Reviewerly/exHarmony huggingface dataset:
An Improved Analysis of Tourism Management in Enhanced Forest Environment using Block Chain based Internet of Things Model

Tourism is something that gives some relief to the human rush. It is an annual event to rest in the lap of nature, cool the eyes in the green meadows, refresh the mind and return to regular work. Those tourist spots are getting more polluted now. We have to think whether we can destroy the nature that gives comfort to the mind. The reason is human negligence. People living in the areas have also started protesting that the environment is getting polluted in the places where tourists come and go. There is a voice of protest that there are many impacts caused by unregulated and unregulated tourism. Forests are a gift of their produce to mankind. Gives life to living things. It has the virtue of providing shade even to those who come with an ax to cut down its trees.
Computer Video Intelligent Monitoring and Tracking System based on GABP Neural Network Algorithm

In recent years, object tracking has become one of the hot topics in the field of computer vision. Video target tracking is an important research topic in computer vision, which has been widely used in video surveillance, robotics, human-computer interaction and so on. This paper mainly studies the computer video intelligent monitoring and tracking system based on GABP neural network algorithm. In this paper, genetic algorithm is used to optimize the problem that BP model is easy to fall into local extreme points. According to the needs of actual application scenarios, this paper uses the target tracking algorithm based on GABP neural network, and uses Django back-end framework and front-end development technology to build a set of video target tracking system. The experimental results show that the GABP neural network algorithm constructed in this paper is superior to the traditional model in video tracking performance.
Analysis of the notation system of the current elementary school music textbook Sijo(時調) score and search for effective notation method

본 연구는 현행 2015 개정 음악과 교육과정에 따른 초등학교 5~6학년군 전 8종 음악 교과서에 수록된 시조(時調) 악보를 대상으로 기보 체계를 분석하고, 중점적으로 고려할 요소 3가지(시조 악곡의 특성과 학습자의 발달 단계, 타 국악 가창곡 악보와의 연계성, 상급학교 시조 악보와의 연계성 등)를 추출하여 효과적인 기보 체계 방안을 모색하였으며, 향후 2022 개정 음악과 교육과정에 따른 교과서 집필 시 시조 악보의 기보 체계에 관한 시사점을 제공하는 데 목적을 둔다. 이에 대한 논의 내용을 정리하면 첫째, 시조 악곡의 음악적 특징을 효과적으로 나타내고 학습자의 시각적․직관적인 이해도 향상과 흥미 유발을 위해 양면의 공간에 하나의 선으로 이어지는 가락선 악보 형태로 기보해야 한다. 또한. 가락선 악보 사이에 사설의 어단성장(語短聲長) 특성이 드러나고 가락선의 높낮이와 관련된 율정(律程)의 차이를 명확히 구분하여 지시해야 하며, 장단보는 5박․8박 장단이 분절되지 않고 이어지도록 기보해야 한다. 둘째, 타 국악 가창곡 악보와의 연계성을 고려할 때 시조의 주된 악보 하단의 주요 활동에서 한자 율명과 서양식 한글 계이름이 병기되고 단순화된 3선 악보를 활용하고, 시조의 오선보를 참고 자료로 제시해야 한다. 셋째, 상급학교 시조 악보와의 연계성을 고려할 때 전통적인 한자 율명(律名)의 정간보를 참고 자료로 제시해야 하고, 이를 통해 정간보에 대한 배경지식을 일정 부분 축적할 수 있도록 도와야 하며, 국악 가창곡 악보와의 일관성을 위해 세로식이 아닌 가로식 선율 진행이어야 한다.This study analyzes the notation system for the Sijo score contained in all eight music textbooks in the 5th to 6th grade of elementary school according to the current 2015 revised music curriculum, and the three factors to be considered(characteristics of Sijo music and learner’s development stage, connection with other Korean traditional music song’s scores, and connection with senior school Sijo sores etc.) were extracted to find an effective notation system, and the purpose is to provide implications for the notation system of Sijo music when writing textbooks according to the music and curriculum revised in 2022. To summarize the discussion on this, first, it should be reported in the form of a musical score that leads to a line in both spaces to effectively show the musical characteristics of the Sijo music and to improve learners' visual and intuitive understanding and interest. In addition, the characteristics of the short-term growth of the editorial between the music scores should be clearly distinguished and instructed the difference in rhythm related to the height of the music line, and Jangdanbo(長短譜) should be notified so that the 5-beat and 8-beat Jangdan(長短) continue without being divided. Second, considering the connection with the scores of other Korean traditional music songs, the three-line music score should be used Chises-language Yulmyeongs(律名) and Western-style Five-line Musical Score in Korean as a reference for the main activities at the bottom of the Sijo's main score. Third, considering the connection with the Sijo score of senior schools, Jeongganbo(井間譜) of traditional Chinese characters should be presented as a reference, and through this, it should help to accumulate some background knowledge of Jeongganbo, for consistency with the score of the Korean traditional music song, it should be a horizontal melody progression, not a vertical one.
White Paper: Setting up CNN (Convolutional Neural Network) for Character Recognition

