A Flexible Hingeless Control Surface Inspired by Aquatic Animals

##正## A flexible hingeless control surface model was proposed for motion control of Underwater Vehicles (UVs),which is inspiredby the flexible bending control surfaces of underwater creatures,such as fish and squid.Computational Fluid Dynamics(CFD) simulation demonstrates that,in comparison with the hinged or rigid control surface,the proposed flexible bendingcontrol surface can suppress the flow separation so as to improve the turning performance.A prototype of the flexible controlsurface was fabricated,in which Shape Memory Alloy (SMA) wires were selected as the actuators.The elastic energy storageand exchange mechanism was incorporated into the actuation of the control surface to improve the efficiency.Thermal analysisof SMA wires was performed to find proper actuating condition.Open-loop bending experiments were carried out.The resultsshow that the proposed control surface can achieve the maximum bending angle of 104°.Moreover,the power and energyconsumption under different pulse conditions were compared.     

基于人工神经网络-遗传算法的樟芝发酵培养基优化

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模,并获得最优发酵培养基组成.对樟芝发酵过程中的形态变化过程进行了观察,并分别采用人工神经网络(ANN)和响应面法(RSM)对樟芝发酵过程进行建模,同时采用遗传算法(GA)优化了发酵培养基组成.结果表明,ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力,GA计算得到樟芝生物量理论最大值为6.2 g/L,并获得发酵最佳接种量及培养基组成:孢子浓度1.76× 105个/mL,葡萄糖29.1 g/L,蛋白胨9.4 g/L,黄豆粉2.8 g/L.在最佳培养条件下,樟芝生物量为(6.1±0.2)g/L.基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程,从而获得生物量或活性代谢产物.     

Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.     

Optimal Control of a Mackerel-Mimicking Robot for Energy Efficient Trajectory Tracking

A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel＇s Carangiform motion. Hydrodynamic characteristics of a fish-mimetic test model are experimentally identified and utilized in order to numerically simulate fish swimming. The discrete set of kinematic and dynamic parameters are obtained by considering required horizontal and lateral forces and minimum energy consumption. Using the optimized parameter set, optimal control of the robot is studied.     

Effect of velocity and added resistance on selected coordination and force parameters in front crawl

The effect of (a) increasing velocity and (b) added resistance was examined on the stroke (stroke length, stroke rate [SR]), coordination (index of coordination [IdC], propulsive phases), and force (impulse and peaks) parameters of 7 national-level front crawl swimmers (17.14 ± 2.73 years of swimming; 57.67 ± 1.62 seconds in the 100-m freestyle). The additional resistance was provided by a specially designed parachute. Parachute swimming (PA) and free-swimming (F) conditions were compared at 5 velocities per condition. Video footage was used to calculate the stroke and coordination parameters, and sensors allowed the determination of force parameters. The results showed that (a) an increase in velocity (V) led to increases in SR, IdC, propulsive phase duration, and peak propulsive force (p < 0.05), but no significant change in force impulse per cycle, whatever the condition (PA or F); and (b) in PA conditions, significant increases in the IdC, propulsive phase duration, and force impulse and a decrease in SR were recorded at high velocities (p < 0.05). These results indicated that, in the F condition, swimmers adapted to the change in velocity by modifying stroke and coordination rather than force parameters, whereas the PA condition enhanced the continuity of propulsive action and force development. Added resistance, that is, "parachute training," can be used for specific strength training purposes as long as swimming is performed near maximum velocity.     

Horizontal visibility of an underwater low-resolution video camera modeled by practical parameters near the sea surface

Underwater video cameras can be used for fisheries, aquacultures, and marine ecology to monitor the existence of fish. In order to assess marine structures, monitoring is also important to understand the effects on the local fish community. Underwater vision is sometimes degraded due to the condition of the water and sunlight. In particular, horizontal visibility is not easily obtained with regard to water quality. So far, an empirical relationship with horizontal visibility has been described according divers' vision, and it states that divers' horizontal visibility is approximately equal to 0.7 times the Secchi depth (Wright and Colling, 1995). If the video camera has a high resolution, visibility may be similar, however, if the camera has a low resolution, detectable distance can deteriorate. For practical usage, a video camera with low resolution has the advantage of reducing storage of data and it is easy to transmit video remotely, in other words, real-time monitoring. Real-time data will provide a prompt understanding of aquatic environmental changes. In order to obtain such data, a low-resolution video camera is still required for usage in a field. There are few models that describe horizontal visibility using a camera in water, therefore this study aims to model one. This paper showed a new approach to model horizontal visibility by water depth and Secchi depth, which are practical and obtainable parameters for underwater monitoring. In addition to this, global solar radiation is adopted for modeling. The model is expressed as relative contrast between target and background. Based on two observations near the sea surface (less than Secchi depth), the model is created. The model showed plausible horizontal visibility using an underwater video camera for real-time monitoring of fish and marine environment.     

