Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

Effects of Sunflower Artificial Aging on Seed Vigor and Physiological Characteristics

[Objective]The aim was to study the effects of sunflower artificial aging on seed vigor and physiological characteristics.[Method] The varieties of seed germination capacity,vigor of germination,germination index,vigor index,peroxidase（POD） activity,superoxide dismutase（SOD） activity and malondialdehyde（MDA） content for four sunflower germplasms such as＇SunM20＇,＇Deep Purple Minle＇,＇Da San Kui Hua 4＇and＇Ji Kui 24＇were studied under high temperature and high humidity conditions（100% RH,45 ℃） for different days（0 d,2 d,4 d,6 d,8 d,10 d）.[Result]The result showed that the germination capacity,vigor of germination,germination index,vigor index,POD activity,SOD activity declined gradually with the increase of seed aging days,whereas MDA content enhanced by degrees;The diggerences of resistance to artifical aging existed among the four accessions,＇SunM20＇was the most resistant one,and exhibited the strongest seed vigor,highest activities of two protective enzyme（POD,SOD） and lowest content of MDA at the uniform condition,moreover,the seed vigor and protective enzyme activities of＇SunM20＇changed slowest among the four materials during the aging process.[Conclusion]The distinct reduction of POD,SOD activities maybe the main reasons for the decrease of sunflower seed vigor at the artificial aging,and the gradual accumulation of a few MDA accelerated seeds aging.

NEW HYBRID AI-SVM ALGORITHM: COMBINATION OF SUPPORT VECTOR MACHINES AND ARTIFICIAL IMMUNE NETWORKS

Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased.

On social-oriented translator training in colleges and universities

With the deepening of globalization, the demand of translators is on the rise. The upsurge of translation does not mean that there is no any worry in translation teaching, since the translators trained in colleges and universities do not satisfy the social demands. The particularity of translation determines the peculiarity of training pattern, so the present translation teaching is out of date. Thus, the translator training should be oriented according to the social demands in certain degree. From the aspects of current situation, social demands, translation teaching and its reform, this paper aims at exploring the pattern of translator training in the light of social demands.

Intelligent PID guidance control for AUV path tracking

Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as planning level during the control system using transverse deviation is came up with. Continuous tracking of path expressed by a point sequence can be realized by the law. Firstly, a path tracking control system based on rational behavior model of three layers is designed, mainly satisfying the needs of underactuated AUV. Since there is no need to perform spatially coupled maneuvers, the 3D path tracking control is decoupled into planar 2D path tracking and depth or height tracking separately. Secondly, planar path tracking controller is introduced. For the reason that more attention is paid to comparing with vertical position control, transverse deviation in analytical form is derived. According to the Lyapunov direct theory, control law is designed using discrete PID algorithm whose parameters obey adaptive fuzzy adjustment. Reference heading angle is given as an output of the guidance controller conducted by lateral deviation together with its derivative. For the purpose of improving control quality and facilitating parameter modifying, data normalize modules based on Sigmoid function are applied to input-output data manipulation. Lastly, a sequence of experiments was carried out successfully, including tests in Longfeng lake and at the Yellow sea. In most challenging sea conditions, tracking errors of straight line are below 2 m in general. The results show that AUV is able to compensate the disturbance brought by sea current. The provided test results demonstrate that the designed guidance controller guarantees stably and accurately straight route tracking. Besides, the proposed control system is accessible for continuous comb-shaped path tracking in region searching.

New steering mechanism for wheeled mobile robots

A new castor wheel mechanism for omni-directional mobile platform is presented. A motion of translation is transformed into a rotation to steer the wheel with the help of a helical path fits into a translation joint and three rollers whose axes are connected to the driving shaft of the wheel. When the path moves in translation it acts on the rollers for steering. The path-roller friction transmission, the wheel kinematics and the maneuverability have been analyzed.

Experimental Study on Cultivation of Multi-head Seedlings of Toona sinensis in Chongqing

Seedlings of Toona sinensis were planted in seeding and breeding ways to increase the yield of T. sinensis by raising the number of lateral branches of T. sinensis seedlings by means of cutting and pinching through sowing and breed- ing in containers and sand bed nursery. The results showed that： after cutting and pinching, seedlings with multi-lateral branches could be formed. The cutting treat- ment in the container had a small number of seedlings with over 2 lateral buds, and the lateral buds were weak. Some of the weak lateral buds disappeared with the growth going on, showing poor growth performance. The pinching treatment in the sand bed nursery found a large number of seedlings with more than 2 lateral buds, and the lateral buds could grow into lateral branches, forming the T. sinensis seedlings with multi-lateral branches. Proceed to cutting processing, the secondary and third-level lateral branches came into being, which could make the seedlings of T. sinensis have multi-head tree shape in 2-3 years.

