# Spatial Clustering Approach for Vessel Path Identification

Mohamed Abuella, M. Amine Atoui, Slawomir Nowaczyk, Simon Johansson, and Ethan
Faghani Manuscript received November 1, 2023; revised November 16, 2023.This
work was supported by the Sweden’s innovation agency (Vinnova).(Corresponding
author: Mohamed Abuella.)
Mohamed Abuella, M. Amine Atoui, and Slawomir Nowaczy are with Center for
Applied Intelligent Systems Research (CAISR), Halmstad University, Kristian
IV:s väg 80523, Halmstad, 30118 Sweden (e-mail<EMAIL_ADDRESS>amine.atoui; Slawomir.Nowaczyk@hh.se). Simon Johansson and Ethan Faghani are
with CetaSol AB, Gothenburg, 41251, Sweden (e-mail: simon.johansson and
<EMAIL_ADDRESS>
###### Abstract

This paper addresses the challenge of identifying the paths for vessels with
operating routes of repetitive paths, partially repetitive paths, and new
paths. We propose a spatial clustering approach for labeling the vessel paths
by using only position information. We develop a path clustering framework
employing two methods: a distance-based path modeling and a likelihood
estimation method. The former enhances the accuracy of path clustering through
the integration of unsupervised machine learning techniques, while the latter
focuses on likelihood-based path modeling and introduces segmentation for a
more detailed analysis. The result findings highlight the superior performance
and efficiency of the developed approach, as both methods for clustering
vessel paths into five classes achieve a perfect F1-score. The approach aims
to offer valuable insights for route planning, ultimately contributing to
improving safety and efficiency in maritime transportation.

###### Index Terms:

Spatial clustering, vessel path identification, maritime transportation,
average nearest neighbor distance, hierarchical clustering, likelihood
estimation.

##  I Introduction

###  I-A Background and Motivation

The maritime transportation is crucial for global trade, generating extensive
vessel trajectory data that reveals intricate spatial and temporal navigation
patterns. Understanding these patterns is vital for effective maritime traffic
surveillance and management [1].

A trajectory refers to a sequence of consecutive geographical points, each
representing a specific location at a given timestamp [2]. Trajectories,
observed in various scenarios such as pedestrian movements, vehicular routes,
and natural events like wildlife migrations or hurricanes, involve time-
evolving position data. Trajectory mining aims to uncover significant patterns
within datasets, enabling tasks like path classification, anomaly detection
for accidents or traffic congestions, surveillance for suspicious activities,
and prediction of vessel trajectories in different landscapes [3].

It is crucial to distinguish between trajectory and path when studying
movement data. The term trajectory typically denotes the movement of an object
over time, while path refers to the specific route taken by the object. For
instance, if an object travels from origin to destination, its trajectory is
the sequence of geographical points it passes through, while its path is the
specific route taken, such as a sidewalk, street, highway, lane, railway, or
waterway.

Additionally, the term route is synonymous with path or trajectory as long as
it shares the same origin and destination. Conversely, any difference in
either the origin or destination represents another route.

Path clustering, a versatile technique, involves grouping paths into clusters
based on their similarity. This approach has found its niche in a wide range
of practical applications. In the realm of navigation, path identification
empowers systems to generate clear and detailed instructions for users seeking
their way. Traffic analysis benefits from path clustering as it facilitates
the identification of diverse traffic patterns, such as the smooth flow of
traffic on highways and the congestion often encountered on city streets. Path
identification proves equally valuable in route planning, enabling the
optimization of routes for transportation systems, including public and
maritime transportation services [4].

In the scope of the maritime industry, path identification from Automatic
Identification System (AIS) data is a challenging task due to the high spatial
freedom and, especially in coastal areas, the high frequency of ship’s
navigation maneuvers. Thus, it is imperative to develop a path identification
tool that integrates with route planning systems for improving maritime safety
and optimizing vessel routing. As data-driven approaches from AIS data
continue to grow and evolve, path clustering will undoubtedly play an
increasingly important role in understanding vessel behavior and supporting
decision-making in maritime transportation [5].

