Monitoring project performance is a cornerstone of success in technology-driven industries. Projects in semiconductors, software/IT, and retail (supply chain) are increasingly complex, requiring robust anomaly detection methods to identify deviations in schedule, cost, quality, and throughput. Traditional approaches are often siloed, applying statistical thresholds or isolated machine learning techniques to single domains. This paper presents an AI-enhanced, KPI-driven anomaly detection framework validated on real-world datasets.
The electric vehicle (EV) and renewable energy generation have achieved considerable development due to the growing energy demand and scarcity in fossil fuels. At the same time, EVs consume a huge amount of electricity when they are clustered in a charging station. In this project we are going to create a Artificial Intelligence based Power Allocation and ev charging system. We are using a deep learning technique called artificial Neural Network and hence we can able to get an accuracy over 90%. We predict the suitable power source for charging the electric vehicles using artificial Neural Network.
In solar photovoltaic (PV) inverter systems, power losses in the input loop significantly impact overall efficiency and performance. This paper presents a Super Capacitor Assisted (SCA) technique to minimize conduction and switching losses in the input stage of an inverter system for solar PV applications. By integrating supercapacitors strategically within the power circuit, the proposed method reduces peak current stress, stabilizes voltage fluctuations, and enhances transient response. The project provides a detailed analysis of the working principle, power loss reduction mechanisms, and the design considerations for implementing the SCA technique.
AI-Enhanced Anomaly Detection for Project Performance: A Cross-Industry Study for Technology-Driven Industries
Shreya Makinani, Pankaj Siri Bharath Bairu
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Monitoring project performance is a cornerstone of success in technology-driven industries. Projects in semiconductors, software/IT, and retail (supply chain) are increasingly complex, requiring robust anomaly detection methods to identify deviations in schedule, cost, quality, and throughput. Traditional approaches are often siloed, applying statistical thresholds or isolated machine learning techniques to single domains. This paper presents an AI-enhanced, KPI-driven anomaly detection framework validated on real-world datasets.
Power Allocation System Using Artificial Neural NetworkAriramar C, Ramraj S
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The electric vehicle (EV) and renewable energy generation have achieved considerable development due to the growing energy demand and scarcity in fossil fuels. At the same time, EVs consume a huge amount of electricity when they are clustered in a charging station. In this project we are going to create a Artificial Intelligence based Power Allocation and ev charging system. We are using a deep learning technique called artificial Neural Network and hence we can able to get an accuracy over 90%. We predict the suitable power source for charging the electric vehicles using artificial Neural Network.
Super Capacitor Assisted Technique for Reducing Losses in the Input Loop of an Inverter System for Solar PV Application
Mr. Anandharaj R, Ms. A. Shiny Pradeepa
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In solar photovoltaic (PV) inverter systems, power losses in the input loop significantly impact overall efficiency and performance. This paper presents a Super Capacitor Assisted (SCA) technique to minimize conduction and switching losses in the input stage of an inverter system for solar PV applications. By integrating supercapacitors strategically within the power circuit, the proposed method reduces peak current stress, stabilizes voltage fluctuations, and enhances transient response. The project provides a detailed analysis of the working principle, power loss reduction mechanisms, and the design considerations for implementing the SCA technique.