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 method of stereoscopic view generation by image-based rendering in wide outdoor environments. The stereoscopic view can be generated from an omnidirectional image sequence by a light field rendering approach which generates a novel view image from a set of images. The conventional methods of novel view generation have a problem such that the generated image is distorted because the image is composed of parts of several omnidirectional images captured at different points. To overcome this problem, we have to consider the distances between the novel viewpoint and observed real objects in the rendering process. In the proposed method, in order to reduce the image distortion, stereoscopic images are generated considering depth values estimated by dynamic programming (DP) matching using the images that are observed from different points and contain the same ray information in the real world. In experiments, stereoscopic images in wide outdoor environments are generated and displayed.

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Novel stereoscopic view generation by image-based rendering coordinated with depth information

This study proposes an artificial hummingbird algorithm (AHA) for energy management (EM) for optimal operation of a microgrid (MG), including conventional sources and renewable energy sources (RES), with an incentive-based demand response (DR). Due to the stochastic nature of solar and wind output power and the uncertainty of prices and load, a probabilistic EM with hybrid AHA and point estimation method (PEM) is proposed to model this uncertainty by utilizing the normal and Weibull distribution functions. The PEM method is considered a good tool for handling stochastic EM problems. It achieves good results using the same procedures used with the deterministic problems while maintaining low computational efforts. The proposed AHA technique is employed to solve a deterministic incentive DR program, with the goal of reducing the overall cost, which includes the cost of conventional generator fuel and the cost of power transaction with the main grid while taking into account the load demand. Two different case studies are tested. The simulation results of the proposed AHA is compared with the results of well-known metaheuristic algorithms to demonstrate its efficacy. According to AHA’s results, a total reduction of energy consumption by 104 KWh for the first case study and 2677 MWh for the second case study is achieved while achieving the lowest overall operating cost. The results demonstrate that the AHA is adequate for tackling the EM problem. Then, to examine the effect of uncertainty on the MG state, a probabilistic EM problem is solved using AHA-PEM.

/pdf/developing-an-artificial-hummingbird-algorithm-for-2tl9ahnh.pdf

Developing an Artificial Hummingbird Algorithm for Probabilistic Energy Management of Microgrids Considering Demand Response

This paper proposes a novel method for generating arbitrary stereoscopic view from multiple omni directional image sequences. Although conventional methods for arbitrary view generation with an image-based rendering approach can create binocular views, positions and directions of viewpoints for stereoscopic vision are limited to a small range. In this research, we attempt to generate arbitrary stereoscopic views from omni directional image sequences that are captured in various multiple paths. To generate a high-quality stereoscopic view from a number of images captured at various viewpoints, appropriate ray information needs to be selected. In this paper, appropriate ray information is selected from a number of omni directional images using a penalty function expressed as ray similarity. In experiments, we show the validity of this penalty function by generating stereoscopic view from multiple real image sequences.

/pdf/arbitrary-stereoscopic-view-generation-using-multiple-22z6guumn9.pdf

Arbitrary Stereoscopic View Generation Using Multiple Omnidirectional Image Sequences

Toward a really useful navigation system, utilizing spherical panoramic photos with maps like Google Street View is efficient. Users expect the system to be available in all areas they go. Conventional shooting methods obtain the shot position from GPS sensor. However, indoor areas are out of GPS range. Furthermore, most urban public indoor areas are crowded with pedestrians. Even if we blur the pedestrians in a photo, the photos with blurring are not useful for scenic information. Thus, we propose a method which simultaneously subtracts pedestrians based on background subtraction method and generates location metadata by manually input from maps. Using these methods, we achieved an underground panoramic view system which displays no pedestrians.

Pano UMECHIKA: A Crowded Underground City Panoramic View System

Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of vehicles. Existing systems largely depend on texture and motion features. Such systems face several challenges, including illumination changes caused by variations in weather conditions, complexity of scenes, vehicle occlusion, and the ambiguity of stopped vehicles. To overcome these issues, this article proposes a rapid and reliable traffic congestion detection method based on the modeling of video dynamics using deep residual learning and motion trajectories. The proposed method efficiently uses both motion and deep texture features to overcome the limitations of existing methods. Unlike other methods that simply extract texture features from a single frame, we use an efficient representation learning method to capture the latent structures in traffic videos by modeling the evolution of texture features. This representation yields a noticeable improvement in detection results under various weather conditions. Regarding motion features, we propose an algorithm to distinguish stopped vehicles and background objects, whereas most existing motion-based approaches fail to address this issue. Both types of obtained features are used to construct an ensemble classification model based on the support vector machine algorithm. Two benchmark datasets are considered to demonstrate the robustness of the proposed method: the UCSD dataset and NU1 video dataset. The proposed method achieves competitive results (97.64% accuracy) when compared to state-of-the-art methods.

/pdf/reliable-and-rapid-traffic-congestion-detection-approach-2s6ldilu8g.pdf

Maiya Hori

Papers

Novel stereoscopic view generation by image-based rendering coordinated with depth information

Developing an Artificial Hummingbird Algorithm for Probabilistic Energy Management of Microgrids Considering Demand Response

Arbitrary Stereoscopic View Generation Using Multiple Omnidirectional Image Sequences

Pano UMECHIKA: A Crowded Underground City Panoramic View System

Reliable and Rapid Traffic Congestion Detection Approach Based on Deep Residual Learning and Motion Trajectories