Research on Location Characteristics and Algorithms based on Frequency Domain for a 2D Underwater Active Electrolocation Positioning System

Weakly electric fish has an ability to generate a low-frequency electric field actively to locate the surrounding object in complete darkness by sensing the change of the electric field.This ability is called active electrolocation.In this paper,we designed a two-dimensional (2D) experimental platform of underwater active electrolocation system by simulating weakly electric fish.On the platform,location characteristics based on frequency domain were investigated.Results indicated that surface shape of 3D location characteristic curves for the 2D underwater active electrolocation positioning system was convex upwards or concave down which was influenced by the material of probed objects and the frequency of the electric field excitation signal.Experiments also confirmed that the amplitude of the electric field excitation signal and the size of the probed object will only influence the amplitude corresponding to 3D location characteristic curves.Based on above location characteristics,we present three location algorithms including Cross Location Algorithm (CLA),Stochastic Location Algorithm (SLA) and Particle Swarm Optimization (PSO) location algorithm in frequency domain and achieved the task of the underwater positioning system.Our work may have reference value for underwater detection study.     

A combined steepest descent and genetic algorithm (SD/GA) approach for the optimization of solvation parameters

An accurate solvation model is essential for computer modeling of protein folding and other biomolecular self-assembly processes. Compared to explicit solvent models, implicit solvent models, such as the Poisson-Boltzmann (PB) with solvent accessible surface area model (PB/SA), offer a much faster speed—the most compelling reason for the popularity of these implicit solvent models. Since these implicit solvent models typically use empirical parameters, such as atomic radii and the surface tensions, an optimal fit of these parameters is crucial for the final accuracy of properties such as solvation free energy and folding free energy. In this paper, we proposed a combined approach, namely SD/GA, which takes the advantage of both local optimization with the steepest descent (SD), and global optimization with the genetic algorithm (GA), for parameters optimization in multi-dimensional space. The SD/GA method is then applied to the optimization of solvation parameters in the non-polar cavity term of the PB/SA model. The results show that the newly optimized parameters from SD/GA not only increase the accuracy in the solvation free energies for ～200 organic molecules, but also significantly improve the free energy landscape of a β-hairpin folding. The current SD/GA method can be readily applied to other multi-dimensional parameter space optimization as well.     

Microhabitat use by brown trout, Salmo trutta L. and Atlantic salmon, S. salar L., in a stream: a comparative study of underwater and river bank observations

Two methods, visual observation from the river bank and visual observation underwater by diving, were compared for microhabitat studies in young brown trout and Atlantic salmon in a stream. A wide range of habitat conditions were surveyed. Each method yielded different results with respect to microhabitat use. River bank observations missed small fish under surface turbulence and in deeper waters. Underwater observations missed small fish in shallow areas.     

Dynamic Modeling and Experiment of a Fish Robot with a Flexible Tail Fin

This paper presents the dynamic modeling of a flexible tail for a robotic fish. For this purpose firstly, the flexible tail was simplified as a slewing beam actuated by a driving moment. The governing equation of the flexible tail was derived by using the Euler-Bernoulli theory. In this equation, the resistive forces were estimated as a term analogous to viscous damping. Then, the modal analysis method was applied in order to derive an analytical solution of the governing equation, by which the relationship between the driving moment and the lateral movement of the flexible tail was described. Finally, simulations and experiments were carried out and the results were compared to verify the accuracy of the dynamic model. It was proved that the dynamic model of a fish robot with a flexible tail fin well explains the real behavior of robotic fish in underwater environment.     

Systematic search for the rate constants that control the exocytotic process from chromaffin cells by a genetic algorithm

We have recently created a kinetic model that reproduces the dynamics of exocytosis with high accuracy. The reconstruction necessitated a search, in a multi-dimensional parameter space, for 37 parameters that described the system, with no assurance that the parameters, which reconstructed the observations, are a unique set. In the present study, a Genetic Algorithm (GA) was used for a thorough search in the unknown parameter space, using a strategy of gradual increase of the complexity of the analyzed input data. Upon systematic incorporation of one to four measurable parameters, used as input signals for the analysis, the constraint set on the GA search imposed the convergence of the free parameters into a single narrow range. The mean values for each adjustable parameter represent a minimum for the fitness function in the multi-dimensional parameter space. The GA search demonstrates that the parameters that control the kinetics of exocytosis are the rate constants of the steps downstream to synaptotagmin binding, and that the equilibrium constant of the binding of calcium to Munc13 controls the calcium-dependent priming process. Thus, the systematic use of the GA creates a link between specific reactions in the process of exocytosis and experimental phenotypes.     

