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Research on Similar Odor Recognition Based on Bid Data Analysis Research on Similar Odor Recognition Based on Bid Data Analysis

Yunxiang Liu, Tingting Xiong, Xinxin Yuan, Chunya Wang

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School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

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Volume 7, Issue 1

Volume and Issue can't be empty

411-415

The Page Numbers field can't be Empty

2432-5465

19-07-2021

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Abstract: In the common olfactory system odor recognition is processed by the electronic nose collecting sensor data, but the odor data collection of substances is easily affected by the environment and the processing is complicated, which is prone to deviation. This paper proposes a method based on big data analysis. According to the different chemical structure characteristics of different odor substances, the BP neural network is used to build a model to classify and recognize similar odors, and compare it with the traditional PCA+LDA recognition method. The results show that the establishment of a similar odor recognition model can accurately classify substances with similar odors, and the BP neural network algorithm is used to identify different substances with a higher rate of odor recognition. This method is stable and simple, and can provide different ideas for odor identification.

Keywords: Smell recognition; Big data analysis; BP neural network; Similar smell

Text Classification Based on Title Sematic Information Text Classification Based on Title Sematic Information

Yunxiang Liu, Qi Xu, Chunya Wang

The Author field can not be Empty

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

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Volume 7, Issue 1

Volume and Issue can't be empty

416-421

The Page Numbers field can't be Empty

2432-5465

19-07-2021

Publication Date field can't be Empty
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Abstract: With the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory (LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.

Keywords: Text classification; natural language processing; deep learning; LSTM

Design of the Automatic Control System for Restaurant Food Delivery Based On PLC Design of the Automatic Control System for Restaurant Food Delivery Based On PLC

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Abstract: The paper designs an automatic control system for restaurant food delivery based on PLC, including the mechanical structure and automatic control system design. The mechanical structure of the system includes horizontal delivery subsystems and a vertical delivery subsystem. The automatic control system includes PLC control and the human-machine interface, which realizes the entire system's automation. At the end of the paper, we analyze the whole system's reliability and economy to reflect the characteristics and practicability of the automatic control system.

Keywords: Food delivery system; PLC control; human-machine interface; reliability

License Plate Recognition Algorithm Based on Convolutional Neural Network License Plate Recognition Algorithm Based on Convolutional Neural Network

Yunxiang Liu, Jinpeng Ren, Xinxin Yuan, Zixuan Lu

The Author field can not be Empty

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

The Institution field can't be Empty

Volume 7, Issue 1

Volume and Issue can't be empty

429-434

The Page Numbers field can't be Empty

2432-5465

19-07-2021

Publication Date field can't be Empty
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Abstract: In order to improve the problem of unequal suspension positions in the traditional license plate recognition system, this paper introduces the convolutional neural network algorithm into the license plate recognition system, and conducts a series of tests and corrections to meet the current license plate recognition system. This paper proposes for the first time that the flood filling algorithm is applied to the preprocessing of the license plate image, the recognized contour is divided into regions, and then the license plate inclination angle is offset, and rough positioning and cutting are performed to make the vehicle shot from the side The picture can also fully identify the license plate, and finally judge according to the aspect ratio of the license plate and the standard aspect ratio, and get whether the recognized license plate is. The experimental results show that the model utilizes the advantages of convolutional neural network so that the model can recognize classification features more accurately. Keywords: License plate recognition, Convolutional neural network, Flood filling algorithm

Research and Implementation of FacialNet Based on Convolutional Neural Network Research and Implementation of FacialNet Based on Convolutional Neural Network

Yunxiang Liu, Chunya Wang, Jinpeng Ren , Xinxin Yuan

The Author field can not be Empty

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

The Institution field can't be Empty

Volume 7, Issue 1

Volume and Issue can't be empty

435-439

The Page Numbers field can't be Empty

2432-5465

19-07-2021

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Abstract: Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people's daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition. In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.

