Enmei Wang1 and Shunan Wu2, 1School of Aeronautics and Astronautics, Dalian University of Technology, Dalian City, China and 2Key Laboratory of Advanced Technology for Aerospace Vehicles, Dalian University of Technology, Dalian City, China
To deal with the issues of vibration suppression of the large space structures (LSS) such as design complexity, fault-tolerant limitation, repeated expansion difficulty and etc., a distributed vibration control approach is proposed in this paper. According to the structure characteristics, the LSS is firstly divided into different control units, and the dynamic model of each unit is developed. The distributed LQR vibration controller of each unit is then designed and the final distributed vibration control system of the whole structure is therefore integrated. Simulations are presented to verify the validity of the proposed controller, and the results demonstrate that repeatable distributed controllers can achieve vibration suppression for LSS and provide good fault-tolerance performance.
Large Space Structure, Distributed Control, Linear Quadratic Regulator, Fault Tolerance
Nataly Ilyasova1,2 and Alexander Shirokanev1,2, 1IPSI RAS - branch of the FSRC «Crystallography and Photonics» RAS, Samara, Russia 2Samara National Research University, Samara, Russia
In this paper, information technology has been developed for automatic highlighting the lungs on x-ray images, based on the images pre-processing, calculation of textural properties and classification of kmeans. In some cases, the highlighted objects can describe not only the current patient’s condition but also specific characteristics regarding age, gender, constitution, etc. While using the k-means method, the relationship between the segmentation error and fragmentation window size was revealed. Within the study, both a visual criterion for evaluating the quality of the segmentation result and a criterion based on calculating the clustering error on a large set of fragmented images were implemented. The study also included image pre-processing techniques. Thus, the study showed that the technology provided key objects highlighting error at 26%. However, the equalizing procedure has lessened this error to 14%. Xray image clustering errors for fragmentation windows of 12x12, 24x24 and 36x36 were presented.
Lungs X-rays Images, Image Processing, Texture Analysis, Selection Technique of Interest Regions
Nataly Ilyasova1,2 and Alexander Shirokanev1,2, 1IPSI RAS - branch of the FSRC «Crystallography and Photonics» RAS, Samara, Russia and 2Samara National Research University, Samara, Russia
The article proposes a new method for analyzing eye fundus images. The method is based on the convolutional neural network (CNN). The CNN architecture was constructed, followed by network learning on a balanced dataset composed of four classes of images, composed of thick and thin blood vessels, healthy areas, and exudate areas. Segmentation of fundus images was performed using CNN. Considering that exudates are a primary target of laser coagulation surgery, the segmentation error was calculated on the exudate class, amounting to 5%. In the course of this research, the HSL color system was found to be most informative, using which the segmentation error was reduced to 3%.
Convolution Neural Networks, Fundus Image, Diabetic Retinopathy, Exudates, Laser Coagulation Image Processing, Image Segmentation
Ibon Merino1, Jon Azpiazu1, Anthony Remazeilles1, and Basilio Sierra2, 1Industry and Transport, Tecnalia Research and Innovation, Donostia-San Sebastian, Spain and 2Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.
Computer vision, Descriptors, Feature-based object recognition, Expert system
Melissa Qian1, Yu Sun1 and Fangyan Zhang2, 1Department of Computer Science, California State Polytechnic University, Pomona, CA, 91768 and 2ASML, San Jose, CA, 95131
This paper presents an innovative redesign of a doorbell system in order to eliminate unnecessary ringing noise from users’ daily life. Employing artificial intelligence for face recognition, the IoT doorbell system define the visitors as complete strangers or someone who is expected. The next step operates based on this result; the doorbell system will either ring or send out notification to the users’ phone depending on the familiarity of the visitor and the user.
Machine Learning, Deep Learning, Artificial Intelligence, Wireless Network
Tianren Dong1, Yu Sun1 and Fangyan Zhang2, 1Department of Computer Science, California State Polytechnic University, Pomona, CA, 91768 and 2ASML, San Jose, CA, 95131
With more and more attentions paid on health, people begin to care about healthy diet options created by experts on nutrition. However, it will take a long time to observe the effects by taking healthy diet. This causes great difficulty for users to follow the healthy diet strictly. Most existing applications are not userfriendly in inputting information to the application. Then it becomes difficulty to track for exact health status. This paper proposes an android application which can be trained to recognize different kinds of food and facilitate the information input through phone camera using machine learning algorithms. Thus, nutritional information can be fed in application accurately.
Machine learning, Android application, Image recognition
Constantina Nicolaou1, Amal Vaidya1, Fabon Dzogang2, David Wardrope1,2 and Nikos Konstantinidis1, 1Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK and 2ASOS AI, Greater London House,Hampstead Road, London NW1 7FB, UK
We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS customer care, namely “Where is my order?”. We conduct extensive experiments to compare the accuracy of two popular classification models: logistic regression via N-grams that account for sequences in the data and recurrent neural networks that perform the extraction of sequential patterns automatically. A Mann-Whitney U test indicated that F1 score on a representative sample of held out labelled messages was greater for linear N-grams classifiers than for recurrent neural networks classifiers (M1=0.828, M2=0.815; U=1,196, P=1.46e-20), unless all neural layers including the word representation layer were trained jointly on the classification task (M1=0.831, M2=0.828, U=4,280, P=8.24e-4). Overall our results indicate that using simple linear models in modern AI production systems is a judicious choice unless the necessity for higher accuracy significantly outweighs the cost of much longer training times.
Natural Language Processing, Intent Classification, Bag-of-words, Recurrent Neural Networks
Clark Ren, Yu Sun and Fangyan Zhang, California State Polytechnic University, USA
As more and more students get access to computers to aid them in their studies, they also gain access to machines that can play games, which can negatively affect a student's academic performance. However, it is also debated that playing video games could also positively affect a student’s academic performance. In order to address both sides of the argument, we can create an app that limits the amount of time a student has to play games while not completely removing the ability for students to play games.
Parental Control, Smart System, Digital Games, Web Service