###  I-B Aims and Contributions

This paper aims to address the challenge of identifying vessel paths in
scenarios characterized by repetitive, semi-repetitive, and novel operations.
In general, the aims and contributions of the proposed approach in this paper
can be outlined as follows:

  * •

The proposed clustering approach of vessel paths requires only position
information, specifically longitude and latitude.

  * •

The clustering approach has a proven added value for clustering challenging
unseen or unknown paths.

  * •

The approach is robust and interpretable by applying a similarity measure that
reduces the influence of noise or outliers and offers a clear interpretation
of path clustering.

  * •

The approach has a customizable parameter to determine the number of path
classes, thereby enhancing the flexibility and adaptability of the framework
and allowing users to tailor it to their specific needs.

  * •

The approach also includes a method to study and analyze the patterns within
specific segments of a path.

  * •

It is a data-driven solution that can be used as a valuable asset for informed
decision-making in route planning and optimization, traffic management, and
resource allocation.

The rest of this paper is organized as follows. Section II reviews the related
work on vessel path identification. Section III describes our proposed spatial
clustering approach in detail. The real-world vessel data of our case study is
described in Section IV. Section V presents the results of our experimental
evaluation. Section VI concludes the paper and discusses future work.

##  II Related Work

Path clustering can be done using a variety of different methods [6]. We will
explore the related works of these various methods.

Clustering is gaining popularity for route extraction. Machine learning (ML)
has recently been applied extensively for vessel path identification by
learning patterns from historical data. The study presented in [7] introduced
a framework, Traffic Route Extraction and Anomaly Detection (TREAD), which
utilizes unsupervised learning for maritime route extraction. The primary
emphasis is on anomaly detection and route prediction, highlighting the
crucial role of AIS data in enhancing maritime situational awareness. The work
specifically addresses challenges related to intermittency and persistence in
AIS data. Another method of route extraction was proposed in [8], transforming
ship trajectories into ship trip semantic objects (STSO) and utilizing graph
theory for route extraction. The method proves robustness in extracting
traffic routes for merchant ships but may have limitations for vessels with
frequent navigation behavior changes, such as fishing vessels. The approach in
[9], on the other hand, adopts a dynamic time warping (DTW) distance as a
similarity measure and considers vessel course changes to analyze its
trajectories. Experiments demonstrated its high accuracy in distinguishing and
detecting similar vessel trajectories, outperforming existing methods in
accuracy and cluster degree evaluation. Moreover, [10] presents a machine
learning framework for maritime vessel trajectory analysis, incorporating
clustering, classification, and outlier detection. It employs piecewise linear
segmentation for compression and alignment kernels to integrate geographical
domain knowledge, enhancing task performance. Results show reduced computation
time without compromising accuracy.

Capobianco et al.[11] proposed a deep learning approach using recurrent neural
networks, employing a Bidirectional Long Short-Term Memory (BiLSTM) layer as
an encoder and a Unidirectional Long Short-Term Memory (LSTM) layer as a
decoder, for vessel trajectory prediction. Their model outperforms baseline
approaches, showcasing the effectiveness of sequence-to-sequence neural
networks. In their study, Li et al. [12] present an AIS data-based machine
learning method for feature extraction and unsupervised route planning for
Maritime Autonomous Surface Ships (MASS). The method uses Automatic and
Adaptive Dynamic Time Warping (AADTW), Spectral Clustering with Affinity
Feature (SCAF), and a route optimization algorithm based on dynamic
programming to extract features, obtain movement patterns, and plan routes.
The proposed method outperforms existing methods by considering the impact of
hidden factors and providing different routes for different types of MASS. The
work in [13] systematically analyzes the performance of twelve ship trajectory
prediction methods, including classical machine learning and emerging deep
learning techniques. It compares twelve methods across three AIS datasets,
representing different maritime traffic scenarios, and evaluates their
effectiveness based on six indexes. The study concludes that traditional
machine learning-based trajectory prediction methods struggle to meet the
rising demands for accuracy and real-time performance, leading to increased
interest in and promising results from deep learning-based approaches.