Keywords: Convolutional neural network, Face recognition, Gender recognition, Age recognition
Analysis and Prediction of COVID-19 in Xinjiang based on Machine Learning Analysis and Prediction of COVID-19 in Xinjiang based on Machine Learning

Yunxiang Liu, Yan Xiao

The Author field can not be Empty

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

The Institution field can't be Empty

Volume 7, Issue 1

Volume and Issue can't be empty

440-443

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2432-5465

04-08-2021

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Abstract: Covid-19 has taken the world by storm, dramatically affecting the lives of people around the world. China is a major country in the fight against the epidemic. It has provided the world with a wealth of valuable experience in the prevention and treatment of COVID-19. Based on the data released by Xinjiang Health Commission, this study used mathematical modeling method to reasonably predict and analyze the trend of the number of coVID-19 confirmed in the recent outbreak in Xinjiang through machine learning polynomial regression under limited data conditions, aiming at the coVID-19 outbreak in Xinjiang in July.

Keywords: COVID-19, estimates of the number of confirmed cases, Machine   learning, Polynomial regression.

The Impact of Trait Anxiety under a Painful Stimulus on the Chaotic Synchronization of Respiration and Pulse Waves The Impact of Trait Anxiety under a Painful Stimulus on the Chaotic Synchronization of Respiration and Pulse Waves $15.00

Ryoko Takikawa, Taira Suzuki, Yasutomo Ishii and Mayumi Oyama-Higa

The Author field can not be Empty

Waseda University

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Volume 7, Issue 1

Volume and Issue can't be empty

471-478

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2432-5465

12-01-2021

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Abstract: Thus far, attention has been paid to the relation between pain and anxiety, which has been studied. On the other hands, the earlier studies have hinted at the importance of considering mental and physical health from a holistic perspective, while taking into consideration the principles that prescribe the chaotic behavior of living organisms. Therefore, the purpose of this study is to use the respiration and pulse waves to examine the nature of the chaotic connection between biosignals involved in people’s mental and physical conditions, particularly those involved in the psychological trait of anxiety under a painful stimulus in this case. The results showed that with the high anxiety group, the extent of the synchronicity between their respiration and pulse waves under a painful stimulus increased, while this decreased for the low anxiety group. In other words, chaos dynamics for living systems are expressed in synchronous phenomena for the LLE for respiration and pulse waves. It also implied that these dynamics are prescribed by trait anxiety under a painful stimulus. This has opened up the possibility that, in the future, the cross-correlation function for LLE in pulse waves and respiration will make contributions to treating and assessing chronic pain in the field of clinical medicine.

User Experience Evaluation on the Cryptocurrency Website by Trust Aspect User Experience Evaluation on the Cryptocurrency Website by Trust Aspect $15.00

Bagus A. Ramadhan, Erlinda Muslim, Billy M. Iqbal, Boy Nurtjahyo

The Author field can not be Empty

The Institution field can't be Empty

Volume 7, Issue 1

Volume and Issue can't be empty

479-485

The Page Numbers field can't be Empty

2432-5465

12-01-2021

Publication Date field can't be Empty
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Abstract:  A portal into the public ledger cryptocurrency makes a competition of the best web sites and easily trusted by the public. These are believed to constitute a measurable dimensions of User Experience (UX). This study aims to evaluate the user experience of the use of the three web sites most frequently accessed cryptocurrency from Indonesia. The evaluation conducted aimed at knowing the factors that influence user trust through the design of the interface and can be installed on a new design. Methods include Performance Metrics, Post-Task Rating, Post-Session Rating, and Experiential Overview and eye-tracking device. Based on the results of research, in the overall evaluation of the web site, the web site is the most superior of Indodax. The results of the evaluation are then applied on a new design using the software Invision and examined again to see a comparison of the respondent at the time of first use. The result of the research is the assessment, recommendations, and design the look of the web site cryptocurrency are trustworthy based on user experience

Keywords: Cognitive Ergonomics, Human-Computer Interaction, User Experience, Cryptocurrency, Website, Emotional Design, Online Design

