Effect of Mechanical Cycling at Elevated Temperatures on The Constitutive Properties and Microstructure of Lead Free Solder Alloys

Solder joints in electronic packages often experience fatigue failures due to cyclic mechanical stresses and strains in fluctuating temperature environments. These stresses and strains are induced by mismatches in coefficients of thermal expansion, and lead to damage accumulation that contributes to crack initiation, crack propagation, and eventually to failure. In this study, we have tried to compare the effects of elevated mechanical cycling on SAC305 and SAC+Bi (SAC_Q). Initially, small uniaxial cylindrical samples of both alloys were prepared and reflowed in a reflow oven. These samples were then mechanically cycled for various durations at testing temperatures of 100 °C. The measured cyclic stress-strain curves were used to characterize the evolution of the hysteresis loop properties (peak stress, hysteresis loop area, and plastic strain range) with high temperature mechanical cycling. In addition, uniaxial tensile tests and creep tests were also conducted on specimens that had been previously mechanically cycled for various durations (e.g 0, 50, 100, 200, and 300 cycles) at an elevated temperature. This allowed us to study the evolution of the constitutive behavior of the solder alloys that occurred during the high temperature mechanical cycling due to the fatigue damage that builds up in the specimens. The reductions in the properties that occur during high temperature mechanical cycling were also correlated with the corresponding changes in the microstructure of the specimens. Rectangular cross-sectioned samples of the two lead free solder alloys were polished and selected regions indented to track the changes in the microstructure of a fixed region with mechanical cycling at T = 100 °C. Using the results of this study, we are working to develop better fatigue criteria for lead free solders which are subjected to variable temperature applications.

Variability of support conditions and effects on the non-linear flexural response of concrete sleepers

Current structural models used for the flexural design of prestressed concrete sleepers assume that ballast bearing support is static and located within a fixed region. This assumption implies a linear relationship between wheel load and bending moment. However, field data gathered from instrumented sleepers shows that this trend is non-linear, and the difference in flexural behavior between model predictions and field-measured demand is significant. Using back-calculation techniques and the development of a sleeper support analysis tool, this paper investigates the load-dependency of sleeper support condition. It is hypothesized that a given support condition redistributes ballast reaction forces due to the mechanical interaction of ballast particles with the sleeper’s deflected shape. It was found that redistribution of support conditions can reduce the expected flexural bending moment up to 45% when compared with moments calculated using traditional design guidelines. This effect (non-linearity) is greater as wheel loads increase. Results from revenue service field experimentation provided insight into the interaction between sleeper and ballast and serve as a foundation for the development of more complex analytical models. This will facilitate revisions to the future flexural design procedures for concrete sleepers, to ensure they are optimized for their expected service loading conditions.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.

ON ONE APPROACH OF FORECASTING NATURAL DISASTERS WITH THE SYSTEM OF PATTERN RECOGNITION WITH LEARNING

There are a number of recognition problems in different fields that can be solved with the system of pattern recognition with learning – SPRL elaborated by us. The problem of forecasting natural disasters (floods, mudslides) in the given year, the fixed region, and the period belongs to it. To solve it, it is set in the terms of pattern recognition with learning according to which it is necessary to pre-determine the learning descriptions in the same region of the previous years using data of the previous 12 months of the period. From learning descriptions, firstly are separated control descriptions, then the variants of learning and learning recognizable descriptions. Besides, it is necessary to determine descriptions in year, in the same region using data of the same previous period of the (the first model). After transformation and increasing the informativity of the learning descriptions, the knowledge and data bases are determined for learning recognizable and control descriptions in relation to the variants and classes (the second model). Using them, one decision is made on belonging to the respective class for learning recognizable descriptions, but for control descriptions – the primary decisions according to the number of variants, and then on their basis – one decision. Exactly according to the results of the recognition of control descriptions a decision is made on the occurrence (non-occurrence) of a natural disaster in the same region and period (the third model). The article discusses the arguments related to this fact. This model considers the correction of data bases with respect to variants and classes, also, defines the effectiveness of working of the SPRL and its detector of trust. Considering the specifics of forecasting, the initial data of at least 5 years are required to select the best knowledge and data bases with the use of which a disaster should be forecasted.

ON THE METHOD OF DETERMINING LEARNING DESCRIPTIONS TO FORECAST NATURAL DISASTERS WITH THE PATTERN RECOGNITION SYSTEM

To forecast natural disasters (floods, mud-slides) in the fixed region and in period T0 with SPRL – the System of Pattern Recognition with Learning (elaborated by us) it is necessary to have the data of the previous 12 months of period T0 and learning descriptions (LDs). To identify this latter, the fact of occurrence or non-occurrence of disasters in the same region and the period T0 should be known in other years and also, the above mentioned 12- month date for each year. Determining LDs based on them is the aim of the article. For this purpose, the method which will be included in the first model of the SPRL is elaborated. The SPRL comprises: 1) preliminary elaboration of the initial information, 2) learning and 3) recognition models. This system is implemented on a PC. It is verified on the basis of the real data to recognize objects of different classis. Primary, additional and formal additional parameters are determined in the method given in the article. On the basis of their values in correlation with the aforementioned 12 months two matrices are determined. The first of them corresponds to the fact of occurrence of disasters and the second one – of non-occurrence. By using these parameter values given in these matrices LDs will be determined. The best LDs will be given to the learning model of the SPRL for transformation and increasing of informativity. Based on the LDs obtained after the transformation, the learning model will make knowledge and data bases.