization in applications related to smart materials is growing in diversified applications. In this keynote lecture, I will cover different case studies in which optimization is integrated into smart materials. The first case study is related to piezoelectric smart material as an actuator for robot gripper design. An...

For a manipulator of a given topology, designing the dimensions based on optimising Jacobianbased performance parameters (such as manipulability index and condition number (Patel & Sobh, 2015)) around a given end-effector point would require only the topological information for the formulation of Jacobian, as every other step can be automated. Form...

Graphology is the study of handwriting and it is used to analyse personality traits and characteristics. It is based on the idea that a person's handwriting has certain physical characteristics that are thought to reflect various aspects of their personality. It has traditionally been a field conducted by human experts who interpret various aspects...

Artificial Intelligence can be used in design of adaptive structures for data-driven modelling, design of intelligent control algorithms, predictive analytics, and design optimization. Adaptive structures for thermal management of electronic devices refer to systems or components that can dynamically adjust their shape, properties, or configuration...

Artificial Intelligence (AI) is the study of designing computers to make decisions like humans do. Using AI, computers are able to solve problems that require knowledge and intelligence. Data Science is the study of analysing raw data and to process it into a useful form, often a form that is ready to be used in Artificial Neural Networks. Artifici...

Journey by aircraft is the only option for long-distance transportation and also one of the frequently used modes of transportation of passengers. As a result, safety of passengers and efficiency of the aircraft depend on maintaining efficient running conditions. Although many safety standards are followed in the design of the aircraft, and thus th...

The multi-objective optimisation problem of the dynamic response of a freight wagon fitted with three-piece bogies is a challenging task due to interdependencies between the decision variables, conflicts between the objective functions and computationally expensive rail vehicle dynamic simulations. In this article, a novel approach of multi-objecti...

In this study, displacement transmissibility based parameter identification of a silicon based organic viscoelastic polymer, polydimethylsiloxane (PDMS) has been proposed. The vision is to fill the identification gap for a mechanical model of soft viscoelastic polymers of average-to-high molecular weight. The present investigation is based on an ex...

In this paper, we propose an optimization system based on an evolutionary algorithm developed to solve a real-life optimization problem related to the optimization of the computer networks. In particular, we focus on an NP-hard flow allocation problem in computer networks related to network survivability and addressing various constraints specific...

Aerofoil self-noise can affect the performance of the overall system. One of the main goals of aircraft design is to create an aerofoil with minimum weight, cost, and self-noise, satisfying all design requirements from the physical and the functional requirements. Aerofoil self-noise refers to the noise produced by the interaction between an aerofo...

The heat sink is one of the most widely used devices for thermal management of electronic devices and automotive systems. The present study approaches the design of the heat sink with the aim of enhancing their efficiency and keeping the material cost to a minimum. The above-mentioned purpose is achieved by posing the heat sink design problem as a...

Adequate sleep is significant for human to actively pursue daily activity. On the other hand, insomnia is directly proportional to aging and health deterioration. Sleep disorder classification is important for medical scientists as well as machine learning researchers. In the paper, we have developed a sleep disorder classification method for Elect...

Constraint normalization ensures consistency in scaling for each constraint in an optimization problem. Most constraint handling studies only address the issue to deal with constraints and use problem information to scale the constraints. In this paper, we propose a hybrid evolutionary algorithm—Constraint Handling with Individual Penalty Approach...

Neural networks perform well when they are built for a specific task and the set of inputs and the set of outputs are well defined. However, these results are very limited in scope, and communication between different neural networks to share knowledge that can lead to the performance of more general tasks is still inadequate. Communication between...

Traditional rheometers and viscometers are useful for low viscosity - viscoelastic materials where complex shear modulus can be estimated from the measurement of shear response against torsional oscillation of the sample material. However, the same technique is not very useful for relatively high modulus viscoelastic materials. In this study, a dis...

An application of reconfigurable parabolic space antenna for satellite is discussed in this paper. The present study focuses on shape morphing of flexible parabolic antenna actuated with Shape Memory Alloy (SMA) wires. The antenna is able to transmit the signals to the desired footprint on earth with a desired gain value. SMA wire based actuation w...

Text summarization aims to generate condensed summary from a large set of documents on the same topic. We formulate text summarization task as a multi-objective optimization problem by defining information coverage and diversity as two conflicting objective functions. The result solutions represent summaries that ensure the maximum coverage of the...

Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for two main reasons: (1) EAs allow finding several members of the Pareto optimal set in a single run of the algorithm and (2) EAs are less suscep...

Constrained optimization is applicable to most real world engineering science problems. An efficient constraint handling method must be robust, reliable and computationally efficient. However, the performance of constraint handling mechanism deteriorates with the increase of multi-modality, non-linearity and non-convexity of the constraint function...

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience...

The field of robotics is evolving at a very high pace and with its increasing applicability in varied fields, the need to incorporate optimization analysis in robot system design is becoming more prominent. The present work deals with the optimization of the design of a 7-link gripper. As actuators play a crucial role in functioning of the gripper,...

In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to satisfy certain restrictions or constraints. Moreover, in some applications, the numerical values of the objectives and constraints are obtained from computationally expensive simulations. Ma...

This research focuses on the establishment of a constructive solid geometry-based topology optimization (CSG-TOM) technique for the design of compliant structure and mechanism. The novelty of the method lies in handling voids, non-design constraints, and irregular boundary shapes of the design domain, which are critical for any structural optimizat...

