A Fine-Tuning Method Using Pruning of Recurrent Neural Network for Prediction of the Anesthetic Effects

2020 ◽  
Author(s):  
Yoshitomo Sakuma ◽  
Takumi Kobayashi ◽  
Chika Sugimoto ◽  
Ryuji Kohno
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Fine Tuning ◽  
Tuning Method

scholarly journals Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4054 ◽  
Author(s):  
Fernandez-Lopez ◽  
Liu-Jimenez ◽  
Kiyokawa ◽  
Wu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Sensors ◽  
Selection Process ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Tuning Method ◽  
Random Grid ◽  
Hyperparameter Selection ◽  
User Recognition

In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

scholarly journals Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 29
Author(s):  
Christian Dengler ◽  
Boris Lohmann
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Trajectory Optimization ◽  
Inverted Pendulum ◽  
Gradient Methods ◽  
Real System ◽  
Fine Tuning ◽  
Imitation Learning ◽  
Parametric Function ◽  
The Real

In this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for this task, which is why we choose to train the controller using imitation learning on optimized trajectories. In contrast to popular approaches like policy gradient methods, this approach allows us to shape the behavior of the system by including equality constraints. When transferring the parametric controller from simulation to the real mobile inverted pendulum, the control performance is degraded due to the reality gap. A robust control design can reduce the degradation. However, for the framework of imitation learning on optimized trajectories, methods that explicitly consider robustness do not yet exist to the knowledge of the authors. We tackle this research gap by presenting a method to design a robust controller in the form of a recurrent neural network, to improve the transferability of the trained controller to the real system. As a last step, we make the behavior of the parametric controller adjustable to allow for the fine tuning of the behavior of the real system. We design the controller for our system and show in the application that the recurrent neural network has increased performance compared to a static neural network without robustness considerations.

Recurrent neural network pruning using dynamical systems and iterative fine-tuning

2021 ◽  
Author(s):  
Christos Chatzikonstantinou ◽  
Dimitrios Konstantinidis ◽  
Kosmas Dimitropoulos ◽  
Petros Daras
Keyword(s):  
Neural Network ◽  
Dynamical Systems ◽  
Recurrent Neural Network ◽  
Fine Tuning ◽  
Network Pruning

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

IMPLEMENTASI CHATBOT PADA PENDAFTARAN MAHASISWA BARU MENGGUNAKAN RECURRENT NEURAL NETWORK

2019 ◽  
Vol 24 (2) ◽  
pp. 91-101
Author(s):  
Tjut Awaliyah Zuraiyah ◽  
Dian Kartika Utami ◽  
Degi Herlambang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network

Chatbot adalah perangkat lunak yang dapat berkomunikasi dengan manusia menggunakan bahasa alami. Model percakapan menggunakan kecerdasan buatan agar mampu memahami ucapan pengguna dan memberi tanggapan yang relevan dengan masalah yang dibahas oleh pengguna. Pendaftaran mahasiswa baru memerlukan banyak informasi mengenai prosedur pendaftaran di perguruan tinggi. Website pendaftaran online di Universitas Pakuan masih sebatas berisi informasi umum. Penelitian ini bertujuan untuk membuat suatu aplikasi Chatbot otomatis yang dapat berkomunikasi dengan manusia mengenai informasi pendaftaran mahasiswa baru di Universitas Pakuan menggunakan Recurrent Neural Network (RNN) untuk klasifikasi teks. Aplikasi Chatbot diimplementasikan menggunakan bahasa pemrograman Python dan Telegram API. Tahapan pada implementasi Chatbot terdiri dari preprocessing, transformasi data ke format .JSON, pelatihan data, bag of word dan full connection. Pengujian aplikasi Chatbot menggunakan data sebanyak 251 kalimat pertanyaan tentang pendaftaran mahasiswa baru di Universitas Pakuan. Hasil pengujian menunjukkan bahwa Chatbot dapat menjawab pertanyaan mengenai pendaftaran mahasiswa baru dengan akurasi sebesar 88%, presisi sebesar 95% dan recall sebesar 92%.

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