A maritime traffic route extraction approach based on multi-dimensional
density-based spatial clustering of applications with noise (MD-DBSCAN) was
developed in [14]. The approach incorporates trajectory compression,
similarity measures, and extraction of ship trajectory clusters. The approach
demonstrates effectiveness in noise reduction and route extraction. The
authors in [15] proposed a trajectory clustering method based on Hierarchical
Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and
Hausdorff distance to generate a similarity matrix. The method adapts to shape
characteristics and exhibits good clustering scalability and improved
clustering results compared to DBSCAN, k-means, and spectral clustering
algorithms.

Eljabu et al. proposed spatial clustering methods (SPTCLUST and SPTCLUST-II)
in [16] and [17] respectively, for maritime traffic routes extraction from AIS
data. The approach consists of data preprocessing, pathfinding, and route
extraction without using traditional clustering algorithms. It achieved high
F1-scores, 97% and 99%, for tankers and cargo maritime traffic routes.

The study in [18] enhanced the DBSCAN method by integrating the Mahalanobis
distance metric for vessel behavior modeling. The proposed methodology
includes clustering historical AIS data and detecting anomalies. The study
showcases applicability to diverse water regions, contributing to situational
awareness, collision prevention, and route planning.

Farahnakian et al. [19] conducted a comprehensive examination of clustering-
based techniques, including k-means, DBSCAN, Affinity Propagation (AP), and
the Gaussian Mixtures Model (GMM), for detecting abnormal vessel behaviors
from AIS data. Results indicate that k-means is particularly effective in
detecting dark ships and spiral vessel movements, which is crucial for
enhancing maritime safety. Furthermore, the study [20] proposed two methods
for trajectory outlier detection, with the first utilizing DBSCAN clustering
and Hausdorff distance, and the second employing Support Vector Machine (SVM)
classifier and the Generalized Sequence Pattern algorithm. Both models
outperform the baselines, with the SVM approach demonstrating superior
performance in the identification of traffic patterns and outliers.

The research paper [21] offers a detailed survey of visual analytics for
vessel trajectory data. The authors discuss a variety of methods, including
map-based visualization, timeline-based visualization, and interactive
visualization.

The paper [22] comprehensively reviews various approaches for vessel
trajectory predicting, including clustering algorithms and machine learning
algorithms. It also discusses the challenges and future research directions,
such as the uncertainty in the data, the dynamic environment, and the
computational complexity.

Among the identified challenges, which are subjects of ongoing research and
require additional attention, three are worthy of specific mention: navigating
dynamic maritime environments poses a substantial challenge in accurately
identifying vessel paths (I); ensuring stability, explainability, and managing
the computational cost of the model add further complexity (II); finally,
addressing the need for flexibility, scalability, and practical applicability
is crucial for a comprehensive solution in the field of vessel path
identification (III). Motivated by these challenges, we aim to develop a
framework that focuses on vessel path identification and potentially tackling
such challenges faced in maritime transportation.

##  III Methodology

This section covers the theoretical background and description of our proposed
framework’s underlying methodology. The framework of vessel path
identification is depicted in Figure 1.

####  III-1 Problem Formulation

The equations (1-3) serve as a mathematical representation to describe the
clustering of vessel paths.
It is worth mentioning that the clustering process is conducted sequentially,
point by point, while the labeling of path classes is performed for the entire
voyage. Therefore, each voyage has a single label of path class.