A Convolutional Neural Network (ConvNet / CNN) is an in-depth Learning algorithm that can take an input image, assign importance (learned weight and bias) to various aspects or objects in the image, and can be differentiated from one another. The initial processing required in a CNN is less compared to other classification algorithms. While in the initial methods the filters were engineered by hand, with adequate training, CNN was capable of knowing these filters or characteristics. The architecture of a CNN is similar to the pattern of connectivity of Neurons in the Human Brain and inspired by the organization of the Visual Cortex. Individual neurons respond only to stimuli in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.
Design of path planning and tracking control for a UGV

This paper designs a path planning and tracking control algorithm for obstacle avoidance of an unmanned ground vehicle (UGV). Firstly, we establish and simplify the kinematics model a UGV by considering only the lateral control. Then, several safe central way points obtained segment-by-segment along the road. Further, a feasible trajectory is optimized by the improved sigmoid function and the full-state feedback controller is designed to track the optimal trajectory. Finally, we demonstrate our method is effective in software and hardware, the result shows that the path planner can find the optimal trajectory and tracking controller is stable.
Automatic accurate positioning and experimental research of robot target based on optical navigator

With the continuous development of robot related technology, robots have the ability to replace people to complete tasks in various fields and scenes. In this context, the demand for acupuncture robots is increasing, and the positioning accuracy of acupuncture robots is also getting more and more attention. This paper uses chessboard calibration for hand eye registration of acupuncture robot, recognizes chessboard corner points through Python compiling algorithm and carries out accurate iterative optimization, finally uses Tasi two-step method to solve the transformation matrix, which is more accurate, and realizes registration of 3D image system and mechanical arm. The preliminary robot positioning accuracy experiment shows that the positioning accuracy of this method is within the allowable range.
Design of VISUAL SLAM indoor disinfection robot based on STM32 control

In recent years, the novel corona virus pandemic is raging around the world, and the safety of home environment and public environment has become the focus of people's attention [2]. Therefore, the research on disinfection robot has become one of the important directions in the field of machinery and artificial intelligence. This paper proposes a robot with the STM32 MCU as the core of disinfection, and is equipped with a variety of sensors and a camera vision, has the original cloud service management platform, the remote deployment of navigation, based on visual SLAM to realize high precision navigation and positioning, can realize to indoor environment autonomously route planning, automatic obstacle avoidance checking, disinfection, epidemic prevention function, at the same time can pass Bit computer software realizes remote control of robot, which has great development potential.
Image semantic segmentation algorithm combined with attention mechanism

