标题：《Multistability Analysis, Coexisting Multiple Attractors, and FPGA Implementation of Yu–Wang Four-Wing Chaotic System》

时间：2020.9.29 晚上7.00

地点：理科楼B311

汇报人：刘莉

摘要：

  In this paper, we further study the dynamic characteristics of the Yu–Wang chaotic system obtained by Yu and Wang in 2012. ,e system can show a four-wing chaotic attractor in any direction, including all 3D spaces and 2D planes. For this reason, our interest is focused on multistability generation and chaotic FPGA implementation. ,e stability analysis, bifurcation diagram, basin of attraction, and Lyapunov exponent spectrum are given as the methods to analyze the dynamic behavior of this system. ,e analyses show that each system parameter has different coexistence phenomena including coexisting chaotic, coexisting stable node, and coexisting limit cycle. Some remarkable features of the system are that it can generate transient one-wing chaos, transient two-wing chaos, and offset boosting. ,ese phenomena have not been found in previous studies of the Yu–Wang chaotic system, so they are worth sharing. ,en, the RK4 algorithm of the Verilog 32-bit floating-point standard format is used to realize the autonomous multistable 4D Yu–Wang chaotic system on FPGA, so that it can be applied in embedded engineering based on chaos. Experiments show that the maximum operating frequency of the Yu–Wang chaotic oscillator designed based on FPGA is 161.212 MHz.

录取期刊：Mathematical Problems in Engineering 

标题：《LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things》

时间：2020.9.29 晚上7.00

地点：理科楼B311

汇报人：唐杨宁

摘要：

Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. More and more works start to apply natural language processing (NLP)

methods, and in particular word2vec, in the log feature extraction. Word2vec can extract the relevance between words and vectorize the words. However, the computing cost of training word2vec is high. Anomalies in logs are dependent on not only an individual log message but also on the log message sequence. Therefore, the vector of words from word2vec can not be used directly, which needs to be transformed into the vector of log events and further transformed into the vector of log sequences. To reduce computational cost and avoid multiple transformations, in this paper, we propose an offline feature extraction model, named LogEvent2vec, which takes the log event as input of word2vec to extract the relevance between log events and vectorize log events directly. LogEvent2vec can work with any coordinate transformation methods and anomaly detection models. After getting the log event vector, we transform log event vector to log sequence vector by bary or tf-idf and three kinds of supervised models (Random Forests, Naive Bayes, and Neural Networks) are trained to detect the anomalies. We have conducted extensive experiments on a real public log dataset from BlueGene/L (BGL). The experimental results demonstrate that LogEvent2vec can significantly reduce computational time by 30 times and improve accuracy, comparing with word2vec. LogEvent2vec with bary and Random Forest can achieve the best F1-score and LogEvent2vec with tf-idf and Naïve Bayes needs the least computational time.

录取期刊：Sensors

标题：《Revenue Optimizatio-n of a UAV-Fog Collaborativ-e Framework  for  Remote Data Collection  Services》

时间：2020.9.29 晚上7.00

地点：理科楼B311

汇报人：胡群钦

摘要：

Unmanned aerial vehicles (UAVs) can provide remote data collection services with quality of service guarantees. The typical application fields include geographic information systems, such as topological survey and natural disasters and hazards monitoring. In the bad geographic environment, wireless communication performance of UAVs cannot be guaranteed. Therefore, the efficiency of remote data collection cannot be guaranteed. This paper proposes a collaborative framework of UAVs and fog computing for remote data collection. Our goal is to maximize the revenue of UAVs with the support of fog computing, so we need to find the optimal computation resources allocation and task assignment scheme. This is a mixed integer nonlinear programming problem. The block coordinate descent method is used to solve this problem, which allows the original problem to be divided into the optimal task assignment sub-problem and the optimal computation resource allocation sub-problem. The greedy algorithm, heuristic algorithm and brute force algorithm are proposed to solve the optimal task assignment sub-problem. The convex optimization analysis method is used to solve the optimal resource allocation sub-problem. The genetic algorithm is used as a benchmark to compare with the heuristic-based block coordinate descent algorithm. The numerical simulation and network simulator based-simulation results show that the proposed UAV-Fog collaborative data collection problem can be efficiently solved by the block coordinate descent algorithm based on the heuristic strategy.

录取期刊：Access