$Voyages\in Path\ Classes\\\ $ (1)
$Voyages=\\{ts_{1}[p_{1},\dots,p_{n}],\dots,ts_{j}[p_{1},\dots,p_{n}]\\}\\\ $
(2) $Path\ Class\ Set=\\{class_{1},\dots,class_{k}\\}$ (3)

where:
$Voyages$: a collection of time series, each representing a voyage of the
vessel taken following a given path, with a predicted clustered class.
$ts_{j}$: a time series corresponding to voyage $j$, i.e., a sequence of $n$
data points, where each data point $p$ represents the vessel position and is
defined by a pair of coordinates, namely latitude and longitude.
$n$: the number of time steps (duration) of each voyage, which can differ from
one voyage to another.
$j$: a total number of voyages.
$Path\ Class\ Set$: a set of $k$ classes into which the path of voyages are
being clustered.

(a)

(b)

Figure 1: Framework of vessel path identification. (a) Flowchart of distance-
based method. (b) Flowchart of segmented Gaussian likelihood method.

####  III-2 Distance-Based Method

The similarity between two paths is measured by the average nearest neighbor
distance (ANND), as shown in Eq. (4).

$ANND(i,j)=\frac{1}{n_{i}}\sum_{k=1}^{n_{i}}Distance(P_{i}^{k},NN(P_{j}^{k}))$
(4)

where:
$ANND(i,j)$: is the average nearest neighbor distance between path $i$ and
path $j$, present in the distance matrix at row $i$ and column $j$. It is a
symmetric, meaning that $ANND(i,j)$ is the same as $ANND(j,i)$
$Distance(P_{i}^{k},NN(P_{j}^{k}))$ : The distance between the $k^{th}$ point
in path $i$, denoted as $P_{i}^{k}$, and its corresponding nearest neighbor
point in path $j$, indicated as $NN(P_{j}^{k})$. $n_{i}$ is the total number
of points in path $i$.
The measure $Distance$ is an Euclidean distance. However, for longer curved
routes, Haversine or Great-circle distance would be more suitable.

The ANND, as expressed in Eq. (4), is computed by averaging the distances
between each point in one path and its nearest neighbor in the other path.
Then, the similarity value (i.e., ANND) of this pair of paths is stored as an
element in the distance matrix.
A lower ANND indicates that the paths within a cluster are more similar. The
distance matrix will have dimensions $(m\times m)$ , where $m$ is the number
of paths.
For instance, the computed distance matrix for a set of 12 paths is
illustrated in Figure 2.

After the construction of the distance matrix, the machine learning (ML)
technique is applied to cluster the paths based on their corresponding values
in this distance matrix. The ML techniques that we used are k-means, Gaussian
Mixture Model (GMM), and hierarchical clustering.

Figure 2: Part of the distance matrix, in this case showing 12 paths.

####  III-3 Segmented Gaussian Likelihood Method

In addition to identifying the vessel’s path, to better understand how the
vessel changes its paths, we employ Gaussian distributions on several distinct
segments of the vessel route. This technique can be summarized as follows:

  * •

Utilize a training dataset comprising vessel position information that should
adequately represent all potential paths of the vessel route.

  * •

Divide the route into different distinct segments.

  * •

Train a single GMM model for each segment to find the Gaussian distributions
of all segments of the route.

  * •

Estimate likelihoods of the segments by using the trained GMM models with
their corresponding segments of each vessel voyage in the test dataset.

  * •

Label the path classes based on the estimated likelihood at the unique
segments of the route.

##  IV Case Study

In this section, we describe the case study, including the data collection,
preprocessing, and analysis.

###  IV-A Data Collection

In this study, we utilized a dataset that was collected from a ship named
Cinderella II, which is a passenger ship that operates in the Stockholm
archipelago. The dataset spans over five months, from July to November 2022.
The dataset comprises information on 124 voyages of this vessel, connecting
the two main ports of Vaxholm in the east and Sodra in the west.

Additional information about Cinderella II can be found in [23]. For instance,
the specifications of this vessel are 19 meters in length and 6.41 meters in
breadth, boasting a gross tonnage of 68. Operating under the flag of Sweden,
its maritime mobile service identity (MMSI) is 265513810 with a reported
draught of 2.5 meters and a recorded average speed of 8.2 knots (4.2 m/s).

