• Smart Structures and Systems
• /
• 제24권5호
• /
• pp.587-595
• /
• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권8호
• /
• pp.3621-3640
• /
• 2016

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
• /
• pp.117-122
• /
• 2006

Modernized GNSS such as new GPS signals updated GLONASS and coming Galileo promises higher quality and higher reliability for users. Powerful technologies such as Internet, ubiquitous network technology and sensor network has been used to promote a safe and more secure lifestyle. This report describes experimental trials to combine these technologies namely GPS and Sensor Network into a high-performance system. GPS is used to enlarge the communication range, resolving the service area limitations, as a wider service area is required at shore areas compared to urban area. GPS position datum is also used as primary network routing information to get practical Sensor Network. Another application is the under water Sensor Network. Accurate GPS position and time are used to establish stable and high reliability underwater acoustic Sensor Network. This paper describes the background of the project 'Harbor area Marine Ubiquitous Sensor Network', preliminary consideration and testing. Radio and acoustic communication is the main focus of this preliminary experiment.

• ETRI Journal
• /
• 제28권1호
• /
• pp.91-94
• /
• 2006

Impulse radio (IR) is a competitive candidate for ultra-wideband (UWB) systems. In this letter, we evaluated the coexistence of an IR-UWB system based on an orthogonal sinusoidal correlation receiver (OSCR) with an IEEE 802.11a WLAN through a detailed simulation. The coexistence performance of the two systems is characterized in terms of the receiver's bit-error rates. Then, some approaches to interference mitigation are discussed.

• 경영과학
• /
• 제17권1호
• /
• pp.55-64
• /
• 2000

By managing not only geographical information but also various kinds of attribute data. GIS presents useful information for decision-makings. Most of decision-making problems using GIS can be formulated into network-optimization problems. In this study we deal with the implementation of network optimization system that extracts data from the database in GIS. solves a network optimization problem and present optimal solutions through GIS' graphical user interface. We design a nitwork optimization system and present some implementation techniques by showing a prototype of the network optimization system. Our network optimization system consists of three components : the interface module for user and GIS the basic network the program module the advanced network optimization program module. To handle large-scale networks the program module including various techniques for large sparse networks is also considered, For the implementation of the network optimization system we consider two approaches : the method using script languages supported by GIS and the method using client tools of GIS. Finally some execution results displayed by the prototype version of network optimization system are given.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2002년도 추계학술발표논문집 (중)
• /
• pp.1667-1670
• /
• 2002

현재 Network 의 취약점인 Node 들의 빠른 이동성을 지원하기 위하여 MANET 이라는 이동단말로만 이루어진 Network model 이 현재 연구 중에 있다. 그러나 MANET 은 별도의 Router 없이 이동 노드로만 이루어져있으므로 각 노드는 자신의 목적지에 대한 경로를 각자 알아내야 한다. 따라서 각 노드는 목적지까지의 경로를 알아내기 위한 RREQ 메시지를 모든 Network 노드로 Broadcasting 하는데 Network 이 커질 경우 각 노드들이 전체 Network으로 Broadcasting하는 RREQ 메시지로 인하여 커다란 Network 부하를 만들어 낸다. 본 논문에서는 이러한 RREQ 메시지에 의한 Network 부하를 줄이고자 MANET의 Routing Protocol의 한 종류인 ZRP를 개선하여 각 Zone들을 그룹으로 묶어 그룹내의 노드 목록을 캐쉬에 저장함으로써 전체 network 으로 RREQ 를 Broadcasting 하지 않고 Group내에서 1차적으로 전송하여 Network의 부하를 줄이고자 한다.

• International Journal of Computer Science & Network Security
• /
• 제21권12호
• /
• pp.9-20
• /
• 2021

Abstraction applied in computer networking hides network details behind a well-defined representation by building a model that captures an essential aspect of the network system. Two current methods of representation are available, one based on graph theory, where a network node is reduced to a point in a graph, and the other the use of non-methodological iconic depictions such as human heads, walls, towers or computer racks. In this paper, we adopt an abstract representation methodology, the thinging machine (TM), proposed in software engineering to model computer networks. TM defines a single coherent network architecture and topology that is constituted from only five generic actions with two types of arrows. Without loss of generality, this paper applies TM to model the area of network monitoring in packet-mode transmission. Complex network documents are difficult to maintain and are not guaranteed to mirror actual situations. Network monitoring is constant monitoring for and alerting of malfunctions, failures, stoppages or suspicious activities in a network system. Current monitoring systems are built on ad hoc descriptions that lack systemization. The TM model of monitoring presents a theoretical foundation integrated with events and behavior descriptions. To investigate TM modeling's feasibility, we apply it to an existing computer network in a Kuwaiti enterprise to create an integrated network system that includes hardware, software and communication facilities. The final specifications point to TM modeling's viability in the computer networking field.

• Journal of Information Processing Systems
• /
• 제14권6호
• /
• pp.1464-1479
• /
• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Journal of Electrical Engineering and Technology
• /
• 제8권3호
• /
• pp.649-657
• /
• 2013

DTN flooding based routing protocol replicate the message copy to increase the delivery like hood that overloads the network resources. The probabilistic routing protocols reduce replication cost by forwarding the message to a node that holds high predictability value to meet its destination. However, the network traffic converges to high probable nodes and produce congestion that triggers the drop of previously stored messages. In this paper, we have proposed a routing protocol called as Connection frequency Buffer Aware Routing Protocol (CFBARP) that uses an adaptive method to maintain the information about the available buffer space at the receiver before message transmission. Furthermore, a frequency based method has been employed to determine the connection recurrence among nodes. The proposed strategy has performed well in terms of reducing message drop, message relay while increases the delivery probability.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.169-180
• /
• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.