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Reinforcement learning relocation assignment in the multiple-deep storage system
Jakob Marolt, Bojan Rosi, Tone Lerher, 2022, objavljeni znanstveni prispevek na konferenci

Opis: The field of reinforcement learning shows promising results in recent publications for solving complex combinatorial problems. In this paper, an agent is trained to tackle the relocation problem that appears in various logistics systems. The relocation problem was formulated as an array that is accessible only from the top side. The agent has to relocate blocking SKUs and thus enable access to the SKU with the highest dispatch priority. We utilised the Deep Q-learning Network (DQN) to train the agent. Two case studies are presented – one with 1.2 ∙ 10 and another with 2 ∙ 10 possible states. The results display that the agents made a significantly better decision toward the learning process’s end than at the beginning
Ključne besede: relocation problem, reinforcement learning, DQN, multiple-deep, storage system
Objavljeno v DKUM: 14.03.2023; Ogledov: 61; Prenosov: 3
.pdf Celotno besedilo (659,02 KB)

Data-driven supply chain operations : the pilot case of postal logistics and the cross-border optimization potential
Tanja Zdolšek Draksler, Miha Cimperman, Matevž Obrecht, 2023, izvirni znanstveni članek

Opis: According to the defined challenge of cross-border delivery, a pilot experiment based on the integration of new digital technologies to assess process optimization potential in the postal sector was designed. The specifics were investigated with events processing based on digital representation. Different events were simulated with scenario analysis with the integration of the Cognitive Advisor and supported by the monitoring of KPIs. The business environment is forcing logistics companies to optimize their delivery processes, integrate new technologies, improve their performance metrics, and move towards Logistics 4.0. Their main goals are to simultaneously reduce costs, environmental impact, delivery times, and route length, as well as to increase customer satisfaction. This pilot experiment demonstrates the integration of new digital technologies for process optimization in real time to manage intraday changes. Postal operators can increase flexibility, introduce new services, improve utilization by up to 50%, and reduce costs and route length by 12.21%. The Cognitive Advisor has shown great potential for the future of logistics by enabling a dynamic approach to managing supply chain disruptions using sophisticated data analytics for process optimization based on the existing delivery infrastructure and improving business processes. Research originality is identified with a novel approach of real-time simulation based on the integration of the Cognitive Advisor in postal delivery.
Ključne besede: cognitive logistics, machine learning, social IoT, last mile, digital twin, supply chain digitization
Objavljeno v DKUM: 24.02.2023; Ogledov: 99; Prenosov: 16
.pdf Celotno besedilo (1,70 MB)
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Razvijanje kompetenc 21. stoletja s pomočjo izdelave računalniških iger : zaključno delo
Jernej Kotnik, 2021, magistrsko delo

Opis: V tej nalogi smo raziskali tako digitalne kot ostale kompetence, ki so pomembne pri poučevanju računalništva. Pogledali bomo različne poznane metode poučevanja računalništva, med katerimi so učenje s pomočjo iger, projektno učenje in raziskovalno učenje. Prav tako bomo pregledali literaturo na temo računalniškega mišljenja in poučevanja računalništva v osnovni in srednji šoli. Pri tem se bomo osredotočili na izobraževalni sistem v Sloveniji, pa tudi v tujini. Predstavili bomo tudi programe, ki lahko pri računalništvu pomagajo pri doseganju višjih kognitivnih ciljev in razvijanju različnih kompetenc. Med temi programi se bomo posebej osredotočili na program GameMaker Studio, na katerem bo temeljil tudi tečaj učenja programiranja. Tečaj bo pomemben del magistrske naloge, v kateri bomo zapisali cilje tečaja ter opisali ciljno skupino in predviden način izvedbe tečaja.
Ključne besede: IKT, računalništvo, GameMaker Studio, programiranje, algoritmično razmišljanje, »game-based learning«
Objavljeno v DKUM: 12.11.2021; Ogledov: 478; Prenosov: 41
.pdf Celotno besedilo (2,28 MB)

Construction of deep neutral networks using swarm intelligence to detect anomalies : master's thesis
Sašo Pavlič, 2021, magistrsko delo