Figure 3: Image of the Cinderella II ship [23]. Figure 4: Map of vessel route.

(a) The vessel route.

(b) Main classes of path

Figure 5: The vessel route before and after applying path identification

###  IV-B Data Preparation and Analysis

In our approach, we emphasize the significance of data representation. As a
result, we group the path points based on their timestamps with a resolution
of one second and store these grouped path points with distinctive Voyage IDs.
Afterward, these paths are ready to be processed by the path clustering
approach to determine the overall path class. In order to exploit the
resulting class information, statistical analysis is conducted to determine
differences in vessel paths concerning fuel, time, distance, and speed. The
histograms representing these quantities across various path classes can be
found in Figure 7. Notably, when the vessel traverses the shorter southern
paths, it employs slower speeds, effectively reducing fuel consumption without
significantly impacting travel time.

Figure 6: Distribution of voyages across the five path classes

Figure 7: Average fuel and time, distance, and speed of five path classes

##  V Results and Discussion

In this section, we present the results of our spatial clustering approach for
vessel path identification and discuss the implications of these findings. The
approach involved the utilization of position information and various
clustering techniques, specifically k-means, hierarchical clustering, and
Gaussian distributions clustering, with the dataset containing 124 voyages.

###  V-A Evaluation of Path Clustering

The results of vessel path identification are evaluated through visual
inspection and tabulation using metrics such as confusion matrix, precision,
recall, and F1-score [24].

The hits and messes of path clustering are presented by the confusion matrix.
For our results of path clustering, the confusion matrix is a one-vs-one type
matrix. Then, the confusion matrix is converted into a one-vs-all type matrix
(binary-class confusion matrix) as shown in Eq. (5), for calculating class-
wise metrics like precision, recall, and F1-score.

$\begin{array}[]{c|cc}&\text{Pred. Pos.}&\text{Pred. Neg.}\\\
\hline\cr\text{Act. Pos.}&\text{TP }&\text{FN }\\\ \text{Act. Neg.}&\text{FP
}&\text{TN }\end{array}$ (5)

where True Positives (TP), False Positives (FP), True Negatives (TN), and
False Negatives (FN) are determined by comparing the predicted (Pred.) and
actual (Act.) path classes.

The following performance metrics were used:

  * •

Precision: the ratio of true positives to the total number of predicted
positives.

  * •

Recall: the ratio of true positives to the total number of actual positives in
the data.

  * •

F1-score: the harmonic mean of precision and recall.

The equations for precision, recall, and F1-score are shown in Eqs. (6), (7),
and (8).

$Precision=\frac{TP}{TP+FP}$ (6)

$Recall=\frac{TP}{TP+FN}$ (7)

$F1\text{-}score=2\times\frac{(Precision\times Recall)}{(Precision+Recall)}$
(8)

###  V-B Discussion

Table I shows the results of applying k-means or Gaussian Mixture Model (GMM)
models to identify the vessel paths from the distance matrix in the distance-
based method of the path identification approach. Notably, the paths with
classes of North-West, South, and South-West achieved an F1-score of 1.0,
indicating that the approach correctly identified all the paths of these
classes.

In contrast, the North-East and North-Middle paths exhibited lower F1 scores
compared to other classes. The path class of North-Middle is the most
challenging path to identify since six such paths have been clustered as
North-East, as can be seen by comparing Figures 8 and 9, which are the
visualization for all the paths, color-marked based on their ground truth
classes. Figure 10 illustrates the probability distribution of mis-clustered
paths with respect to latitude and longitude coordinates. It is obvious that
these paths have nearly identical coordinates, which makes them challenging
paths to cluster with k-means or GMM. This suggests that there is still room
for improvement by using other ML clustering methods.