There are still some shortcomings in the mainstream semantic segmentation algorithm, such as the loss of small size target and the segmentation inaccuracy. This paper improved the algorithm based on the High-Resolution Net (HRNet). Firstly, the channel attention mechanism and the spatial attention mechanism were integrated, which leads to a complementary relationship between spatial and channel information of features, retain more effective feature information, and improve the accuracy of feature extraction. In view of the large number of parameters in HRNet network model and the time-consuming training, this paper uses depth-separable convolution to reduce the parameters of the model without affecting the segmentation accuracy. Since the model maintains a high resolution of the image and retains the spatial information of the feature map, the segmentation effect of the small size target has been significantly improved. The mIoU in the PASCAL VOC2012 enhanced dataset reached 80.39%, Which is improved by 1.21% compared with the original model.
Power Security Detection Based on Improved YOLOX Algorithm

There are problems with diversified objects and complex scenarios in power site safety inspection. To achieve efficient and accurate security detection, this paper proposes an improved algorithm based on the YOLOX model. First, it improves network detection by introducing a shallow feature layer to retain more object information and line to the output; Then, it uses a fusion of RFB (Receptive Field Block) at the feature map output node to increase the receptive field and solve the detection accuracy degradation caused by the drastic change of multiple object detection sizes. Last, the feature fusion network uses ECA-Net (Efficient Channel Attention Neural Networks), which gives different weights to the information channels to extract objects from complex backgrounds. Experiment with power security dataset to verify the effectiveness and feasibility of the improved algorithm. Experiments are carried out on the power security data set to verify the effectiveness and feasibility of the improved algorithm. The results show that the mean average precision (mAP) of the improved algorithm is 92.29%, which is 7.41% higher than that before the improvement, and the detection speed FPS (Frames Per Second) is 36.06 frames/s, which meets the requirements of power safety field detection accuracy and detection speed.
Image noise recognition algorithm based on data enhancement

Image denoising has always been an indispensable part of image processing tasks, and the quality of image preprocessing algorithms is directly related to the effect of subsequent image processing. Most existing denoising algorithms explicitly define the type and distribution of noise, for example, the artificially set additive Gaussian white noise which is based on prior knowledge. But on the one hand, the actual real image noise is not equivalent to Gaussian noise. The noise distribution of different noise types such as Gaussian, Salt and pepper noise, Rayleigh, Uniform noise is very different. It is not possible to generalize in the process of image denoising. On the other hand, images in real scenarios often have problems such as unrepeatable acquisition, lack of data information, or even only single image information, which can't support large-scale deep learning and discrimination. The above two points limit the application of most algorithms in real scenarios.
Research on Credit Card Fraud Detection Based on GAN

With the advancement of technology, credit card transactions have become more and more popular, and the number of credit card frauds has also increased at the same time. In order to reduce property loss of cardholders and banks, many traditional machine learning algorithms based on binary classification are applied to detect fraudulent transactions. However, the number of fraudulent transactions and normal transactions in the dataset used to train the classifier is seriously imbalanced, which causes the classifier with the goal of accuracy to tend to classify all transactions as normal transactions. It is meaningless to detect fraudulent transactions with this trained classifier. Oversampling the minority fraudulent transaction classes to rebalance the training set is an effective way to address the problem of class imbalance. The method proposed in this paper uses an improved conditional generative adversarial network to rebalance dataset, then combines with random forests classifier for fraud detection, and experiments prove that the proposed method is effective for credit card fraud detection.
The Control of intelligent building robot patrol technology based on Raspberry Pi

With the rapid development of intelligent buildings, the intelligent research of mobile robots has set off an upsurge. In this paper, the research is carried out on building intelligent security robots, the wheeled robot platform is built, and an autonomous inspection system is designed. Through communication, electronics, Internet, Internet of Things, satellite remote sensing, laser ranging, radar ranging, satellite positioning and inertial navigation combination, computer, machinery manufacturing, and other technologies, the robot construction and equipment, hardware and circuit integration, embedded software development, etc. are designed and manufactured to realize intelligent work. Robot participation in inspection makes up for the empty period of manual inspection, avoids the risks and disadvantages of manual inspection, improves production efficiency, reduces the burden of operations and maintenance personnel, and saves costs.
Research on Face Detection and Recognition with Face Mask Based on FaceNet

