Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient’s status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients’ streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.     

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources”). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.     

The min-dist location selection and facility replacement queries

We propose and study a new type of location optimization problem, the min-dist location selection problem: given a set of clients and a set of existing facilities, we select a location from a given set of potential locations for establishing a new facility, so that the average distance between a client and her nearest facility is minimized. The problem has a wide range of applications in urban development simulation, massively multiplayer online games, and decision support systems. We also investigate a variant of the problem, where we consider replacing (instead of adding) a facility while achieving the same optimization goal. We call this variant the min-dist facility replacement problem. We explore two common approaches to location optimization problems and present methods based on those approaches for solving the min-dist location selection problem. However, those methods either need to maintain an extra index or fall short in efficiency. To address their drawbacks, we propose a novel method (named MND), which has very close performance to the fastest method but does not need an extra index. We then utilize the key idea behind MND to approach the min-dist facility replacement problem, which results in two algorithms names MSND and RID. We provide a detailed comparative cost analysis and conduct extensive experiments on the various algorithms. The results show that MND and RID outperform their competitors by orders of magnitude.     

An FAR-SW based approach for webpage information extraction

Automatically identifying and extracting the target information of a webpage, especially main text, is a critical task in many web content analysis applications, such as information retrieval and automated screen reading. However, compared with typical plain texts, the structures of information on the web are extremely complex and have no single fixed template or layout. On the other hand, the amount of presentation elements on web pages, such as dynamic navigational menus, flashing logos, and a multitude of ad blocks, has increased rapidly in the past decade. In this paper, we have proposed a statistics-based approach that integrates the concept of fuzzy association rules (FAR) with that of sliding window (SW) to efficiently extract the main text content from web pages. Our approach involves two separate stages. In Stage 1, the original HTML source is pre-processed and features are extracted for every line of text; then, a supervised learning is performed to detect fuzzy association rules in training web pages. In Stage 2, necessary HTML source preprocessing and text line feature extraction are conducted the same way as that of Stage 1, after which each text line is tested whether it belongs to the main text by extracted fuzzy association rules. Next, a sliding window is applied to segment the web page into several potential topical blocks. Finally, a simple selection algorithm is utilized to select those important blocks that are then united as the detected topical region (main texts). Experimental results on real world data show that the efficiency and accuracy of our approach are better than existing Document Object Model (DOM)-based and Vision-based approaches.     

Social media assimilation in firms: Investigating the roles of absorptive capacity and institutional pressures

Firms are increasingly employing social media to manage relationships with partner organizations, yet the role of institutional pressures in social media assimilation has not been studied. We investigate social media assimilation in firms using a model that combines the two theoretical streams of IT adoption: organizational innovation and institutional theory. The study uses a composite view of absorptive capacity that includes both previous experience with similar technology and the general ability to learn and exploit new technologies. We find that institutional pressures are an important antecedent to absorptive capacity, an important measure of organizational learning capability. The paper augments theory in finding the role and limits of institutional pressures. Institutional pressures are found to have no direct effect on social media assimilation but to impact absorptive capacity, which mediates its influence on assimilation.     

A hybrid clustering algorithm based on PSO with dynamic crossover

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in $k$ -means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into $k$ -means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of $k$ -means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance.     

MPaaS: Mobility prediction as a service in telecom cloud

Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.     

Algebraic operations in fuzzy object-oriented databases

The fuzzy object-oriented databases have been proposed to meet the need of dealing with fuzzy as well as complex objects. In this paper, we present a formal fuzzy object-oriented database model. Based on the semantic measure of fuzzy data, we first identify two kinds of fuzzy object redundancies, which are inclusion redundancy and equivalence redundancy, and then define three kinds of merging operation for redundancy removal. On the basis, we define some fuzzy algebraic operations for fuzzy classes and fuzzy objects. Finally, in the paper, we discuss fuzzy querying strategies and give the form of SQL-like fuzzy querying for the fuzzy object-oriented databases.     

The removal of dichloromethane from atmospheric pressure air streams using plasma-assisted catalysis

Plasma-assisted catalysis was used for the destruction of 500 ppm of dichloromethane, CH₂Cl₂ (DCM), in gas streams of air using a non-thermal, atmospheric pressure plasma utilising a dielectric packed bed. The combination of plasma and catalyst gave improved destruction of DCM. Eight catalysts which including alumina, TiO₂ and various zeolites were investigated with the finding that alumina in a one-stage reactor configuration and TiO₂ and HZSM-5 in two-stage configurations gave the best DCM destructions. The sodium zeolites are capable of reducing by 50% the unwanted NO_x by-products, formed by plasma processing in air. The nature of the catalyst is important in terms of the destruction efficiency, end-product selectivity and NO_x reduction.     

建筑工程机电安装施工技术

机电安装在建筑工程中涉及的专业面非常广泛,学科跨度较大。虽然机电安装有自身的固有特征,但它的通用性很强。分析了机电安装工程的概况、特点和相关技术,研究了机电安装中出现的问题,并提出相应的解决措施。