Robot gripper design is an active research area due to its wide spread applicability in automation. The present work deals with the actuator analysis of a non-linear, multi-modal and multi-objective optimization problem which is originally formulated by Osyczka [1]. The previous work [1] had treated the actuator as a blackbox. In the present work,...

The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also...

Robot gripper design is an active research area due to its wide spread applicability in automation, especially for high-precision micro-machining. This paper deals with a multiob-jective optimization problem which is nonlinear, multimodal, and originally formulated. The previous work, however, had treated the actuator as a blackbox. The system mode...

The holy grail of constrained optimization is the development of an efficient, scale invariant, and generic constraint-handling procedure in single- and multi-objective constrained optimization problems. Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and nonconvex. As a generic c...

This paper proposes dual multiobjective quantum-inspired evolutionary algorithm (DMQEA) for a sensor arrangement problem in a 2D environment. DMQEA has a dual stage of dominance check by introducing secondary objectives in addition to primary objectives. In an archive generation process, the secondary objectives are to maximize global evaluation va...

This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint ha...

Piezoelectric (PZ) actuator is widely recognized for its high precision and displacement accuracy even at nanometer ranges. A minimalistic model is proposed in the present work, for PZ stack actuators. In the proposed model, various stack assembly arrangements have been assumed. Separate series and parallel assembly arrangements are suggested for b...

Passive use of smart materials for vibration isolation of dynamic systems is drawing wide attention due to its flexibility, vast applicability and effectiveness in aerospace and auto-mobile applications. In the present work, we design and investigate experimentally, the application of two vibration isolation systems based on such passive smart mate...

Active and passive vibration control of dynamic systems using Shape Memory Alloys (SMAs) have received significant attraction because of its effectiveness in wide range of applications including aerospace, automobile and biomedical domain. A vibration isolation system comprising four bar linkage mechanism along with SMA wire is proposed in the pres...

Constrained optimization is one of the popular research areas since constraints are usually present in most real world optimization problems. The purpose of this work is to develop a gradient free constrained global optimization methodology to solve this type of problems. In the methodology proposed, the single objective constrained optimization pr...

The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure in single and multi-objective constrained optimization problems. In this paper, an individual penalty parameter based methodology is proposed to solve constrained optimization problems. The individual penalty para...

Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and non-convex. As a generic classical approach, the penalty function approach is a popular methodology which degrades the objective function value by adding a penalty proportional to the constraint violation. However, the penalty fu...

Optimization for single main objective with multi constraints is considered using a probabilistic approach coupled to evolutionary search. In this approach the problem is converted into a bi-objective problem, treating the constraint ensemble as a second objective subjected to multi-objective optimization for the formation of a Pareto front, and th...

Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining...

A hybrid adaptive normalization based constraint handling approach is proposed in the present study. In most constrained optimization problems, constraints may be of different scale. Normalization of constraints is crucial for the efficient performance of a constraint handling algorithm. A growing number of researchers have proposed different strat...

This paper is concerned with the determination of optimum forces extracted by robot grippers on the surface of a grasped rigid object -- a matter which is crucial to guarantee the stability of the grip without causing defect or damage to the grasped object. A multi-criteria optimization of robot gripper design problem is solved with two different c...

Equality constraints are difficult to handle by any optimization algorithm, including evolutionary methods. Much of the existing studies have concentrated on handling inequality constraints. Such methods may or may not work well in handling equality constraints. The presence of equality constraints in an optimization problem decreases the feasible...

Over the past few years there have been considerable interest in optimal reconstruction of cross-sectional images from tomographic data using evolutionary algorithms. Reconstruction of image data from projections is a key problem in tomographic image analysis. It is essential to choose some proper objective functions of the problem. We use sum of s...

Constrained engineering design optimization problems are usually computationally expensive due to non-linearity and non convexity of the constraint functions. Penalty function methods are found to be quite popular due to their simplicity and ease of implementation, but they require an appropriate value of the penalty parameter. Bi-objective approac...

Genetic Algorithms (GAs) are a highly successful population based approach to solve global optimization problems. They have carved out a niche for themselves in solving optimization problems of varying difficulty levels involving single and multiple objectives. Most real-world optimization problems involve equality and / or inequality constraints a...

Evolutionary algorithms are modified in various ways to solve constrained optimization problems. Of them, the use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective, has received a significant attention. Classical penalty function approach is another common methodolo...

Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective Evolutionary Algorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constrain...

Optimal machining parameters are very important for every machining process. This paper presents an Evolutionary Multi-objective Genetic Algorithm based optimization technique to optimize the machining parameters (cutting speed, feed and depth of cut) in a turning process. The effect of these parameters on production time, production cost and surfa...

In this paper, we propose a hybrid reference-point based evolutionary multi-objective optimization (EMO) algorithm coupled with the classical SQP procedure for solving constrained single-objective optimization problems. The reference point based EMO procedure allows the procedure to focus its search near the constraint boundaries, while the SQP met...

Evolutionary multi-objective optimization (EMO) has received significant attention in re-cent studies in engineering design and analysis due to their flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machini...

Optimization for single main objective with multi constraints is considered using a probabilistic approach coupled to evolutionary search. In this approach the problem is converted into a bi-objective problem, treating the constraint ensemble as a second objective subjected to multi-objective optimization for the formation of a Pareto front, and th...