Estimation of User-Indoor Spatial Information Using Deep Neural Networks Selective Ventilation for Living Area Estimated by Deep Neural Network

Cho, Jungrae

doi:10.1109/icce-berlin.2018.8576235

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Deep learning networks (DNN) have been applied to estimate the indoor partial space for an air conditioner to blow air selectively into the main living area of residents. The DNN learns the human body detection saliency map to estimate the living or non-living area of the residents by accumulating sequential predictions (Cho and Lee, 2018).…”

Section: Spatial Predictionmentioning

confidence: 99%

A survey of technologies supporting design of a multimodal interactive robot for military communication

Paul

2023

JDAL

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PurposeThis paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.Design/methodology/approachThis review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.FindingsUsing insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.Research limitations/implicationsMultimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.Practical implicationsA multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.Social implicationsInteraction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.Originality/valueThe objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

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Section: Spatial Predictionmentioning

confidence: 99%

A survey of technologies supporting design of a multimodal interactive robot for military communication

Paul

2023

JDAL

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“…In this research, we propose an efficient air conditioner control method based on the indoor user localization which is obtained from human body detection with spatial learning [20][21][22][23]. The main contribution of this work is a new method to implement an intelligent air conditioning for energy efficiency, which monitors human activity in a living room and adaptively controls airflow based on human locations.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Network Based Ambient Airflow Control through Spatial Learning

2020

Self Cite

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As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than ± 5 ° . In addition, the target temperature can be reached 19.8% faster and the power consumption can be saved up to 20.5% by the time the target temperature is achieved.

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“…In 2006, Huang et al [30] proposed the concept of deep neural networks (DNN). Since then, deep learning (DL) has gradually become the most popular method in the field of machine learning [31]- [39]. In recent years, deep learning methods and deep learning platforms have developed rapidly.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method for Short-Term Time Series Forecasting Based on EEMD

Yang

2020

IEEE Access

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To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several prediction methods. In particular, much research has been done on the selection of experimental data because representative time series data can better test the effectiveness and practicability of the prediction method. Based on the idea of divide and conquer of complex problems and the strategy of continuous optimization of machine learning, we proposed the prediction methods of LSTM-TFE, LR-TFE, and BR-TFE combined the EEMD, LSTM, LR, and BR methods in this paper. These methods use EEMD to decompose complex time series into several relatively milder, more regular and stable subsequences. Then the prediction model of each subsequence based on machine learning is carried out by using the LSTM, LR, or BR methods. We use these prediction models to predict the value of each subsequence. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series. To verify the proposed method comprehensively, we select three representative time series data to test this paper. From the experimental results, we found that the proposed method has a good effect. INDEX TERMS LSTM, short-term forecasting, EEMD, price prediction, time series.

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Estimation of User-Indoor Spatial Information Using Deep Neural Networks Selective Ventilation for Living Area Estimated by Deep Neural Network

Cited by 3 publications

References 6 publications

A survey of technologies supporting design of a multimodal interactive robot for military communication

A survey of technologies supporting design of a multimodal interactive robot for military communication

Deep Neural Network Based Ambient Airflow Control through Spatial Learning

Hybrid Method for Short-Term Time Series Forecasting Based on EEMD

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