Arpan Pal

Bio: Arpan Pal is an academic researcher from Tata Consultancy Services. The author has contributed to research in topics: Photoplethysmogram & Analytics. The author has an hindex of 17, co-authored 184 publications receiving 1283 citations.

Digital twin: current scenario and a case study on a manufacturing process

Abstract: In the current scenario, industries need to have continuous improvement in their manufacturing processes. Digital twin (DT), a virtual representation of a physical entity, serves this purpose. It aims to bridge the prevailing gap between the design and manufacturing stages of a product by effective flow of information. This article aims to create a state-of-the-art review on various DTs with their application areas. The article also includes schematic representations of some of the DTs proposed in various fields. The concept is also represented by a case study based on a DT model developed for an advanced manufacturing process named friction stir welding. Towards the end, a model for implementing DT in a factory has been proposed.

A method and system for effective management of energy consumption by household appliances

Abstract: A method and system for effective management of energy consumption by household appliances, using a home energy information gateway. Particularly, the invention provides a method and system for an efficient and economic home energy information gateway which can provide the energy consumption information and communication interface through a set top box like gateway device on the existing display device such as television thereby reducing extra display medium cost. More particularly, the invention provides a method and system for managing energy consumption effectively by monitoring, controlling and displaying energy usage of household appliances by way of collecting smart meter data and generating user friendly reports and graphs.

Real time monitoring and control of friction stir welding process using multiple sensors

Abstract: In the present work, a novel cloud-based remote and real time monitoring and control scheme has been developed for a manufacturing process named friction stir welding (FSW) to avoid occurrence of weld defects. This model acquires data from multiple sensors associated with the FSW machine and transmits them to the cloud. The signals are analyzed and processed in the cloud in real time through various signal processing and machine learning techniques. The model provides a feedback to the machine regarding the desired controlled parameters to achieve an improved weld quality. This is an example of Industry 4.0 where a manufacturing process can be controlled in real time from any location.

Estimation of blood pressure levels from reflective Photoplethysmograph using smart phones

Abstract: As part of preventive healthcare, there is a need to regularly monitor blood pressure (BP) of cardiac patients and elderly people. Mobile Healthcare, measuring human vitals like heart rate, Spo2 and blood pressure with smart phones using the Photoplethysmography technique is becoming widely popular. But, for estimating the BP, multiple smart phone sensors or additional hardware is required, which causes uneasiness for patients to use it, individually. In this paper, we present a methodology to estimate the systolic and diastolic BP levels by only using PPG signals captured with smart phones, which adds to the affordability, usability and portability of the system. Initially, a training model (Linear Regression Model or SVM Model) for various known levels of BP is created using a set of PPG features. This model is later used to estimate the BP levels from the features of the newly captured PPG signals. Experiments are performed on benchmark hospital dataset and data captured from smart phones in our lab. Results indicate that by additionally adding information of height, weight and age play a vital role in increasing the accuracy of the estimation of BP levels.

Historical Data Based Real Time Prediction of Vehicle Arrival Time

Abstract: In recent times, most of the industries provide transportation facility for their employees from scheduled pick-up and drop points. In order to reduce longer waiting time, it is important to accurately predict the vehicle arrival in real time. This paper proposes a simple, lightweight yet powerful historical data based vehicle arrival time prediction model. Unlike previous work, the proposed model uses very limited input features namely vehicle trajectory and timestamp considering the scarcity and unavailability of data in the developing countries regarding traffic congestion, weather, scheduled arrival time, leg time, dwell time etc. The authors proposed model is evaluated against standard Artificial Neural Network (ANN) and Support Vector Machine (SVM) regression models using real bus data of an industry campus at Siruseri, Chennai collected over four months of time period. The result shows that proposed historical data based model can predict two and half (approx.) times faster than ANN model and two (approx.) times faster than SVM model while it also achieves a comparable accuracy (75.56%) with respect to ANN model (76%) and SVM model (71.3%). Hence, the proposed historical data based model is capable of providing a real time system by balancing the trade-off between prediction time and prediction accuracy.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).