Journal of Controller and Converters

Design and Development of Automatic System for Polymer (HDPE) Bag Counting

Fri, 29 Dec 2023 00:00:00 +0530

This project revolves around crafting a smart system for accurately counting High-Density Polyethylene (HDPE) bags, addressing the growing demand in various industries. The main objective of the project is to implement automation in the (HDPE) baggage counting system at a manufacturing Industry Bhoomi Polymers. Currently, the bag-counting process is quite manual and requires a lot of work and time. So the goal is to automate the system by integrating sensors, and controllers to increase process efficiency and accuracy of the counting process. By automating this task, industries can save time and reduce labour costs. At the heart of this automatic configuration, the PIC18F4520 microcontroller is used as the central controller. The traditional counting mechanism has been replaced by the sensor mechanism that performs the counting process. This project consists of two different set-ups which are used at Bhoomi Polymers to count the manufactured bags.

Smart Shopping Trolley

Komal Jadhav, Rohit K. Naik, Siddharth S. Kolekar, Nikita S. Jadhav — Tue, 19 Dec 2023 00:00:00 +0530

Nowadays, using barcodes on products comes with several drawbacks, including low range, reduced security, and the need for line of sight. We suggested a solution in our project that makes use of radio frequency identification, or RFID. This technique offers a way to cut down on the amount of time spent grocery shopping. Shopping trolleys are used by every supermarket to assist shoppers in choosing the items they want to buy. Customers may experience a variety of issues at the billing counter, such as waiting and not knowing if they have enough money to pay for the goods they have chosen. The innovative product that receives social recognition is the one that will direct comfort, accommodation, and efficiency in day-to-day living. Purchasing and shopping at massive malls are becoming less commonplace in metro areas daily. A wide range of goods are purchased by customers and placed in the trolley. It takes a long time and can be rather annoying to go to the billing counter after the purchase is finished to make the payment. This prototype's primary goal was to minimize human labour, get rid of the line, and shorten the billing process so that customers might have an easier time. In addition to taking a lot of time, the counter-billing process requires additional staff. We have put up a method to address this issue, which makes use of a smart shopping cart to get around these obstacles.

IoT in the Electric Power Industry

Dr. Kazi Kutubuddin, Kazi Sultanabanu Sayyad Liyakat — Fri, 15 Dec 2023 00:00:00 +0530

The Internet of Things (IoT) has revolutionized the electric power industry and has enabled efficient, secure, and cost-effective processes. IoT-enabled technologies are transforming the way energy is generated, distributed, and consumed. IoT provides a platform to collect, analyze, and share data from various sources and to enable better decision-making. The IoT technology helps to monitor and optimize the performance of the energy grid and enables the tracking of energy usage in real-time. Additionally, the implementation of IoT solutions has made it possible to detect potential faults and failures faster, ensuring a reliable and safe energy delivery. The application of IoT technology has also enabled the emergence of a “Smart Grid”, which is an intelligent and highly automated energy distribution network. Smart Grids use advanced analytics and machine learning to optimize energy production and consumption. This allows for better energy management and increases the reliability of the power grid. In conclusion, the power industry is rapidly changing due to the introduction of IoT technology. IoT is helping to improve the efficiency, safety, and reliability of the energy grid. It is also helping to reduce energy wastage and costs. As the technology evolves, it is expected that more sophisticated applications of IoT will be seen in the power industry, leading to further improvements in energy production, distribution, and consumption.

Smart Sensors and Real-Time Data Analysis for Advanced Bomb Detection Robotics

Kajal Pawar — Tue, 26 Dec 2023 00:00:00 +0530

Bomb detection and disposal technology must always evolve due to the growing threat posed by explosive devices. The goal of this project is to improve bomb detection robot capabilities through the integration of real-time data analysis methodologies and smart sensor technology. In this work, we introduce a novel method that integrates state-of-the-art sensor technologies-such as chemical, acoustic, and advanced image sensors-into a well-integrated robotic system.

The proposed system employs state-of-the-art smart sensors capable of detecting a wide range of explosive materials with high sensitivity and specificity. These sensors provide the robot with real-time data streams, capturing diverse environmental information crucial for effective threat identification. To cope with the complexity of data generated by multiple sensors, we introduce sophisticated data analysis algorithms that enable the robot to differentiate between benign and potentially hazardous materials. Furthermore, the research investigates the integration of machine learning algorithms for adaptive learning and pattern recognition, allowing the robot to continuously improve its detection accuracy over time. Real-time decision-making skills improve the autonomy of the system by allowing for quick and well-informed responses to changing threat scenarios. Our findings, which were obtained after extensive testing and validation, show a notable increase in the operating effectiveness and detection accuracy of bomb-detecting robots fitted with intelligent sensors and real-time data processing. This work establishes the groundwork for the creation of more resilient and intelligent systems to prevent explosive threats and advances the field of robotics in security applications.

Neural Net-Based Industrial Control System Fault Detection and Diagnosis

Hitesh Maidurkar — Mon, 18 Dec 2023 00:00:00 +0530

Industrial control systems (ICS) are essential to the reliable and effective operation of complicated operations. Faults, however, can result in disruptions, malfunctions, and jeopardised security. Conventional failure detection and diagnosis (FDD) techniques in industrial control systems (ICS) frequently depend on rule-based methodologies, which may make it difficult to adjust to dynamic and nonlinear system behaviours. In this paper, a new method for FDD in industrial control systems based on neural network approaches is presented.

The suggested approach makes use of artificial neural networks (ANNs) capacity to recognise abnormal patterns linked to different failure scenarios and to capture complex relationships within the dynamics of the system. The purpose of the multi-layered neural network architecture is to process sensor data and distinguish between optimal and unsatisfactory operating conditions. Using historical data that includes both fault-free and fault-ridden cases, the neural network is trained, allowing the model to learn the intricate relationship between input variables and fault categories. The created neural network-based FDD system's performance is assessed using in-depth simulations and experiments on actual industrial processes. The outcomes show how well the method works to precisely identify and diagnose errors even when noise and uncertainty are present. Moreover, the suggested approach demonstrates a strong degree of flexibility for various industrial uses, indicating its potential for broad adoption. By utilising neural networks, this research advances fault detection and diagnostic methods in industrial control systems (ICS) and opens the door to increased operational safety, less downtime, and improved system reliability.