Opis: The design of neural network architecture is becoming more difficult as the complexity of the problems we tackle using machine learning increases. Many variables influence the performance of a neural model, and those variables are often limited by the researcher's prior knowledge and experience. In our master's thesis, we will focus on becoming familiar with evolutionary neural network design, anomaly detection techniques, and a deeper knowledge of autoencoders and their potential for application in unsupervised learning. Our practical objective will be to build a neural architecture search based on swarm intelligence, and construct an autoencoder architecture for anomaly detection in the MNIST dataset.
Ključne besede: neural architecture search, machine learning, swarm intelligence
Objavljeno v DKUM: 18.10.2021; Ogledov: 654; Prenosov: 86
.pdf Celotno besedilo (3,18 MB)
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The use of Past Simple and Present Perfect Tense and common errors of students in 9th grade
Kaja Podgoršek, 2021, magistrsko delo

Opis: In master's thesis entitled The Use of Past Simple and Present Perfect Tense and Common Errors of Students in 9th Grade theoretical part thoroughly presents English as a global language, language acquisition and language learning, teaching English as a foreign language, grammar and its correlation to education, deductive and inductive approach, the two tenses – Past Simple and Present Perfect and the most common errors in learning English grammar. The empirical part is based on a research where 100 9th graders from 6 different schools answered questions from a questionnaire regarding English tenses. With the help of questionnaire which had 9 tasks in total, we were able to examine and research to what extent do the Slovenian 9th graders know and successfully use Past Simple and Present Perfect. The questionnaire was created accordingly to the syllabus. The learners were the most successful when dealing with the theoretical part of the questionnaire. Even though they had some problem when choosing the correct tense when dealing with practical tasks, they had a higher success rate when dealing with the tasks that were theoretical. The learners from 9th grade achieved overall positive success rate in all tasks which means that they successfully know, recognize and properly use Past Simple and Present Perfect Tense.
Ključne besede: English tenses, Teaching English as a foreign language, Language learning, English grammar, English as a global language
Objavljeno v DKUM: 07.10.2021; Ogledov: 760; Prenosov: 31
.pdf Celotno besedilo (1,20 MB)

Implementation of a new reporting process in a group x
Sara Črešnik, 2021, magistrsko delo

Opis: Reporting is present in every company. Whether it is small or big, it cannot be avoided. It plays a crucial role in the process and progress of business. The quality of reporting affects the development of the work environment and the company. Since business report is a document that contains business information, which supports the decisions about the future-oriented business decisions, it is very important for it to be designed in such a way that it contains the key information for the recipient and provides support for business decisions. The reporting process can take place horizontally upwards or downwards. Content and structure vary depending on the recipient of the report. We live in an age when our every step is accompanied by digitization, computerization, artificial intelligence, mass data, the Internet of Things, machine learning, and robotics. These changes have also affected the reporting process as well as its processes. The processes of data acquisition, processing and sharing have changed. Furthermore, the data quantity has increased, whereas the speed of the time in which to prepare the reports has decreased. We can have data without information, but we cannot have information without data. There is never enough time, especially nowadays when we are used to having everything at our fingertips. These are two conflicting factors – having more data and less time to prepare quality reports. The systems are developed to optimize the process, increase efficiency and quality and, what is nowadays most important, they have been created to obtain mass data in the shortest possible time. Therefore, it is important to adapt and implement software that can help achieve our daily tasks. We must know how to process huge amounts of real-time data and deliver the information they contain. It is crucial for companies to keep up with the environment and implement changes and innovations into their business process. A company is like a living organism for it must constantly evolve and grow. As soon as it stops growing and evolving, it can fail because it starts lagging and is therefore no longer competitive to others. To deliver faster feedback, companies need data of better quality. There are tools that can improve the business process, better facilitating the capacity of the human agents. The goal is to harness the employees’ full potential and knowledge for important tasks, such as analyzing, reviewing, and understanding data and acting upon them, invoking information technology to automate repetitive processes and facilitate better communication. The focus in this master’s thesis is on the reporting process in Group X. Group X is one of the world leaders in the automotive industry, a multinational corporation based in Canada with subsidiaries around the world. The complexity of the business reporting that is implemented for the Headquarters in Canada has to address the complexity of the multinational corporation to support the decision process. The aim of the thesis is to propose a reporting process for preparing and producing reports with a huge amount of data in a very time-efficient manner. We start by examining the existing processes and upon that, identifying the processes required for the reports to reach the final recipients. Our goal is to identify the toolset, which would increase efficiency, accuracy, credibility, and reduce errors in the fastest possible time. We investigate a short-term and a long-term solution. By a short-term solution, we mean a system, program, or a tool that can help us increase our potential by using digital resources, which are already existing in the organization. By a long-term solution, we mean a solution, which requires employment of specialized future tools in the field of reporting and in repetitive processes, which we can identify with current knowledge and expectations for development. This includes machine learning, robotic process automatization, artificial intelligence.
Ključne besede: Consolidated reporting, reporting process, robotic process automatization, business intelligence, artificial intelligence, machine learning, SharePoint, Big Data, digital transformation, electronic data interchange.
Objavljeno v DKUM: 01.09.2021; Ogledov: 518; Prenosov: 3
.pdf Celotno besedilo (1,71 MB)

Energy efficient system for detection of elephants with Machine Learning : master's thesis
Marko Sagadin, 2020, magistrsko delo

Opis: Human-Elephant Conflicts are a major problem in terms of elephant conservation. According to WILDLABS, an average of 400 people and 100 elephants are killed every year in India alone because of them. Early warning systems replace the role of human watchers and warn local communities of nearby, potentially life threatening, elephants, thus minimising the Human-Elephant Conflicts. In this Master's thesis we present the structure of an early warning system, which consists of several low-power embedded systems equipped with thermal cameras and a single gateway. To detect elephants from captured thermal images we used Machine Learning methods, specifically Convolutional Neural Networks. The main focus of this thesis was the design, implementation and evaluation of Machine Learning models running on microcontrollers under low-power conditions. We designed and trained several accurate image classification models, optimised them for on-device deployment and compared them against models trained with commercial software in terms of accuracy, inference speed and size. While writing firmware, we ported a part of the TensorFlow library and created our own build system, suitable for the libopencm3 platform. We also implemented reporting of inference results over the LoRaWAN network and described a possible server-size solution. We finally a constructed fully functional embedded system from various development and evaluation boards, and evaluated its performance in terms of power consumption. We show that embedded systems with Machine Learning capabilities are a viable solution to many real life problems.
Ključne besede: machine learning, microcontroller, on-device inference, thermal camera, low-power system
Objavljeno v DKUM: 06.01.2021; Ogledov: 823; Prenosov: 133
.pdf Celotno besedilo (13,35 MB)

Mards Workshop Proceedings
2020, zbornik strokovnih ali nerecenziranih znanstvenih prispevkov na konferenci

Opis: The collection contains contributions by authors who were presented at a workshop within the Erasmus + project MARDS in Maribor, at the University of Maribor.
Ključne besede: doctoral study, higher education, e-learning, distance learning, study evaluation
Objavljeno v DKUM: 25.11.2020; Ogledov: 552; Prenosov: 30
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Deep Learning on Low Power Embedded Devices Using RISC-V Cores with an Extended Instruction Set : master's thesis
Jure Vreča, 2020, magistrsko delo

Opis: This thesis explores the possibility of running neural networks on microcontrollers and how to optimize their performance using instruction set extensions. Microcontrollers are seen as too weak to run neural networks. We challenge this view and show that stripped-down neural networks can run and be useful for some applications. We used an open-source microcontroller called PULPino to run our neural network. The benefit of various instructions and optimizations for minimizing energy consumption to run deep learning algorithms was evaluated. Hardware loops, loop unrolling, and the dot-product unit were implemented and tested. We developed an FPGA-based testing system to evaluate our hardware. We also developed a deep learning library and a test neural network for our hardware. We wrote two versions of the deep learning library. One version is the reference code, and the other is the optimized code that uses the dot product unit. Using the testing system, we tested the performance of the two versions. The synthesis was run to determine the power and energy consumption. We also tried out various optimizations to see if the performance could be improved. Using instruction set extensions and algorithmic optimizations we reduced the clock cycle count by 72% for the convolutional layers and by 78% for fully-connected layers. This reduced power consumption by 73%. We compare our results with related research.
Ključne besede: deep learning, embedded system, instruction set, RISC-V
Objavljeno v DKUM: 03.11.2020; Ogledov: 977; Prenosov: 137
.pdf Celotno besedilo (2,69 MB)

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