Table II presents the results of employing hierarchical clustering to the
distance matrix in the distance-based method for clustering the vessel paths.
In hierarchical clustering, there is a parameter called ”Dendrogram Cut-off
threshold,” and its value should be selected depending on the number of path
classes. Hence, as illustrated in Figure 11, this parameter is denoted by the
Y-axis as a clustering height, and its value is set to 100 for clustering the
vessel path into five classes.

Remarkably, all path classes achieved an F1-score of 1, indicating that
hierarchical clustering successfully identified all paths with high accuracy
from the distance matrix using the distance-based method. This suggests that
the choice of ML clustering technique with the distance matrix can influence
the accuracy of path identification. Table II displays the outcomes of
clustering, now by applying the segmented likelihood Gaussian method. This
method achieved perfect precision, recall, and F1-score for all path classes.
Figures 12, 13, and 14 present visualizations for the segmented Gaussian
likelihood method.

The accuracy in results by hierarchical and segmented Gaussian likelihood
clustering for path classes indicates the efficacy of the developed approach
of spatial clustering for vessel path identification.

Figure 8: Display of the five path classes (ground truth). TABLE I: Result of
implementing of both k-means and GMM clustering to five path classes

(a) Precision, Recall, and F1-score Paths | Precision | Recall | F1-score
---|---|---|---
North-East (NE) | 0.7 | 1 | 0.824
North-Middle (NM) | 1 | 0.85 | 0.919
North-West (NW) | 1 | 1 | 1
South (S) | 1 | 1 | 1
South-West (SW) | 1 | 1 | 1

(b) Confusion Matrix Actual | Predicted | Total
---|---|---
NE | NM | NW | S | SW
NE | 14 | 0 | 0 | 0 | 0 | 14
NM | 6 | 34 | 0 | 0 | 0 | 40
NW | 0 | 0 | 16 | 0 | 0 | 16
S | 0 | 0 | 0 | 52 | 0 | 52
SW | 0 | 0 | 0 | 0 | 2 | 2
Total | 20 | 34 | 16 | 52 | 2 | 124

Figure 9: Results of both k-means and GMM clustering to five path classes.
Figure 10: Probability distribution of location coordinates for mis-clustered
paths by both k-means and GMM. Figure 11: Results of hierarchical clustering
to five path classes. TABLE II: Result of implementing both hierarchical
clustering and segmented likelihood Gaussian clustering to five path classes

(a) Precision, Recall, and F1-score Paths | Precision | Recall | F1-score
---|---|---|---
North-East (NE) | 1 | 1 | 1
North-Middle (NM) | 1 | 1 | 1
North-West (NW) | 1 | 1 | 1
South (S) | 1 | 1 | 1
South-West (SW) | 1 | 1 | 1

(b) Confusion Matrix Actual | Predicted | Total
---|---|---
NE | NM | NW | S | SW
NE | 14 | 0 | 0 | 0 | 0 | 14
NM | 0 | 40 | 0 | 0 | 0 | 40
NW | 0 | 0 | 16 | 0 | 0 | 16
S | 0 | 0 | 0 | 52 | 0 | 52
SW | 0 | 0 | 0 | 0 | 2 | 2
Total | 14 | 40 | 16 | 52 | 2 | 124

Figure 12: Distribution of the route into eight segments. Figure 13:
Probability distributions of location coordinates for the eight segments of
the route. Figure 14: Gaussian distributions for seven segments of the route.

##  VI Conclusion

The proposed approach is able to identify the vessel paths with partially
defined or unknown paths. In the distance-based method, the hierarchical
clustering used in the approach outperforms k-means and GMM clustering
techniques.

The approach of hierarchical clustering includes a user-customizable
parameter, a cut-off threshold, which allows desired control for the number of
path classes, enhancing the flexibility and adaptability of the proposed
approach.

In the distance-based method, adopting ANND as a measure of similarity makes
path clustering less affected by noise or outliers and provides a more
intuitive interpretation of path similarity, ultimately enhancing the
robustness and interpretability of our approach.

