scholarly journals Target Tracking Algorithm for Table Tennis Using Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hongtu Zhao ◽  
Fu Hao
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Deep Neural Network ◽  
Tracking Algorithm ◽  
Tennis Ball ◽  
Table Tennis ◽  
Ball Speed ◽  
Position Information ◽  
Offline Learning ◽  
Hidden Layer

The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game’s data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

scholarly journals Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

2020 ◽  
Vol 10 (5) ◽  
pp. 1657 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Training Data ◽  
Open Pit ◽  
Percentage Error ◽  
Open Pit Mines ◽  
Determination Coefficient ◽  
Packet Data ◽  
Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

scholarly journals Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network

2021 ◽  
Vol 7 (2) ◽  
pp. 108-118
Author(s):  
Erwin Yudi Hidayat ◽  
Raindy Wicaksana Hardiansyah ◽  
Affandy Affandy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Application Programming Interface ◽  
Learning Rate ◽  
Application Programming ◽  
Hidden Layer ◽  
Programming Interface

Dalam menaikkan kinerja serta mengevaluasi kualitas, perusahaan publik membutuhkan feedback dari masyarakat / konsumen yang bisa didapat melalui media sosial. Sebagai pengguna media sosial Twitter terbesar ketiga di dunia, tweet yang beredar di Indonesia memiliki potensi meningkatkan reputasi dan citra perusahaan. Dengan memanfaatkan algoritma Deep Neural Network (DNN), neural network yang tersusun dari layer yang jumlahnya lebih dari satu, didapati hasil analisa sentimen pada Twitter berbahasa Indonesia menjadi lebih baik dibanding dengan metode lainnya. Penelitian ini menganalisa sentimen melalui tweet dari masyarakat Indonesia terhadap sejumlah perusahaan publik dengan menggunakan DNN. Data Tweet sebanyak 5504 record didapat dengan melakukan crawling melalui Application Programming Interface (API) Twitter yang selanjutnya dilakukan preprocessing (cleansing, case folding, formalisasi, stemming, dan tokenisasi). Proses labeling dilakukan untuk 3902 record dengan memanfaatkan aplikasi Sentiment Strength Detection. Tahap pelatihan model dilakukan menggunakan algoritma DNN dengan variasi jumlah hidden layer, susunan node, dan nilai learning rate. Eksperimen dengan proporsi data training dan testing sebesar 90:10 memberikan hasil performa terbaik. Model tersusun dengan 3 hidden layer dengan susunan node tiap layer pada model tersebut yaitu 128, 256, 128 node dan menggunakan learning rate sebesar 0.005, model mampu menghasilkan nilai akurasi mencapai 88.72%. 

scholarly journals A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1619
Author(s):  
Kim ◽  
Sok ◽  
Kang ◽  
Lee ◽  
Nam
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Deep Neural Network ◽  
Phase Estimation ◽  
Fault Current ◽  
Dc Offset ◽  
Intensive Training ◽  
Unsupervised Training ◽  
Training Methodologies ◽  
Hidden Layer

The purpose of this paper is to remove the exponentially decaying DC offset in fault current waveforms using a deep neural network (DNN), even under harmonics and noise distortion. The DNN is implemented using the TensorFlow library based on Python. Autoencoders are utilized to determine the number of neurons in each hidden layer. Then, the number of hidden layers is experimentally decided by comparing the performance of DNNs with different numbers of hidden layers. Once the optimal DNN size has been determined, intensive training is performed using both the supervised and unsupervised training methodologies. Through various case studies, it was verified that the DNN is immune to harmonics, noise distortion, and variation of the time constant of the DC offset. In addition, it was found that the DNN can be applied to power systems with different voltage levels.

scholarly journals Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion

2021 ◽  
Vol 25 (4) ◽  
pp. 442-449
Author(s):  
Liqi Liu ◽  
◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Semantic Similarity ◽  
Relative Position ◽  
Deep Neural Network ◽  
Syntactic Structure ◽  
Dimensional Space ◽  
High Dimensional ◽  
Vector Representation ◽  
High Dimensional Space ◽  
Position Information

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.

scholarly journals Learning algorithm analysis for deep neural network with ReLu activation functions

2018 ◽  
Vol 19 ◽  
pp. 01009
Author(s):  
Stanisław Płaczek ◽  
Aleksander Płaczek
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Activation Function ◽  
Point Of View ◽  
Algorithm Analysis ◽  
Theoretic Point ◽  
Neural Network Structure ◽  
Null Value ◽  
Hidden Layer

In the article, emphasis is put on the modern artificial neural network structure, which in the literature is known as a deep neural network. Network includes more than one hidden layer and comprises many standard modules with ReLu nonlinear activation function. A learning algorithm includes two standard steps, forward and backward, and its effectiveness depends on the way the learning error is transported back through all the layers to the first layer. Taking into account all the dimensionalities of matrixes and the nonlinear characteristics of ReLu activation function, the problem is very difficult from a theoretic point of view. To implement simple assumptions in the analysis, formal formulas are used to describe relations between the structure of every layer and the internal input vector. In practice tasks, neural networks’ internal layer matrixes with ReLu activations function, include a lot of null value of weight coefficients. This phenomenon has a negatives impact on the effectiveness of the learning algorithm convergences. A theoretical analysis could help to build more effective algorithms.

scholarly journals Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

2019 ◽  
Vol 9 (19) ◽  
pp. 4180 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Deep Neural Network ◽  
Underground Mine ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Hidden Layer ◽  
System Characteristics ◽  
Haulage System

A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

Sign in / Sign up

Export Citation Format

Share Document