Electronic and Control

The choice of the best set of universal ternary gates for quantum circuits is an open problem. We create exact minimum cost ternary reversible gates with quantum multiplexers using the method of iterative deepening depth-first search (IDDFS) . Such search is better for small problems than evolutionary algorithms or other search methods. Several new gates that are provably exact minimum cost have been discovered. These gates are next used as library building blocks in the minimization of larger ternary quantum circuits like highly testable GFSOP cascades (that generalize ESOP) as well as the wave cascades generalized to ternary logic. They are useful to design oracles for multivalued algorithms such as Deutsch-Jozsa and Grover.

This study proposes a three-dimensional co-culturing system of mesenchymal stem cells (MSCs) and nucleus pulposus (NP) cells from New Zealand white male rabbits to differentiate MSCs into NP-like cells. The preferable ratio of MSCs to NP cells and the effects of mechanical stimulation were investigated without biochemical reagents. The preferable ratio was investigated without mechanical stimulation using five groups: Group I (MSC control); Group II (NP cell control); and Groups III, IV, and V, for which the ratios of NP cells to MSCs were 1:1, 1:2, and 2:1, respectively. During culture for 10 days without stimulation, the proliferation of MSCs did not increase after day 4. NP cells proliferated more when co-cultured as in Group V. However, the degree of differentiation of MSCs increased significantly in Group V. The differentiation of NP cells decreased gradually over time. When mechanical stimulation was applied to Groups I, II, and V, it contributed to the differentiation of MSCs into NP-like cells, as well as to that of NP cells, but did not contribute to the proliferation of either cell type. The contribution of mechanical stimulation to differentiation was also confirmed by RT-PCR.

Strictly maintaining the steam temperature can be diflcult due to heating value variation 10 the fuel source, time delay changes in the main steam temperature, the change of the dynamic characteristics in the reheater. Up to ihepresent time, PID Controller hac been used to operate this system because of its implementational advantages. However, it is very di#iculr Io achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the PID Controller with disturbance rejection using immune network algorirhm. To decide the performance of response, an ITSE (integral of time weighted squared error)

This paper deals with design method of fuzzy controller for improving efficiency of DC-DC power converter. To design optimal control by fuzzy, this paper introduces optimal switching time and optimal switching mode of PWM. DC-DC Power converter is one of energy conversion device to transfer DC input source to DC output. When they transfer DC to DC, they have been using PID controller or fuzzy controller. Therefore, the efficiency of DC conversion strongly depends on PID parameter. Some papers illustrate tuning method of PID controller for this but have not been mentioning about the switching time and switching mode that can influence on the efficiency of DC-DC conversion. This paper suggests effective DC-DC conversion method by means of introducing switching time and switching mode into fuzzy based PID tuning.

This paper suggests novel hybrid optimization system (BF-CL) based on the bacterial foraging and clonal selection of immune system. A foraging strategy involves finding a patch of optimal condition (e.g., group of objective with conditions), deciding whether to enter it and search for optimal conditions, and when to leave the patch. There are predators and risks, energy required for optimization, and physiological constraints (sensing, memory, cognitive capabilities). Foraging scenarios can be modeled and optimal policies can be found using, for instance, dynamic programming. This approach provides us with novel hybrid model based on foraging behavior and clonal selection for a higher running time and with also a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces for control system.

This paper suggests novel hybrid optimization system (BF-CL) based on the bacterial foraging and clonal selection of immune system. A foraging strategy involves finding a patch of optimal condition (e.g., group of objective with conditions), deciding whether to enter it and search for optimal conditions, and when to leave the patch. There are predators and risks, energy required for optimization, and physiological constraints (sensing, memory, cognitive capabilities). Foraging scenarios can be modeled and optimal policies can be found using, for instance, dynamic programming. This approach provides us with novel hybrid model based on foraging behavior and clonal selection for a higher running time and with also a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces for control system.

A novel nano-biocomposite scaffold was fabricated in bead form by applying simple foaming method, using a combination of natural polymers─chitosan, gelatin, alginate and a bioceramic─nano-hydroxyapatite (nHAp). This approach of combining nHAp with natural polymers to fabricate the composite scaffold, can provide good mechanical strength and biological property mimicking natural bone. Environmental scanning electron microscopy (ESEM) images of the nano-biocomposite scaffold revealed the presence of interconnected pores, mostly spread over the whole surface of the scaffold. The nHAp particulates has covered the surface of the composite matrix and made the surface of the scaffold rougher. The scaffold has a porosity of 82% with a mean pore size of 112±19.0μm. Swelling and degradation studies of the scaffold showed that the scaffold possesses excellent properties of hydrophilicity and biodegradability. Short term mechanical testing of the scaffold does not reveal any rupturing after agitation under physiological conditions, which is an indicative of good mechanical stability of the scaffold. In vitro cell culture studies by seeding osteoblast cells over the composite scaffold showed good cell viability, proliferation rate, adhesion and maintenance of osteoblastic phenotype as indicated by MTT assay, ESEM of cell-scaffold construct, histological

Up to the present time, PID Controller has been widely used to control industrial process loops because of its implementational advantages. However, it is very difficult to achieve an optimal PID gain with no experience, since the parameters of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the PID controller using gain/phase margin and immune algorithm. After deciding optimal gain/phase margin specifications for the given process, the gains of PID controller using fitness value of immune algorithm depending on error between optimal gain/phase margin and the gain/phase margin obtained by tuning is tuned for the required response. To improve effectiveness of the suggested scheme, simulation results are compared with the FNN (Fuzzy Neural Network) based responses and illustrate more desirable performance.

