Volume 13, Number 2, 2016
Special Issue on Intelligent Computing and Modeling in Life System and Sustainable Environment (pp.89-158)
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Emergencies, which are very difficult to be forecasted, would always bring about huge harm to people. Therefore, to find ways to reduce such devastating effects, researches on emergency management have turned to be paramount. Nowadays, the rapid development of computer technology has supplied a new and effective idea for the researches of emergency management, namely that the researches can be done in computers by performing simulation experiments according to the artificial societies, computational experiments, parallel execution (ACP) approach. Guided by this approach, this paper has proposed one agent-based prototype simulation system to research emergency management. Firstly, structure of the simulation system oriented to emergency management was analyzed and designed. Then a simulation system oriented to public health emergency management was constructed to study the transmission of infectious diseases. Finally, several experiments were carried out based on the system, with several significant conclusions having also been obtained.
A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics on computers, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation parameters in a real-time fashion. One of the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit (GPU) programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units (GPGPU) programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling. Due to performance being an important factor, a performance model has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.
Obstructive sleep apnea syndrome (OSAS) is a respiratory disease characterized by the upper airway collapses and reopens repeatedly during sleep. Though the nerve control plays a key role in the upper airway collapse, it has been considered in previous studies only with lumped parameter models. Based on a finite element model including airway and surrounding structures, the effect of nerve control on the upper airway collapse was studied with fluid-structure interaction method. Spring elements were used to simulate the function of the muscle group. The simulation results show that the nerve control reduces the deformation of airway successfully and avoids the risk of OSAS.
The problems of identification and stabilization of a class of Hammerstein systems over a wireless network are investigated in this paper. A new approach for the proof of iterative identification is presented first. Then a guaranteed performance controller is designed to stabilize the system. The effectiveness of the proposed approach is demonstrated by numerical examples.
In this paper, effects of environmental and hunting parameters on the interspecific interacting populations are considered by applying the Rosenzweig-MacArthur model with the Holling type II functional response. Attenuating functions of the carrying capacity are introduced with a concern on the hunting parameters. We carry out numerical study to investigate how the population densities behave when environmental quantities change. We obtain the Hopf bifurcation diagrams from numerical results.
It is always quite difficult to accurately measure boiler drum water level in power plant. Though the effect of false measurement of water level can be reduced with some devices, the effect of deviation of boiler drum water level to the monitoring and alarm system, even to the control of drum water level, and so on, cannot be surmounted. Because of these reasons, the accurate water level alarm signal cannot be provided and the water level control measures cannot be applied. In order to solve this problem, a water level deviation analysis method is presented for analyzing boiler drum water level. Based on the analysis of boiler drum water level related running parameters, the relational model of water level deviation under different working conditions and its parameters is constructed. By analyzing this model, the specific impacts of the main factors can be fixed. Thus the drum water level deviation can be reduced by adjusting running parameters without changing unit load. And then the measurement of drum water level can be more accurate only if power plants have accurate measuring devices. Therefore, the boiler drum water level can be more accurately monitored and controlled. So, this innovation is important in ensuring the safe running of power plant.
In this paper, we propose multi-characteristics based data scheduling over smart grid. Three different pricing strategies are presented based on user priority and load rate. Then the corresponding novel scheduling algorithms are introduced by the proposed data priority and pricing strategies. The simulation experiments are carried out to evaluate the proposed algorithms based on trace data. And the results show that our methods can outperform the conventional method.
Brain hypothermia treatment (BHT) is an active therapy for severe brain injury. It makes the temperature of the brain track a given temperature input curve so as to reduce the risk of tissue damage. BHT requires a brain-temperature control system because of environmental disturbances and changes in the human body. The thermal models of the human body devised so far are usually of a very high order and are not suitable for controlling brain temperature. This paper presents a method of finding a reducedorder thermal model of the human body for use in BHT. It combines minimal realization and balanced realization. Unlike other methods, this method yields a reduced-order model that is based on system theory and that takes the frequency characteristics of human thermal sensation into account. It features high precision in the frequency band for BHT and is suitable for the control of brain temperature.
Usually, the security requirements are addressed by abstracting the security problems arising in a specific context and providing a well proven solution to them. Security patterns incorporating proven security expertise solution to the recurring security problems have been widely accepted by the community of security engineering. The fundamental challenge for using security patterns to satisfy security requirements is the lack of defined syntax, which makes it impossible to ask meaningful questions and get semantically meaningful answers. Therefore, this paper presents an ontological approach to facilitating security knowledge mapping from security requirements to their corresponding solutions-security patterns. Ontologies have been developed usingWeb Ontology Language (OWL) and then incorporated into a security pattern search engine which enables sophisticated search and retrieval of security patterns using the proposed algorithm. Applying the introduced approach allows security novices to reuse security expertise to develop secure software system.
A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.
This paper is devoted to identifying the biomarkers of rat liver regeneration via the adaptive logistic regression. By combining the adaptive elastic net penalty with the logistic regression loss, the adaptive logistic regression is proposed to adaptively identify the important genes in groups. Furthermore, by improving the pathwise coordinate descent algorithm, a fast solving algorithm is developed for computing the regularized paths of the adaptive logistic regression. The results from the experiments performed on the microarray data of rat liver regeneration are provided to illustrate the effectiveness of the proposed method and verify the biological rationality of the selected biomarkers.
IJAC CiteScore keeps raising in 2018
IJAC receives a CiteScore as high as 2.34 in 2018 which is 1.37 times higher than that in 2017. Being in the top 15%, it ranks #69 among 460 journals in respective categories.
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During the past two years, IJAC has published a series of high-quality papers by famous scholars around the world, including professor Tomaso Poggio from MIT, professor Brian Anderson from Australian National University, professor Yike Guo from Imperial College London, etc.. All the papers are open access, covering topics of Deep Learning, Artificial Intelligence, Neural Networks, and so on. You’ll never miss it!
2019 International Academic Conference List
International Journal of Automation and Computing (IJAC) maintains this list of conferences at the beginning of each year that are highly relevant to current hot research topics, including artificial intelligence, machine learning, computer vision, pattern recognition, robotics and automatic control.