24 research outputs found
On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
Out-of-distribution (OOD) detection is concerned with identifying data points
that do not belong to the same distribution as the model's training data. For
the safe deployment of predictive models in a real-world environment, it is
critical to avoid making confident predictions on OOD inputs as it can lead to
potentially dangerous consequences. However, OOD detection largely remains an
under-explored area in the audio (and speech) domain. This is despite the fact
that audio is a central modality for many tasks, such as speaker diarization,
automatic speech recognition, and sound event detection. To address this, we
propose to leverage feature-space of the model with deep k-nearest neighbors to
detect OOD samples. We show that this simple and flexible method effectively
detects OOD inputs across a broad category of audio (and speech) datasets.
Specifically, it improves the false positive rate (FPR@TPR95) by 17% and the
AUROC score by 7% than other prior techniques
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Within the domain of e-commerce retail, an important objective is the
reduction of parcel loss during the last-mile delivery phase. The
ever-increasing availability of data, including product, customer, and order
information, has made it possible for the application of machine learning in
parcel loss prediction. However, a significant challenge arises from the
inherent imbalance in the data, i.e., only a very low percentage of parcels are
lost. In this paper, we propose two machine learning approaches, namely, Data
Balance with Supervised Learning (DBSL) and Deep Hybrid Ensemble Learning
(DHEL), to accurately predict parcel loss. The practical implication of such
predictions is their value in aiding e-commerce retailers in optimizing
insurance-related decision-making policies. We conduct a comprehensive
evaluation of the proposed machine learning models using one year data from
Belgian shipments. The findings show that the DHEL model, which combines a
feed-forward autoencoder with a random forest, achieves the highest
classification performance. Furthermore, we use the techniques from Explainable
AI (XAI) to illustrate how prediction models can be used in enhancing business
processes and augmenting the overall value proposition for e-commerce retailers
in the last mile delivery
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
Cost-effective asset management is an area of interest across several
industries. Specifically, this paper develops a deep reinforcement learning
(DRL) solution to automatically determine an optimal rehabilitation policy for
continuously deteriorating water pipes. We approach the problem of
rehabilitation planning in an online and offline DRL setting. In online DRL,
the agent interacts with a simulated environment of multiple pipes with
distinct lengths, materials, and failure rate characteristics. We train the
agent using deep Q-learning (DQN) to learn an optimal policy with minimal
average costs and reduced failure probability. In offline learning, the agent
uses static data, e.g., DQN replay data, to learn an optimal policy via a
conservative Q-learning algorithm without further interactions with the
environment. We demonstrate that DRL-based policies improve over standard
preventive, corrective, and greedy planning alternatives. Additionally,
learning from the fixed DQN replay dataset in an offline setting further
improves the performance. The results warrant that the existing deterioration
profiles of water pipes consisting of large and diverse states and action
trajectories provide a valuable avenue to learn rehabilitation policies in the
offline setting, which can be further fine-tuned using the simulator.Comment: Published Neural Comput & Applic (2023), 12 pages, 8 Figur
From Analysis of Information Needs towards an Information Model of Railway Infrastructure
Railway is a tightly coupled network, where the operations are directly effected by the condition of rail infrastructure. With the advancement of ICT, a railway network exploit various computerized systems for efficient railway monitoring, maintenance and operations. However, these systems suffer from number of limitations, mainly, the data related to each asset type (e.g. Track, Bridge, etc) are stored in separate database management system. Such scattered and isolated nature of data present the island of information, while making it impossible to perform the sound decision analysis. In this paper, we propose a nework wide information model of railway infrastructure that structure the railway object, specify their properties and identify their inter-relationships. The presented information model supports the railway monitoring, maintenance and operations by providing the layout of railway infrastructure. Structuring data in the form of railway assets, railway risk assessment, railway load management, railway maintenance, and railway failure will provide a solid base to railway stakeholders, e.g. infrastructure managers, to take informed decisions based on data properties
Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization
Evolutionary algorithms (EA) are efficient population-based stochastic algorithms for solving optimization problems. The performance of EAs largely depends on the configuration of values of parameters that control their search. Previous works studied how to configure EAs, though, there is a lack of a general approach to effectively tune EAs. To fill this gap, this paper presents a consistent, automated approach for tuning and controlling parameterized search of an EA. For this, we propose a deep reinforcement learning (DRL) based approach called āDRL-APC-DEā for online controlling search parameter values for a multi-objective Differential Evolution algorithm. The proposed method is trained and evaluated on widely adopted multi-objective test problems. The experimental results show that the proposed approach performs competitively to a non-adaptive Differential Evolution algorithm, tuned by grid search on the same range of possible parameter values. Subsequently, the trained algorithms have been applied to unseen multi-objective problems for the adaptive control of parameters. Results show the successful ability of DRL-APC-DE to control parameters for solving these problems, which has the potential to significantly reduce the dependency on parameter tuning for the successful application of EAs
A multi-objective decision making model for risk-based maintenance scheduling of railway earthworks
Aged earthworks constitute a major proportion of European rail infrastructures, the re-placement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multiāattribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization.Geo-engineerin
Operator Selection in Adaptive Large Neighborhood Search using Deep Reinforcement Learning
Large Neighborhood Search (LNS) is a popular heuristic for solving
combinatorial optimization problems. LNS iteratively explores the neighborhoods
in solution spaces using destroy and repair operators. Determining the best
operators for LNS to solve a problem at hand is a labor-intensive process.
Hence, Adaptive Large Neighborhood Search (ALNS) has been proposed to
adaptively select operators during the search process based on operator
performances of the previous search iterations. Such an operator selection
procedure is a heuristic, based on domain knowledge, which is ineffective with
complex, large solution spaces. In this paper, we address the problem of
selecting operators for each search iteration of ALNS as a sequential decision
problem and propose a Deep Reinforcement Learning based method called Deep
Reinforced Adaptive Large Neighborhood Search. As such, the proposed method
aims to learn based on the state of the search which operation to select to
obtain a high long-term reward, i.e., a good solution to the underlying
optimization problem. The proposed method is evaluated on a time-dependent
orienteering problem with stochastic weights and time windows. Results show
that our approach effectively learns a strategy that adaptively selects
operators for large neighborhood search, obtaining competitive results compared
to a state-of-the-art machine learning approach while trained with much fewer
observations on small-sized problem instances
Network level bridges maintenance planning using Multi-Attribute Utility Theory
Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers