基于小波核SVM的水声信道盲均衡算法

推导并提出了一种快速收敛的基于小波核支持向量机的恒模盲均衡器(WSVM-CMBE)。该方法以支持向量机为框架,利用信号的恒模特性构造代价函数,采用小波核函数,并自适应调整核函数中的伸缩因子。通过水声信道仿真实验,并与采用高斯核函数的支持向量机恒模盲均衡器(SVM-CMBE)进行比较,结果证明该方法提高了收敛速度。     

A very fast TS/SA algorithm for the job shop scheduling problem

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10–15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.     

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.     

基于上下文的网络监控模型研究与实现

论文针对目前网络的安全威胁提出了一种基于上下文的网络监控（Context-Based Network Monitor,CBNM）模型。该模型能够通过基于通信上下文的协议分析来全面并准确地提取通信过程中的各类信息,并根据制定的安全策略对协议分析得到的结果执行响应操作,从而更有效地防止各种网络安全威胁产生的危害。     

飞行目标红外多光谱高效识别算法

随着新型诱饵的快速发展,在日益复杂的目标环境中探测识别真假目标是红外探测识别系统最难解决的技术问题之一.通过分析天空背景下红外小目标,干扰物,噪音及背景的光谱特性,利用人造飞行目标光谱辐射强度高且相邻波段光谱辐射强度连续性特点,提出了一种以多光谱辐射强度和梯度相组合的目标识别高效算法.就此给出了相应的仿真算例,验证了算法在获取的红外图像信噪比很低,背景高亮度,多个诱饵干扰的条件下也能准确识别目标,具有更强的自适应性,更高的识别率和更为快捷的处理能力.     

一种新的最小二乘支持向量机稀疏化算法

普通的最小二乘支持向量机(LS-SVM)稀疏化算法在处理有些常见的模式识别问题时,随着训练样本的删减,识别率下滑很快,往往达不到稀疏化的目的。针对这种情况,提出了一种新的LS-SVM稀疏化算法来弥补这种不足,从而使得LS-SVM稀疏化算法体系更加完善。将新算法应用到雷达一维距离像的识别中,实验结果证明了新算法的有效性。     

Fast intrusion detection based on a non-negative matrix factorization model

In this paper, we present an efficient fast anomaly intrusion detection model incorporating a large amount of data from various data sources. A novel method based on non-negative matrix factorization (NMF) is presented to profile program and user behaviors of a computer system. A large amount of high-dimensional data is collected in our experiments and divided into smaller data blocks by a specific scheme. The system call data is divided into blocks by processes, while command data is divided into consecutive blocks with a fixed length. The frequencies of individual elements in each block of data are computed and placed column by column as data vectors to construct a matrix representation. NMF is employed to reduce the high-dimensional data vectors and anomaly detection can be realized as a very simple classifier in low dimensions. Experimental results show that the model presented in this paper is promising in terms of detection accuracy, computation efficiency and implementation for fast intrusion detection.     

Performance analysis of a threshold-based discrete-time queue using maximum entropy

The provision of guaranteed QoS for various Internet traffic types has become a challenging problem for researchers. New Internet applications, mostly multimedia-based, require differentiated treatments under certain QoS constraints. Due to a rapid increase in these new services, Internet routers are facing serious traffic congestion problems. This paper presents an approximate analytical performance model in a discrete-time queue, based on closed form expressions using queue threshold, to control the congestion caused by the bursty Internet traffic. The methodology of maximum entropy (ME) has been used to characterize closed form expressions for the state and blocking probabilities. A discrete-time GGeo/GGeo/1/{N₁, N₂} censored queue with finite capacity, N₂, external compound Bernoulli traffic process and generalised geometric transmission times under a first come first serve (FCFS) rule and arrival first (AF) buffer management policy has been used for the solution process. To satisfy the low delay along with high throughput, a threshold, N₁, has been incorporated to slow the arrival process from mean arrival rate λ₁ to λ₂ once the instantaneous queue length has been reached, otherwise the source operates normally. This creates an implicit feedback from the queue to the arrival process. The system can be potentially used as a model for congestion control based on the Random Early Detection (RED) mechanism. Typical numerical experiments have been included to show the credibility of ME solution against simulation for various performance measures and to demonstrate the performance evaluation of the proposed analytical model.     

Carbon Black-Supported Single-Atom Co?N?C as an Efficient Oxygen Reduction Electrocatalyst for H2O2 Production in Acidic Media and Microbial Fuel Cell in Neutral Media

Single metal atom isolated in nitrogen-doped carbon materials (M N C) are effective electrocatalysts for oxygen reduction reaction (ORR), which produces H₂O₂ or H₂O via 2-electron or 4-electron process. However, most of M N C catalysts can only present high selectivity for one product, and the selectivity is usually regulated by complicated structure design. Herein, a carbon black-supported Co N C catalyst (CB@Co N C) is synthesized. Tunable 2-electron/4-electron behavior is realized on CB@Co-N-C by utilizing its H₂O₂ yield dependence on electrolyte pH and catalyst loading. In acidic media with low catalyst loading, CB@Co N C presents excellent mass activity and high selectivity for H₂O₂ production. In flow cell with gas diffusion electrode, a H₂O₂ production rate of 5.04 mol h⁻¹ g⁻¹ is achieved by CB@Co N C on electrolyte circulation mode, and a long-term H₂O₂ production of 200 h is demonstrated on electrolyte non-circulation mode. Meanwhile, CB@Co N C exhibits a dominant 4-electron ORR pathway with high activity and durability in pH neutral media with high catalyst loading. The microbial fuel cell using CB@Co N C as the cathode catalyst shows a peak power density close to that of benchmark Pt/C catalyst.