Determining turbulent flow friction coefficient using adaptive neuro-fuzzy computing technique

In the analysis of water distribution networks, the main required design parameters are the lengths, diameters, and friction coefficients of rough-pipes, as well as nodal demands and water levels in the reservoirs. Although some of these parameters such as the pipe lengths are precisely known and would remain the same at different points of the networks whereas some parameters such as the pipe diameters and friction coefficients would changed during the life of network and therefore they can be treated as imprecise information. The primary focus of this study is to investigate the accuracy of a fuzzy rule system approach to determine the relationship between pipe roughness, Reynolds number and friction factor because of the imprecise, insufficient, ambiguous and uncertain data available. A neuro-fuzzy approach was developed to relate the input (pipe roughness and Reynolds number) and output (friction coefficient) variables. The application of the proposed approach was performed for the data derived from the Moody’s diagram. The performance of the proposed model was compared with respect to the conventional procedures using some statistic parameters for error estimation. The comparison test results reveal that through fuzzy rules and membership functions, the friction factor can be identified, precisely.     

Spectral irradiance profiles of suspended marine clay for the estimation of suspended sediment concentration in tropical waters

Reflectance spectra of water containing varying concentrations of organic and inorganic sediments isolated from the coastal waters of Singapore were measured using a portable spectroradiometer under controlled experimental conditions and natural sunlight. The effect of different sizes of sediments on the spectral profile of water was also investigated. In the presence of organic sediments, maximum reflectance of water was observed over a broad band between 440-580 nm, with a peak reflectance at about 550 nm, followed by two smaller peaks at 600-645 nm and 665-690 nm. In contrast, inorganic sediments produced a distinct band peak between 595 and 690 nm. For both sediment types, characteristic features in the infra-red region include a reflectance trough at 754 nm and peak at 814 nm. The empirical colour ratios, OD550-OD754 and OD 595-OD754, were used to estimate the organic and inorganic sediment concentrations respectively. In the case of organic sediments, a power function was found to fit the data well ( R 2 =0.89-0.98), whereas a linear fit was found for inorganic sediments ( R 2 =0.53-0.86). In general, decreases in particle size resulted in overall increases in spectral reflectance.     

A neuro-fuzzy technique for document binarisation

This paper proposes a new neuro-fuzzy technique suitable for binarisation or, in general, the colour reduction of digital documents. The proposed approach uses the image colour values and additional local spatial features extracted in the neighbourhood of the pixels. Both image and local features values feed a Kohonen self-organised feature map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define a first approach of the final classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to obtain the colours of the final document. The method can be applied to greyscale and colour documents; it is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Adaptive offloading for pervasive computing

Delivering a complex application on a resource-constrained mobile device is challenging. An adaptive offloading system enables dynamic partitioning of the application and efficient offloading of part of its execution to a nearby surrogate. To deliver pervasive services without modifying the application or degrading its fidelity, we propose an adaptive offloading system that includes two key parts: a distributed offloading platform and an offloading inference engine. There are two important decision-making problems for adaptive offloading: adaptive offloading triggering and efficient application partitioning.     

Resource estimation for heterogeneous computing

Code-profiling is the process of determining the types of codes found in a given heterogeneous task. Once this information is available, it is desirable to know how many processors are needed for each of the code types. In this paper, we propose two methods for estimating the minimum number of processors required for each of these code types. The first method involves making use of task compatibility graphs. We show that a task compatibility graph can be generated by analyzing certain compatible relations between task module pairs of a given task flow graph. We define the resource (processor) minimization problem therefore to be equivalent to finding the minimal number of cliques that cover the task compatibility graph, or to finding the minimal number of colors that color the vertices of its complement graph, called task conflict graph. We solve this problem using a greedy approach in O(¦V¦log¦V¦¦E¦) time, where ¦V¦ and ¦E¦ are the number of vertices and edges of the task compatibility graph. We further show that for three special types of task compatibility graphs, optimal solution can be obtained in polynomial time. The second method studied in this paper uses the Cluster-M methodology for estimating the minimum number of processors. Examples are shown to compare the estimated results obtained using different techniques.     

Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.     

