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Based on the contributions of carbon nanostructures and their composited species, great advances in electromagnetic wave interference shielding have been achieved. In this article, recent progress in electromagnetic wave shielding enabled by the synergism of carbon nanostructures and their corresponding composites is discussed encompassing the factors of microstructural defects, filler concentration, filler alignment, filler inherent conductivity and the surrounding temperature. Carbon nanostructures and their composites would energize the advanced electromagnetic wave shielding because of their light weight, high corrosion resistance, excellent thermal, mechanical, and electrical properties, broad absorption frequency bandwidth and cost-effectiveness. In this context of identifying suitable carbon composites that can enhance electromagnetic wave absorption. This review provides updated electromagnetic wave shielding knowledge of carbon nanostructures and their composites as well as their prospects and challenges.

Occupational hearing loss (OHL) is a common and serious health problem, so early hearing hazard signal detection is important in occupational health management. As some people may be more vulnerable to certain specific occupational exposures, it is necessary to provide them better protection by adaptively detecting constrained background based signals from occupational health examination data (OHED). In this paper we propose a bottom-up subspace clustering-based signal detection approach, which consists of three steps: subgroup generation, signal extraction, and signal selection. Bottom-up subspace clustering is utilized to generate background subgroups, which serve as constrained backgrounds in disproportionality analysis. To avoid missing important subgroups, we apply various algorithms and validity indices to construct initial subgroups. As subgroups are constructed by iteratively intersecting existing subgroups, most of them are small in size. As a result, traditional proportional reporting ratio (PRR) will lead to an unacceptable fake positive rate in signal extraction. Here a simulation based approach is proposed for evaluating small samples' disproportionality. Yet, the number of extracted signals remains very large because there are many similar signals. So a hybrid score, which combines the simulation based score and shrinkage PRR, is presented for selecting representative signals. We have analysed an OHED of 2051 records. Experimental results suggest the algorithm can effectively detect potential occupational hearing hazard signals.

Evolutionary K-Means (EKM), which combines K-Means and genetic algorithm, solves K-Means’ initiation problem by selecting parameters automatically through the evolution of partitions. Currently, EKM algorithms usually choose silhouette index as cluster validity index, and they are effective in clustering well-separated clusters. However, their performance of clustering noisy data is often disappointing. On the other hand, clustering stability-based approaches are more robust to noise; yet, they should start intelligently to find some challenging clusters. It is necessary to join EKM with clustering stability-based analysis. In this paper, we present a novel EKM algorithm that uses clustering stability to evaluate partitions. We firstly introduce two weighted aggregated consensus matrices, positive aggregated consensus matrix (PA) and negative aggregated consensus matrix (NA), to store clustering tendency for each pair of instances. Specifically, PA stores the tendency of sharing the same label and NA stores that of having different labels. Based upon the matrices, clusters and partitions can be evaluated from the view of clustering stability. Then, we propose a clustering stability-based EKM algorithm CSEKM that evolves partitions and the aggregated matrices simultaneously. To evaluate the algorithm’s performance, we compare it with an EKM algorithm, two consensus clustering algorithms, a clustering stability-based algorithm and a multi-index-based clustering approach. Experimental results on a series of artificial datasets, two simulated datasets and eight UCI datasets suggest CSEKM is more robust to noise.

Disproportionality based methods are widely used in detecting occupational hazard signals. Yet, deciding on a proper background is hard because there are subgroups vulnerable to given exposures, and in many cases such subgroups can not be defined by job. This paper presents an interactive way to do disproportionality analysis on occupational health examination data. It firstly uses multi-dimensional scaling to project data from a subspace to a two-dimensional space, which is further divided into rectangle cells. Then each cell is served as a background to evaluate disproportionality for potential hazard record(PHR), which is jointly defined by a cell and a company. The PHRs with high disproportionalities are checked interactively to select interesting ones. The method has been used to analyse an occupational hearing loss data, and has found an interesting potential signal for further analysis.

The evolution of molecular cloud(MC) brings about various stars and galaxies. A simple two-stage MC based evolutionary model is proposed to reorganize a word cloud into a galaxy-like cloud. Based on the model, an evolutionary algorithm simulates the moving and colliding of stage-1 MCs, and the merging and collapsing of stage-2 MCs. In addition, it considers long range force based disturbance, which often starts the formation of stars. Our experiment suggests that it produces galaxy-like word clouds for users to choose from.

We propose a new immune binary particle swarm optimization algorithm (IBPSO) to solve the problem of instance selection for time series classification, whose objective is to find out the smallest instance combination with maximal classification accuracy. The proposed IBPSO is based on the basic binary particle swarm optimization (BPSO) algorithm proposed by Kennedy and Eberhart. Its immune mechanism includes vaccination and immune selection. Vaccination employs the hubness score of time series and the particles' inertance as heuristic information to direct the search process. Immune selection procedure always discards the particle with the worst fitness in the current swarm for preventing the degradation of the swarm. Experimental results on small and medium datasets show that IBPSO outperforms BPSO and deterministic INSIGHT in terms of storage requirement and classification accuracy, and presents better robustness to noise than BPSO. In addition, experimental results on larger datasets indicate that IBPSO has better scalability than BPSO.

In recent years, with the growth of P2P software, the operation mechanism has been much diversified. It is very difficult to classify P2P flow and normal flow based on single testing method. The increasing number of encrypted P2P protocols posed big challenge to testing. This thesis analyzes the identifying method for P2P flow based on port number, payload signature inspection, reverse connection ratio, convection analysis. Finally, these four analysis method is in comparison.

The next generation network (NGN) is driven by business. The business identification and control is meaningful to management of NGN and safety. Aimed at the accuracy and effectiveness of network business identification mechanism, one systematic model is developed with accurate and intelligent identification function. The research on business flow identification technology is conducted. Through testing the identification accuracy has been improved a lot.

Evolutionary K-Means (EKM) is a non-parametric approach proposed to improve K-Means algorithm. Current EKM approaches are ineffective in deciding the correct cluster number of real datasets. This paper uses instance-level constraints to solve this problem and presents a Constrained Silhouette (CS) based algorithm, namely CS-EAC. Firstly CS is defined to combine constraints into the computation of Silhouette Information (SI). Updated from the Fast Evolutionary Algorithm for Clustering algorithm (F-EAC), CS-EAC uses CS instead of SI to guide the genetic operations. Experimental results suggest that CS-EAC is effective in both deciding the correct number of clusters and improving the accuracy of clustering for real datasets.

This paper proposes four novel parallelization methods of a modified Ant Colony Optimization algorithm. The parallelization methods are aiming at finding the optimal segmentation scheme of time series with a low execution time. The series is decomposed into different sub-series firstly, and then each sub-series can be solved by colonies independently, finally merge the solutions of each colony to obtain the full segmentation scheme. According to the synchronization of individuals and colonies, we design four types of dual parallel models, and implement the parallel versions by using OpenMP library on a computing platform with a multi-core processor for time series segmentation. Experiment results suggest that the parallel algorithms can greatly shorten the execution time without reducing the quality of the final solution.

A two-group resource allocation scheme enhanced with a capacity-approaching modulation and coding scheme is proposed to improve the effective transmission bit rate over wireless ad hoc networks. Improvement in the transmission bit rate reduces the end-to-end delay of the selected routing paths. With computationally efficient loading scheme and reduced gap values, the energy is efficiently used to improve the total bit rate.