Face Orientation Normalization Using Eye Positions

Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.

NEW RANGE ALIGNMENT ALGORITHM FOR ISAR BASED ON HIGH ORDER MOMENT

A new technique is proposed for range alignment in Inverse Synthetic Aperture Radar （ISAR）. The basic idea is to perform range alignment using a maximum kurtosis （fourth-order central moment） criterion. After maximizing the kurtosis of the combined range profile of two adjacent echoes, the amount of range shift between them can be automatically tracked out. The combined range profile is constructed by a max operation, which only reserves the larger elements of the two echoes, and the echoes＇ amplitudes are limited before they are combined. This algorithm has bee~ used to process real ISAR data and the results demonstrate the effectiveness of the method. Compared with the correlation method and the minimum entropy method, the proposed algorithm obtains much better results in both examples in this paper. Its computation complexity has the same order of magnitude as the minimum entropy method.

Influence of dominance rank and affiliation relationships on selfdirected behavior in female Tibetan macaques (Macaca thibetana)

Self-directed behavior(SDB) is characterized as an indicator of anxiety, frustration and stress in nonhuman primates. In this study, we collected self-directed behavior data from one group of free-ranging Tibetan macaques(Macaca thibetana) at Mt. Huangshan, China(September 2012–May 2013) using a combination of behavioral sampling methods including focal animal sampling, behavioral sampling, continuous sampling and instantaneous sampling. Our results showed that females engaged in significantly higher rates of self-directed behavior when they were in proximity to dominant individuals compared to subordinate ones. Conflict losers significantly increased their SDB rates after agonistic episodes, indicating that SDB might also serve as an index of anxiety in M. thibetana. We further found that females significantly increased their SDB rates when focal individual was proximity to weakly affiliation relationship higher rank members than to strongly affiliation relationship higher rank members. If conflicts were not reconciled, the postconflict SDB rates of losers were higher when they stayed with strongly affiliation opponents; if conflicts were reconciled, victims of strongly affiliation relationships opponents engaged in more SDB rates before reconciliation than after reconciliation, while victims of moderately affiliation relationships opponents did not engaged in more SDB rates before reconciliation than after reconciliation. We conclude that both of dominance rank and affiliation relationships might both influence the SDB rates of female Tibetan macaques significantly, suggesting that SDB is not only an index of anxiety in Tibetan macaques, but also can provide a new insight into evaluation of social relationships between individuals.

Improved BP Neural Network for Transformer Fault Diagnosis

The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method (RTR). Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR.

Study on Distributed Motion of Self-Reconfigurable Robot Based on Local Rules

The eigenvector of a module with six adjacent module's state was constructed according to self-reconfigurable robot M-Cubes and the configuration of system was expressed with the eigenvectors of all modules.According to the configuration and motion characteristics of the modules,a 3-dimension motion rule set was provided.The rule sets of each module was run according to eigenvector of the module after the motion direction of system decided and motion rules were selected.At last,the rapid and effective motion and metamorphosis were realized in system.The rule sets are operated on three systems and the distributed motion of system is fully realized.The result of simulation shows that the 3-dimension motion rule sets has perfect applicability and extensibility.The motion steps and communication load of the modules increase with the module number in linear.

Orientation of larval and juvenile horseshoe crabs Limulus polyphemus to visual cues: Effects of chemical odors