High-performance computing for real-time spectral estimation

This paper presents two separate investigations into the real-time implementation of the modified covariance spectral estimator: one comparing performances on digital signal processors, the TMS320C40, and the recently released ADSP2016x (SHARC); another using application-specific custom circuitry. The advantages and disadvantages of each of these different approaches are reviewed, resulting in the design of a field-programmable gate array/digital signal processor-based high-performance system that combines the hardware and software approaches. In conjunction with pulsed Doppler ultrasound blood-flow detectors, the spectral estimator offers increased sensitivity in the non-invasive detection of arterial disease.     

Long short-term memory for predicting daily suspended sediment concentration

Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal relationships on long time scales. To build the model, daily observed time series of river discharge (Q) and SSC in the Schuylkill River in the United States were used. The results of the proposed model were evaluated and compared with the feedforward neural network and the adaptive neuro fuzzy inference system models which were trained using three different learning algorithms and widely used in the literature for prediction of daily SSC. The comparison of prediction accuracy of the models demonstrated that the LSTM model could satisfactory predict SSC time series, and adequately estimate cumulative suspended sediment load (SSL).

     

Toward universal multispectral suspended sediment algorithms

A data acquisition and analysis program has been undertaken to demonstrate the feasibility of remote multispectral techniques for monitoring suspended sediment concentrations in natural water bodies. Two hundred surface radiance measurements (400–1000 nm) were made at Lake Mead with coincident water sampling for laboratory analysis. Water volume spectral reflectance is calculated from the recorded surface radiance and volume reflectance-suspended sediment relationships investigated. Statistical analysis indicates that quantitative estimates of nonfilterable residue and nephelometric turbidity can be obtained from volume spectral reflectance data with sufficient accuracy (based on U.S. Environmental Protection Agency standards) to make the multispectral technique feasible for sediment monitoring. Algorithms exhibit sufficient universality to indicate they can be implemented in many cases with little or no ground truth for calibration.     

Adaptive security protocol selection for mobile computing

The mobile computing paradigm has introduced new problems for application developers. Challenges include heterogeneity of hardware, software, and communication protocols, variability of resource limitations and varying wireless channel quality. In this scenario, security becomes a major concern for mobile users and applications. Security requirements for each application are different, as well as the hardware capabilities of each device. To make things worse, wireless medium conditions may change dramatically with time, incurring great impact on performance and QoS guarantees for the application. Currently, most of the security solutions for mobile devices use a static set of algorithms and protocols for services such as cryptography and hashes.In this work we propose a security service, which works as a middleware, with the ability to dynamically change the security protocols used between two peers. These changes can occur based on variations on wireless medium parameters and system resource usage, available hardware resources, application-defined QoS metrics, and desired data “security levels”. We compare our solution to some widespread static security protocols, demonstrate how our middleware is able to adapt itself over different conditions of medium and system, and how it can provide a performance gain in the execution of cryptographic primitives, through the use of data semantics.     

Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper

The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping objects. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance. The optimal topology of the gripper structure was obtained by iterative finite element method (FEM) optimization procedure. The main points of this paper are in explanation of a new sensing capability of the gripper for grasping and lifting up the gripping objects. Since the sensor stress depends on weight of the grasping object it is appropriate to establish a prediction model for estimation of the grasping object weight in relation to sensor stress. A soft computing based prediction model was developed. In this study an adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology to conduct prediction of the grasping objects weight. The training and checking data for the ANFIS network were obtained by FEM simulations.     

Adaptive neuro-fuzzy modeling of a soft finger-like actuator for cyber-physical industrial systems

Soft robotics is a trending area of research that can revolutionize the use of robotics in industry 4.0 and cyber-physical systems including intelligent industrial systems and their interactions with the human. These robots have notable adaptability to objects and can facilitate many tasks in everyday life. One potential use of these robots is in medical applications. Due to the soft body of these robots, they are a suitable replacement for applications like rehabilitation and exoskeletons. In this paper, we present the neuro-fuzzy modeling of a soft pneumatic finger-like actuator. This actuator is a fiber-reinforced soft robot with the shape and dimensions of a real finger and moves in planar motion. A bending sensor is used as a feedback for curvature motion of this actuator. In order to model this actuator, an adaptive neuro-fuzzy inference system is utilized to overcome the hardship in the modeling of the nonlinear performance of the soft materials. An experimental setup is designed to obtain suitable input–output data needed for modeling. The results show the applicability of the utilized method in the modeling of the soft actuator.

