A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

     

Exploring creative thinking in graphically mediated synchronous dialogues

This paper reports on an aspect of the EC funded Argunaut project which researched and developed awareness tools for moderators of online dialogues. In this study we report on an investigation into the nature of creative thinking in online dialogues and whether or not this creative thinking can be coded for and recognized automatically such that moderators can be alerted when creative thinking is occurring or when it has not occurred after a period of time. We outline a dialogic theory of creativity, as the emergence of new perspectives from the interplay of voices, and the testing of this theory using a range of methods including a coding scheme which combined coding for creative thinking with more established codes for critical thinking, artificial intelligence pattern-matching techniques to see if our codes could be read automatically from maps and ‘key event recall’ interviews to explore the experience of participants. Our findings are that: (1) the emergence of new perspectives in a graphical dialogue map can be recognized by our coding scheme supported by a machine pattern-matching algorithm in a way that can be used to provide awareness indicators for moderators; (2) that the trigger events leading to the emergence of new perspectives in the online dialogues studied were most commonly disagreements and (3) the spatial representation of messages in a graphically mediated synchronous dialogue environment such as Digalo may offer more affordance for creativity than the much more common scrolling text chat environments. All these findings support the usefulness of our new account of creativity in online dialogues based on dialogic theory and demonstrate that this account can be operationalised through machine coding in a way that can be turned into alerts for moderators.     

一种基于数据流跟踪的无线传感网能量模型及网络优化

提出了一种基于数据流跟踪的能量模型,通过跟踪数据流在网络中的整个过程来计算全网的能量消耗,是一种不受网络结构限制的普遍适用的能量模型;在此基础上.建立了基于能耗的网络优化模型,针对链式和簇式结构进行了拓扑、功率和路由方面的优化设计,仿真结果证明了理论分析的正确性.     

Repeated Budgeted Second Price Ad Auction

Our main goal is to abstract existing repeated sponsored search ad auction mechanisms which incorporate budgets, and study their equilibrium and dynamics. Our abstraction has multiple agents bidding repeatedly for multiple identical items (such as impressions in an ad auction). The agents are budget limited and have a value per item. We abstract this repeated interaction as a one-shot game, which we call budget auction, where agents submit a bid and a budget, and then items are sold by a sequential second price auction. Once an agent exhausts its budget it does not participate in the proceeding auctions. Our main result shows that if agents bid conservatively (never bid above their value) then there always exists a pure Nash equilibrium. We also study simple dynamics of repeated budget auctions, showing their convergence to a Nash equilibrium for two agents and for multiple agents with identical budgets.     

New Hybrid Access Method for Femtocell through Adjusting QoS

The increasing number of mobile users raises issues about coverage extension in some areas such as rural zones, indoor or underground locations： one of suggestion solution to accommodate this growing of mobile user is femtocell. Femtocell have been attracting considerable attention in mobile communications, it is a small base station that install to improve the indoor coverage of a given cellular and to enhance user＇s capacity. Call admission control and resource allocation infemtocell＇s hybrid mode are an essential performance promotion issue. Developing methods for femtocell utilization is very comparative nowadays. The two major limitations of wireless communication are capacity and range. The main contribution of our paper is developing resource allocation scheme that can manage the femocell resources between subscriber （femtocell user） and non-subscriber （macrocell user in order to maximizing the system utilizations, we provide a mechanism that leads to serve more users by admitting more subscribers basing on adjusting QoS of the connected users.     

