3D Step-by-step inversion strategy for audio magnetotellurics data based on unstructured mesh

A three-dimensional(3D)step-by-step inversion strategy for audio magnetotellurics(AMT)is investigated in this study.The objective function is minimized by iteratively solving the Gauss-Newton normal equation,and the inversion region is discretized with unstructured tetrahedral elements.The inversion proceeds step-by-step from a coarse mesh to a fine mesh.In the inversion iteration process,a mesh is adaptively optimized according to the spatial gradient information about the model resistivity to fine delineate the boundaries of abnormal bodies.In the early stage of inversion execution,a coarse mesh is used for inversion,and the inversion stability is improved by reducing the number of inversion elements.In addition,mesh refinement is performed in the iterative inversion process.The inversion results obtained from the previous mesh are used as the reference and initial models for the next mesh iterative inversion.The step-by-step inversion strategy can ensure that the inversion is performed in the correct direction,improving the inversion stability and results gradually.Synthetic results show that the step-by-step inversion strategy with a Gauss-Newton method for 3D AMT inversion is stable and reliable,which lays a foundation for further practical 3D AMT data inversion.

Physical contributors to glenohumeral internal rotation deficit in high school baseball players

Background： Glenohumeral internal rotation deficit （GIRD） is a risk factor for shoulder and elbow injury in baseball players. Although this evidence forms a basis for recommending stretching, clinical measures of internal rotation range of motion （ROM） do not differentiate if GIRD is due to muscular, capsuloligamentous, or osseous factors. Understanding the contributions of these structures to GIRD is important for the development of targeted interventions. We hypothesize that the osseous component will have the greatest relative contribution to GIRD, followed by muscle stiffness and posterior capsule thickness. Methods： Internal rotation ROM, muscle stiffness （teres minor, infraspinatus, and posterior deltoid）, posterior capsule thickness, and humeral retrotorsion were evaluated on 156 baseball players. A side-to-side difference was calculated for each variable. Variables were entered into a multivariable linear regression to determine the significant predictors of GIRD. Results： The regression model was statistically significant （R2 = 0.134, F（1, 156） = 24.0, p 〈 0.01） with only humeral retrotorsion difference remaining as a significant predictor （β = -0.243, t156 = -4.9, p 〈 0.01）. A greater humeral retrotorsion side-to-side difference was associated with more GIRD. Conclusion： Humeral retrotorsion accounted for 13.3% of the variance in GIRD. The stiffness of the superficial shoulder muscles and capsular thickness, as measured in this study, were not predictors of GIRD. Factors not assessed in this study, such as deeper muscle stiffness, capsule/ ligament laxity, and neuromuscular regulation of muscle stiffness may also contribute to GIRD. Since it is the largest contributor to GIRD, causes of changes in humeral retrotorsion need to be identified. The osseous component only accounted for 13.3% of the variance in GIRD, indicating a large contribution from soft tissues factors that were not addressed in this study. These factors need to be identified to develop evidence-based evaluations and intervention programs to decrease the risk of injury in baseball players.

A Machine Vision System for Ball Grid Array Package Inspection

An optical inspection method of the Ball Grid Array package(BGA) was proposed by using a machine vision system. The developed machine vision system could get main critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity. The experiment has proved that this system is available for BGA failure detection.

Knowledge acquisition method for case-knowledge-based product family design

Knowledge acquisition has always been the bottleneck of artificial intelligence. It is the critical point in product family design. Here a knowledge acquisition method was introduced based on scenario model and repository grid and attribute ordering table technology. This method acquired knowledge through providing product design cases to expert, and recording the means and knowledge used by the expert to describe and resolve problems. It used object to express design entity, used scenario to describe the design process, used Event-Condition-Action（ECA） nile to drive design process, and with the help of repository grid and attribute ordering table technology to acquire design knowledge. It＇ s a good way to capture explicit and implicit knowledge. And its validity is proved with respective examples.