Underwater acoustics in marine fisheries and fisheries research

Underwater acoustics enables the detection and precise location of fish and is therefore a prerequisite for effective fishing methods such as pelagic trawling and purse seining. The application of acoustic instruments to detect fish and monitor gear performance in modern commercial fisheries is outlined. The latest developments in obtaining information such as bottom roughness and determining such characteristics of fish detected as size and species are presented.Echo integration is now widely used to estimate the abundance of commercially important fish stocks. The principles of the method are outlined briefly, and special emphasis is put on such effects of fish behaviour as the dramatic influence of fish orientation on its backscattering cross section, the possible effects of vessel avoidance, and the uncertainties connected with spatial variability.The use of acoustic tags, echosounders and sonar to study and quantify fish behaviour and distribution is outlined, with particular attention to new developments that provide detailed information on fish behaviour and distribution in relation to environmental parameters.Future developments and improvements in the application of underwater acoustics to commercial fisheries and fisheries research are suggested     

Ecology of fish hearing

Underwater sound is directional and can convey important information about the surrounding environment or the animal emitting the sound. Therefore, sound is a major sensory channel for fishes and plays a key role in many life-history strategies. The effect of anthropogenic noise on aquatic life, which may be causing homogenisation or fragmentation of biologically important signals underwater is of growing concern. In this review we discuss the role sound plays in the ecology of fishes, basic anatomical and physiological adaptations for sound reception and production, the effects of anthropogenic noise and how fishes may be coping to changes in their environment, to put the ecology of fish hearing into the context of the modern underwater soundscape.     

Genetic algorithm based fuzzy logic control of a fed-batch fermentor

In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.     

An SOM-based algorithm for optimization with dynamic weight updating

The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning efficiency; this may dynamically adjust the neighborhood function for the SOM in learning system parameters. As a demonstration, the proposed SOMS is applied to function optimization and also dynamic trajectory prediction, and its performance compared with that of the genetic algorithm (GA) due to the similar ways both methods conduct searches.     

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes.     

Fish and chips: implementation of a neural network model into computer chips to maximize swimming efficiency in autonomous underwater vehicles

Recent developments in the design and propulsion of biomimetic autonomous underwater vehicles (AUVs) have focused on boxfish as models (e.g. Deng and Avadhanula 2005 Biomimetic micro underwater vehicle with oscillating fin propulsion: system design and force measurement Proc. 2005 IEEE Int. Conf. Robot. Auto. (Barcelona, Spain) pp 3312-7). Whilst such vehicles have many potential advantages in operating in complex environments (e.g. high manoeuvrability and stability), limited battery life and payload capacity are likely functional disadvantages. Boxfish employ undulatory median and paired fins during routine swimming which are characterized by high hydromechanical Froude efficiencies (approximately 0.9) at low forward speeds. Current boxfish-inspired vehicles are propelled by a low aspect ratio, 'plate-like' caudal fin (ostraciiform tail) which can be shown to operate at a relatively low maximum Froude efficiency (approximately 0.5) and is mainly employed as a rudder for steering and in rapid swimming bouts (e.g. escape responses). Given this and the fact that bioinspired engineering designs are not obligated to wholly duplicate a biological model, computer chips were developed using a multilayer perception neural network model of undulatory fin propulsion in the knifefish Xenomystus nigri that would potentially allow an AUV to achieve high optimum values of propulsive efficiency at any given forward velocity, giving a minimum energy drain on the battery. We envisage that externally monitored information on flow velocity (sensory system) would be conveyed to the chips residing in the vehicle's control unit, which in turn would signal the locomotor unit to adopt kinematics (e.g. fin frequency, amplitude) associated with optimal propulsion efficiency. Power savings could protract vehicle operational life and/or provide more power to other functions (e.g. communications).     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

Design Study of a Cable-based Gait Training Machine

This paper deals with a design approach of a gait training machine based on a quantitative gait analysis.The proposed training machine is composed of a body weight support device and a cable-driven parallel robot.This paper is focused on the cable-driven robot,which controls the pose of the lower limb through an orthosis placed on the patient's leg.The cable robot reproduces a normal gait movement through the motion of the orthosis.A motion capture system is used to perform the quantitative analysis of a normal gait,which will be used as an input to the inverse dynamic model of the cable robot.By means of an optimization algorithm,the optimal design parameters,which minimize the tensions in the cables,are determined.Two constraints are considered,i.e.,a non-negative tension in the cables at all times,and a free cable/end-effector collision.Once the optimal solution is computed,a power analysis is carried out in order to size the robot actuators.The proposed approach can be easily extended for the design study of a similar type of cable robots.     

Determining the Optimum Power Load in Jump Squat Using the Mean Propulsive Velocity

The jump squat is one of the exercises most frequently used to improve lower body power production, which influences sports performance. However, the traditional determination of the specific workload at which power production is maximized (i.e., optimum power load) is time-consuming and requires one-repetition maximum tests. Therefore, the aim of this study was to verify whether elite athletes from different sports would produce maximum mean propulsive power values at a narrow range of mean propulsive velocities, resulting in similar jump heights. One hundred and nine elite athletes from several individual/team sport disciplines underwent repetitions at maximal velocity with progressive loads, starting at 40% of their body mass with increments of 10% to determine the individual optimum power zone. Results indicated that regardless of sport discipline, the athletes’ optimum mean propulsive power was achieved at a mean propulsive velocity close to 1.0 m.s⁻¹ (1.01 ± 0.07 m.s⁻¹) and at a jump height close to 20 cm (20.47 ± 1.42 cm). Data were narrowly scattered around these values. Therefore, jump squat optimum power load can be determined simply by means of mean propulsive velocity or jump height determination in training/testing settings, allowing it to be implemented quickly in strength/power training.