Xiaoxiao Dai

Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.

Fall Detection Based on Sequential Modeling of Radar Signal Time-Frequency Features

Falls are one of the greatest threats to elderly health as they carry out their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be rendered. Radar is an effective non-intrusive sensing modality which is well suited for this purpose. It can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. In this paper, we use micro-Doppler features in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition for feature extraction and fall detection. The extracted features include the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, we use the extracted signal features for training and testing hidden Markov models and support vector machines indifferent falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

Image-Based Fall Detection with Human Posture Sequence Modeling

In this paper, an image-based method is presented for fall detection using statistical human posture sequence modeling. Specifically, a series of laboratory simulated falls and activities of daily living (ADLs) are performed and recorded by a Kinect sensor as training video data. The skeleton view of a human body in these video recordings is extracted using the Kinect for Windows SDK. Hidden Markov Models are used for modeling the fall posture sequences and distinguishing different fall activities and ADLs. Our experimental results demonstrate an average fall recognition rate above 80% and the capability of early warning for falls.

Prediction of a bed-exit motion: Multi-modal sensing approach and incorporation of biomechanical knowledge

This paper aims to answer the following questions: 1) How to detect and predict a bed-exit movement, and 2) How early a bed-exit movement can be predicted before it actually occurs. To achieve the above goals we consider the following sensing modalities for observing the human motion during a bed-exit: RGB images, depth images and radio frequency (RF) sensing. Using the measurements from the aforementioned sensing modalities, we investigate different approaches to infer information on the human motion. Specifically, motion history images are extracted from the RGB-Depth images for motion classification. Depth images complement the analysis with the lost range information of the two dimensional RGB images, which enables three dimensional human motion analysis. The combination of RGB and depth images significantly enhances the performance of motion recognition. A RF sensor, a ultrawideband radar in this research work, is used for performance improvement and for detecting human motion in the cases where image sensors lose the vision.

Social energy: mining energy from the society

The inherent nature of energy, i.e., physicality, sociality and informatization, implies the inevitable and intensive interaction between energy systems and social systems. From this perspective, we define U+201C social energy U+201D as a complex sociotechnical system of energy systems, social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions. The recent advancement in intelligent technology, including artificial intelligence and machine learning technologies, sensing and communication in Internet of Things technologies, and massive high performance computing and extreme-scale data analytics technologies, enables the possibility of substantial advancement in socio-technical system optimization, scheduling, control and management. In this paper, we provide a discussion on the nature of energy, and then propose the concept and intention of social energy systems for electrical power. A general methodology of establishing and investigating social energy is proposed, which is based on the ACP approach, i.e., U+201C artificial systems U+201D U+0028 A U+0029, U+201C computational experiments U+201D U+0028 C U+0029 and U+201C parallel execution U+201D U+0028 P U+0029, and parallel system methodology. A case study on the University of Denver U+0028 DU U+0029 campus grid is provided and studied to demonstrate the social energy concept. In the concluding remarks, we discuss the technical pathway, in both social and nature sciences, to social energy, and our vision on its future.

Ultra-wideband radar based human body landmark detection and tracking with biomedical constraints for human motion measuring

In this manuscript, we propose and investigate a methodology for detecting and tracking human body landmarks using ultra-wideband (UWB) radars. The detection of multiple human body landmarks (HBLs) is achieved by motion target indication techniques, and the multi-HBL tracking is accomplished by a novel iterative convex optimization based approach with considerations of biomechanics constraints. Specifically, the radar signals returned from radio frequency (RF) reflective markers attached to the HBLs are extracted and processed. Then moving target indication (MTI) and constant false alarm rate (CFAR) detection techniques are then used for detecting the reflectors. The data association (DA) is then applied to validate and relate the detection results to target landmarks for generating range measurements. The range measurements are used for a convex optimization based sequential estimation algorithm to sequentially estimate the accurate marker locations. It is noted that the proposed optimization based sequential estimation is able to incorporate biomechanical constraints. In our field experiment, two RF reflective markers are attached to the wrist and elbow of one human arm for reflecting radar signals. It is demonstrated that detection and tracking of the moving trajectories of two markers are feasible and successfully achieved, and thus, the human arm motion is accurately measured using one UWB radar.

Composite socio-technical systems: A method for social energy systems

In order to model and study the interactions between social on technical systems, a systemic method, namely the composite socio-technical systems (CSTS), is proposed to incorporate social systems, technical systems and the interaction mechanism between them. A case study on University of Denver (DU) campus grid is presented in paper to demonstrate the application of the proposed method. In the case study, the social system, technical system, and the interaction mechanism are defined and modelled within the framework of CSTS. Distributed and centralized control and management schemes are investigated, respectively, and numerical results verifies the feasibility and performance of the proposed composite system method.

Distribution locational real-time pricing based smart building control and management

This paper proposes an real-virtual parallel computing scheme for smart building operations aiming at augmenting overall social welfare. The University of Denvers campus power grid and Ritchie fitness center is used for demonstrating the proposed approach. An artificial virtual system is built in parallel to the real physical system to evaluate the overall social cost of the building operation based on the social science based working productivity model, numerical experiment based building energy consumption model and the power system based real-time pricing mechanism. Through interactive feedback exchanged between the real and virtual system, enlarged social welfare, including monetary cost reduction and energy saving, as well as working productivity improvements, can be achieved.

Causality graph learning on cortical information flow in Parkinson's disease patients during behaviour tests

Electroencephalographs (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this paper is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinsons Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include motor and phonemic fluency. This method uses verbal generation skills, activating different Brocas areas of the Brodmanns areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI).