Faculty Publications
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Publications by NITK Faculty
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Item A Heuristic Algorithm to Find a Path to be Blocked by Minimizing Traffic Disruption(Institute of Electrical and Electronics Engineers Inc., 2020) Das, M.; Ambati, S.S.; Chandrasekaran, K.This paper discusses the problem of finding a path to be blocked from the source to the destination for a vehicle to pass by in such a way that the traffic disruption caused is minimum. The traffic disruption caused by blocking a path is measured by estimating the number of vehicles that would have crossed any of the vertices in the path if the path had not been blocked. It also presents a heuristic algorithm 'Aggregate Traffic Minimization' (ATM) to solve the above problem. The traffic disruption caused by the path chosen by the ATM algorithm was compared with that of a popular baseline algorithm and was found that ATM outperforms the baseline alzorithm in most cases. © 2020 IEEE.Item Performance evaluation of Nuclear Power Plant Injection Systems using PetriNets(Institute of Electrical and Electronics Engineers Inc., 2021) Patidar, K.; Tushar; Das, M.; Mohan, B.R.The failure of safety critical systems like Nuclear Power Plant can lead to disastrous effects which may lead to death of people and harm to the environment. Thus it becomes very important to model these systems to meet the reliability, safety requirements without compromising with the high performance. Petri Nets(PNs) is a type of modelling languages which is widely used for validation of real time systems. Timed Petri Nets(TPNs) are popularly use to model such critical systems primarily for non-functional analysis. In this paper have used Timed petri net and Markov chains methodology for reliability and performance analysis for Nuclear Power Plant Injection Systems. This paper will be finding the time spent by the tokens in the Petri Net System as an evaluation metric. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.Item Program Slicing Analysis with KLEE, DIVINE and Frama-C(Institute of Electrical and Electronics Engineers Inc., 2021) Kumar, N.; Neema, S.; Das, M.; Mohan, B.R.Optimizing the Time complexity of any program is still the most researched and sought area for researchers. At the industry level, the Software execution timing is the dominant criteria for Workload selection. One prominent method for reducing the Time complexity of a program is by using program slicing configuration, without affecting the program flow. Program slicing is the process of slicing a program in such a way that it reduces the time of debugging. This paper presents a timed-based analysis of a program with and without slicing with the help of different verification tools, namely KLEE, DIVINE, and Frama-C. This paper aims to compare these tools based on the timing of debugging and validity of a program before and after slicing. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.Item Formal Specification and Verification of Drone System using TLA+: A Case Study(Institute of Electrical and Electronics Engineers Inc., 2022) Das, M.; Mohan, B.R.; Guddeti, R.M.R.A Safety-Critical System is a System whose break-down may cause disastrous effects to the environment, damage the system, or cause loss of life. Sometimes loss or misuse of information can indirectly cause harmful impacts due to system failure. In this paper, we study the various components of a drone system and analyze the safety of this Safety-Critical System (SCS) by looking into the potential failure using Fault Tree Analysis (FTA). Drone system failure or crash has been specified and verified using the Temporal Logic of Actions (TLA+) tool. The TLA+ tool consists of mathematical notations to describe the system specification using discrete mathematical concepts or formal methods. We tried to build a TLA+ Specification and Verification for this drone system, parse it using the TLC model checker successfully, and observed the final number of states to justify the correctness of the specification. © 2022 IEEE.Item Bayesian Belief Network Analysis for SPAD System in Railways(Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Reddy G, R.M.; Chinmaya, C.; Umesh; Reddy G, V.M.; Vismay, P.Even with a very strong network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even in the modern day. These occurrences, known as Signal Passed at Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach. However, when the system grows more complex, FTA too becomes more complex, and tough to maintain simplicity and ease of analysis. This opens the gateway to the exploration of other methods to model and assess such SPAD incidents and similar safety-critical systems in railways. Bayesian belief network (BBN) is considered to be a better model to represent this situation when it comes to handling complexity. This paper focuses on the implementation and advantages of the BBN model over FTA by considering the SPAD system as a case study. Both the FTA and BBN methods are then compared concerning modeling and analysis aspects. © 2024 IEEE.Item Safety Analysis of Shutdown System in Nuclear Power Plants through Petri Nets(Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Ram Mohana Reddy, G.; Abhishek; Kumar, A.; Kumar, P.; Khan, J.Addressing the critical need for rigorous safety analysis, the research focuses on the intricate shutdown mechanisms, particularly the Rod Control System and Poison Injection System. The methodology commenced with exhaustive system requirements gathering to grasp the operational nuances and emergency protocols. A meticulous Petri Net representation followed, encapsulating system components and concurrent processes within a cohesive model. The framework facilitated rigorous safety checks, formal verification, and simulation-based o ptimization. Through iterative design and validation against established research, the model underwent continuous refinement. The findings illuminate the robustness of safety protocols and offer a transformative outlook on shutdown procedures, laying a foundation for further research and practical implementation to secure NPP operations. © 2024 IEEE.Item Reliability Analysis Using Bayesian Belief Network on Drone System: A Case Study(Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Ram Mohana Reddy, G.; Chhaparwal, E.; Krishna Kumar, K.; Chowdhury, S.; Sharma, S.Ensuring the reliability of software components is of paramount importance in safety-critical systems. Grave consequences might occur if software failures in such systems. Hence, predicting software reliability is important in these systems. This research uses Bayesian Belief Network(BBN) and leverages historical failure data to find fault interdependencies, giving much more insights than methodologies like Fault Tree Analysis (FTA) and Reliability Block Diagrams (RBD). By comparing BBNs with these traditional methods, the research shows the dynamic capabilities of BBNs. BBN also shows the capability of using real-time data and machine learning together to increase the software reliability of the software components, making this system much safer. © 2024 IEEE.Item Hybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling(Institute of Electrical and Electronics Engineers Inc., 2024) Harsha, S.S.; Muddi, K.S.; Jindrali, S.S.; Reji, S.; Das, M.; Mohan, B.R.This paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. © 2024 IEEE.Item Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Kelkar, S.; Vishvasrao, S.P.; Agarwal, A.; Rajput, C.; Mohan, B.R.; Das, M.Software reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. © 2024 IEEE.Item Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.