Pētniecība
Publikācijas
729 publikācijas
- 2023
Synthetic Data Generation for Visual Detection of Flattened PET Bottles(atveras jaunā logā)
Vitalijs Fescenko, Janis Arents and Roberts Kadikis
Machine Learning and Knowledge Extraction, Sēj. 5 (1), lpp. 14-28, MDPI
Kopsavilkums
Polyethylene terephthalate (PET) bottle recycling is a highly automated task; however, manual quality control is required due to inefficiencies of the process. In this paper, we explore automation of the quality control sub-task, namely visual bottle detection, using convolutional neural network (CNN)-based methods and synthetic generation of labelled training data. We propose a synthetic generation pipeline tailored for transparent and crushed PET bottle detection; however, it can also be applied to undeformed bottles if the viewpoint is set from above. We conduct various experiments on CNNs to compare the quality of real and synthetic data, show that synthetic data can reduce the amount of real data required and experiment with the combination of both datasets in multiple ways to obtain the best performance.
Indeksēts Scopus / WoS · Scopus procentile: 91 · SNIP: 1.822
- 2023
Can renewable energy communities enable a just energy transition? Exploring alignment between stakeholder motivations and needs and EU policy in Latvia, Norway, Portugal and Spain(atveras jaunā logā)
Standal K., Leiren M.D., Alonso I., Azevedo I., Kudrenickis I., Maleki-Dizaji P., Laes E., Di Nucci M.R., Krug M.
Energy Research & Social Science, Sēj. 106
- 2023
A Vision-Based Micro-Manipulation System(atveras jaunā logā)
Oskars Vismanis, Janis Arents, Jurga Subačiute-Žemaitieṅe, Vytautas Bučinskas, Andrius Dzedzickis, Brijesh Patel, Wei-Cheng Tung, Po-Ting Lin, Modris Greitans
Applied Sciences, Sēj. 13 (24), MDPI
Kopsavilkums
This research article outlines the design and methodology employed in the development of a vision-based micro-manipulation system, emphasizing its constituent components. While the system is initially tailored for applications involving living cells, its adaptability to other objects is highlighted. The integral components include an image enhancement module for data preparation, an object detector trained on the pre-processed data, and a precision micro-manipulator for actuating towards detected objects. Each component undergoes rigorous precision testing, revealing that the proposed image enhancement, when combined with the object detector, outperforms conventional methods. Additionally, the micro-manipulator shows excellent results for working with living cells the size of yeast. In the end, the components are also tested in a combined system as a proof-of-concept.
Indeksēts Scopus / WoS · Scopus procentile: 63
- 2023
Assessment of Power System Asset Dispatch under Different Local Energy Community Business Models(atveras jaunā logā)
T. Korõtko, F. Plaum, T. Häring, A. Mutule, R. Lazdins, O. Borščevskis, A. Rosin and P. Carroll
Energies, Sēj. 16
- 2023
Agriculture Electrification, Emerging Technologies, Trends and Barriers: A Comprehensive Literature Review(atveras jaunā logā)
J. Stakens, A. Mutule and R. Lazdins
Latvian Journal of Physics and Technical Sciences, Sēj. 60, lpp. 18-32
- 2023
Evaluation of Materials and Structures with a Multistatic Ultra-Wideband Impulse Radar: A Concept Validation(atveras jaunā logā)
Gatis Gaigals, Romans Maliks, Vladimir Aristov, Rolands Savelis, Janis Simanovics, Eduards Lobanovs, Haralds Egliens, Dans Laksis, Kristaps Maris Greitans, Modris Greitans.