A new receiver scheme is developed to reduce the end-to-end delay in the ad hoc networks without increasing the energy required. The proposed Pulse Amplitude Modulation–Minimum Mean Square Error (PAM–MMSE) receiver reduces the granularity of the discrete bit values compared to the current multi-code Code-Division Multiple Access (CDMA) network, increasing transmission data rate in an ad hoc network without reducing the network lifetime

This paper presents a time-efficient optimal resource allocation algorithm aiming to maximize the system throughput of the single-user High Speed Downlink Packet Access (HSDPA) deployed in femtocell base station. The system throughput maximization with constrained total power is rst formulated as a constrained integer programming problem. We rst prove that a two-group bit and energy allocation provides the global optimum solution in the system without multipath. We then focus on the use of the two-group allocation method over frequency selective channels. A pre-processing method was used to systematically cluster and remove channels to stop using energies over severely degraded channels with the two-group allocation approach. This improves the system throughput whilst greatly reducing the computation complexity. The proposed twogroup approach with channel removal is suitable for femtocell base station with limited signal processing capability. Accepted version

When using an adaptive modulation and coding scheme over multicode CDMA transmission channels, the High Speed Downlink Packet Access (HSDPA) allocates rates equally over parallel channels to provide a wide range of data rates. However, the HSDPA system uses the radio resources inefficiently and suffers from severe performance loss when encountering inter-symbol interference (ISI) over frequency selective channels. This paper proposes a new extended-receiver model and a novel ISI-suppressing linear equalizer for a two group approach whilst nearly doubling the data rate achieved by the HSDPA systems for a given receiver signal-to-noise ratio.

Vehicle Traveling Data Recorders (VTDRs) have been universally installed on fixed-route intercity buses, and the data collected is growing rapidly. It becomes urgent to utilize VTDR data to give drivers and managers some helpful feedbacks. Two novel representations of VTDR data: Location Based Speed Series (LBSS) and Behavior Sequence (BS) are presented for the analysis of these data. The data gathered directly from VTDR is Time Based Speed Series (TBSS), which defines the speeds at different time points. The fixed intercity route makes it feasible to covert a TBSS to an LBSS, which defines the speeds at different locations. BS is then proposed to extract the dynamic information, such as the speed change pattern at a segment of an LBSS. For converting an LBSS to a BS, a Background Based Symbolization (BBS) approach is presented. Based on the background of an average speed curve, five typical behavior patterns are defined. During the BBS process, each length-fixed segment of an LBSS is labeled as one of the five patterns, and the consequent pattern sequence is a BS. A simple example is presented to illustrate the transformations. The applications of LBSS and BS are illustrated with the analysis of FuQuan Highway data. The LBSS is effective in the evaluation of the driving behavior at Xiangsiling Tunnel. And BS is more suitable in mining sequential patterns than those symbolized by Symbolic Aggregate Approximation (SAX).

This paper studies an efficient radio resource allocation method for the multicode transmission of High-Speed Downlink Packet Access system. The current HSDPA system allocates equal data rates to multiple channels, causing an inefficient use of radio resources in multipath fading channel. To maximize the system throughput subject to limited radio resources, this paper proposes a computing-efficient 2 group allocation method, which assigns two distinct data rates to 2 groups of channels respectively. The two data rates and the division of the channels can be derived iteratively. The 2 group allocation method significantly outperforms the link adaptation method in the HSDPA system in system throughput, especially when the system operates in high SNR region.

This paper describes a novel technique to analyze newly available Vehicle Traveling Data Recorder (VTDR) data from Passenger Transport Company. To help us be more conducive to study, we preprocessed the VTDR data firstly. 1) Algorithm that detects highway range in the VTDR data is proposed. 2) We transformed VTDR data from their original Time based Speed Series (TSS) to Location based Speed Series (LSS), which makes a link between road location and speed. 3) Then, we applied a self-training technique to distinguish two route directions of the VTDR data. After preprocessing, we extracted general speed pattern from the VTDR data. This information greatly improves our understanding on the road traffic influence to speed. In addiction, based on this, novel driver behavior analysis and outlier analysis are given.The technique introduced in this paper utilizes the huge amount of VTDR data to extract useful information, so that enhances transportation safety management.