Quantitative evaluation of intellectual productivity considering depth of concentration

Ueda, Kimi; Shimonaka, Shota; Shimoda, Hiroshi; Ishii, Hirotake; Obayashi, Fumiaki

doi:10.1109/smc.2017.8122699

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Heating Ventilation and Control (HVAC) systems are contributing to the ongoing damage we inflict on our planet. In the United States of America, one of the highest pollution factors is from the industrial and commercial electricity sector of which HVAC units play a major part (Ueda, & Shimonaka, 2017;Edenhofer, et al, 2014). In many environments these systems are left on for extended periods of time or are increased to temperatures which make the room uncomfortable and at times unusable.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning System for Optimised Environmental Control of Consecutive Areas

Perry

Fallon

2019

Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; And Web Based Co

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Building management systems have led to artificially controlled environmental conditions. While new infrastructure will lead to a lower carbon footprint and a better working environment, the costs cannot justify the rewards at this present time. Ambient temperature regulation has the potential to mitigate excessive energy consumption. This work proposes an externally influenced environmental control Artificial Neural Network (ANN) implementation to optimise ambient temperature for a given specific area whether that be internal or external to the building. The approach uses a multi-aspect ANN. Two architectural components are introduced, an Agent ANN (A-ANN) and a Coordinating ANN (C-ANN). The Agent ANNs (A-ANN) are deployed to provide temperature control at the extremities of the open plan area. The A-ANN operates with a degree of autonomy. A Coordinating ANN (C-ANN) considers the optimal ambient temperature of the room and consequently effects the surrounding area(s). These A-ANNs have internal and external factors acting as effectors to the system such as outdoor environmental conditions and internally located effectors such as adjacent rooms. Results are presented which diagnose the effort applied by A-ANN instances in varying environmental conditions both internally and externally.

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Section: Introductionmentioning

confidence: 99%

A Federated Learning System for Optimised Environmental Control of Consecutive Areas

Perry

Fallon

2019

Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; And Web Based Co

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“…The concentration state is also divided into two states which are working state and short-term pause state. Based on this concept, indices indicating the ratio of concentration time (Concentration Time Ratio; CTR) [5] and the depth of concentration (Concentration Depth Index; CDI) [6] were developed.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Relationship Between Intellectual Concentration and Personal Mental Characteristics

Takekawa

Ueda

Ogata

et al. 2018

Human Systems Engineering and Design

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As a proposal of new diagnosis for mental diseases, this study focused on the relationship between intellectual concentration and personal mental characteristics. It is expected that the measurement of concentration characteristics may help the diagnosis of the mental disorders because the mental characteristics such as psychiatric disease, developmental disorder and behavioral feature are supposed to be closely related to their mental activity such as concentration. When analyzing the relationship, the characteristics of concentration are expressed as 36 feature values by analyzing answering time distribution of cognitive task, and the values of concentration were compressed to 5 main factors by principal component analysis. Then the combination of the factors and one of 36 parameters of mental characteristics were given to a decision tree analysis tool.

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An Evaluation Method for Intellectual Concentration Based on Concentration Depth

Ueda

Shimonaka

Shimoda

et al. 2018

Human Systems Engineering and Design

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Quantitative evaluation of intellectual productivity considering depth of concentration

Cited by 4 publications

References 5 publications

A Federated Learning System for Optimised Environmental Control of Consecutive Areas

A Federated Learning System for Optimised Environmental Control of Consecutive Areas

An Experimental Study on Relationship Between Intellectual Concentration and Personal Mental Characteristics

An Evaluation Method for Intellectual Concentration Based on Concentration Depth

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