Prof. Aleksandar Rikalovic, PhD
Senior Research Associate
Digital Transformation and Advanced Technologies
AI Implementation in Business and Industry
Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective
Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes
Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).
Implementation of Deep Learning to Prevent Peak-Driven Power Outages Within Manufacturing Systems
In this paper, a solution to effective energy consumption monitoring of fast-response energy systems in industrial environments was deployed, while the research focuses on the manner and intensity of energy use in the observed system as a consequence of nonlinearity in the performance of the dynamic system, to predict the near future relatively accurately. The paper addresses the quite common but still an inevitable case for the majority of manufacturing systems where constant jumps in peak loads on several machines simultaneously lead to the situation that the entire system remains without a power supply. This paper proposes a deep learning method, based on enhanced recurrent neural network (RNN), more precisely LSTM network (Long Short-Term Memory) to effectively predict future machine states in terms of energy consumption five steps ahead. The data sets were obtained for eight machines in one CNC metal-forming center on a monthly level at a one-second sampling rate by means of using a previously developed IoT device.
Digital Kaizen: Opportunities And Challenges In Industry 5.0
Kaizen, a continuous improvement methodology, serves as a fundamental element for companies
to maintain their competitiveness in an increasingly demanding market. The advent of Industry 4.0, with the
support of Kaizen, has been considered to answer these never-ending demands by bringing significant
transformations in manufacturing processes through the integration of digital technologies. However, most
of the Industry 4.0 implementation challenges report a lack of human resources, as one of the biggest
managerial obstacles. Therefore, society moved toward the next phase of the industrial revolution, Industry
5.0, which has a focus shift beyond automation to the integration of human intelligence with advanced
digital systems. Thus, Industry 5.0 promotes a human-centric approach to implementing digital sustainable
technologies for continuous improvement – Digital Kaizen. This research examines Digital Kaizen, as Industry
5.0 continuous improvement methodology enabled by advanced technologies, mostly human-cyberphysical systems (HCPS) and artificial intelligence of things (AIoT) to be used to improve the workforce’s
productivity, rather than simply to replace workers.
A verifiable model of a minimal market operating sequentially, with price and time discrete
This research presents a minimal computational market model, i.e.,a model of a trading venue, with sequential order matching, in a declarative style, and proceeds to demonstrate how some fundamental properties can be formally proved. It is a challenging task to formally certify properties for a fundamental system in any realm of human endeavor, especially for systems with infinite state space. With the recent development of theoretical frameworks based on formal logic, it is now possible (albeit very difficult) to both formalize and reason about an object system in the same language. This research derives from the previous research presented in , and represents a simplification to obtain a minimal model. The computational model of a minimal market, presented here in a declarative style, is of importance from the perspective of both market design and verification.