According to the requirements of face recognition with masks in epidemic environment, this paper analyzes the difficulties of face recognition with masks, and puts forward a scheme combining face detection, data enhancement and face recognition. Firstly, face detection is performed by MtCNN algorithm; Then, data enhancement technology is used to enhance the complexity of sample data sets and further optimize FaceNet model. Finally, the face feature vector is extracted by FaceNet model to realize the correct recognition of mask face. Experiments show that this scheme can improve the accuracy of face recognition with masks to a certain extent, and at the same time improve the traffic speed.
Design of an automatic cleaning robot for land-based factory aquaculture ponds

In response to the problems of labor-intensive and inefficient manual cleaning of land-based factory aquaculture ponds, this paper designs an automatic cleaning robot for aquaculture ponds based on rail movement and with the function of over-pond cleaning. The robot consists of moving mechanism, a cleaning mechanism, and a control system. The moving mechanism moves backwards and forwards, left and right along the rails above the pond, the cleaning mechanism cleans the bottom and wall of the pond by rotating and telescoping up and down, and the control system realizes the precise control of the cleaning robot by means of an STM32F103 microcontroller. Path planning is carried out by establishing a full-coverage raster map, and automatic obstacle avoidance is achieved by using algorithms such as shortest tangent and wildfire method.
Parallel Optimized Attribute-Based Encryption Scheme Based on SM9

With the development of cloud computing, secure file storage based on it is even more widely popular. Attribute-based encryption (ABE) is one of the key cryptographic techniques to solve the security of cloud storage. China has independently developed its own cryptography industry standard - SM9 identification encryption, which will be wildly used in many areas. However, the current SM9-based ABE scheme cannot effectively utilize the parallelized resources of cloud computing. With the parallel computing technology, a parallel optimized ABE scheme based on SM9 is proposed. The scheme's accuracy and security are carefully examined, and its effectiveness is examined through tests.
Gaussian process multi-label classification based on multi-view similarity fusion

Multi-view multi-label learning serves as a vital framework for learning from objects with diverse representations and rich semantics. The Gaussian process (GP), as an efficient and flexible Bayesian nonparametric model, the GP has effectively solved various tasks in machine learning. However, the Gaussian process has rarely been directly applied to multi-view multi-label learning scenarios. Therefore, in this paper, we propose a new multi-view multi-label learning method (mvml-DSimGP) based on the Gaussian pass latent variable model (GPLVM), which consists of similar non-parametric mapping function learning, rule constraint learning and label learning. Similarity nonparametric study mapping function for multiple-views data analysis, it studied the view within the similarity and latent mapping function between the parameters. On the basis of the similarity of nonparametric function learning, regular constraints are introduced, and put forward multi-label learning. Instances with similar label information are encouraged to have similar output representations in the embedded space, making it sufficient discernment in the embedded space. Numerous experiments on real-world datasets clearly show that mvml-DSimGP has good performance compared to other general multi-view multi-label learning methods.
The design research on the design of the elderly Smart wheelchair based on the analysis of the AHP user needs level

