| 1. |
- Ahmadi, Zahra, 1966-
(författare)
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Market orientation and public housing companies in the Swedish declining market
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The licentiate thesis consists of three papers with the particular topic in public housing. They discuss how the public housing companies manage the transition to higher economic demands meeting increased customer and market requirements. These studies focus specifically on how the public housing company deal with market challenges associated with the decision to demolish, maintain and/or new construction. Market-oriented perspective can be a tool for the public housing companies to achieve better customer value and enhance economic development. Although the market orientation concept has contributed to valuable improvements in research, the thesis assumes that it is necessary to distinguish between that the public housing companies operate market-oriented to meet customer requirements and their focus on innovation.Paper I develops market/innovation types and then investigates how public housing companies adapt to these types. It was found that economic conditions in the municipality have a major impact on the housing companies, causing them to act innovatively and create superior customer value by innovations. The study confirms that the implementation of market and innovation orientation contributes to competitive advantages in growing markets, while weak economic conditions impair implementation in declining markets.Paper II addresses how public housing companies in declining markets act based on the concept of market intelligence. This study suggested and tested whether there is a positive link between collecting customer information, disseminating it in the organization, and responding to customer needs, and whether this link has an impact on strategic performance. The result shows that weak links exist in the process; the efficiency of intelligence distribution in public housing companies is affected mainly by their responsiveness to customer needs.Paper III also addresses the public housing companies’ market strategies in declining markets. This study, based on a market-strategic perspective, compares how public housing companies act in relation to customer wants compared to the private housing market. The result shows that public housing companies are more engaged in carrying out new construction, renovation, and reconstruction, as well as taking more social responsibility compared to the private sector. In particular, their concern for the customers’ social needs is evident.
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| 2. |
- Kalantari, Zahra
(författare)
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Adaptation of road drainage structures to climate change
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Climate change is expected to lead to more frequent extreme precipitation events, floods and changes in frost/thawing cycles. The frequency of road closures and other incidents such as flooding, landslides and roads being washed away will probably increase. Stronger demands will be placed on the function of road drainage systems. The overall aim of this thesis was to produce scientifically well-founded suggestions on adaptation of road drainage systems to climate change involving more frequent floods. The work began by examining current practice for road drainage systems in Sweden and gathering experience from professionals working with various problems concerning surface and subsurface drainage systems. Various hydrological models were then used to calculate the runoff from a catchment adjacent to a road and estimate changes in peak discharge and total runoff resulting from simulated land use measures. According to these survey and hydrological modelling studies, adaptation of road drainage systems to climate change can be grouped into two categories: i) institutional adaptation; and ii) technical adaptation. The main approaches in institutional adaptation are to: i) raise the awareness of expected climate change and its impact on drainage systems in transport administration and relevant stakeholders; ii) include adaptation measures in the existing funding programme of the transport administration; and iii) develop an evaluation tool and action plans concerning existing road drainage systems. Technical adaptation will involve ensuring that road constructions are adapted to more frequent extreme precipitation events and responsive to changes in activities and land use in areas adjacent to roads. Changes in climate variables will have effects on watershed hydrological responses and consequently influence the amount of runoff reaching roads. There is a great need for tools such as hydrological models to assess impacts on discharge dynamics, including peak flows. Improved communication between road managers and local actors in the forestry and agriculture sectors can be a means to reduce the impacts of, e.g., clear-cutting or badly managed farmland ditches.
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| 3. |
- Ramezani, Zahra, 1988
(författare)
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Enhancing Temporal Logic Falsification of Cyber-Physical Systems using multiple objective functions and a new optimization method
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cyber-physical systems (CPSs) are engineering systems that bridge the cyber-world of communications and computing with the physical world. These systems are usually safety-critical and exhibit both discrete and continuous dynamics that may have complex behavior. Typically, these systems have to satisfy given specifications, i.e., properties that define the valid behavior. One commonly used approach to evaluate the correctness of CPSs is testing. The main aim of testing is to detect if there are situations that may falsify the specifications. For many industrial applications, it is only possible to simulate the system under test because mathematical models do not exist, thus formal verification is not a viable option. Falsification is a strategy that can be used for testing CPSs as long as the system can be simulated and formal specifications exist. Falsification attempts to find counterexamples, in the form of input signals and parameters, that violate the specifications of the system. Random search or optimization can be used for the falsification process. In the case of an optimization-based approach, a quantitative semantics is needed to associate a simulation with a measure of the distance to a specification being falsified. This measure is used to guide the search in a direction that is more likely to falsify a specification, if possible. The measure can be defined in different ways. In this thesis, we evaluate different quantitative semantics that can be used to define this measure. The efficiency of the falsification can be affected by both the quantitative semantics used and the choice of the optimization method. The presented work attempts to improve the efficiency of the falsification process by suggesting to use multiple quantitative semantics, as well as a new optimization method. The use of different quantitative semantics and the new optimization method have been evaluated on standard benchmark problems. We show that the proposed methods improve the efficiency of the falsification process.
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| 4. |
- Shahroozi, Zahra
(författare)
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Prediction horizon requirement in control and extreme load analyses for survivability : Advancements to improve the performance of wave energy technologies
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The main objective of wave energy converters (WECs) is to ensure reliable electricity production at a competitive cost. Two challenges to achieving this are ensuring an efficient energy conversion and offshore survivability. This thesis work is structured in three different sections: Control and maximum power optimization, forces and dynamics analysis in extreme wave conditions, and statistical modeling of extreme loads in reliability analysis. The need for prediction and future knowledge of waves and wave forces is essential due to the non-causality of the optimal velocity relation for wave energy converters. Using generic concepts and modes of motion, the sensitivity of the prediction horizon to various parameters encountered in a real system is elaborated. The results show that through a realistic assumption of the dissipative losses, only a few seconds to about half a wave cycle is sufficient to predict the required future knowledge for the aim of maximizing the power absorption. The results of a 1:30 scaled wave tank experiment are used to assess the line force and dynamic behaviour of a WEC during extreme wave events. Within the comparison of different wave type representations, i.e. irregular, regular and focused waves, of the same sea state, the results show that not all the wave types deliver the same maximum line forces. As a strategy of mitigating the line forces during extreme wave events, changing the power take-off (PTO) damping may be employed. With consideration of the whole PTO range, the results indicate an optimum damping value for each sea state in which the smallest maximum line force is obtained. Although wave breaking slamming and end-stop spring compression lead to high peak line forces, it is possible that they level out due to the overtopping effect. Waves with a long wavelength result in large surge motion and consequently higher and more damaging forces. On the investigation of reliability assessment of the wave energy converter systems, computing the return period of the extreme forces is crucial. Using force measurement force data gathered at the west coast of Sweden, the extreme forces are statistically modelled with the peak-over-threshold method. Then, the return level of the extreme forces over 20 years for the calm season of the year is computed.
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| 5. |
- Stromann, Oliver, 1992-
(författare)
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Data-Driven Classification in Road Networks
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Connected and autonomous vehicles (CAVs) are an emerging trend in the transport sector and their impact on transportation, the economy, society and the environment will be tremendous. Much like the automobile shaped the way humans travelled, lived and worked during the 20th century, CAVs have yet again the potential to affect and reform all of these areas. Besides the imminent technological challenges on the robotic aspect of making CAVs become a market-ready reality, a plethora of ethical, social and legal questions will have to be addressed along the line. Knowledge of and interaction with the surrounding infrastructure and other actors in the system will be essential for CAVs in order to pave the way for progressive solutions to urgent sustainability and mobility issues in transportation.Road networks, i.e. the networks of roads and intersections, are the core infrastructure on which CAVs will operate. Thus, having detailed knowledge about them is key for CAVs in order to take the right decisions on both short-term actions that will affect individual traffic users in immediate situations and long-term actions that will affect entire transportation systems in the long run. Machine learning is nowadays a popular choice to extract and conglomerate knowledge from large amounts of data – and large amounts of data can be obtained about road networks. However, classical machine learning models are incapable of harnessing the graph-structured nature of road networks sufficiently.Graph neural networks (GNNs) are machine learning models of growing popularity that can explicitly leverage the complex topological structure of node dependencies in graphs, such as the ones observed in road networks. Road networks are sparse graphs that reside in a euclidean space, and therefore different to typical graphs studied in the literature. Also, crowd-sourced road network graphs often have incomplete attributes and are generally lacking the fine-grained level of detail in their encoded information that would be required for CAVs. Identifying the best representation of road network graphs and complementing their lacking detail with auxiliary data is therefore an important research direction.This thesis, therefore, addresses data-driven classification in road networks from two directions: A) the general approach of learning on spatial graphs of road network with GNNs, and B) complementing road network graphs with auxiliary data. Specifically, this thesis and the included papers address the exemplary task of road classifications and make the following contributions to the field:Paper A analyses how GNNs can be applied to road networks and how the networks are best represented. Different aggregator functions are compared on final classification performances. A novel aggregator and a neighbourhood sampling method are introduced, and the line graph transformation is identified as a suitable representation of road network graphs for GNNs.Paper B complements the road network graphs with mobility data from millions of GPS trajectories and introduces an equitemporal node spacing to create road segments of equal travel time. It further introduces remote sensing vision data as a potent complement to overcome shortcom-ings of the graph-based representation for road networks. Simple hand-crafted low-level vision features are used in this work. However, both the equitemporal node spacing and the simple vision features clearly exhibit improved classification performances.Finally, Paper C consolidates the complement of remote sensing data to the road network graphs. Through a general visual feature encoding of state-of-the-art pretrained vision back-bones that are carefully fine-tuned to the remote sensing domain, a further performance boost on the road classification task is achieved.