Applied Sciences, Sēj. 13 (3), lpp. 1636, MDPI
Kopsavilkums
This document describes the results of the study contributing to the methods and tools applicable in plastic waste sorting systems that exploit the multistatic ultra-wideband impulse radar enforced with a deep learning signal processing back-end. The novelty of the research is the use of synthetic data for the development of a trained neural network before real data are available, and the use of a multistatic radar for the improvement of the training data set. The study results are described in multiple publications; the current paper shows the applicability of the described approach. The main results are as follows: a monostatic impulse radar can be used for the determination of material properties, such as thickness, dielectric permittivity, and losses, with limited accuracy; multistatic radar configuration increases the accuracy of the material property estimation; an open source finite difference time domain simulator can be used to simulate electromagnetic wave propagation in dielectric structures in order to generate synthetic data for development of optimized artificial neuron network structures used for the estimation of dielectric material properties, and the developed network can successfully be used for multistatic radar data processing.
Scopus procentile: 73 · SNIP: 1.026
- 2023
Infrared Image Pre-Processing and IR/RGB Registration with FPGA Implementation(atveras jaunā logā)
Edgars Lielāmurs, Andrejs Cvetkovs, Rihards Novickis, Kaspars Ozols
Electronics, Sēj. 12 (4), lpp. 4, MDPI
Kopsavilkums
Infrared imaging sensors are frequently used in thermal signature detection applications in industrial, automotive, military and many other areas. However, advanced infrared detectors are generally associated with high costs and complexity. Infrared detectors usually necessitate a thermoelectric heater–cooler for temperature stabilization and various computationally complex preprocessing algorithms for fixed pattern noise (FPN) correction. In this paper, we leverage the benefits of uncooled focal plane arrays and describe a complete digital circuit design for Field Programmable Gate Array (FPGA)-based infrared image acquisition and pre-processing. The proposed design comprises temperature compensation, non-uniformity correction, defective pixel correction cores, spatial image transformation and registration with RGB images. When implemented on Xilinx Ultrascale+ FPGA, the system achieves a throughput of 30 frames per second using the Fraunhofer IMS Digital 17 μm QVGA-IRFPA with a microbolometer array size of 320 × 240 pixels and an RGB camera with a 1024 × 720 resolution. The maximum ratio of the standard deviation to the mean of 0.35% was achieved after FPN correction.
Indeksēts Scopus / WoS · Scopus procentile: 60 · SNIP: 1.01
- 2023
Comparison of Canopy Height Metrics from Airborne Laser Scanner and Aerial/Satellite Stereo Imagery to Assess the Growing Stock of Hemiboreal Forests(atveras jaunā logā)
Grigorijs Goldbergs
Remote Sens, Sēj. 2023 (6), lpp. 1688, MDPI
Kopsavilkums
This study compared the canopy height model (CHM) performance obtained from large-format airborne and very high-resolution satellite stereo imagery (VHRSI), with airborne laser scanning (ALS) data, for growing stock (stand volume) estimation in mature, dense Latvian hemiboreal forests. The study used growing stock data obtained by ALS-based individual tree detection as training/reference data for the image-based and ALS CHM height metrics-based growing stock estimators. The study only compared the growing stock species-specific area-based regression models which are based solely on tree/canopy height as a predictor variable applied to regular rectangular 0.25 and 1 ha plots and irregular forest stands. This study showed that ALS and image-based (IB) height metrics demonstrated comparable effectiveness in growing stock prediction in dense closed-canopy forests. The relative RMSEs did not exceed 20% of the reference mean values for all models. The best relative RMSEs achieved were 13.6% (IB) and 15.7% (ALS) for pine 0.25 ha plots; 10.3% (IB) and 12.1% (ALS) for pine 1 ha plots; 16.4% (IB) and 12.2% (ALS) for spruce 0.25 ha plots; 17.9% (IB) and 14.2% (ALS) for birch 0.25 ha plots; 15.9% (IB) and 18.9% (ALS) for black alder 0.25 ha plots. This research suggests that airborne imagery and, accordingly, image-based CHMs collected regularly can be an efficient solution for forest growing stock calculations/updates, in addition to a traditional visual forest inventory routine. However, VHRSI can be the fastest and cheapest solution for monitoring forest growing stock changes in vast and dense forestland under optimal data collection parameters.
