Sanjoy Das
YOU?
Author Swipe
View article: Imitation Learning with Deep Attentive Tabular Neural Networks for Environmental Prediction and Control in Smart Home
Imitation Learning with Deep Attentive Tabular Neural Networks for Environmental Prediction and Control in Smart Home Open
Automated indoor environmental control is a research topic that is beginning to receive much attention in smart home automation. All machine learning models proposed to date for this purpose have relied on reinforcement learning using simp…
View article: Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review
Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review Open
Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The model…
View article: Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review
Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review Open
Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-ma…
View article: Reinforcement Learning: Theory and Applications in HEMS
Reinforcement Learning: Theory and Applications in HEMS Open
The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management s…
View article: Reinforcement Learning: Theory and Applications in HEMS
Reinforcement Learning: Theory and Applications in HEMS Open
The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management s…
View article: Reinforcement Learning: Theory and Applications in HEMS
Reinforcement Learning: Theory and Applications in HEMS Open
The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allow…
View article: Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning
Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning Open
Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the…
View article: Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble
Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble Open
Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is…
View article: EFFECT OF IODINE AND MOISTURE ON THE MICROSTRUCTURE OF ZIRCALOY-4 UNDER SERVICE CONDITION IN PHWR
EFFECT OF IODINE AND MOISTURE ON THE MICROSTRUCTURE OF ZIRCALOY-4 UNDER SERVICE CONDITION IN PHWR Open
Fuel failures are always a cause of concern in any nuclear reactors as it increases the manrem consumption of radiation workers. Although performance of the fuels in pressurized heavy water reactors is good, but still fuel failures occur o…
View article: A Data-Driven Machine Learning Approach for Consumer Modeling with Load Disaggregation
A Data-Driven Machine Learning Approach for Consumer Modeling with Load Disaggregation Open
While non-parametric models, such as neural networks, are sufficient in the load forecasting, separate estimates of fixed and shiftable loads are beneficial to a wide range of applications such as distribution system operational planning, …
View article: An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads
An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads Open
Energy disaggregation refers to the decomposition of energy use time series data into its constituent loads. This paper decomposes daily use data of a household unit into fixed loads and one or more classes of shiftable loads. The latter i…
View article: Pareto-Optimal Allocation of Transactive Energy at Market Equilibrium in Distribution Systems: A Constrained Vector Optimization Approach
Pareto-Optimal Allocation of Transactive Energy at Market Equilibrium in Distribution Systems: A Constrained Vector Optimization Approach Open
In a grid constrained transactive distribution system market, distribution locational marginal pricing DLMP is influenced by the distance from the substation to an energy user, thereby causing households that are further away from the subs…
View article: Fairness-Regularized DLMP-Based Bilevel Transactive Energy Mechanism in Distribution Systems
Fairness-Regularized DLMP-Based Bilevel Transactive Energy Mechanism in Distribution Systems Open
Distribution locational marginal pricing (DLMP) can adversely affect users in a grid-constrained transactive distribution system market (DSM) that are at a distance away from the substation, requiring longer paths to connect to the substat…
View article: An Agent Based-Model and Equilibrium Analysis of Academic P&T Decisions: The Effects of Inbreeding
An Agent Based-Model and Equilibrium Analysis of Academic P&T Decisions: The Effects of Inbreeding Open
In academic institutions, merit based promotion & tenure decisions have always been beset with controversy.This paper suggests an agent based model of the decision making process using spectral graph theory, where the voting agents are the…
View article: Distributed Bilevel Energy Allocation Mechanism With Grid Constraints and Hidden User Information
Distributed Bilevel Energy Allocation Mechanism With Grid Constraints and Hidden User Information Open
A novel distributed energy allocation mechanism for Distribution System Operator (DSO) market through a bi-level iterative auction is proposed. With the locational marginal price at the substation node known, the DSO runs an upper level au…
View article: Deconstructing Anomalies in Academic Promotion & Tenure Decisions Using Spectral Graph Theory
Deconstructing Anomalies in Academic Promotion & Tenure Decisions Using Spectral Graph Theory Open
Merit based promotion & tenure decision have always been controversial. This paper suggests an agent based model of the decision making processs using spectral graph theory, where the voting agents are the vertices of the graph, and edge w…
View article: Deconstructing Anomalies in Academic Promotion & Tenure Decisions Using Spectral Graph Theory.
Deconstructing Anomalies in Academic Promotion & Tenure Decisions Using Spectral Graph Theory. Open
Merit based promotion & tenure decision have always been controversial. This paper suggests an agent based model of the decision making processs using spectral graph theory, where the voting agents are the vertices of the graph, and edge w…
View article: Opinion based on Polarity and Clustering for Product Feature Extraction
Opinion based on Polarity and Clustering for Product Feature Extraction Open
In recent time, with the rapid development of web 2.0 the number of online user-generated review of product is increases very rapidly.It is very difficult for user to read all reviews and handle all websites to make a valuable decision at …
View article: Transactive Energy Auction with Hidden User Information in Microgrid
Transactive Energy Auction with Hidden User Information in Microgrid Open
This research proposes a novel auction mechanism for transactive energy exchange between buyers and sellers, modeled as agents in a microgrid. The mechanism is implemented by a separate microgrid controller (MC) agent, and requires big dat…
View article: Double-Sided Energy Auction Equilibrium Under Price Anticipation
Double-Sided Energy Auction Equilibrium Under Price Anticipation Open
This paper investigates the problem of proportionally fair double sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by…
View article: Double-Sided Energy Auction in Microgrid: Equilibrium Under Price Anticipation
Double-Sided Energy Auction in Microgrid: Equilibrium Under Price Anticipation Open
This paper investigates the problem of proportionally fair double-sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by…
View article: Estimating Animal-Related Outages on Overhead Distribution Feeders Using Boosting
Estimating Animal-Related Outages on Overhead Distribution Feeders Using Boosting Open
Faults on overhead distribution feeders have significant impact on the distribution reliability. Literature review on outages shows that overhead lines are highly susceptible to environmental factors such as weather, trees and animal. Hist…