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Automation of the parcel distribution process in a postal department

This thesis is devoted to automating the process of parcel distribution in the post office to increase the speed and accuracy of processing in the face of the growing demand of e-commerce. The limitations of the existing manual sorting system were analyzed and an automated solution was developed. The work defines the technical parameters and functions of the system, including the operation of conveyors, accurate data collection and parcel sorting. Structural and functional diagrams are developed, and the selection of components such as scanning devices, sorting machines, conveyors, sensors and PLCs is described. The algorithm and control program for the system were created and tested using the STEP 7 development environment and FactoryIo. The proposed automated system is aimed at significantly increasing productivity and reducing errors in the parcel processing process.

Author: Aleksandrs Panteļejevs

Supervisor: Vasilijs Gredasovs

Degree: Bachelor

Year: 2024

Work Language: English

Comparative analysis of RPL protocol objective functions impact on energy consumption in low-power and lossy networks

This bachelor thesis explores the impact of different RPL protocol objective functions on energy consumption in low-power and lossy networks. Given the critical role of low-power and lossy networks in applications like environmental monitoring, industrial automation, and smart cities, optimising energy consumption is paramount. Using the Contiki-NG operating system and the Cooja network simulator, this study conducts a comparative analysis of two primary objective functions: Objective Function Zero and Minimum Rank with Hysteresis Objective Function. Simulations are performed under 18 network scenarios including different node densities (10, 30, 50), node positioning, and receive ratios. The findings indicate that Objective Function Zero generally consumes less energy, particularly in high-density networks and environments with high packet delivery ratios. Conversely, Minimum Rank with Hysteresis Objective Function may be more suitable for dynamic and unpredictable environments despite its higher energy consumption. This research offers practical recommendations for selecting objective functions to optimise the energy consumption of constrained networks, contributing to the development of more sustainable IoT solutions.

Author: Deniss Bogdans

Supervisor: Jeļena Baranova

Degree: Bachelor

Year: 2024

Work Language: English

Vibration Analysis-Based Fault Diagnosis of Tool Conditions on Electric Motor-Powered Machine Tools Using Convolutional Neural Networks

This thesis explores and evaluates techniques for utilizing vibration analysis and Convolutional Neural Networks (CNNs) to assess the condition of drill bits installed on electric motor-driven drills. By strategically positioning an Inertial Measurement Unit (IMU) sensor to capture acceleration data, a wide range of vibration signals can be gathered in different operational scenarios. The CNN models undergo training and validation utilizing this data to precisely detect various fault conditions and operational states of the drill bits, showcasing the possibility of implementing scalable and reliable fault detection systems in industrial environments. The research attains a Technology Readiness Level (TRL) of 3, as demonstrated by trials that effectively categorize machine conditions using CNNs, hence confirming the critical functions of the proposed technology. The aim of this study is to assess the efficacy of vibration analysis in classifying the operational state of a drilling machine as either good, moderate, or bad.Vibration analysis is a method used to analyze the oscillation patterns of a machine in order to identify problems such as misalignment, imbalance, and wear.

Author: Marawan Mohamed Ahmed Elsayed Youssef

Supervisor: Emmanuel Alejandro Merchan Cruz

Degree: Master

Year: 2024

Work Language: English

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