Indeksēts Scopus / WoS · Scopus procentile: 90
- 2023
The Synthetic Data Application in the UAV Recognition Systems Development(atveras jaunā logā)
D. Duplevska, V. Medvedevs, D. Surmacs, A. Aboltins
Advances in Information, Electronic and Electrical Engineering (AIEEE)
Kopsavilkums
The increasing popularity and accessibility of unmanned aerial vehicles (UAVs) presents both opportunities and challenges. On the one hand, UAVs has a wide range of civilian, industrial, and military applications. On the other hand, the popularity of UAVs can lead to illegal or dangerous usage. Thus, the development of UAV recognition systems is crucial for ensuring safety and security. However, collecting and labeling large amounts of real-world data for training these systems can be time-consuming and labor-intensive. In this study, we propose a methodology, which can help to accelerate the development of new UAV recognition systems. This work demonstrates the effectiveness of training a neural network using a combination of real-world and synthetic data that can achieve similar performance to a network trained on real-world data only.
- 2023
Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review(atveras jaunā logā)
Jānis Judvaitis, Valters Āboliņš, Amr Elkenawy, Rihards Balašs, Leo Seļāvo, Kaspars Ozols
Journal of Sensor and Actuator Networks, Sēj. 12 (2), lpp. 48, MDPI
Kopsavilkums
Abstract As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains.
Indeksēts Scopus / WoS · Scopus procentile: 94 · SNIP: 1.389
- 2023
Constructing Maps for Autonomous Robotics: An Introductory Conceptual Overview(atveras jaunā logā)
Peteris Racinskis, Janis Arents, Modris Greitans.
Electronics, Sēj. 12 (13), lpp. 2925, MDPI
Kopsavilkums
Mapping the environment is a powerful technique for enabling autonomy through localization and planning in robotics. This article seeks to provide a global overview of actionable map construction in robotics, outlining the basic problems, introducing techniques for overcoming them, and directing the reader toward established research covering these problem and solution domains in more detail. Multiple levels of abstraction are covered in a non-exhaustive vertical slice, starting with the fundamental problem of constructing metric occupancy grids with Simultaneous Mapping and Localization techniques. On top of these, topological meshes and semantic maps are reviewed, and a comparison is drawn between multiple representation formats. Furthermore, the datasets and metrics used in performance benchmarks are discussed, as are the challenges faced in some domains that deviate from typical laboratory conditions. Finally, recent advances in robot control without explicit map construction are touched upon.
Indeksēts Scopus / WoS · Scopus procentile: 68 · SNIP: 1.045
- 2023
Impact of Variable Factors on the Viability and Efficiency of Energy Communities: A Scenario Simulation Study in Latvia(atveras jaunā logā)
R. Lazdins and A. Mutule
2023 IEEE 64th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), lpp. 1-5
- 2023
Robotic System for Post Office Package Handling(atveras jaunā logā)
Oskars Vismanis, Janis Arents, Karlis Freivalds, Vaibhav Ahluwalia and Kaspars Ozols.
Applied Sciences, Sēj. 11 (13), lpp. 7643, MDPI
Kopsavilkums
Parcel sorting is becoming a significant challenge for delivery distribution centers and is mostly automated by using high-throughput sorting machinery, but manual work is still used to feed these machines by placing the parcels on the conveyor belt. In this paper, an AI-based robotic solution that automates the parcel placement task was developed. The architecture of the proposed system along with methods on how to implement it are described by using the currently available hardware and software components. The described choices lead to a well-functioning system and the gained insights will facilitate building similar systems for parcel delivery automation.
Indeksēts Scopus / WoS · Scopus procentile: 75 · SNIP: 0.974
- 2023
Supplementation of synthetic object replicas for increasing precision of microrobot trajectory keypoints(atveras jaunā logā)
M. Laizans, J. Arents, O. Vismanis, V. Bučinskas, A. Dzedzickis, and M. Greitans.
Robotic Systems and Applications, Sēj. 3 (1), lpp. 47-58, JVE International Ltd.