Adult horseshoe crabs Limulus polyphemus have long served as models for the study of vision in marine arthropods. Yet, little is known about the ability of early life history stages to detect and respond to visual cues. We examined the visually directed movements of larvae and first stage juveniles to horizons containing dark visual targets of different sizes. The study tested the hypotheses that （1） larval and juvenile crabs can detect and respond to visual targets and （2） the direction of orientation varies with the presence of chemical cues associated with settlement habitats. Orientation of larval and juvenile crabs to rectangles subtending angles from 30--330~ was tested in a circular arena containing water that either lacked estuarine chemical cues （offshore water） or contained odors from aquatic vegetation or known predators. In the absence of chemical odors, larvae oriented toward and juveniles moved away from dark horizons subtending angles 〉 60~. When placed in water containing chemical odors from potential nursery habitats, including the seagrasses Halodule wrightii and Syringodium filiforme, crabs reversed their direction of orientation relative to their responses in offshore water. Odors from two known predators, the mummichug Fundulus grandis and blue crab Callinectes sapidus, had no affect on the orientation of larvae. Yet, juveniles responded to both odors by moving toward the visual target. Results support the hypothesis that the visual orientation of larval and juvenile horseshoe crabs changes upon exposure to habitat and predator cues and that the direction of the response undergoes an ontogenetic shift following metamorphosis

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.

Population and Environment:Necessary Transition Initiatives for Developing Countries

Now environment is an important topic in academic field.Many researches focus on the negative outcomes of nature which are being continually created by human and much attention has been paid to how the environment is protected through integrated research,movement and policy.But few studies are concentrated on population and environment and are to find out the interconnectivity and underlying mechanism that have an effect on people's preferring additional children and perception to environment.Many researchers claim that population growth is a great problem for environment but they do not provide the indepth integrated knowledge and mechanism that influence fertility trend and environmental problems.It is very crucial to develop practical and concrete initiatives to confirm a transition to reduce population growth and redirect the perception on population and environment.

Modeling and motion control simulation of tendon based parallel manipulator translation mechanism for sensor based high value waste processing

A novel sorting system based on one degree of freedom (DOF) tendon based parallel manipulator (TBPM) for high value waste processing was presented and designed. In order to control the motion of loads, nonlinear state feed forward control algorithm in the tendon length coordinate was used. Considering the system redundancy and actuation behavior, algorithms of optimal tension distribution and forward kinematics were designed. Then, the simulation experiments of motion control were implemented. The results demonstrate that the proposed TBPM translation system performs robust capacities. It can transfer the loads 1 m away within 1.5 s. With further optimization, the translation duration can be further reduced to be about 1 s and the optimized translation is followed with 43.59 m/s2 maximum acceleration. The translation errors at the aim position remain below 0.4 mm.

Nonlinear dynamics and coupling effect of libration and vibration of tethered space robot in deorbiting process

In order to control the growth of space debris,a novel tethered space robot(TSR) was put forward.After capture,the platform,tether,and target constituted a tethered combination system.General nonlinear dynamics of the tethered combination system in the post-capture phase was established with the consideration of the attitudes of two spacecrafts and the quadratic nonlinear elasticity of the tether.The motion law of the tethered combination in the deorbiting process with different disturbances was simulated and discussed on the premise that the platform was only controlled by a constant thrust force.It is known that the four motion freedoms of the tethered combination are coupled with each other in the deorbiting process from the simulation results.A noticeable phenomenon is that the tether longitudinal vibration does not decay to vanish even under the large tether damping with initial attitude disturbances due to the coupling effect.The approximate analytical solutions of the dynamics for a simplified model are obtained through the perturbation method.The condition of the inter resonance phenomenon is the frequency ratio λ_1=2.The case study shows good accordance between the analytical solutions and numerical results,indicating the effectiveness and correctness of approximate analytical solutions.

Face mask detection algorithm based on HSV+HOG features and SVM

To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.

Precise navigation for a 4WS mobile robot

Position and orientation estimation with high accuracy based on GPS and encoders for a four-wheel-steering vehicle (4WS) mobile robot is presented. A GPS receiver working in Real-Time Kinematics (RTK) mode can offer centimeter-level accuracy for our vehicle. In addition to GPS, the vehicle is equipped with four incremental encoders and two absolute encoders to provide information on wheels for estimation of velocity and sideslip angle of vehicle. The proposed architecture of position and orientation estimation consists of two extended Kalman filters and a processing unit of Runga-Kutta based dead reckoning. The first EKF fuses data from six encoders to estimate the vehicle velocity and the sideslip angle. The second EKF is applied to the estimation of position and orientation based on the measurement from precise RTK GPS data and output from first EKF. To obtain higher accuracy of estimation, an arbitrator is designed to switch between EKF2 and dead reckoning. The results and analysis of experiments are presented to show the effectiveness of the proposed approach. Limitations of the proposed approach and future works are also pointed out and discussed in this paper.