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results sug...

A PID Controller has been used to operate this system because of its implementational advantages. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the PID Controller with disturbance rejection using immune network algorithm. To decide the performance of response, an ITSE (Integral of time weighted squared error) is used in this paper. 2 Control Characteristics of Thermal Power Plant for Controller Design A thermal power plant is mainly composed of one boiler whose steam output feeds one or two turbine, driving an electric generator. There can be many available models for each subsystem with a varying degree of complexity and accuracy. The models

Traditionally intelligence systems are popular in robotics, decision-making, multimedia processing, data mining etc. Recently, green technology and CO 2 gas reduction issues have been a major issue, paving the way for artificial intelligence researchers to formulate effective solutions. Therefore, there are good prospects for researchers to study green technology and C0 2 gas reduction problems because those issues are directly affecting our daily life. Energy saving and development, power management for renewable energy sources are very important for green technology and CO 2 gas reduction. This paper describes our application methods studied up to now for evaluating green technology.

This lecture deals with intelligent method based emotional actuator design approaches. Emotional intelligence has been described as the capacity to monitor and regulate one's own feeling and others' feelings, and to use feelings to guide thought and action by Salovey and Mayer in 1990. In light of the above agent definitions, we may have a different decision because entity whose state includes not only mental components of belief, capabilities, choices, and commitments, but emotional components of mood, preference, attitude, and feeling. Here, technologies for emotionally oriented control strategies or actuator design or programming architectures should be allowed to compute with consistency and appropriate relatedness between emotion and intellect. From the biological information processing view, emotional intelligence may be practically defined as the ability to use "emotional knowledge" in the mapping from percepts to actions. Recently, theoretically or neurologically we have commonly been researching that most artificial intelligences have come from emulating activities in bio areas or mathematical tools.

We propose a design approach to PID controllers with resistance to external disturbance in motor-controlled systems using a bacterial foraging-based optimal algorithm. PID controllers are used to operate AC motor drives because of their practical implementation and simple structure. Inexperienced personnel find it difficult, however, to achieve optimal PID gain because this is manually tuned by trial and error in industrial environments full of disturbances. To design disturbance-resistance tuning, we use disturbance-resistance conditions based on H∞ and calculcate response the performance based on bacterial foraging for the PID controller as an integral of time-weighted squared error. Hence, parameters for the PID controller are selected by our bacterial foraging-based optimal algorithm to obtain the required response.

This study introduces the control method of duct cleaning robot that enables real-time position tracking and self-driving over L-shaped and T-shaped duct sections. The developed robot has three legs and is designed to flexibly respond to duct sizes. The position of the robot inside the duct is identified using the UWB communication module and the location estimation algorithm. Although UWB communication has relatively large distance error within the metal, the positional error was reduced by introducing appropriate filters to estimate the robot position accurately.  TCP/IP communication allows commands to be sent between the PC and the robot and to receive live images of the camera attached to the robot. Using Haar-like and classifiers, the robot can recognize the type of duct that is difficult to overcome, such as L-shaped and T-shaped duct, and it moves successfully inside the duct according to the corresponding moving algorithms.

This paper suggests that the immune network algorithm based on fuzzy set can effectively be used in tuning of a PID controller for multivariable process or nonlinear process. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through t he stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used the fuzzy set in order that the stimulation and suppression relationship between antibody and antigen can be more adaptable controlled against the external condition, including noise or disturbance of plant. The immune network based on fuzzy set suggested here is applied for the PID controller tuning of multivariable process with two inputs and one output and is simulated. The result of study shows the artificial immune based on fuzzy set can effectively be used to tune the nonlinear process or the multivariable process, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods, against the noise or disturbance, various inputs, and coupling action between loops.

control, multivariable DCS control. Abstract—Process systems such as the raw water and chemical injection line in purification, the flow line of the waste water system, and the feed water, or the circulation system of a power plant system must be controlled accurately, because the system's performance and the energy saving rate in the whole system depend on the control method and precision. Generally, a PI controller is used in these systems, but it is very difficult to find an optimum parameter for the controller, because of the coupling action among loops and the disturbance in the system loop. There are few experimental systems or educational courses for such processes. This paper introduces an experimental method and educational course into the curriculum, to build up effective instruction of this complicated multivariable porcess system. Also, this paper proposes a new control method that changes the fluid system of a multivariable control loop and applies a NN-Tuning 2-DOF...

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper proposes a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR). Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model. Keyword:Fuzzy neural network; Immune algorithm; Multiobjective control; Optimzation.

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.