Enhance Energy Conservation Based on Residual Energy and Distance for WSNs

As the advancements in the field of artificial intelligence technologies continue to grow, robots are being built by the researchers as an attempt to render services to the people. In this regard, the robots can communicate effectively with the people and be able to complete all the tasks adequately given to them. These service robots while being developed requires the dialogue services to be developed to interact effectively with human beings providing far better user experience. Thus, the robot been built can provide domain-specific knowledge as well as able to provide consultations in various domains. We in this paper adopted a service-oriented approach for developing context-aware communication services for the cloud robot. The proposed work aims at training the context-aware model developed. The context-aware model is responsible for answering the questions put forward by the users and possess the ability to exploit the answers corresponding to the questions presented. An integrated framework is used to combine the contextual information and communication services. The performance of the proposed model can be evaluated based on Inverse Rank Mean (IRM). Evolutionary testing methods are used for testing the data in the proposed model. The results thus obtained shows the effectiveness of the proposed approach.

     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

Formal analysis of seamless application execution in mobile cloud computing

Mobile cloud computing augments the resource-constrained mobile devices to run rich mobile applications by leveraging the cloud resources and services. Compute-intensive mobile apps require significant communication resources for migrating the code from mobile devices to the cloud. For such apps, distributed application execution frameworks (DAEF) have been proposed in the literature. These frameworks either migrate the mobile app code during runtime or keep the app synchronized with another remotely executed app on the cloud. Frameworks also support mobile app live migration to cater for compute node mobility. One key research question arises is how successful are these DAEFs in achieving the seamless application execution under various network conditions? The answer to this question entails formal analysis of the DAEFs to determine the realistic bounds on propagation delay, bandwidth and application interaction with mobile device for various types and sizes of apps. In this research, we apply formal analysis techniques to define the execution time of the app and the time required for code migration. We also define three conditions for seamless application execution. Given realistic values for processor speed, application executable size, possible number of executed instructions, network propagation delay and transmission delay, we show what components of the mobile app need to be migrated during execution to the cloud. Finally, we compute realistic bounds for the app size (that can be executed seamlessly) based on important features which include cloud and device resources, bandwidth and latency profile.     

Multi-Directional Maximum-Entropy Approach to the Evolutionary Design Optimization of Water Distribution Systems

A new multi-directional search approach that aims at maximizing the flow entropy of water distribution systems is investigated. The aim is to develop an efficient and practical maximum entropy based approach. The resulting optimization problem has four objectives, and the merits of objective reduction in the computational solution of the problem are investigated also. The relationship between statistical flow entropy and hydraulic reliability/failure tolerance is not monotonic. Consequently, a large number of maximum flow entropy solutions must be investigated to strike a balance between cost and hydraulic reliability. A multi-objective evolutionary optimization model is developed that generates simultaneously a wide range of maximum entropy values along with clusters of maximum and near-maximum entropy solutions. Results for a benchmark network and a real network in the literature are included that demonstrate the effectiveness of the procedure.     

ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.     

Volumetric quantification of atherosclerotic plaque in CT considering partial volume effect

Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation maximization (MEM) of a statistical model for the calcified and background material is used to estimate the partial calcium content of the voxels. The algorithm limits the region over which MEM is performed. By using MEM, the statistical properties of the model are iteratively updated based on the calculated resultant calcium distribution from the previous iteration. The estimated statistical properties are used to generate a map of the partial calcium content in the calcified region. The volume of calcium in the calcified region is determined based on the map. The experimental results on a cardiac phantom, scanned 90 times using 15 different protocols, demonstrate that the proposed method is less sensitive to partial volume effect and noise, with average error of 9.5% (standard deviation (SD) of 5-7mm(3)) compared with 67% (SD of 3-20mm(3)) for conventional techniques. The high reproducibility of the proposed method for 35 patients, scanned twice using the same protocol at a minimum interval of 10 min, shows that the method provides 2-3 times lower interscan variation than conventional techniques.     

Interpreting the hydrogen adsorption on organic groups functionalized MOF-5s by statistical physics model

The hydrogen adsorption isotherms at equilibrium on four adsorbents (MOF-5 and three modified MOF-5s named, CH₃-MOF-5, Br-MOF-5 and Cl-MOF-5) were studied using a monolayer model with four adsorption sites energies. The analytical expression of this model was developed using the grand canonical ensemble in statistical physics by taking some working hypotheses involving some physicochemical parameters which can describe the adsorption process. These parameters are: four numbers of hydrogen adsorbed molecules per site (n₁, n₂, n₃ and n₄), four receptor site densities (N_M1, N_M2, N_M3 and N_M4), four saturation adsorbed quantities (Q₁, Q₂, Q₃ and Q₄) and four adsorption energies (??₁, ??₂, ??₃ and ??₄). The evolutions of these parameters in relation with temperature were discussed to understand and interpret the adsorption process at different temperatures. Fitting results revealed that the adsorption of hydrogen on MOF-5 is an exothermic physisorption process. The adsorption surface is inhomogeneous with many site energies. The fitting of the adsorption site is achieved by an aggregate of hydrogen molecules. The adopted model expression is used to derive the thermodynamic potential functions which govern the sorption mechanism such as entropy S_a, free enthalpy of Gibbs G and internal energy E_int.     

Physicochemical Properties of Different Types of Starch Phosphate Monoesters

Different types of starch were phosphorylated to different degrees of substitution using monosodium and disodium hydrogen orthophosphate at 160 °C under vacuum. Generally, phosphation enhanced the physicochemical properties of the modified starches compared to their native counterparts. Solubility and swelling power greatly increase when phosphorylation was carried out to a low degree of substitution, while the solubility and swelling power decreased gradually by increasing the degree of substitution. However, the values of the monoesters were still higher than those of the corresponding native polysaccharides. Viscosities of different starch types except corn amylose showed the highest values at the lowest degree of substitution, when the degree of phosphation increased the viscosity values decreased. Native potato starch formed a clear paste (96% transmittance) due to the presence of phosphate groups while the paste clarity of potato starch decreased gradually by increasing the degree of phosphation. Generally, phosphorylation increased the light transmittance of the other starches investigated at the lowest degree of substitution but the clarity decreased by increasing the degree of substitution.     

Application of time series and polynomial learning networks to robot trajectory error control

A compensatory control scheme based on measured errors at the end-effector is proposed using polynomial learning networks and time series modeling. Based on experimental data from an industrial manipulator programmed forms straight-line motion, trajectory deviations are modeled using both techniques. The performance of the models are compared at different locations in the workspace. It is shown that the robot arm signature can be obtained and that models from both techniques can be used to forecast trajectory errors. A method to implement the proposed scheme is also given.     

Pressure control in a PEM fuel cell via second order sliding mode

Pressure difference inside the Polymer Electrolyte Membrane Fuel Cells (PEMFC) arises due to load variations, during which the pressure difference between anode and cathode rises. Practically, this problem can be avoided by equalizing anode and cathode pressures, to protect the fuel cell from permanent damage. This paper focuses on pressure regulation in the anode and cathode sides of the PEMFC. The control objective is achieved using second order sliding mode multi-input multi-output (MIMO) controller based on “Twisting algorithm”. Parametric uncertainty is formally presented and included in a nonlinear dynamic fuel cell model. The resultant nonlinear controller is robust and is proved to guarantee performance around any equilibrium point and under parametric uncertainty. Simulation results show that the proposed controller has a good transient response under load variations.