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Inverse kinematics is a general method for defining the joint angles of the robot arm. This method provides an efficient way to control the robot arm for several tasks. However, the server motors or the mechanism design of the robot arm may not always be ideal. If the motor consumption is existed, the error of the final position of the robot arm will be increased. In order to solve this problem, this...
Machine learning, a powerful technique for building models, can rapidly provide accurate predictions. Since Integrated Circuit (IC) design and manufacturing have tremendously high complexity and enormous data, there is a surge in adapting machine learning approach in IC Design stages, as machine learning can provide fast predictions. Recently, machine learning has been used in some IC Design stages...
Text line detection and localisation is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a novel approach for text line localisation based on Convolutional Neural Networks and Multidimensional Long Short-Term Memory cells as a regressor in order to predict the coordinates of the text line bounding boxes directly...
Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation...
In this study, we present a new phoneme-based deep neural network (DNN) framework for single microphone speech enhancement. While most speech enhancement algorithms overlook the phoneme structure of the speech signal, our proposed framework comprises a set of phoneme-specific DNNs (pDNNs), one for each phoneme, together with an additional phoneme-classification DNN (cDNN). The cDNN is responsible...
Driving environment is a complex acoustic environment with a variety of noises. It requires more precise speech enhancement of noisy signals, for speech recognition when driving. In this paper, a new two-stage neural network speech enhancement algorithm is proposed. First, the feature vector of noisy signals is used to training BP network and RBP network. And then, the BP and RBP networks are merged...
This paper considers the usage of artificial intelligence, in particular, neural networks, to correct and compensate thermocouple errors. There are the correction of the thermocouple tolerance, the error due to conversion characteristic drift under the influence of high operating temperatures as well as the compensation of the error due to acquired thermoelectric inhomogeneity of thermocouple legs...
In this study, we investigate the control performance of an adaptive controller using multilayer hypercomplex-valued neural networks, such as complex, hyperbolic, bicomplex and quaternion neural networks. The control system is a direct controller whereby the control input of a plant is synthesized online using the multilayer hypercomplex-valued neural network to track the plant output to the desired...
Plants are related to human. Recognize an unfamiliar plant correctly without any expert understanding is big task. Due to Improvement in image processing, it is likely to know leaf image rapidly from which species it is. Pulse coupled neural network is a helpful tool for feature extraction. Entropy sequence is key feature which is obtained from pulse-coupled neural network. Along with entropy sequence...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Text detection is a difficult task due to the significant diversity of the texts appearing in natural scene images. In this paper, we propose a novel text descriptor, SPP-net, extracted by equipping the Convolutional Neural Network (CNN) with spatial pyramid pooling. We first compute the feature maps from the original text lines without any cropping or warping, and then generate the fixed-size representations...
In still images, multi-scale regions contain rich information of different granularity. However, only semantically meaningful regions provide auxiliary cues for action recognition. Moreover, regions at different scales contribute differently. Motivated by the two observations, we propose an approach that is composed of three components: 1) detecting semantic region candidates at multiple scales, 2)...
The Human Activity Recognition is a context awareness application, which has, for example, sports, security and health monitoring applications. As a way to acquire the human activity data, there are external approaches (e.g. cameras data) and embedded approaches (e.g. accelerometer data). In this area, we can find solutions using multiple sensors simultaneously supporting the real time data acquisition...
This research proposes an approach for text classification that uses a simple neural network called Dynamic Text Classifier Neural Network (DTCNN). The neural network uses as input vectors of words with variable dimension without information loss called Dynamic Token Vectors (DTV). The proposed neural network is designed for the classification of large and short text into categories. The learning...
In this paper we present a novel architecture design called SpecNN for artificial neural networks. Our approach allows to consider prior probability distributions and leverage samples similarity to handle the problem of fine-grained samples, thus improving the classification accuracy. We present two different learning algorithms for SpecNN. SpecNN is especially useful in moot cases, when classification...
Satellite imagery analysis is becoming increasingly important in a variety of fields. Automation of analysts' workflows has the potential to greatly increase their efficiency and effectiveness. Specifically, developing automatic parking lot extraction from overhead imagery has huge importance to predicting stores future sales. However, to the best of our knowledge, there is no prior work for the development...
The establishment of early warning model of steel industry based on BP neural network is discussed in this paper. The topology of the network chooses three layers of BP network structure. Hidden layer nodes selects Sigmoid function as the activation function, and the output layer select purelin function as the activation function. Error function and capacity utilization composite index are combined...
An open world turn based monster battle game was developed in Java using the popular LibGDX game framework applying multiple machine learning algorithms for its mechanics consisting of an ID3 decision tree, perceptron, naïve Bayes classifier and A∗ pathfinding in an attempt to imitate ‘machine intelligence’. A tiled map was used as the game area containing multiple AI agents with different personalities...
A real-time neuro car detection system based on the Haar-like feature is presented in this paper. The proposed system relies on an artificial neural network (ANN) to recognize the car object. ANN was trained using the Haar-like features extracted from the negative and positive car image data. The car objects vary with their sizes and trademarks. However, they have common features which can be assumed...
Biometrics play a crucial role in establishing an individuals identity. A signature is one of the most widely recognized way to authorize transactions and authenticate the human identity as compared to other electronic identification methods such as fingerprint and retina scans. Due to a huge demand for authentication, fast algorithms need to be assimilated for signature recognition and verification...
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