Papers by Keyword: Localization

Abstract: The wireless sensor network localization algorithm in this paper combines hop-count information and distributed learning. The network is classified into many classes based on sensors’ location, and then the class that each sensor falls into is specified. There are a certain number of beacon nodes with position coordinate in network, and they use their own locations as training data in performing above classification. This positioning method merely uses the partial hop-count information between target sensor and reference node in specifying the class of each node. The final simulation experiment will analyze the excellent performance of this method under different system parameters.

Abstract: Android platform is not implemented Tibetan localization currently. Tibetan glyphs superimposed incorrect. This paper researched Android system source code and Tibetan text features, elaborated Tibetan localized operating on Android system. The main contents include downloaded and compiled Android4.1 source, added the Tibetan language settings options, modified the ICU4C module, added Tibetan font, translated into Tibetan language, solved the problem of Tibetan superposition display, added Tibetan input method, etc. The implementation of the Android platform Tibetan localization is the foundation for Tibetan Localization on Android devices.

Abstract: This paper introduces the electric cable partial discharge detection technology and elaborates pulse waveform characteristics of cable partial discharge, PD signal’s detection theory and basic method of cable PD source localization. On this basis, application examples of substation cable-site detection are given.

Abstract: A range-based localization approach which named gravitational particle swarm optimization localization algorithm (GL) has been proposed. This algorithm considered the influence from the position of anchor nodes to the localization results, GL can directly searched out the coordinates of unknown nodes by the distance from anchor nodes to unknown nodes. As is shown in the experiment results, GL not only has high positioning accuracy, but also overcomes the defect that location error increases rapidly as the ranging error increases, compares with normal schemes (such as method of least squares, ML ) GL’s accuracy can improve 40% as the ranging error is 35%.

Abstract: This article deals with numerical simulation of necking. It draws the attention onto the importance of the description of strain-hardening and the effects on the evolution of necking. In order to compare necking evolution in relation with different plasticity models, a tracking procedure which consists in determining the evolution over time of discharged volumes of the sample is adopted. Models that take into account physical phenomena at the microscopic level and especially the heterogeneities of materials from a mechanical point of view seem well suited to fit experimental evidence connected to necking.

Abstract: With the requirement of vehicle performance and fuel economy, dual-phase (DP) steels as one of the advanced high stress steels (AHSS) are increasingly used in the automotive industry due to the excellent combination of the tensile strength and ductility. On a microscale the ductile fracture is governed by the void nucleation, growth and coalescence mechanism. In the dual-phase steels this damage mechanism exhibits a rather complex situation: voids are generated by the debonding of the hard phase from the matrix and the inner cracking of the hard phase besides by inclusions. On a macroscale fracture of these materials is observed in the automotive industry with the absence of strain localization or minimal post-necking deformation. Consequently the failure during the forming process is caused by a competitive or combined mechanism of internal damage evolution and metal instability. In this study, the target is to develop a simple and generalized model for metal forming processes accounting for instability, damage and ductile fracture. Theoretical predictions of metal instability by the Hill–Swift necking criterion and the modified maximum force criterion are considered. The damage model is developed by the combination of the prediction of metal instability and ductile fracture of sheet metals. The model is developed in 3D triaxial stress state and the accumulation of damage is stress state dependent. Furthermore, the influence of the hardening curve effected by damage on the forming limit curve is investigated.

Abstract: Plastic deformation induces various types of dislocation microstructures at different length scales, which eventually results in a heterogeneous deformation field in metallic materials. Development of such structures manifests themselves as macroscopic hardening/softening response and plastic anisotropy during strain path changes, which is often observed during forming processes. In this paper we present two different non-local plasticity models based on non-convex potentials to simulate the intrinsic rate-dependent and rate-independent development of plastic slip patterns, which is the simplified mechanism for the intrinsic microstructure development. For the sake of mechanistic understanding, the formulation and the simulations will be conducted in one-dimension which does not exclude its extension to multi-dimensions resulting in a crystal plasticity framework.

Abstract: From the theoretical point of view, network localization can be viewed as finding a unique solution from distances constraint among points. The one of the difficulties is that even if the network is uniquely localizable, it is proven to be an NP-Hard [1]. It is also true that the network graph has to be sufficiently dense [2]. This poses even more challenges to the original problem as we often work on sparse networks. To cope with this, in [3], we introduce priori knowledge to assist the process of finding the unique localization solution. It helps to speed up the searching algorithm; however, the ambiguity still exists among sparse networks. In this paper we try to bring as much priori knowledge as possible to assist or to be used as constraints. Hopefully this will reduce search space and reach the unique solution quickly. In clean environment, this extra info will, by some magnitude, bring the graph closer to the unique answer. We start from integer-coordinate noise-free position and then add sources of priori knowledge. Then we examine the case where assisted data can be noisy. A search is used within the noisy but useful constraint. The justification of using the assisted knowledge is from the practical uses of some networks, e.g. sensor network, where other measurements are available and they are often correlated and can be helpful in determining the positions.

Abstract: The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

Abstract: Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.