In order to improve the reusability of existing mobile terminals for condition monitoring systems and reduce system development cost,this paper proposes a design scheme of universal mobile terminals for condition monitoring systems.The principle of the universal mobile terminal is introduced,and the interaction design of it is also explained.The universal realization of the mobile terminal is illustrated in detail,and the universality is verified by applying the mobile terminal to two different monitoring systems of tall buildings and green houses.The results show that the universal mobile terminal can automatically change display interface and display content according to the monitoring requirements of different condition monitoring systems and monitoring objects.It has strong improves the adaptability and universality.
It is very meaningful for software development to identify design patterns automatically from Unified Modeling Language(UML) models.Formalization is the base of automatic identification of design patterns,so a method based on Petri net is proposed to describe design patterns.Conversion rules of design patters to Petri net are defined,and observer pattern is formalized by those rules.Preliminary process of automatic identification of design patterns is given.Analysis results show that this method can describe design patterns graphically.On this basis,automatic identification of design patterns can be achieved by means of the mathematical theory of Petri net.
Aiming at the problem that traditional Wireless Sensor Network(WSN) data compression algorithms cannot take both compression efficiency and data loss into account,a fast and efficient Lossless Adaptive Compression(LAC) algorithm based on adaptive Huffman coding and Golomb-Rice coding is proposed.Hybrid coding of adaptive Huffman and Golomb-Rice is used to solve the problem of variable length and dynamic.Heuristic method is used to simply estimate non-negative Golomb-Rice coding parameters proposed.A rice mapping function is used to transform the Laplace distribution error term so as to approximate the geometric distribution of nonnegative integers,which are used as the input of entropy encoder.Adaptive entropy coding is used to independently compress sampling data block.Experimental results on real environment WSN dataset from SensorScope show that the proposed algorithm acnieves a compression ratio of 4.11 per sample,and can realize power savings of up to 70.61%.Besides,compression performance and compression rate of the proposed algorithm are better than that of S-LZW,LEC and other compression algorithms.
Since various types of information security risk assessment standards are too complex,as an alternative,most enterprises can only choose to do construction in accordance with safety standards in implementation.This always leads to a substantial gap that security measures are not for particular systems and cannot be quickly adjusted according to the changes of the system.To deal with these problems,this paper proposes a risk assessment method with low difficulty of implementation.In this method,Fuzzy Cognitive Map(FCM) is used to capture dependencies between assets FCM reasoning process is used to calculate the value of systemic risks.An application of the method is studied using an example of a mobile office system.Results indicate that the proposed method is efficient and low-cost.It can reflect the risk status of the system promptly and appropriately.
This paper proposes a two-way protocol to authenticate the security of nodes in the wireless sensor network based on clusters.The protocol is composed of lightweight verification based on solution of equation and digital signature verification based on bilinear mapping.The former is based on that it is difficult to solve a univariate equation of higher degree,and the latter is based on properties of bilinear mapping.In this protocol,nodes in the wireless sensor network are pre-constructed by the lightweight verification as a system structure in cluster units.Digital signatures are verified during the exchange of information between the nodes,so as to constitute a secure wireless sensor network system structure.In the process of generating digital signatures,both nodes achieve the exchange of symmetric key to encrypt information exchange.Security analysis results show that the nodes in the wireless sensor network can not only quickly join the cluster but also safely communicate with each other.
In traffic videos,the impact of environment,installation angle of equipment and other factors may cause vehicle occlusion,resulting in the error of vehicle detecting and tracking.Hence,this paper presents a method based on contour feature points to detect and segment the overlapped vehicles.It uses the background difference method to obtain the target areas,detects the contour points of target area by Freeman chain code,and identifies the feature points by chain code pairs.The number of feature points and the occupancy ratio of target area are used to judge the overlapped vehicles.If they are overlapped,convex hull analysis is carried out in the target area to find the optimal segmentation points and segment the overlapped vehicles.Experimental results show that compared with related methods based on concavity analysis and ellipse fitting,this method can segment the overlapped vehicles more precisely and has better adaptability without any prior knowledge except the vehicle shape.
According to the problem that travel service mode and travel demand data sources are limited in current traffic information service, this paper proposes a method for modeling the behavior of a traveler by studying the travel behavior of individual traveler. It designs a Stay Point Recognition Algorithm(SPRA) based on conditional constraint, to construct the model of traveler’s mobile behavior. Finite Stay Point(FSP) clustering algorithm is put forward to eliminate behavior trajectory dissimilarity caused by GPS error. Intelligent Semantic Matching(ISM) algorithm based on Points of Interest(POI) is presented, which can establish a travel mode sequence containing POI. The effectiveness of the above methods is validated by using simulation experiments. Results show that the stay point recognition accuracy can reach 90% after using SPRA and FSP, while ISM algorithm has a higher recall ratio compared with the behavior recognition algorithm based on cosine similarity.