Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Application of six sigma methodology to reduce defects of a grinding process

Six Sigma is a data-driven leadership approach using specific tools and methodologies that lead to fact-based decision making. This paper deals with the application of the Six Sigma methodology in reducing defects in a fine grinding process of an automotive company in India. The DMAIC (Define–Measure–Analyse–Improve–Control) approach has been followed here to solve the underlying problem of reducing process variation and improving the process yield. This paper explores how a manufacturing process can use a systematic methodology to move towards world-class quality level. The application of the Six Sigma methodology resulted in reduction of defects in the fine grinding process from 16.6 to 1.19%. The DMAIC methodology has had a significant financial impact on the profitability of the company in terms of reduction in scrap cost, man-hour saving on rework and increased output. A saving of approximately US$2.4 million per annum was reported from this project

Multimodal hybrid powerplant for unmanned aerial systems (UAS) robotics

Most UAS propulsion systems currently utilize either Internal Combustion Engines (ICE) or Electric Motor (EM) prime movers. ICE are favoured for aircraft use due to the superior energy density of fuel compared to batteries required for EM, however EM have several significant advantages. A major advantage of EM is that they are inherently self starting have predictable response characteristics and well developed electronic control systems. EMs are thus very easy to adapt to automatic control, whereas ICE have more complex control response and an auxiliary starting motor is required for automated starting. This paper presents a technique for determining the performance, feasibility and effectiveness of powerplant hybridisation for small UAS. A Hybrid Powerplant offers the possibility of a radical improvement in the autonomy of the aircraft for various tasks without sacrificing payload range or endurance capability. In this work a prototype Aircraft Hybrid Powerplant (AHP) was designed, constructed and tested. It is shown that an additional 35% continuous thrust power can be supplied from the hybrid system with an overall weight penalty of 5%, for a given UAS. Dynamometer and windtunnel results were obtained to validate theoretical propulsion load curves. Using measured powerplant data and an assumed baseline airframe performance characteristic, theoretical endurance comparisons between hybrid and non-hybrid powerplants were determined. A flight dynamic model for the AHP was developed and validated for the purposes of operational scenario analysis. Through this simulation it is shown that climb rates can be improved by 56% and endurance increased by 13%. The advantages of implementing a hybrid powerplant have been baselined in terms of payload range and endurance. Having satisfied these parameters, a whole new set of operational possibilities arises which cannot be performed by non-self-starting ICE only powered aircraft. A variety of autonomous robotic aircraft tasks enabled by the hybrid powerplant is discussed

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Monitoring Cluster on Online Compiler with Ganglia

Ganglia is an open source monitoring system for high performance computing (HPC) that collect both a whole cluster and every nodes status and report to the user. We use Ganglia to monitor our spasi.informatika.lipi.go.id (SPASI), a customized-fedora10-based cluster, for our cluster online compiler, CLAW (cluster access through web). Our experience on using Ganglia shows that Ganglia has a capability to view our cluster status and allow us to track them

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment