| 1. |
- Saeed, Nausheen, et al.
(author)
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A multimodal deep learning approach for gravel road condition evaluation through image and audio integration
- 2024
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In: Transportation Engineering. - : Elsevier. - 2666-691X. ; 16
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Journal article (peer-reviewed)abstract
- This study investigates the combination of audio and image data to classify road conditions, particularly focusingon loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturinggravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep ConvolutionalNetworks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forestclassifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevelprocessing with logical disjunction and conjunction gates (AND and OR) in combination with individualclassifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121),demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusionmethod enhances adaptability by compensating for limitations in one modality with information from the other.Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. Thismethod can help to identify loose gravel on gravel roads, substantially improving road safety and implementing aprecise maintenance strategy through a data-driven approach.
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| 2. |
- Saeed, Nausheen, et al.
(author)
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Gravel road classification based on loose gravel using transfer learning
- 2022
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In: The international journal of pavement engineering. - : Taylor & Francis. - 1029-8436 .- 1477-268X. ; , s. 1-8
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Journal article (peer-reviewed)abstract
- Road maintenance agencies subjectively assess loose gravel as one of the parameters for determininggravel road conditions. This study aims to evaluate the performance of deep learning-based pretrainednetworks in rating gravel road images according to classical methods as done by humanexperts. The dataset consists of images of gravel roads extracted from self-recorded videos andimages extracted from Google Street View. The images were labelled manually, referring to thestandard images as ground truth defined by the Road Maintenance Agency in Sweden (Trafikverket).The dataset was then partitioned in a ratio of 60:40 for training and testing. Various pre-trainedmodels for computer vision tasks, namely Resnet18, Resnet50, Alexnet, DenseNet121, DenseNet201,and VGG-16, were used in the present study. The last few layers of these models were replaced toaccommodate new image categories for our application. All the models performed well, with anaccuracy of over 92%. The results reveal that the pre-trained VGG-16 with transfer learning exhibitedthe best performance in terms of accuracy and F1-score compared to other proposed models.
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| 3. |
- Saeed, Nausheen
(author)
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Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation
- 2024
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Doctoral thesis (other academic/artistic)abstract
- A well-maintained road network is essential for sustainable economic development, providing vital transportation routes for goods and services while connecting communities. Sweden's public road network includes a significant portion of gravel roads, particularly cost-effective for less populated areas with lower traffic volumes. However, gravel roads deteriorate quickly, leading to accidents, environmental pollution, and vehicle tire wear when not adequately maintained. The Swedish Road Administration Authority (Trafikverket) assesses gravel road conditions using subjective methods, analysing images taken during snow-free periods. Due to cost constraints, this labour-intensive process is prone to errors and lacks advanced techniques like road profilometers.This thesis explores the field of assessing gravel road conditions. It commences with a comprehensive review of manual gravel road assessment methods employed globally and existing data-driven smart methods. Subsequently, it harnesses machine hearing and machine vision techniques, primarily focusing on enhancing road condition classification by integrating sound and image data.The research examines sound data collected from gravel roads, exploring machine learning algorithms for loose gravel conditions classification with potential road maintenance and monitoring implications. Another crucial aspect involves applying machine vision to categorise image data from gravel roads. The study introduces an innovative approach using publicly available resources like Google Street View for image data collection, demonstrating machine vision's adaptability in assessing road conditions.The research also compares machine learning methods with manual human classification, specifically regarding sound data. Automated approaches consistently outperform manual methods, providing more reliable results. Furthermore, the thesis investigates combining audio and image data to classify road conditions, particularly loose gravel scenarios. Early feature fusion using pre-trained models significantly improves classifier accuracy.The research proposes using cost-effective devices like mobile phones with AI applications attached to car windshields to collect audio and visual data on gravel road conditions. This approach can provide more accurate and efficient data collection, resulting in real-time mapping of road conditions over considerable distances. Such information can benefit drivers, travellers, and road maintenance agencies by identifying problematic areas with loose gravel, enabling targeted and efficient maintenance efforts, and minimising disruptions to traffic flow during maintenance operations.
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| 4. |
- Salin, Hannes, et al.
(author)
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Quality Metrics for Software Development Management and Decision Making : An Analysis of Attitudes and Decisions
- 2022
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In: Product-Focused Software Process Improvement. 23rd International Conference, PROFES 2022, Jyväskylä, Finland, November 21–23, 2022, Proceedings. - Cham : Springer. - 9783031213885 - 9783031213878 ; , s. 525-530
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Conference paper (peer-reviewed)abstract
- We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-making methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 5. |
- Westergren, Jens, et al.
(author)
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Acute exercise as active inference in chronic musculoskeletal pain, effects on gait kinematics and muscular activity in patients and healthy participants : a study protocol for a randomised controlled laboratory trial
- 2023
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In: BMJ Open. - : BMJ. - 2044-6055. ; 13
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Journal article (peer-reviewed)abstract
- Introduction: Chronic musculoskeletal pain is a highly prevalent, complex and distressing condition that may negatively affect all domains of life. In view of an active inference framework, and resting on the concept of allostasis, human movement per se becomes a prerequisite for health and well-being while chronic pain becomes a sign of a system unable to attenuate an allostatic load. Previous studies on different subgroups of chronic pain conditions have demonstrated alterations in gait kinematics and muscle activity, indicating shared disturbances in the motor system from long-term allostatic load. We hypothesise that such alterations exist in heterogenous populations with chronic musculoskeletal pain, and that exposure to acute and controlled exercise may attenuate these alterations. Therefore, the main aim of this study is to investigate the acute effects of exercise on gait kinematics and activity of the back and neck muscles during diverse walking conditions in patients with chronic musculoskeletal pain compared with a reference sample consisting of healthy participants.Methods and analysis: This two-sample two-armed parallel randomised controlled laboratory trial will include 40 participants with chronic musculoskeletal pain (>3 months) and 40 healthy participants. Participants will be randomly allocated to either 30 min of aerobic exercise or rest. Primary outcomes are gait kinematics (walking speed, step frequency, stride length, lumbar rotation, gait stability) and muscular activity (spatial and temporal) of the back and neck during diverse walking conditions. Secondary outcomes are variability of gait kinematics and muscle activity and subjective pain ratings assessed regularly during the trial.Ethics and dissemination: The study has been approved by the Regional Ethics Review Board in Uppsala, Sweden (#2018/307). Findings will be disseminated via conference presentations, publications in peer-reviewed journals and engagement with patient support groups and clinicians.
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| 6. |
- Zhu, Yurong, et al.
(author)
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A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls
- 2023
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In: Buildings. - : MDPI. - 2075-5309. ; 13:9
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Journal article (peer-reviewed)abstract
- To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.
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| 7. |
- Zhu, Yurong, et al.
(author)
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A Review on Data-driven Methods for Studying Hygrothermal Transfer in Building Exterior Walls
- 2023
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In: ICBDT '23: Proceedings of the 2023 6th International Conference on Big Data Technologies. - : ACM Press. ; , s. 33-41
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Conference paper (peer-reviewed)abstract
- This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.
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