     

Adaptive neuro-fuzzy inference approach for prediction the stiffness modulus on asphalt concrete

In this study, stiffness modulus parameters of asphalt concrete were determined experimentally for different temperature and exposure times. The stiffness modules were calculated according to Nijboer stiffness module. Basic physical properties and the quantity of bitumen of asphalt core samples were designated for determining the stiffness modules. The samples were exposed to 17 °C (reference temperature), 30, 40 and 50 °C temperatures for 1.5, 3, 4.5 and 6 h respectively and then Marhall Stability tests were done for each samples. By using the test results a prediction model with Sugeno type based on the adaptive neuron-fuzzy inference system (ANFIS) was alternatively developed to predict the stiffness modules of asphalt core samples. As a result, it was seen that the developed prediction model could be used as a prediction model for unperformed situations which are not suitable for experiments.     

Adaptive fault-tolerant scheduling strategies for mobile cloud computing

Mobile cloud computing is a form of cloud computing that incorporates mobile devices such as smartphones and tablet PCs into the cloud infrastructure. As mobile devices are resource-constrained in nature, new scheduling strategies are required when using them as resource providers. Based on our previous group-based scheduling algorithm, we present fault-tolerant scheduling algorithms considering checkpoint and replication mechanisms to actively cope with faults. We carried out the performance evaluation with simulation to demonstrate that our algorithm is more efficient than the existing one lacking fault tolerance in terms of accuracy rate, resource consumption, and average execution time. In particular, the average execution time was reduced by about 60%, resulting in the reduction of resource consumption.

     

一种面向可靠云计算的自适应故障检测方法

提出了一种保障云系统可靠性的自适应故障检测方法。首先基于步进指标搜索算法和指标空间分离技术进行云指标提取, 得到最能刻画云行为和健康状况的最大关联性标准和最小冗余度标准, 然后使用最小封闭球体将云指标数据点从数据空间映射到内核空间进行降维, 最后将低维数据输入故障检测器。故障检测器在内核空间产生一个能够容纳数据的最小封闭球面, 确定潜在故障, 如果以前故障历史无法获得, 则故障检测器通过寻找与其他状态存在明显不同的云健康状态来确定故障。当检测结果被云运营商验证后, 要么被判定为真实故障, 要么被判定为正常状态（虚警）。实现了一种故障检测原型系统, 并在校园云计算环境下展开实验。实验结果表明, 与其他现有故障检测技术相比, 该方法的检测更高效、更准确。     

面向普适计算的自适应模糊访问控制方法

普适环境中的上下文信息是普适访问控制的关键因素,对主体的授权和对主体使用权限过程的控制具有决定性影响。系统安全强度和安全策略应随上下文的变化而动态改变。传统访问控制模型均未考虑上下文对安全强度和安全策略的动态影响,不适合普适计算环境。提出了普适环境下安全强度和安全策略随上下文动态变化的思想,基于区间值模糊集合理论建立了上下文信息相关的产生式规则,设计了一种简单高效的区间值模糊访问控制方法,以提高普适计算系统中安全强度和安全策略的自适应性,更符合普适环境。     

单应性矩阵自适应估计方法

如何从初始匹配点集中估计出精确的单应性矩阵,有效地剔除误匹配,一直以来都是视觉领域研究的重点和难点,也是实际相关技术应用中最为关键的一步。通过将特征点对相似度概念应用于LMedS的样本选取过程,提出了一种新的单应性矩阵自适应的估计方法。区别于传统LMeds方法从无序匹配点集中随机选取样本的过程,该方法首先以点对间的相似度对整个初始匹配点进行降序排列,然后从前往后依次选取样本。实验结果表明,与LMedS相比,该方法估计出的单应性矩阵更精确、鲁棒,效率更高（得到最佳模型所需的迭代次数仅约为LMedS的1/5）,同时弥补了RANSAC及其改进方法需预先设置距离偏差阈值的不足。