Kopsavilkums
Artificial neural networks are becoming more popular with the development of artificial intelligence. These networks require large amounts of data to function effectively, especially in the field of computer vision. The quality of an object detector is primarily determined by its architecture, but the quality of the data it uses is also important. In this study, we explore the use of novel data set enhancement technique to improve the performance of the YOLOv5 object detector. Overall, we investigate three methods: first, a novel approach using synthetic object replicas to augment the existing real data set without changing the size of the data set; second - rotation augmentation data set propagating technique and their symbiosis, third, only one required class is supplemented. The solution proposed in this article improves the data set with a help of supplementation and augmentation. Lower the influence of the imbalanced data sets by data supplementation with synthetic yeast cell replicas. We also determine the average supplementation values for the data set to determine how many percent of the data set is most effective for the supplementation.
Scopus procentile: 91 · SNIP: 1.822
- 2023
Development of an MCTS Model for Hydrogen Production Optimisation(atveras jaunā logā)
Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Kaspars Ozols, Artūrs Ņikuļins, Kaspars Sudars
Processes, Sēj. 11 (7), lpp. 16, MDPI
Kopsavilkums
Hydrogen has the potential to revolutionize the energy industry due to its clean-burning and versatile properties. It is the most abundant element in the universe and can be produced through a variety of methods, including electrolysis. The widespread adoption of hydrogen faces various challenges, including the high cost of production; thus, it is important to optimise the production processes. This research focuses on development of models for hydrogen production optimisation based on various external factors and parameters. Models based on electricity prices are developed and compared between different market situations. To run hydrogen production more effectively, it is required to use renewable energy sources for the production process. Adding the solar power component to the economic evaluation model outcome is more positive. The Monte Carlo tree search (MCTS) algorithm is adapted to effectively control the electrolysis process. MCTS schedule optimization was performed for a 24 h time horizon applying two time-resolution settings—1 h and 15 min. The results demonstrate the potential of the MCTS algorithm for finding good schedules for water electrolyser devices by taking into account variable environmental factors. Whereas the MCTS with a 15 min resolution ensures mathematically better results, it requires more computational power to solve the decision tree.
Indeksēts Scopus / WoS · Scopus procentile: 66 · SNIP: 2
- 2023
RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection(atveras jaunā logā)
Sarmīte Strautiņa, Ieva Kalniņa, Edīte Kaufmane, Kaspars Sudars, Ivars Namatēvs, Arturs Ņikuļins, Edgars Edelmers
Data, Sēj. 8 (5), lpp. 5, MDPI
Kopsavilkums
The RaspberrySet dataset is a valuable resource for those working in the field of agriculture,particularly in the selection and breeding of ecologically adaptable berry cultivars. This is becauselong-term changes in temperature and weather patterns have made it increasingly important for cropsto be able to adapt to their environment. To assess the suitability of different cultivars or to makeyield predictions, it is necessary to describe and evaluate berries’ characteristics at various growthstages. This process is typically carried out visually, but it can be time-consuming and labor-intensive,requiring significant expert knowledge. The RaspberrySet dataset was created to assist with thisprocess, and it includes images of raspberry berries at five different stages of development. Thesestages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry imagesclassified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated usingground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGBimages, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). Theimages were taken in various weather conditions, at different times of the day, and from differentangles, and they include fully visible buds, flowers, berries, and partially obscured buds. This datasetis intended to improve the efficiency of berry breeding and yield estimation and to identify theraspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allowsfor the reliable detection and phenotyping of yield components at different stages of development. Byproviding a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele,Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture.
