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1572. A Neo-Babylonian Colony in Transpotamia (?) /
- Creator:
- Dandamajev, Muchammed Abdulkadyrovič,
- Subject:
- kolonie and Přední Asie v pravěku a starověku
- Language:
- English
- Rights:
- unknown
1573. A Nephrite Axe from the Jeseník Museum /
- Creator:
- Přichystal, Antonín,
- Subject:
- sbírky muzejní, eneolit, nástroje kamenné, and české země v eneolitu
- Language:
- English
- Description:
- Nefritová sekerka z Vlastivědného muzea Jesenicka.
- Rights:
- unknown
1574. A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm
- Creator:
- Tian , Z., Li , S., Wang, Y., and Wang, X.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- network traffic, grey model, Elman neural network, prediction, and improved harmony search
- Language:
- English
- Description:
- The telecommunication and Ethernet trafic prediction problem is studied. Network traffic prediction is an important problem of telecommunication and Ethernet congestion control and network management. In order to improve network traffic prediction accuracy, a network traffic hybrid prediction model was proposed by using the advantages of grey model and Elman neural network, grey model and Elman neural network predictive values were independently obtained, the different weight coefficients of two prediction models were given. In terms of weight coefficients optimization, an improved harmony search algorithm with better convergence speed and accuracy was proposed, the optimal weight coefficients of network traffic hybrid prediction model were determined through this algorithm, two prediction models results were multiplied by the weight coefficients to obtain the final prediction value. The network traffic sample data from an actual telecommunication network was collected as simulation object. The simulation results verified that the proposed network traffic hybrid prediction model based on improved harmony search algorithm has higher prediction accuracy.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1575. A Neural Network Approach for Assessing the Relationship between Grip Strength and Hand Anthropometry
- Creator:
- Cakit, Erman, Durgun , Behice , and Cetik , Oya
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- hand dimensions, grip strength, artificial neural network, stepwise regression analysis, and sensitivy analysis
- Language:
- English
- Description:
- This study aimed to determine grip strength data for Turkish dentistry students and developed prediction models that allow: i) investigation of the relationship between grip strength and hand anthropometry using artificial neural networks (ANNs) and stepwise regression analysis, ii) prediction of the grip strength of Turkish dentistry students, and iii) assessment of the potential impact of hand anthropometric variables on grip strength. The study included 153 right-handed dentistry students, consisting of 81 males and 72 females. From 44 anthropometric and biomechanical measurements obtained from the right hands of the participants; five anthropometric measurements were selected for ANN and regression modeling using stepwise regression analysis. We included stepwise regression analysis results to assess the predictive power of the neural network approach, in comparison to a classical statistical approach. When the model accuracy was calculated based on the coefficient of determination (R2), the root mean squared error (RMSE) and the mean absolute error (MAE) values for each of the models, ANN showed greater predictive accuracy than regression analysis, as demonstrated by experimental results. For the best performing ANN model, the testing values of the models correlated well with actual values, with a coefficient of determination (R2) of 0.858. Using the best performing ANN model, sensitivity analysis was applied to determine the effects of hand dimensions on grip strength and to rank these dimensions in order of importance. The results suggest that the three most sensitive input variables are the forearm length, the hand breadth and the finger circumference at the first joint of digit 5 and that the ANNs are promising techniques for predicting hand grip strength based on hand breadth, finger breadth, hand length, finger circumference and forearm length.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1576. A neural network approach for global optimization with applications
- Creator:
- Li, Leong - Kwan and Shao, S.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Global optimization, nonlinear least square problem, and state space search algorithm
- Language:
- English
- Description:
- We propose a neural network approach for global optimization with applications to nonlinear least square problems. The center idea is defined by the algorithm that is developed from neural network learning. By searching in the neighborhood of the target trajectory in the state space, the algorithm provides the best feasible solution to the optimization problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. Our examples show that the method is effective and accurate. The simplicity of this new approach would provide a good alternative in addition to statistics methods for power regression models with large data.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1577. A neural network based selection method for genetic algorithms
- Creator:
- Yalkin, Can and Korkmaz, Erkan
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Genetic algorithms, neural networks, selection, and hybrid algorithms
- Language:
- English
- Description:
- Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1578. A neural network based summarizing method of periodic image sequences
- Creator:
- Berkane, Mohamed, Clarysse, Patrick, Njiwa , Josiane Yankam , Zhu , Yue Min , and Magnin , Isabelle E.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Neural network, periodic motion, image sequence, and summarozed sequence
- Language:
- English
- Description:
- This work relates to the study of periodic events such as the ones that can be observed in biomedicine. Currently, biological processes exhibiting a periodic behaviour can be observed through the continuous recording of signals or images. Due to various reasons, cycle duration may slightly vary over time. For further analysis, it is important to be able to extract meaningful information from the mass of acquired data. This paper presents a new neural network based method for the extraction of a summarized cycle from long and massive cycle recordings. Its concept is simple and it could be naturally implemented on a hardware architecture to speed up the process. The proposed method is demonstrated on synthetic image sequences of the beating heart, and exploited as a prior in a new approach for the fast reconstruction of Magnetic Resonance Image sequences.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1579. A neural network controller for unmanned underwater vehicles
- Creator:
- Kodogiannis, V. and Tomtsis, D.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- neural networks, underwater vehicles, stability theory, adaptive control, and marine systems
- Language:
- English
- Description:
- Underwater robotic vehicles have become an important tool for various underwater tasks because they háve greater speed, endurance, depth capability, and safety than human divers. The problem of controlling a remotely operated underwater vehicle in 6 degrees of freedom (DOF) is addressed in this paper, as an example of a system containing severe non-linearities. Neural networks are been used in a closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the bounded-ness of signals are guaranteed by applying Barbalaťs Lyapunov-like lemma. In this páper, a neural network architecture based on radial basis functions has been ušed to evaluate the performance of the proposed adaptive controller for the motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV).
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
1580. A neural network for analysis of vibration in mechanical systems arising from unbalance
- Creator:
- Kalkat, Menderes, Yildirim, Sahin, and Uzmay, Ibrahim
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- vibration, rotating unbalance, displacement, velocity, acceleration, and neural network
- Language:
- English
- Description:
- The paper preseiits an investigation on vibrations of mechanical systems arising from unbalanced masses. At the experimental stage, a power transmission shaft is driven at different operating speeds, therefore, the paranieters, such as displacement, velocity and acceleration in vertical direction due to body vibrations are measured at various points on the frame before and after balancing. Balancing has provided a definite decrease in the amplitudes of vibration parameters. In addition to these studies mentioned above, the use of Neural Network (NN) for vibration analysis of a frame due to unbalanced transmission shaft is also achieved. The results show that the NN approach exactly follows the foregoing results. This implies the necessity of the non-linear modelling capabilities of the NN for vibration problems of mechanical systems.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public