Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.     

一种指令特性可扩展的汇编器体系结构

对嵌入式处理器的汇编器进行讨论,提出一种指令特性可扩展的汇编器体系结构.在这种体系结构下能有效地加入新的指令或是改进已有指令的功能,并根据此体系结构为汇编器加入两条编译预处理命令.采用该体系结构的汇编器,能够有效降低汇编器的开发维护难度,同时能够增加汇编程序的编写效率.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Cross‐talk acceptability in natural still images for different (auto)stereoscopic display technologies

Abstract— One of the most annoying distortions in (auto)stereoscopic displays is probably cross‐talk, visible as double edges, which is mainly caused by an imperfect separation of left‐ and right‐eye images. For different types of three‐dimensional (3‐D) displays, cross‐talk is caused by different origins, which could result in different levels of perceived image distortion. To evaluate the influence of (auto)stereoscopic display technology on cross‐talk perception, optical measurements and subjective assessments were performed with three different types of 3‐D displays. It is shown with natural still images that the 3‐D display technology with the lowest luminance and contrast level tolerates the highest level of cross‐talk, while still maintaining an acceptable image‐quality level.     

万能式断路器多类别多规格测试的几个关键问题

提出了万能式断路器综合性能检测系统设计中的几个关键技术问题并给出解决方案;为了兼容测试不同类别、不同电流规格的万能式断路器产品,系统将虚拟仪器技术与计算机控制技术相结合,采用三级变换结构和闭环控制方式提高三相大电流发生器的输出精度,设计水平轴向滑动式气动夹具解决不同外形尺寸断路器的不同接线方式问题,软件设计采用分层结构和基于配置文件的设计方法;实际应用表明,这套方案可以满足万能式断路器综合性能检测系统对多类别多规格产品的测试要求.     

基于WEB嵌入式自适应智能控制系统的研究与开发

提出一种基于WEB嵌入式自适应智能控制系统设计方案,提供了各组成模块软硬件的详细设计与具体实现;系统采用主流的S3C2410作为处理器,通过I2C总线实现各硬部件的连接;开发的嵌入式WEB服务器模块,使得用户能够对温度传感器读数进行互联网远程监测和多路数字、模拟量输出智能控制;并且神经网络模块能够自适应地学习和控制温度读数与制冷设备输出数之间的关系。实验结果显示改进后的Hopfield算法的收敛性和记忆性得到了提高,并使该系统具有较大的应用价值。     

Fire detection model in Tibet based on grey-fuzzy neural network algorithm

The fire signals are much weaker in low oxygen concentration and low pressure environment such as Tibet. Fire detectors which were calibrated in correlating standard conditions cannot work well in such condition. This paper presents a synthesis method of GM(1, 1) grey prediction model and adaptive neuro-fuzzy inference system (ANFIS) in advance to detect fire and to make it work in the environment. The theoretical analysis of the algorithm and experimental evaluation in Tibet are presented. In this process, the grey GM(1, 1) predict model can anticipate the development of fire signals without any assumption, thus allowing earlier fire alarm than traditional fire detection equipments, meanwhile, ANFIS can make sure the data processing more accurate to avoid false alarms. This work will supply useful suggestions with the fire detectors design in low ambient pressure and low oxygen concentration such as Tibet, etc.