Communication without words

Technological innovations to assess and include the human dimension in the building-performance loop: A review

Statistical analysis and control of dynamic systems

We show that the seismicity rate increase in the Kanto region around Tokyo following the 2011 Tohoku-Oki earthquake (M w9.0) was well correlated with the static increases in the Coulomb failure function ( ∆CFF) transferred from the Tohoku-Oki earthquake sequence. Because earthquakes in the Kanto region exhibit various focal mechanisms, the receiver faults for the ∆CFF were assumed to be reliable focal mechanism solutions of ̃3,000 earthquakes compiled from three networks (F-net, JMA network, and MeSO-net). The histograms of ∆CFF showed that more events in the postseismic period had positive ∆CFF values than those in the preseismic period (2008 April 1 2011 March 10). Among the 928 receiver faults showing the significant ∆CFF with absolute values≥ 0.1 bars in the preseismic period, 717 receiver faults (77.3 %) indicated positive ∆CFF. On the contrary, 1,334 (88.2 %) out of 1,513 receiver faults indicated positive ∆CFF in the postseismic period. We confirmed that the result is similar for the longer preseismic period, between 1997 October 1 and 2011 March 10. To test the significance of the difference in the distribution of ∆CFF between preseismic and postseismic periods, we used a Monte Carlo method with bootstrap resampling. As a result, the ratio of positive ∆CFF randomly resampled from∆CFF values in the preseismic period never exceeded 83.1%, even after 10,000 iterations. This supports the findings of Toda & Stein [2013]; however, our calculation is more reliable than theirs because we used a much larger number of focal mechanisms compiled from the three networks. It also proves that the static stress changes transferred from the Tohoku-Oki earthquake sequence are responsible for the changes in the seismicity rate in the Kanto region. Earthquakes of focal mechanisms with positive ∆CFF values drastically increased, while those with negative ∆CFFs showed no obvious changes except for immediately after the mainshock. This fault-dependent seismicity rate change strongly supports the contribution of the Coulomb stress transferred from the Tohoku-Oki sequence to the seismicity rate change in the Kanto region. Immediately following the mainshock, earthquakes of all types of focal mechanisms were activated, but the increased seismicity rate of earthquakes with negative ∆CFFs returned to the background level within a few months. This suggests that there might be other contributing factors to the seismicity rate change such as dynamic stress triggering or pore-fluid pressure changes.

Correlation between Coulomb stress imparted by the 2011 Tohoku-Oki earthquake and seismicity rate change in Kanto, Japan

Anomaly detection in home power monitoring can be categorized into two main types: detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the daily activities of residents. Focusing on the application and practicality of anomaly detection, we propose sample efficient home power anomaly detection (SEPAD) with improved monitoring performance in terms of electricity usage as well as changes in the daily living activities of residents via provision of detailed feedback. SEPAD consists of two classifiers: an appliance pattern matching classifier (APMC) and an energy consumption habit classifier (ECHC). The APMC uses a single-source separation framework based on a semi-supervised support vector machine (semi-SVM) model. This semi-supervised learning method requires only a small amount of labeled data to achieve high accuracy in near real time and is a sample efficient detection method. The hidden Markov model (HMM)-based ECHC improves the rationality of SEPAD by providing anomaly detection functionality with respect to the daily activities of householders, especially the elderly and residents in developing areas. When SEPAD detects the appearance of an unknown pattern or known patterns contrary to the household’s electricity usage habits, it triggers an alarm. SEPAD was applied to monitor power consumption data from Mkalama, a rural area in Tanzania with 52 households containing nearly 150 occupants connected to a solar powered off-grid network. The results of the practical test demonstrate the high accuracy and practicality of the proposed method.

Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

This paper describes a navigation system that is guided by a CG avatar using augmented reality AR technology. Some existing conventional AR navigation systems use arrows for route guidance. However, the positions to which the arrows point can be unclear because the actual scale of the arrow is unknown. In contrast, a navigation process conducted by a person indicates the routes clearly. In addition, this process offers a sense of safety with its expectation of arrival at the required destination, because the user can reach the destination as long as he/she follows the navigator. Moreover, the user can communicate easily with the navigator. In this research, we construct an AR navigation system using a CG avatar to perform interactively in place of a real person.

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AR Navigation System Using Interaction with a CG Avatar

We propose a method of energy management aimed at reducing the emission of carbon dioxide by changing people’s behavior in small and medium-sized electricity communities. In the conventional energy management system, a power peak is cut and shifted mainly using solar power generation and batteries. In this research, a power peak is cut and shifted by controlling the power demand. The power demand for each facility in small communities is controlled by changing crowd behavior. In experiments, models for predicting power demand according to crowd congestion are constructed for each facility and the accuracies of prediction are verified.

Simulation of Energy Management by Controlling Crowd Behavior

This paper proposes a new method of early change detection for people flow analysis. Some conventional methods often focus on a single location (spot) to demonstrate how the number of people changes over time. In contrast, our proposed method takes into account the links between the spots to grasp a foretaste of congestion of a specific spot as early as possible. The main advantage of the proposed method is that it not only describes the characteristics of each spot, but also the relationships among spots, i.e., whether the connectivities are strong/weak. We introduce an idea of PageRank, which is based on a centrality of graph theory and extend that idea to represent the amount of people flow among spots. We call the extended method "SpotRank". SpotRank assigns an importance score to each spot. The score of a particular spot is calculated by the number of paths and the amount of people flow from other spots. Therefore, the more paths and people flow, the importance score (ranking) increases. The proposed method begins with the calculation of SpotRank every 10 min, followed by change detection, i.e., how much the ranking changes over time. In our experiments, we measured people flow using Wi-Fi packet sensors for a period of over 16 weeks. We confirmed the effectiveness of the proposed method, which successfully grasped a foretaste of congestion at a restaurant in our university.

Early Change Detection Based on SpotRank

We assume that the forecast error follows a probability distribution which is symmetric and monotonically non-increasing on non-negative real numbers, and if there is a mismatch between observed and predicted value, then we suffer a loss. Under the assumptions, we solve a minimization problem with an asymmetric loss function. In addition, we give an inequality for the variance of the loss.

Inequality for the variance of an asymmetric loss

We propose an emotion transmission system using a cellular phone-type teleoperated robot with a mobile projector. Elfoid has a soft exterior that provides the look and feel of human skin and is designed to transmit a speaker’s presence to their communication partner using a camera and microphone. To transmit the speaker’s presence, Elfoid transmits not only the voice of the speaker but also their facial expression as captured by the camera. In this research, facial expressions are recognized by a machine learning technique. Elfoid cannot, however, physically display facial expressions because of its compactness and a lack of sufficiently small actuator motors. The recognized facial expressions are displayed using a mobile projector installed in Elfoid’s head to convey emotions. We build a prototype system and experimentally evaluate its subjective usability.

/pdf/emotion-transmission-system-using-a-cellular-phone-type-53tblmupvw.pdf

Maiya Hori

Papers

AR Navigation System Using Interaction with a CG Avatar

Simulation of Energy Management by Controlling Crowd Behavior

Early Change Detection Based on SpotRank

Inequality for the variance of an asymmetric loss

Emotion Transmission System Using a Cellular Phone-Type Teleoperated Robot with a Mobile Projector