Renewable and Storage Energy Integration for Smart Grid Housing

This paper presents the design, implementation and testing of an embedded system that integrates solar and storage energy resources to smart homes within the smart mierogrid. The proposed system provides the required home energy by installing renewable energy and storage devices. It also manages and schedules the power flow during peak and off-peak periods. In addition, a two-way communication protocol is developed to enable the home owners and the utility service provider to improve the energy flow and the consumption efficiency. The system can be an integral part for homes in a smart grid or smart microgrid power networks. A prototype for the proposed system was designed, implemented and tested by using a controlled load bank to simulate a scaled random real house consumption behavior. Three different scenarios were tested and the results and findings are reported. Moreover, data flow security among the home, home owners and utility server is developed to minimize cyber-attaeks.

Decentralized Power Control Strategy in Microgrid for Smart Homes

In this paper, Nanogrid System and working is presented for the future sustainable power system. This system is for small scale for smart homes with decentralized power system. A Nanogrid is the combination of energy system such as using of sources such as solar cells, fuel cells, micro turbines, wind turbines, energy storage devices and AC, DC power systems and controllable loads. This grid may use single mode or island mode with soft switching. The Dynamic of power system capability increases reliability, in case if one system fails it switch to other system to continue proving electric power with losing quality. The energy storage system is used to maintain stability during transition between the operating modes is emphasized. The Simulink model is used to present the working of system.

MSDA:multi-subset data aggregation scheme without trusted third party

Data aggregation has been widely researched to address the privacy concern when data is published,meanwhile,data aggregation only obtains the sum or average in an area.In reality,more fine-grained data brings more value for data consumers,such as more accurate management,dynamic price-adjusting in the grid system,etc.In this paper,a multi-subset data aggregation scheme for the smart grid is proposed without a trusted third party,in which the control center collects the number of users in different subsets,and obtains the sum of electricity consumption in each subset,meantime individual user’s data privacy is still preserved.In addition,the dynamic and flexible user management mechanism is guaranteed with the secret key negotiation process among users.The analysis shows MSDA not only protects users’privacy to resist various attacks but also achieves more functionality such as multi-subset aggregation,no reliance on any trusted third party,dynamicity.And performance evaluation demonstrates that MSDA is efficient and practical in terms of communication and computation overhead.

Dual-stage constructed random graph algorithm to generate random graphs featuring the same topological characteristics with power grids

It is a common practice to simulate some historical or test systems to validate the efficiency of new methods or concepts. However, there are only a small number of existing power system test cases, and validation and evaluation results, obtained using such a limited number of test cases, may not be deemed sufficient or convincing. In order to provide more available test cases, a new random graph generation algorithm, named ‘‘dualstage constructed random graph’’ algorithm, is proposed to effectively model the power grid topology. The algorithm generates a spanning tree to guarantee the connectivity of random graphs and is capable of controlling the number of lines precisely. No matter how much the average degree is,whether sparse or not, random graphs can be quickly formed to satisfy the requirements. An approach is developed to generate random graphs with prescribed numbers of connected components, in order to simulate the power grid topology under fault conditions. Our experimental study on several realistic power grid topologies proves that the proposed algorithm can quickly generate a large number of random graphs with the topology characteristics of real-world power grid.

顶级游戏体验的完美解决方案 英伟达GIRD云游戏服务

游戏的含义,其实就是规范化的玩乐。当游戏变成一个事业和行业的时候,它就要在"规范化"方面下功夫,并且要把玩乐这种感觉做到纯粹并且从中盈利。很快,电子游戏出现了。通过计算机程序,去实现可操作、可模拟的音画效果,成为游戏行业的不二选择。于是"电子游戏"行业变成了独立于"游戏"的一个单独词汇。而在人的五感中,视觉是吸收信息最多,最有效也是最直接的感官,所以如何进化数字图形表现,很快就成为游戏机行业的主攻方向。

Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.