Tuomas Jalonen

Tampere University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Moncef Gabbouj

Tampere University

Mohammad Al-Sa’d

Tampere University

Serkan Kıranyaz

Qatar University

Roope Mellanen

Marlon Löppenberg

South Westphalia University of Applied Sciences

Ran Ji

Second Affiliated Hospital of Zhejiang University

Luo Yiming

IRD Fuel Cells (Denmark)

Fankai Wen

IRD Fuel Cells (Denmark)

Xubin Lin

China Southern Power Grid (China)

Peter A. Larsen

University of Minnesota

Cooperative Institutions

IRD Fuel Cells (Denmark)

360

Chinese Academy of Sciences

Zhejiang University

Centre National de la Recherche Scientifique

Shanghai Jiao Tong University

Beijing Institute of Technology

Northwestern Polytechnical University

Chinese Academy of Medical Sciences & Peking Union Medical College

Tampere University

ORCID

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Visual Product Tracking System Using Siamese Neural Networks

IEEE Access (2021)

Tuomas Jalonen Firas Laakom Moncef Gabbouj Tuomas Puoskari

Management of unstructured production data is a key challenge for Industry 4.0. Effective product tracking endorses data integration and productivity improvements throughout the manufacturing processes. Radio-frequency identification (RFID) tags are used in many tracking cases, but in some manufacturing environments, those cannot be used as they might get damaged or removed during processing. In this paper, we propose an alternative visual product tracking system. The physical system uses two cameras placed at the two ends of the tracked process(es). Product pairs are then matched with a Siamese neural network operating on the product images and trained offline on the problem at hand with labeled data. The proposed system can track products solely based on their visual appearance and without any physical interference with the products or production processes. Unlike other existing image-based methods, the proposed system is invariant to major positional and visual changes in the products. As a proof-of-concept, we tested the proposed system with real plywood factory data and were able to track the products with 98.5 % accuracy in a realistic test scenario. The implementation of the proposed method and the Veneer21 dataset are publicly available at https://github.com/TuomasJalonen/visual-product-tracking-system.

Tracking system

Factory (object-oriented programming)

Tracking (education)

10.1109/access.2021.3082934

Cite

Citations (5)

Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks

IEEE Sensors Journal (2024)

Tuomas Jalonen Mohammad Al-Sa’d Roope Mellanen Serkan Kıranyaz Moncef Gabbouj

10.1109/jsen.2024.3521118

Cite

Citations (0)

Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks

arXiv (Cornell University) (2023)

Tuomas Jalonen Mohammad Al-Sa’d Roope Mellanen Serkan Kıranyaz Moncef Gabbouj

The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes. Specifically, we present a novel vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN). We use a camera-based apparatus to photograph the lifting rope's surface, while in operation, and capture the progressive wear-and-tear as well as the more significant degradation in the rope's health state. Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged. Then, we pre-process the images, design a CNN model in a systematic manner, evaluate its detection and prediction performance, analyze its computational complexity, and compare it with various other models. Experimental results show the proposed model outperforms other techniques with 96.4% accuracy, 95.8% precision, 97.2% recall, 96.5% F1-score, and 99.2% AUC. Besides, they demonstrate the model's real-time operation, low memory footprint, robustness to various environmental and operational conditions, and adequacy for deployment in industrial systems.

Robustness

10.48550/arxiv.2302.11947

Cite

Citations (3)

Management Accounting Information in Decision-making: Unveiling Possibilities for AI

Tuomas Jalonen

Source

Cite

Citations (0)

Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults

arXiv (Cornell University) (2024)

Mohammad Al-Sa’d Tuomas Jalonen Serkan Kıranyaz Moncef Gabbouj

Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the bearing fault-induced vibrations and discuss the link between their non-stationarity and the bearing's inherent and operational parameters. We also elucidate quadratic time-frequency distributions and validate their effectiveness in resolving distinctive dynamic patterns associated with different bearing faults. Based on this, we design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings. Our experimental findings undeniably demonstrate the superior performance of TF-CNN in comparison to recently developed techniques. They also assert its versatility in capturing fault-relevant non-stationary features that couple with speed changes and show its exceptional resilience to noise, consistently surpassing competing methods across various signal-to-noise ratios and performance metrics. Altogether, the TF-CNN achieves substantial accuracy improvements up to 15%, in severe noise conditions.

Rolling-element bearing

SIGNAL (programming language)

10.48550/arxiv.2401.01172

Cite

Citations (0)

Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

arXiv (Cornell University) (2023)

Tuomas Jalonen Mohammad Al-Sa’d Serkan Kıranyaz Moncef Gabbouj

Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges.

Rolling-element bearing

Adaptability

10.48550/arxiv.2311.18547

Cite

Citations (0)

Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

2022 IEEE International Conference on Industrial Technology (ICIT) (2024)

Tuomas Jalonen Mohammad Al-Sa’d Serkan Kıranyaz Moncef Gabbouj

Rolling-element bearing

Adaptability

10.1109/icit58233.2024.10540813

Cite

Citations (0)

Discourse analysis on sustaining the maieutic role “when management accounting goes digital”

Qualitative Research in Accounting & Management (2024)

Vesa Tiitola Tuomas Jalonen Mirva Rantanen-Flores Tuomas Korhonen Johanna Ruusuvuori

Purpose This paper aims to explore how the maieutic role of management accounting (MA) can be sustained in the context of MA digitalization. Design/methodology/approach The paper begins with practitioners’ descriptions of the context that makes the MA support of non-routine decisions maieutic. To understand how the maieutic characteristics can be sustained in future MA digitalization, the authors then analyze the discourses these practitioners have about artificial intelligence (AI) in providing MA support. Findings As a basis, the authors’ data show various maieutic characteristics within the use of MA answers in decision-making as well as within the MA process of generating such answers. The paper then identifies three MA digitalization discourses, namely, “computation,” “judgment” and human-AI “interaction” discourse, each with their unique agendas on how AI should be used. Originality/value The paper is based on the premises that AI and digitalization are often discussed without sufficient understanding about the context being digitalized. The authors’ data suggest that MA support in non-routine decision-making is fundamentally maieutic, and AI – as it currently stands – is not expected to change this by providing perfect answers. The authors provide novel insights about maieutic MA support and the current discourses on using AI in MA support, and how digitalization does not necessarily compromise maieutic MA support but instead has the potential to sustain or even enhance it.

Compromise

Value (mathematics)

10.1108/qram-11-2022-0198

Cite

Citations (1)

Table of Contents

2022 IEEE International Conference on Industrial Technology (ICIT) (2024)

Yanze Liu Shuo Sun Gregoire Goff Marc Bodson Maurice Fadel

Increasing the linear modulation range

Table (database)

10.1109/icit58233.2024.10540753

Cite

Citations (0)