Formation of efficient and inefficient social convention driven by conformity bias Formation of efficient and inefficient social convention driven by conformity bias

The Author field can not be Empty

National Institute of Technology, Okinawa College, Japan

The Institution field can't be Empty

Vol.6, Issue 2

Volume and Issue can't be empty

404-401

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2432-5465

31-12-2020

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Abstract: Social conventions govern social behavior in many ways, ranging from left- and right-hand traffic to greetings. We sometimes find that inefficient social conventions, such as bullying in a class, are spontaneously formed, where almost all the people in the group are at a disadvantage. Although such a convention can be disadvantageous for all the people in the group, why are those conventions formed and continue to be maintained? A conformity bias, the behavioral tendency with which people take an action that a majority of the group take, can be one of the key ingredients of this phenomenon. In this study, we investigated the impact of conformity bias and an individual’s trial-and-error learning on the spontaneous formation of social conventions. Analyzing the stationary states of the dynamics and the tipping points at which the dynamics are divided into one converging to the efficient and the other converging to the inefficient convention, we found that by increasing the extent of conformity bias, an inefficient convention tends to be formed. On the other hand, an individual’s trial-and-error learning can suppress conformity bias and promote the formation of efficient conventions.

 

Keywords: Social convention; Conformity bias; Positive externality; Reinforcement learning model; Tipping points…

Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network $15.00

Zhang ZENG Wan-dan, WU Cheng-wei, Shi Ru-jin,IA Zhi-ping, LI Qian-xue, Li zhi-ping Qu han

The Author field can not be Empty

Shanghai Institute of Technology Shanghai, China

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

360-363

The Page Numbers field can't be Empty

2432-5465

01-06-2020

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Abstract: In the terahertz time-domain spectroscopy technique, 16 common pathogens were experimentally studied and their characteristics absorption spectra in the frequency range of 0.1 to 2.2THz were obtained. The terahertz absorption spectra of 16 common pathogens were trained and identified by Siamese neural network method. First, the terahertz absorption spectra of the 16 pathogens were reduced by PCA to construct training data. Then, the constructed Siamese neural network model was trained by back propagation. Finally, the pathogen measured at different times was used as the target spectrum to evaluate the model, after comparing with the training data, the matching absorption spectrum was obtained, and the recognition rate reached 97.34%. The recognition results fully indicate that the identification of different kinds of pathogens can be recognized by Siamese neural network, which provides an effective method of the detection and identification of pathogens by terahertz spectroscopy.

Keywords: THz spectroscopy; machine learning; Siamese neural network; similarity learning

Application of vertical switching signal prediction based on ship networking in heterogeneous networks Application of vertical switching signal prediction based on ship networking in heterogeneous networks $15.00

Yunxiang Liu and Jing Li

The Author field can not be Empty

School of Computer Science & Information Engineering Shanghai Institute of Technology Shanghai, China

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

301-306

The Page Numbers field can't be Empty

2432-5465

01-06-2020

Publication Date field can't be Empty
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Abstract: With the rise of ship networking, in view of the coexistence of many heterogeneous networks, the vertical switching technology of heterogeneous networks is studied, and a vertical signal prediction algorithm is proposed by using grey theory. Through the simulation analysis of MATLAB test, it is verified that the algorithm can effectively improve the accuracy of the prediction signal and is suitable for ship terminals.

Keywords: Ship Networking, Heterogeneous Network, Vertical Switching, Prediction Algorithm.