Indeksēts Scopus / WoS · Scopus procentile: 70 · SNIP: 0.918
- 2023
Semantic Segmentation Using U-Net Deep Learning Network for Quince Phenotyping on RGB and HyperSpectral Images(atveras jaunā logā)
Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Rihards Balass, Astile Peter, Sarmite Strautina, Edite Kaufmane, Ieva Kalnina
27th International Conference "Electronics", IEEE
Kopsavilkums
Semantic segmentation based on the deep learning techniques can be used for the non-invasive phenotyping of quinces. In this paper we present a deep neural network for generating pixel wise mask from RGB and Hyperspectral images of the quinces using the U-Net architecture. The generated mask will be very useful for the experts involved in the phenotyping in order to get the dimension of the quinces. Also it can be used in the future for automatic plucking of quinces by the robot. This paper also compares the evaluation metrics of the model trained on both RGB and HSI data. We were able to achieve an accuracy of 93.33% and 70.225% for HSI and RGB data respectively. The developed segmentator is freely available in the GIT repository. The future works will include the model for detecting the ripeness of the quinces from the HSI data and also HSI images will be included in the dataset which will be helpful for the experts who are making research for other fruits.
- 2023
Automated Vehicle Platform with Connected Driving Capabilities
Oskars Teikmanis, Aleksandrs Levinskis, Andris Ivars Mackus, Artis Rušiņš, Amr Elkenawy, Marta Tropa, Modris Greitans
2023 IEEE International Conference on Intelligent Transportation Systems (ITSC), IEEE
Kopsavilkums
Augmenting automated vehicles to wirelessly detect and respond to external events before they are detectable by onboard sensors is crucial for developing context-aware driving strategies. To this end, we present an automated vehicle platform, designed with connectivity, ease of use and modularity in mind, both in hardware and software. It is based on the Kia Soul EV with a modified version of the Open-Source Car Control (OSCC) drive-by-wire module, uses the open-source Robot Operating System (ROS and ROS 2) in its software architecture, and provides a straightforward solution for transitioning from simulations to real-world tests. We demonstrate the effectiveness of the platform through a synchronised driving test, where sensor data is exchanged wirelessly, and a model-predictive controller is used to actuate the automated vehicle.
Indeksēts Scopus / WoS
- 2023
Gates Are Not What You Need in RNNs(atveras jaunā logā)
R. Zakovskis, A. Draguns, E. Gaile, E. Ozolins, K. Freivalds
The 22nd International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, Sēj. 14125, Springer Cham
Kopsavilkums
Recurrent neural networks have flourished in many areas. Consequently, we can see new RNN cells being developed continuously, usually by creating or using gates in a new, original way. But what if we told you that gates in RNNs are redundant? In this paper, we propose a new recurrent cell called Residual Recurrent Unit (RRU) which beats traditional cells and does not employ a single gate. It is based on the residual shortcut connection together with linear transformations, ReLU, and normalization. To evaluate our cell's effectiveness, we compare its performance against the widely-used GRU and LSTM cells and the recently proposed Mogrifier LSTM on several tasks including, polyphonic music modeling, language modeling, and sentiment analysis. Our experiments show that RRU outperforms the traditional gated units on most of these tasks. Also, it has better robustness to parameter selection, allowing immediate application in new tasks without much tuning. We have implemented the RRU in TensorFlow, and the code is made available at https://github.com/LUMII-Syslab/RRU.
Indeksēts Scopus / WoS · Scopus procentile: 46
- 2023
Does step frequency as a single factor affect running economy? A systematic review(atveras jaunā logā)
Edgars Bernans, Valters Abolins, Janis Lanka
Journal of Human Sport and Exercise, Sēj. 18 (3), lpp. 743-754, Universidad de Alicante. Área de Educación Física y Deporte
Kopsavilkums
Is step frequency (SF) related to running economy and how? This is the main question we have tried to answer in this study. It is a well-known biomechanical variable used in scientific and practical analysis of running technique. A systematic review was carried out to analyse available literature of the field. Scopus, Web of Science and ScienceDirect databases were used for article search. In total 249 articles matched defined queries and after exclusion, 12 were used in the review. After analysis of the review, our hypothesis remained unconfirmed. In some cases, step frequency affected the running economy, while in others it did not have a direct impact on it. It might be that SF cannot be considered in isolation from other biomechanical characteristics and it cannot be analysed as a universal criterion for improving running technique and economy.
Indeksēts Scopus / WoS · Scopus procentile: 52