Computation of the stability radius of a Schur polynomial: an orthogonal projection approach

The robust Schur stability of a polynomial with uncertain coefficients will be investigated. A formula for the stability radius of a Schur polynomial is established. The result is the counterpart of [1] for linear discrete-time systems     

Substitution of G.728 vocoder’s codebook search module with SOM array trained by PSO-optimized supervised algorithm

Low delay-code excited linear prediction (LD-CELP) is an attractive algorithm in implementing vocoders in voice over Internet protocol networks. This algorithm has been proposed for the coding of speech at 16 kbps with toll quality. However, operation at transmission rates lower than 16 kbps is desirable, so that traffic can be accommodated during system overload conditions. In this paper, an array of self-organizing maps (SOMs) is employed instead of traditional codebook search module, recommended in ITU-T G.728, to determine the optimum index value of shape codebook. It is noted that a modified supervised training algorithm is used for SOMs in which some of the training parameters are optimized using particle swarm optimization (PSO) algorithm. Based on the occurrence frequency characteristics of codevectors, six bits for shape codebook and two bits for gain codebook are used in this work to produce a vocoder with lower bit rate as compared with traditional ITU-T G.728 vocoder. The performance comparison of the proposed SOM array trained by PSO-optimized supervised algorithm as the codebook search module in the structure of LD-CELP with a conventional implementation of LD-CELP coder shows that execution time of the algorithm is reduced up to 44 %. However, the degradation of voice quality in terms of mean opinion score, perceived evaluation of speech quality and segmental signal-to-noise ratio (SNR_seg) is acceptable.     

Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

Feature selection is one of the most important techniques for data preprocessing in classification problems. In this paper, fuzzy grids–based association rules mining, as an effective data mining technique, is used for feature selection in misuse detection application in computer networks. The main idea of this algorithm is to find the relationships between items in large datasets so that it detects correlations between inputs of the system and then eliminates the redundant inputs. To classify the attacks, a fuzzy ARTMAP neural network is employed whose training parameters are optimized by gravitational search algorithm. The performance of the proposed system is compared with some other machine learning methods in the same application. Experimental results show that the proposed system, when choosing optimum “feature subset size-adjustment” parameter, performs better in terms of detection rate, false alarm rate, and cost per example in classification problems. In addition, employing the reduced-size feature set results in more than 8.4 percent reduction in computational complexity.     

Robust tracking design based on adaptive fuzzy control of uncertain nonlinear MIMO systems with time delayed states

It is proposed here to use a robust tracking design based on adaptive fuzzy control technique to control a class of multi-input-multi-output (MIMO) nonlinear systems with time delayed uncertainty in which each uncertainty is assumed to be bounded by an unknown gain. This technique will overcome modeling inaccuracies, such as drag and friction losses, effect of time delayed uncertainty, as well as parameter uncertainties. The proposed control law is based on indirect adaptive fuzzy control. A fuzzy model is used to approximate the dynamics of the nonlinear MIMO system; then, two on-line estimation schemes are developed to overcome the nonlinearities and identify the gains of the delayed state uncertainties, simultaneously. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with an online update law. The adaptive fuzzy scheme uses a Variable Structure (VS) scheme to resolve the system uncertainties, time delayed uncertainty and the external disturbances such that H_∞ tracking performance is achieved. The control laws are derived based on a Lyapunov criterion and the Riccati-inequality such that all states of the system are uniformly ultimately bounded (UUB). Therefore, the effect can be reduced to any prescribed level to achieve H _∞ tracking performance. A two-connected inverted pendulums system on carts and a two-degree-of-freedom mass-spring-damper system are used to validate the performance of the proposed fuzzy technique for the control of MIMO nonlinear systems.     

Design of automated hybrid work stations: An evaluation of robot sensory systems for safety

Hybrid robotic systems necessitate a new integrated approach to the design of tasks and the performance requirements for human operators and robots. The presence of operators in hybrid work stations adds to the complexity and unpredictability of such design requirements. An important component of the hybrid system design is the integration of both human and robot sensory capabilities for task completion. A model for the integration of human and robot sensory information collection, processing, and action is presented. Robot sensory systems are evaluated with respect to the safety of operators within a hybrid work station. Four sensory technologies of optical (vision), sonar, capacitance, and infrared are compared. Optically-based and infrared sensors appear to be the most promising in terms of the safety and efficiency of hybrid work stations.