Suspicious Bank Card Transaction Recognition Based on K-means Clustering and Random Forest Algorithm Suspicious Bank Card Transaction Recognition Based on K-means Clustering and Random Forest Algorithm $15.00

Yunxiang Liu, Zeshen Tang, Wenjie Zheng

The Author field can not be Empty

School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai, China

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

307-3012

The Page Numbers field can't be Empty

2432-5465

01-06-2020

Publication Date field can't be Empty
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Abstract: Suspicious transactions are hidden in thousands of massive transaction data, causing incalculable losses and risks, but the detection is very difficult. In terms of how to effectively explore and identify suspicious transactions from massive transaction data accurately and quickly, this paper adopts the method based on the combination of k-means algorithm and random forest algorithm to solve the problem of data imbalance in the identification of suspicious transactions in bank accounts, and proposes an effective suspicious transaction detection model. At the same time, the AUC(Area Under Curve) and Recall indicators such as the unbalanced data classification standard of performance evaluation, and finally to Kaggle data platform for the bank account of suspicious transactions data set, the results show that the proposed detection model of performance evaluation index AUC increased by 5%, F1-measure increases by 1%, show that the method has some reference value to the suspicious transactions recognition, limited information utilization rate is higher, which makes all kinds of Banks prediction speed and accuracy of suspicious transactions events get improved, can to some extent, reduce the operating cost and risk of the banking sector.

Association Analysis of primary Liver Cancer based on Apriori Algorithm Association Analysis of primary Liver Cancer based on Apriori Algorithm $15.00

Yunxiang Liu, Qi Pan

The Author field can not be Empty

School of Computer Science & Information Engineering Shanghai Institute of Technology Shanghai, China

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

313-317

The Page Numbers field can't be Empty

2432-5465

01-06-2020

Publication Date field can't be Empty
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Abstract: This paper briefly describes the Apriori algorithm for primary liver cancer data, and then performs data preprocessing based on the characteristics of primary liver cancer patients, including data import and extraction, and embedding the algorithm into primary liver cancer. The implementation of clinical warning. After that, the Apriori algorithm is used to realize the association of the data association rules of primary liver cancer, and the internal valuable association rules are obtained, which provides suggestions for improving the doctor's remediation and prevention, so as to prevent the occurrence and reduction of primary liver cancer. The incidence of primary liver cancer has important practical significance.

Keywords: Apriori algorithm; primary liver cancer; Association rules; association analysis

Development of Drone Detecting Free Parking Space for Car Parking Guidance Development of Drone Detecting Free Parking Space for Car Parking Guidance $15.00

Tasato Yuske, Zacharie Mbaitiga

The Author field can not be Empty

Department of Media Information Engineering National Institute of Technology, Okinawa College

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

318-320

The Page Numbers field can't be Empty

2432-5465

01-06-2020

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Abstract: In the course of transportation of medical drugs, the requirements for temperature and humidity are high. We should strictly control it within a certain range, otherwise the deterioration of drugs will occur, which will lead to serious consequences. Therefore, a cold chain transport vehicle with temperature and humidity sensors should be used in medical transportation. This paper designs a wireless monitoring system based on LoRa technology, which can collect the data of temperature and humidity sensors on the transport vehicle in real time, and transmit them to the background through the LoRa gateway. Solve the technical problems of high power consumption and data upload failure. A wireless cold chain monitoring system with high data accuracy, strong expansibility and convenient networking is realized ensure the quality of drugs quantity safety.

Keywords: Drone; ardrone 2.0; template matching; parking lot

Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization $15.00

Zhen Nie, Huailin Zhao

The Author field can not be Empty

School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai, China

The Institution field can't be Empty

Volume 6, Issue 1

Volume and Issue can't be empty

231-327

The Page Numbers field can't be Empty

2432-5465

01-06-2020

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Abstract: This paper studied on the path planning problem in known environments. According to Dijkstra algorithm and ant colony optimization (ACO), a hybrid algorithm to search the path was designed. Based on the environment model, constructed by using visual graph method, Dijkstra algorithm was used for initial path planning. Then the ACO was improved and used to optimize the initial path to minimize the path of the robot. The simulation on MATLAB showed that the path planning algorithm based on Dijkstra-ACO has higher efficiency of path search and good effect of path planning, and the algorithm is effective and feasible.

Keywords: Dijkstra Algorithm, Ant Colony Optimization (ACO), Path Planning
 

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