The segmented Gaussian likelihood method is particularly useful for
identifying and analyzing the vessel path alterations at different segments of
the vessel route.

The proposed approach is computationally efficient and has the potential to be
a valuable tool for planning vessel paths. Accurate path identification can
contribute to safer and more efficient maritime transportation practices,
aiding in route planning, collision avoidance, and navigation optimization.

Nevertheless, the framework has some potential limitations, such as the
segmented Gaussian likelihood method exhibiting sensitivity to segment
definition, which could affect its salable performance, particularly in
complex maritime scenarios. Moreover, while the study case demonstrates that
the framework is computationally efficient, it is essential to discuss any
potential scalability issues, especially when dealing with large datasets,
since the computational efficiency may vary depending on the dataset size and
the nature of the paths.

Further work could explore the scalability and real-world applicability of the
proposed clustering approach, as well as its integration with related systems
of maritime transportation.

## Acknowledgment

This research project is funded by Sweden’s innovation agency (Vinnova).
The authors wish to thank the diverse group at the Center for Applied
Intelligent Systems Research (CAISR), Halmstad University, for helpful
discussions.

[Supplementary Materials] The source codes that are implemented on Python
3.9.7 to produce the results are available at:
https://github.com/MohamedAbuella/Path_Clustering.
The dataset is private and cannot be shared due to the crucial commercial
interests of the startup company operating the iHelm system.

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|  Mohamed Abuella received his M.S. and PhD degrees in Electrical and
Computer Engineering from Southern Illinois University at Carbondale and
University of North Carolina at Charlotte, in 2012 and 2018 respectively. He
is a postdoctoral researcher at Halmstad University since 2022. His research
interests include energy analytics and AI for sustainability.
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|  M. AMINE ATOUI obtained his Ph.D. degree from LARIS, Polytech’ Angers,
France, in 2015. Currently, he is affiliated with the Center for Applied
Intelligent Systems Research at Halmstad University, Sweden. His research
interests encompass probabilistic and explainable Machine Learning, causal and
Bayesian Inference, transmission/communication, and automatic control.
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|  Slawomir Nowaczyk is a Professor in Machine Learning at the Center for
Applied Intelligent Systems Research, Halmstad University, Sweden. He received
his MSc degree from Poznan University of Technology in 2002 and his PhD from
the Lund University of Technology in 2008. During the last decades, his
research has focused on machine learning, knowledge representation, and self-
organising systems. The majority of his work concerns industrial data streams,
often with predictive maintenance as the goal. Given that accurate and
relevant labels are usually impossible to obtain in such settings, Slawomir’s
contributions primarily take advantage of proxy labels, such as transfer
learning and multi-task learning, or concern semi-supervised and unsupervised
modelling. He is a board member of the Swedish AI Society and a research
leader for the School of Information Technology at Halmstad University.
Slawomir has led multiple research projects on applying Artificial
Intelligence and Machine Learning in different domains, such as transport and
automotive, energy, smart cities, and healthcare. In most cases, this research
was done in collaboration with industry and public administration
organisations – inspired by practical challenges and leading to tangible
results and deployed solutions.
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|  Simon Johansson is a MSc graduate of Chalmers University of Technology’s
program in Engineering Mathematics and Computational Science in 2020,
currently works in Cetasol, a marine company specialising in CO2 reduction and
energy optimisation for vessels. His practical application of computational
methods and dedication to environmental sustainability align with his role,
contributing to global efforts to mitigate climate change. Simon’s commitment
to advancing eco-friendly solutions in the marine industry reflects a seamless
integration of academic excellence and real-world impact.
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|  Ethan Faghani is the CEO and founder of Cetasol. Before Cetasol, Ethan was
Chief Engineer of Automation and AI at Volvo Penta. Ethan has experience
working with cutting-edge technologies in other transportation segments in
both big enterprises and his own founded startup. Ethan obtained his Ph.D. in
mechatronics from UBC and Innovation and Entrepreneurship from Stanford
Business School.
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