Research

Author: Hang Yuan and C.-C. Jay Kuo

Large-scale video-sharing services, such as YouTube, have come to dominate the Internet traffic. To meet the rapid growth in both data and user demand in VSS, multi-layer infrastructures with parallel architectures have been deployed. In parallel video servers, each video file is divided into a number of blocks and spread across different disks. By breaking a relatively long and continuous video workload into smaller tasks, the system can serve more concurrent requests. The use of massive data centers for large-scale VSS has led to ever-increasing energy cost. In particular, video servers rely on the storage and bandwidth of the parallel disk system, which is among the heaviest energy consumer. It has been reported that such a storage system typically accounts for 27% of the total energy consumption in data centers.

My research focuses on energy management in this kind of parallel storage systems. In particular, we study how to make the best use of the low power modes in disks and how to optimize the usage of memory cache to improve energy efficiency. The goal is to not only minimize energy consumption but also control the impact of energy saving techniques on service delays. We developed a model that efficiently facilitates the selection of power modes for disks, and extended it to optimize cache utilization. Using the model, we can effectively minimize the energy consumption under different service delay constraints.

Our work can be extended in several areas. First, we are in the process of designing better data placement policies that can improve the performance of the algorithm. Second, we only optimized energy consumption for the idle periods of the disk, which prevented us from achieving more energy saving especially [...]

Author: Sachin Chachada and C.-C. Jay Kuo

Research on Environmental Sound Recognition (ESR) has significantly increased in the past decade. With a growing demand on example-based search such as content-based image and video search, ESR can be instrumental in efficient audio search applications. ESR can be also useful for automatic tagging of audio files with descriptors for keyword-based audio retrieval, robot navigation, home-monitoring system, surveillance, recognition of animal and bird species, etc. Among various types of audio signals, speech and music are two categories that have been extensively studied. In their infancy, ESR algorithms were a mere reflection of speech and music recognition paradigms. However, on account of considerably non-stationary characteristics of environmental sounds, these algorithms proved to be ineffective for large-scale databases. Recent publications have seen an influx of substantial new features and algorithms catering to ESR. However, the problem largely remains unsolved.

Owing to non-stationary characteristics of environmental sounds, recent works have focused on time-frequency features [1-3]. Efforts have also been made to incorporate non-linear classifiers for ESR [4]. A comprehensive coverage of recent developments can be found in [5]. These recently developed features perform well on sounds which exhibit non-stationarity but have to compete with conventional features like Mel-Frequency Cepstral Coefficients (MFCC) for other sounds. A set of features with simplicity of stationary methods and accuracy of non-stationary methods is still a puzzle piece. Moreover, considering the numerous types of environmental sounds, it is hard to fathom a single set of features suitable for all sounds. Another problem with using a single set of features is that different features need different processing schemes, and hence several meaningful combination of features, that would be otherwise functionally complementary to each other, are incompatible in practice. [...]

Author: Martin Gawecki and C.-C. Jay Kuo
This work is part funded by the Pratt &Whitney Institute for Collaborative Engineering, in collaboration with Pratt & Whitney, Korean Airlines, and INHA University.

As a supplement to previous work on vibration and acoustic approaches [1], Gas Path Analysis (GPA) is the study of a gas-turbine jet engine’s temperature, pressure, and rotational parameters with the aim of detecting and diagnosing problems that may occur during flight (see Figure 1), commonly known as the field of Prognostics and Health Management (PHM). General applications of GPA for steady-state engine operation have been used successfully for over two decades in a limited capacity, due to the technological restrictions of on-board computing, costly transmission capabilities, and a lack of comprehensive analysis tools. While this foundational problem is slowly being addressed, even fewer answers exist to the question of whether GPA can be used for the immediate recognition and identification of faults that may occur during engine transients (non-steady-state regions of operation, shown in gray in Figure 2). Our research aims to explore this problem in order to build real-time tools that improve the maintenance workflow and to help develop the next generation of “intelligent engines.”

Using a variety of data created using the state-of-the-art Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) [2, 3, 5], we propose a contextualized combination of state variable and empirical models for the purposes of feature extraction [4], coupled with a machine learning framework for the detection and identification of faults. In essence, we hope to use existing normal (non-fault) signatures of GPA parameters at steady-state as a reference for transient points of operation, in the hopes of finding and classifying abnormal behavior. Figure 3 outlines the feature extraction process [...]