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| 6. |
- Taghiyarrenani, Zahra, 1987-
(författare)
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Learning from Multiple Domains
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Domain adaptation (DA) transfers knowledge between domains by adapting them. The most well-known DA scenario in the literature is adapting two domains of source and target using the available labeled source samples to construct a model generalizable to the target domain. Although the primary purpose of DA is to compensate for the target domain’s labeled data shortage, the concept of adaptation can be utilized to solve other problems.One issue that may occur during adaptation is the problem of class misalignment, which would result in a negative transfer. Therefore, preventing negative transfer should be considered while designing DA methods. In addition, the sample availability in domains is another matter that should also be taken into account.Considering the two mentioned matters, this thesis aims to develop DA techniques to solve primary predictive maintenance problems.This thesis considers a spectrum of cases with different amounts of available target data. One endpoint is the case in which we have access to enough labeled target samples for all classes. In this case, we use the concept of DA for 1) Analyzing two different physical properties, i.e., vibration and current, to measure their robustness for fault identification and 2) Developing a denoising method to construct a robust model for a noisy test environment.Next, we consider the case where we have access to unlabeled and a few labeled target samples. Using the few labeled samples available, we aim to prevent negative transfer while adapting source and target domains. To achieve this, we construct a unified features representation using a few-shot and an adaptation learning technique.In the subsequent considered setting, we assume we only have access to very few labeled target samples, which are insufficient to train a domain-specific model. Furthermore, for the first time in the literature, we solve the DA for regression in a setting in which it adapts multiple domains with any arbitrary shift.Sometimes, due to the dynamic nature of the environment, we need to update a model to reflect the changes continuously. An example is in the field of computer network security. There is always the possibility of intrusion into a computer network, which makes each Intrusion Detection System (IDS) subject to concept shifts. In addition, different types of intrusions may occur in different networks. This thesis presents a framework for handling concept shift in one single network through incremental learning and simultaneously adapting samples from different networks to transfer knowledge about various intrusions. In addition, we employ active learning to use expert knowledge to label the samples for the adaptation purpose.During adaptation, all cases mentioned so far have the same label space for the source and target domains. Occasionally, this is not the case, and we do not have access to samples for specific classes, either in the source or target; This is the final scenario addressed in this thesis.One case is when we do not have access to some classes in the source domain. This setting is called Partial Domain Adaptation (PDA). This setting is beneficial to network traffic classification systems because, in general, every network has different types of applications and, therefore, different types of traffic. We develop a method for transferring knowledge from a source network to a target network even if the source network does not contain all types of traffic.Another case is when we have access to unlabeled target samples but not for all classes. We call this Limited Domain Adaptation (LDA) setting and propose a DA method for fault identification. The motivation behind this setting is that for developing a fault identification model for a system, we don’t want to wait until the occurrence of all faults for collecting even unlabeled samples; instead, we aim to use the knowledge about those faults from other domains.We provide results on synthetic and real-world datasets for the scenarios mentioned above. Results indicate that the proposed methods outperform the state-of-art and are effective and practical in solving real-world problems.For future works, we plan to extend the proposed methods to adapt domains with different input features, especially for solving predictive maintenance problems. Furthermore, we intend to extend our work to out-of-distribution learning methods, such as domain generalization.
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