Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

Silva, Daswin De; Sierla, Seppo; Alahakoon, Damminda; Osipov, Evgeny; Yu, Xinghuo; Vyatkin, Valeriy

doi:10.1109/mie.2019.2952165

Cited by 56 publications

(18 citation statements)

References 126 publications

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“…Some highlights from our recent work are Bunji, an empathic chatbot for mental health support; 12 solar nowcasting for optimal renewable energy generation; 13 robust multi-step predictor for energy markets; 14 unsupervised learning with vector symbolic architectures; 15 emotions of COVID-19 from self-reported information; 16 machine learning for online cancer support; 17 , 18 self-building AI for smart cities; 19 intelligent driver behavior change detection; 20 an incremental learning platform for smart traffic management; 21 and a reference architecture for industrial applications of AI. 22 We anticipate that our contribution will create awareness, instill knowledge, and stimulate discussion and debate that will inform research, applications, and policy developments of AI for humanity.…”

Section: Resultsmentioning

confidence: 95%

An artificial intelligence life cycle: From conception to production

Silva

Alahakoon

2022

Patterns

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Section: Resultsmentioning

confidence: 95%

An artificial intelligence life cycle: From conception to production

Silva

Alahakoon

2022

Patterns

Self Cite

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“…The proliferation of voice assistant chatbots such as Siri, Alexa, Cortana, and Google [ 34 ], as well as the numerous chatbot functions in online retail has familiarised a majority of modern society with the utility, engagement, and operation of a chatbot. For instance, in industrial settings, chatbots are used to provide information, instructions, detect fatigue, and address exceptions [ 35 , 36 ], while in healthcare, chatbots have been used for automated post-treatment communications and support groups, counselling, and healthcare service administrative support [ 37 , 38 ]. However, most chatbots are designed using Frequently Asked Questions (FAQs) for information provision or process specific in executing a well-defined repetitive or sequential series of tasks via conversational inputs.…”

Section: Related Workmentioning

confidence: 99%

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Rathnayaka

Mills

Burnett

et al. 2022

Sensors

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Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

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“…De Silva et al [49] propose a machine learning architecture for exploiting distributed energy resources in various energy markets. Forecasts of market prices and energy resource availability are fed into a bidding optimizer.…”

Section: Reinforcement Learning Applications For Batteriesmentioning

confidence: 99%

Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach

et al. 2022

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Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics-based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.

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Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

Cited by 56 publications

References 126 publications

An artificial intelligence life cycle: From conception to production

An artificial intelligence life cycle: From conception to production

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach

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