The article provides theoretical basis and guidance for designing smart wheelchairs by establishing a smart wheelchair function demand index system. Through the research and market product analysis of smart wheelchairs, the functional requirements indicators of smart wheelchairs are used to use the KJ affinity graph. The weight value of each functional indicator is calculated through the questionnaire survey method, interview method, and AHP hierarchical analysis method, and the consistency test is completed. The target layer of the smart wheelchair function requirements indicators include 4 first-level indicators, and 11 second-level indicators. The important sorting of the first-level indicators is security, comfort, convenience and intelligence, respectively. In the secondary indicator of smart wheelchair design, the guardian has high requirements for the intelligent braking auxiliary system, speed adjustment, intelligent assistance and real -time positioning function that affects security. At the same time, the guardian hopes that the smart wheelchair will have the functions of intelligent sitting position, intelligent health monitoring, intelligent positioning, and real-time power monitoring. In the end, the author designed a smart wheelchair in combination with the research conclusion and design practice, and was designed and applied from intelligent power, intelligent positioning, intelligent monitoring, intelligent security, intelligent voice, APP interface, etc.
Multi-channel Vision Platform of Intelligent Robot Based on Deep Learning

With the development of Internet and multimedia technology, the network server provides a large number of images, which brings great challenges to the subsequent image processing and analysis. This paper mainly studies the multi-channel vision platform of intelligent robot based on deep learning. This paper first Outlines the level and structure of multi-channel information fusion. Second, this paper proposes a multi-channel visual information fusion method based on convolutional neural network. It can be seen from the experimental results that the proposed algorithm has excellent visual detection results.
Intelligent fitness system design based on ESP32 and human posture recognition

Aiming at the fixed and single function of fitness device, this system designs an intelligent fitness system that monitors the human body status in real time by the lower computer and judges whether the fitness action is standard by the upper computer. The system uses ESP32 as the main control chip and obtains environmental information and user's body data in real time through temperature and humidity sensor, pH strength sensor and ECG detection sensor. The lower computer uploads the data to the upper computer through UDP communication protocol and visualizes the data. The upper computer connects to the monocular camera and identifies and determines whether the user's fitness movements are standard or not by using the human posture estimation algorithm in deep learning and gives tips. The user performs the exercise according to the prompts. The system ensures the efficiency of fitness and increases the fun of human-computer interaction.
Few Shot Feature Extraction Network Based on Autoencoder

Few Shot Object Detection is becoming a hot issue in the problem of deep learning object detection. Its main goal is to use very few samples for training and learning to obtain a model with good detection speed and accuracy. However, the most significant problem of few shot object detection detection is that the extraction of target feature information is insufficient and incomplete due to the sparse sample data. The solution to this problem in main methods is often to increase the number of samples through data enhancement, which can greatly enhance sample feature information. However, there is still the problem of inaccurate extraction of target feature information. This paper proposes a feature extraction network based on an autoencoder, that is, under the framework of autoEncode, refer to the network structure of Vgg-16 and integrate the SeNet channel attention mechanism to adjust the channel feature information weight parameters in the feature extraction network, and then design the similarity evaluation method between the sample input image and the reconstructed image based on the autoencoder is used to quantify the index representation feature extraction ability. Finally, the experiments show, compared with other main feature extraction algorithms, our method proposed in this paper has a better performance on the feature extraction ability index on the small-sample fine-grained dataset Oxford-102 Flower, and the speed reaches the level of main algorithm, which shows that the scheme proposed in this paper effectively improves the feature learning and extraction ability in the condition of FSOD.
Brain MRI synthesis based on dual-generator generative adversarial network

Various contrast Magnetic Resonance Imaging (MRI) of can increase the available information in clinical diagnosis. However, due to the time limit, cost, and the patient cooperation in the scanning process, some contrast images may not be obtained in time. Moreover, some images may be damaged during the sampling process. Therefore, the synthesis model is designed to obtain the missed contrast image from the existing image, which is beneficial to improve the effect of diagnosis. Hence, we propose a new brain MRI synthesis method based on generative adversarial networks (GAN). This method leverages a coarse-to-fine dual network structure based on the generative adversarial network and uses FID to make T2 weighted images generate more reliable T1 weighted images. We conducted experiments on 2730 T1 and T2 weighted images, and used structural similarity, peak signal-to-noise ratio, and mean square error as evaluation indicators. Quantitative and qualitative experimental results show that our method is superior to the original one.