scholarly journals Voice Communication in Noisy Environments in a Smart House Using Hybrid LMS+ICA Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6022
Author(s):  
Radek Martinek ◽  
Jan Vanus ◽  
Jan Nedoma ◽  
Michael Fridrich ◽  
Jaroslav Frnda ◽  
...  
Keyword(s):  
Success Rate ◽  
Additive Noise ◽  
Building Automation ◽  
Voice Communication ◽  
Voice Control ◽  
Additive Interference ◽  
Actual Recognition ◽  
Technical Functions ◽  
Recognition Success ◽  
Noise Canceling

This publication describes an innovative approach to voice control of operational and technical functions in a real Smart Home (SH) environment, where, for voice control within SH, it is necessary to provide robust technological systems for building automation and for technology visualization, software for recognition of individual voice commands, and a robust system for additive noise canceling. The KNX technology for building automation is used and described in the article. The LabVIEW SW tool is used for visualization, data connectivity to the speech recognizer, connection to the sound card, and the actual mathematical calculations within additive noise canceling. For the actual recognition of commands, the SW tool for recognition within the Microsoft Windows OS is used. In the article, the least mean squares algorithm (LMS) and independent component analysis (ICA) are used for additive noise canceling from the speech signal measured in a real SH environment. Within the proposed experiments, the success rate of voice command recognition for different types of additive interference (television, vacuum cleaner, washing machine, dishwasher, and fan) in the real SH environment was compared. The recognition success rate was greater than 95% for the selected experiments.

scholarly journals Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Yang ◽  
Junkai Zhou
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Customer Satisfaction ◽  
Success Rate ◽  
Insurance Industry ◽  
Service Innovation ◽  
Test Set ◽  
Test Sets ◽  
Recognition Success ◽  
Insurance Data

With the advent of the era of big data, great changes have taken place in the insurance industry, gradually entering the field of Internet insurance, and a large amount of insurance data has been accumulated. How to realize the innovation of insurance services through insurance data is crucial to the development of the insurance industry. Therefore, this paper proposes a ciphertext retrieval technology based on attribute encryption (HP-CPABKS) to realize the rapid retrieval and update of insurance data on the premise of ensuring the privacy of insurance information and puts forward an innovative insurance service based on cloud computing. The results show that 97.35% of users are successfully identified in test set A and 98.77% of users are successfully identified in test set B, and the recognition success rate of the four test sets is higher than 97.00%; when the number of challenges is 720, the modified data block is less than 9%; the total number of complaints is reduced from 1300 to 249; 99.19% of users are satisfied with the innovative insurance service; the number of the insured is increased significantly. To sum up, the insurance innovation service based on cloud computing insurance data can improve customer satisfaction, increase the number of policyholders, reduce the number of complaints, and achieve a more successful insurance service innovation. This study provides a reference for the precision marketing of insurance services.

scholarly journals Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

2021 ◽  
Vol 11 (20) ◽  
pp. 9583
Author(s):  
Bongki Lee ◽  
Donghwan Kam ◽  
Yongjin Cho ◽  
Dae-Cheol Kim ◽  
Dong-Hoon Lee
Keyword(s):  
Success Rate ◽  
Sweet Pepper ◽  
Local Features ◽  
Bp Algorithm ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Plant Parts ◽  
Speeded Up Robust Features ◽  
Recognition Success

For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.

scholarly journals Sparse representation discretization errors in multi-sensor radar target motion estimation

2017 ◽  
Vol 15 ◽  
pp. 69-76
Author(s):  
Hossein Azodi ◽  
Uwe Siart ◽  
Thomas F. Eibert
Keyword(s):  
Compressed Sensing ◽  
Success Rate ◽  
Analytical Model ◽  
Additive Noise ◽  
Probability Density Functions ◽  
Analytical Models ◽  
Density Functions ◽  
Radar Target ◽  
Reconstruction Algorithms ◽  
Discretization Errors

Abstract. In a multi-sensor radar for the estimation of the targets motion states, more than one module of transmitter and receiver are utilized to estimate the positions and velocities of targets, also known as motion states. By applying the compressed sensing (CS) reconstruction algorithms, the surveillance space needs to be discretized. The effect of the additive errors due to the discretization are studied in this paper. The errors are considered as an additive noise in the well-known under-determined CS problem. By employing properties of these errors, analytical models for its average and variance are derived. Numerous simulations are carried out to verify the analytical model empirically. Furthermore, the probability density functions of discretization errors are estimated. The analytical model is useful for the optimization of the performance, the efficiency and the success rate in CS reconstruction for radar as well as many other applications.

scholarly journals Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjia Liu ◽  
Bo Chen ◽  
R. Andrew Swartz
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Success Rate ◽  
Test Data ◽  
Structural Damage ◽  
Series Representation ◽  
Similarity Measures ◽  
Damage Pattern ◽  
Recognition Success

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate.

scholarly journals Telephone Damage Analysis on Voice Control Unit (VCU) At Perum LPPNPI Manado Branch Office

2021 ◽  
Vol 4 (1) ◽  
pp. 47-51
Author(s):  
Adhitya Octavianie ◽  
Aqram Adi Putra
Keyword(s):  
Control Unit ◽  
Control Panel ◽  
Voice Communication ◽  
Control Center ◽  
Voice Control ◽  
Communication Devices ◽  
Screen System ◽  
Branch Office ◽  
Communication Control ◽  
And Control

Voice Communication Control System (VCCS) is a tool designed to make it easier for users to communicate voice by integrating all users (clients) and means of communication in one system and controlled using a control panel. VCCS is a voice switching device used in VHF A / G and Direct Speech communications. The benefit of using VCCS is that when the user makes flight communication it becomes easy because all frequencies and telephones are combined in a VCU (Voice Control Unit) so that the ATC desk/work desk is not filled with communication devices. In addition, to facilitate its use, the VCCS client control panel uses a touch screen system. AirNav Indonesia Manado Branch has a LESS brand VCCS equipment server manufactured by China. VCCS consists of two main parts, namely the server and client. The server is a processing center and control center for switching input and output, while the client or VCU (Voice Control Unit) is the device used by the user in VCCS operations. There are 7 VCUs at AirNav Indonesia Manado which are placed in the Tower room (3 VCU), APP room (3 VCU), and FSS Kompen room (1 VCU).

Pattern recognition of decorative elements based on neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8665-8673
Author(s):  
Liang Tingting ◽  
Liu Zhaoguo ◽  
Wang Wenzhan
Keyword(s):  
Pattern Recognition ◽  
Success Rate ◽  
Detection Method ◽  
Reference Value ◽  
Pattern Detection ◽  
Evaluation Index ◽  
Complex Environment ◽  
Classification Evaluation ◽  
Recognition Success ◽  
High Degree

The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic.

Single Camera for Multiple Vehicles License Plate Localization and Recognition on Multilane Highway

2013 ◽  
Vol 418 ◽  
pp. 120-123
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Sun Yen Tan ◽  
Chiu Ching Tuan ◽  
Chi Ping Lee ◽  
...  
Keyword(s):  
Success Rate ◽  
Cost Effective ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Success Rates ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Multiple Vehicles ◽  
Recognition Success

License plate recognition systems can be classified into several categories: systems with single camera for motionless vehicle, systems with single camera for moving vehicle, and systems with multiple cameras for moving vehicles on highways (one camera for each lane). In this paper we present an innovative system which can locate multiple moving vehicles and recognize their license plates with only one single camera. Obviously, our system is highly cost effective in comparison with other systems. Our system has license plate localization success rate 94% and license plate recognition success rate 88%. These success rates are pretty satisfiable considering the system is working on fast moving vehicles on highway.

scholarly journals Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Zixiang Wang
Keyword(s):  
Mental Health ◽  
Face Recognition ◽  
Emotion Recognition ◽  
Success Rate ◽  
Treatment System ◽  
Diagnosis And Treatment ◽  
Behavior Prediction ◽  
The Government ◽  
Recognition Success ◽  
And Behavior

The phenomenon of teenage campus suicide has become the focus of attention of parents, schools and the society. The causes behind it are extremely complicated, and the root cause is psychological and spiritual problems. However, one's negative psychology is often hidden, and it is difficult to detect and effectively intervene before the tragedy. How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government, academia and the public. Through investigation and research, it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction, and put into use in schools as a mental health auxiliary diagnosis and treatment system, thus effectively reducing the suicide rate on campus. The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions. Based on this, this paper proposes an effective solution for campus suicide prediction, and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis. Through preliminary experimental analysis and verification, the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate, emotion recognition success rate and behavior prediction success rate. With the input of more experimental data and the increase of self-training time, the prediction system will perform better.

scholarly journals A Field-Tested Harvesting Robot for Oyster Mushroom in Greenhouse

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1210
Author(s):  
Jiacheng Rong ◽  
Pengbo Wang ◽  
Qian Yang ◽  
Feng Huang
Keyword(s):  
Success Rate ◽  
Point Cloud ◽  
Field Experiments ◽  
Oyster Mushroom ◽  
Robot System ◽  
End Effector ◽  
Rgb Images ◽  
Oyster Mushrooms ◽  
Recognition Success ◽  
Harvesting Robot

The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the harvesting robot are the perception module and the end-effector. Intel RealSense D435i is adopted to collect RGB images and point cloud images in real time; an improved SSD algorithm is proposed to detect mushrooms, and finally, the existing soft gripper is manipulated to grasp oyster mushrooms. Field experiments exhibit the feasibility and robustness of the proposed robot system, in which the success rate of the mushroom recognition success rate reaches 95%, the harvesting success rate reaches 86.8% (without considering mushroom damage), and the harvesting time for a single mushroom is 8.85 s.

scholarly journals Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Zixiang Wang
Keyword(s):  
Mental Health ◽  
Face Recognition ◽  
Emotion Recognition ◽  
Success Rate ◽  
Treatment System ◽  
Diagnosis And Treatment ◽  
Behavior Prediction ◽  
The Government ◽  
Recognition Success ◽  
And Behavior

The phenomenon of teenage campus suicide has become the focus of attention of parents, schools and the society. The causes behind it are extremely complicated, and the root cause is psychological and spiritual problems. However, one's negative psychology is often hidden, and it is difficult to detect and effectively intervene before the tragedy. How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government, academia and the public. Through investigation and research, it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction, and put into use in schools as a mental health auxiliary diagnosis and treatment system, thus effectively reducing the suicide rate on campus. The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions. Based on this, this paper proposes an effective solution for campus suicide prediction, and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis. Through preliminary experimental analysis and verification, the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate, emotion recognition success rate and behavior prediction success rate. With the input of more experimental data and the increase of self-training time, the prediction system will perform better.

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