| 1. |
- Fan, Han, 1989-, et al.
(författare)
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A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
- 2018
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Ingår i: Sensors and actuators. B, Chemical. - Amsterda, Netherlands : Elsevier. - 0925-4005 .- 1873-3077. ; 259, s. 183-203
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Tidskriftsartikel (refereegranskat)abstract
- Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.
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| 2. |
- Banaee, Hadi, 1986-, et al.
(författare)
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Data-Driven Conceptual Spaces : Creating Semantic Representations for Linguistic Descriptions of Numerical Data
- 2018
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Ingår i: The journal of artificial intelligence research. - : AAAI Press. - 1076-9757 .- 1943-5037. ; 63, s. 691-742
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Tidskriftsartikel (refereegranskat)abstract
- There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore, comparisons are made with descriptions derived on alternative approaches for generating semantic models.
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| 3. |
- Banaee, Hadi, 1986-
(författare)
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From Numerical Sensor Data to Semantic Representations : A Data-driven Approach for Generating Linguistic Descriptions
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In our daily lives, sensors recordings are becoming more and more ubiquitous. With the increased availability of data comes the increased need of systems that can represent the data in human interpretable concepts. In order to describe unknown observations in natural language, an artificial intelligence system must deal with several issues involving perception, concept formation, and linguistic description. These issues cover various subfields within artificial intelligence, such as machine learning, cognitive science, and natural language generation.The aim of this thesis is to address the problem of semantically modelling and describing numerical observations from sensor data. This thesis introduces data-driven approaches to perform the tasks of mining numerical data and creating semantic representations of the derived information in order to describe unseen but interesting observations in natural language.The research considers creating a semantic representation using the theory of conceptual spaces. In particular, the central contribution of this thesis is to present a data-driven approach that automatically constructs conceptual spaces from labelled numerical data sets. This constructed conceptual space then utilises semantic inference techniques to derive linguistic interpretations for novel unknown observations. Another contribution of this thesis is to explore an instantiation of the proposed approach in a real-world application. Specifically, this research investigates a case study where the proposed approach is used to describe unknown time series patterns that emerge from physiological sensor data. This instantiation first presents automatic data analysis methods to extract time series patterns and temporal rules from multiple channels of physiological sensor data, and then applies various linguistic description approaches (including the proposed semantic representation based on conceptual spaces) to generate human-readable natural language descriptions for such time series patterns and temporal rules.The main outcome of this thesis is the use of data-driven strategies that enable the system to reveal and explain aspects of sensor data which may otherwise be difficult to capture by knowledge-driven techniques alone. Briefly put, the thesis aims to automate the process whereby unknown observations of data can be 1) numerically analysed, 2) semantically represented, and eventually 3) linguistically described.
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| 4. |
- Lundell, Jens, et al.
(författare)
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Safe-To-Explore State Spaces : Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
- 2018
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Ingår i: IEEE-RAS Conference on Humanoid Robots. - : IEEE. ; , s. 132-138, s. 132-138
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Konferensbidrag (refereegranskat)abstract
- Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
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