Compact Indexes Based on Core Content in Personal Dataspace Management System

A Personal DataSpace Management System is a platform to manage personal data with heterogeneous data types, in which keyword query is a primary query form for users who know little about the structure of the dataspace. Unlike exploratory queries in web search, a user in a personal dataspace usually has a specific search target and wants to find some known items in mind. To improve result quality in terms of query relevance in a personal dataspace, we propose the concept of compact index in this paper. We refer to the most important and representative semantics from documents as core content, and build compact index on it. We propose algorithm for selecting core content from a document based on semantic analysis, which can process English and Chinese documents uniformly. Furthermore, a software platform named Versatile is introduced for flexible personal data management, in which core content is extracted for building compact indexes and generating query-biased snippet efficiently and accurately. Finally, extensive experiments have been conducted to show the effectiveness and feasibility of compact indexes in personal dataspace management system

A hybrid localization approach in 3D wireless sensor network

Location information acquisition is crucial for many wireless sensor network (WSN) applications. While existing localization approaches mainly focus on 2D plane, the emerging 3D localization brings WSNs closer to reality with much enhanced accuracy. Two types of 3D localization algorithms are mainly used in localization application: the range-based localization and the range-free localization. The range-based localization algorithm has strict requirements on hardware and therefore is costly to implement in practice. The range-free localization algorithm reduces the hardware cost but at the expense of low localization accuracy. On addressing the shortage of both algorithms, in this paper, we develop a novel hybrid localization scheme, which utilizes the range-based attribute RSSI and the range-free attribute hopsize, to achieve accurate yet low-cost 3D localization. As anchor node deployment strategy plays an important role in improving the localization accuracy, an anchor node configuration scheme is also developed in this work by utilizing the MIS (maximal independent set) of a network. With proper anchor node configuration and propagation model selection, using simulations, we show that our proposed algorithm improves the localization accuracy by 38.9% compared with 3D DV-HOP and 52.7% compared with 3D centroid

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Review on recent liquefied natural gas cold energy utilization in power generation cycles

Liquefied natural gas (LNG) needs to be gasified before supplied to the users, and considerable amount of cold energy, about 830 kJ/kg, will be released during this process. Recovery of LNG cold energy bears significance of energy-saving and environmental protection. Among the many ways of using LNG cold energy, power generation is the most effective and suitable one for large-scale applications. Many novel power generation cycles have been designed for utilizing LNG cold energy so far. This paper reviews the recent researches on LNG cold energy utilization in power generation, and discusses 15 novel power generation cycles utilizing LNG cold energy.Cited as: Yu, G., Jia, S., Dai, B. Review on recent liquefied natural gas cold energy utilization in power generation cycles. Advances in Geo-Energy Research, 2018, 2(1): 86-102, doi: 10.26804/ager.2018.01.0