bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Modified Gamma Correction Algorithm Applied to the Medium and Bright Light Problems in AI Image Recognition
Authors: Yu Lu , Wang Yali , Li Jie 
Published: November 2021

AbstractIn this paper, an improved block gamma correction algorithm is proposed for the problem of bright light repair in outdoor scenes in the field of artificial intelligence image recognition, and simulation verification is carried out. Firstly, the strong light area is quickly extracted to meet the high-speed and low-calculation requirements of the artificial intelligence image recognition algorithm. Secondly, the strong light area is divided into sub-blocks, and different gamma values are used according to the standard deviation of each sub-block, which effectively reduces the strong light area. Finally, high image detail information and color confidence are preserved, and at the same time, it meets the requirements of the subsequent image recognition network for the input image.
An Intelligent Path Selection Algorithm Based on Deep Reinforcement Learning
Authors: Sun Mengchen 
Published: November 2021

Abstract
Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy.
CNN-BiLSTM water level prediction method with attention mechanism
Authors: Nie Qingqing , Wan Dingsheng , Wang Rui 
Published: November 2021

Abstract
Hydrological time series data is stochastic and complex, and the importance of its historical features is different. A single model is difficult to overcome its own limitations when dealing with hydrological time series prediction problems, and the prediction accuracy of a single model can be further improved. According to the characteristics of hydrological time series data, a CNN-BiLSTM water level prediction method with attention mechanism is proposed. In this paper, CNN extracts the spatial characteristics of water level data and BiLSTM learns the time period characteristics by combining the past and future sequence information, attention mechanism is introduced to focus the salient features in the sequence. Taking the hourly water level data of Pinghe basin in China as experimental basis, experimental result shows that this method is more accuracy than Support Vector Machine (SVM), Temporal Convolutional Neural network (TCN), and Bidirectional Long Short-Term Memory network (BiLSTM) model.
Application and Research of Convolution Neural Network in MRI Image Classification and Recognition
Authors: Hou Xuemei , Gao Fei , Wu Jianping , Wu Minghui 
Published: November 2021

Abstract
The traditional hepaticcell carcinoma (HCC) pathological grading depends on biopsy, which will cause damage to the patient's body and is not suitable for everyone's pathological grading diagnosis. The purpose of this paper is to study the pathological grading of liver tumors on MRI images by using deep learning algorithm, so as to further improve the accuracy of HCC pathological grading. An improved network model based on SE-DenseNet is proposed. The nonlinear mapping relationship between feature channels is modeled and recalibrated using attention mechanism, and rich deep-seated features are extracted, so as to improve the feature expression ability of the network. The method proposed in this paper is verified on the data set including 197 patients, including 130 training sets and 67 test sets. The experimental results are evaluated by receiver operating characteristic (ROC) and area under the ROC curve (AUC). The improved SE-Densenet network achieves good results, and AUC 0.802 is obtained on the test set. The experimental results show that the method proposed in this paper can well predict the pathological grade of HCC.
A Skin Cancer Detection Interactive Application Based on CNN and NLP
Authors: Gong Xuping , Xiao Yuting 
Published: November 2021

Abstract
Skin cancer is the most common cancer with several different types. According to current estimations, one in five Americans will develop skin cancer in their lifetime. Therefore, early diagnosis and treatment of it is of crucial significance. Several advanced image processing methods have been applied to predict skin cancer. However, few researchers utilize those methods to build an interactive application. In this work, we implemented an interactive skin cancer diagnosis website, combining the convolutional neural network (CNN) and natural language processing (NLP) technology. The neural network model uses four convolutional layers and dense layers respectively to improve the accuracy. Two max-pooling layers were used to reduce redundant information. To address the severe overfitting problem, we chose to utilize the batch normalization along with dropout layers. Based on our results, 0.9935 in accuracy and 0.0225 loss is realized for training data, and accuracy of 0.8393 and 0.6648 loss for testing data. Natural language processing (NLP) was used to implement a chatbot for interaction with users. We crawled skin cancer related questions and answers from Quora and used them to train our chatbot. Lastly, we combined CNN and NLP to build an interactive skin cancer diagnosis website. VUE.js and Django were used to build the front-end and back-end of our website. These results offer a guideline for combining artificial intelligence with not only medicine but also interactive network, which enables people to get medical care more easily.
Design of a Skin Cancer Diagnosing Web Application Based on Convolutional Neural Network Model and Chatterbot Application Programming Interface
Authors: An Qi 
Published: November 2021

Abstract
Skin cancer has become a great concern for people's wellness. With the popularization of machine learning, a considerable amount of data about skin cancer has been created. However, applications on the market featuring skin cancer diagnosis have barely utilized the data. In this paper, we have designed a web application to diagnose skin cancer with the CNN model and Chatterbot API. First, the application allows the user to upload an image of the user's skin. Next, a CNN model is trained with a huge amount of pre-taken images to make predictions about whether the skin is affected by skin cancer, and if the answer is yes, which kind of skin cancer the uploaded image can be classified. Last, a chatbot using the Chatterbot API is trained with hundreds of answers and questions asked and answered on the internet to interact with and give feedback to the user based on the information provided by the CNN model. The application has achieved significant performance in making classifications and has acquired the ability to interact with users. The CNN model has reached an accuracy of 0.95 in making classifications, and the chatbot can answer more than 100 questions about skin cancer. We have also done a great job on connecting the backend based on the CNN model as well as the Chatterbot API and the frontend based on the VUE Javascript framework.
A Plug-and-play Attention Module for CT-Based COVID-19 Segmentation
Authors: Zhang Zhengqian , Xue Haoqian , Zhou Guanglu 
Published: November 2021

AbstractAt the end of 2019, a new type of coronavirus (COVID-19) rapidly spread globally, even if the penetration of vaccination is getting higher and higher, the emergence of viral variants has increased the number of new coronal pneumonia infections. The deep learning model can help doctors quickly and accurately divide the lesion zone. However, there are many problems in the segmentation of the slice from the CT slice, including the problem of uncertainty of the disease area, low accuracy. At the same time, the semantic segmentation model of the traditional CNN architecture has natural defects, and the sensing field restrictions result in constructing the relationship between pixels and pixels, and the context information is insufficient. In order to solve the above problems, we introduced a Transformer module. Visual Transformer has been proved to effectively improve the accuracy of the model. We have designed a plug-and-play spatial attention module, on the basis of attention, increased positional offset, effective aggregate advanced features, and improve the accuracy of existing models.
Characteristic of Neural Signal Feature for Spike Sorting and Detection
Authors: Wang Tongwei 
Published: November 2021

Abstract
Neural spike plays an important role in understanding brain activities, and in neural spike sorting, the features of signal are of great importance. This paper aims to have a review on features used to discriminate different originated spikes. The features are divided into three categories: features in the time domain, features in the transformation domain, and features of dimensional reduction. For each kind of feature, the basic principle, advantages, and disadvantages are described and discussed. Results showed that features in the time domain are suitable for on-chip or real-time spike sorting, while features in the transformation domain can be used in offline spike sorting aiming at high performance. For features of dimensional reduction, it makes a large number of features available in spike sorting. In conclusion, researchers need to determine features by balancing the minimization of calculation complexity and maximizing sorting performance according to different occasions and demands. Expectations are also made for future directions of spike feature studies. The article may guide both the physiologists who want to determine features in neural spike sorting and researchers who want to work on feature extracting algorithms further to achieve better performance in experimental challenges.
Research on the Application of Uninterrupted 5G Private Network in Smart Grid
Authors: Lu Guosheng , Tian Lin , Liu Hang , Zhu Hailong , Wang Long , Zeng Jielin 
Published: November 2021

Abstract
Based on the demand for smart grid 5G network, this paper studies 5G mobile communication technology and the industry's 5G private network construction mode. Combined with power grid requirements, this paper focuses on analyzing the uninterrupted 5G private network solution, the principal of the solution is also explained. Taking a 5G test network deployed at a converter station as an example, the construction, isolation, operation, and maintenance plans of the uninterrupted 5G private network solution in the power communication network is proposed, the actual tests verify the feasibility of the continuous 5G private network. This paper will provide reference for the promotion and application of 5G private network technology in the power grid.
Indoor Positioning Method Based on Infrared Vision and UWB Fusion
Authors: Zhang Qianrong , Li Yi 
Published: November 2021

Abstract
Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.
Research on Intelligent Fire Risk Perception System of High-rise Building Based on Big Data of Internet of Things
Authors: Wu Xiaosha , Tian Shixiang , Wang Wei , Jiang Zebiao , Guo Kai 
Published: November 2021

Abstract
Due to the characteristics of high-rise buildings, such as dense personnel, complex structure and many combustibles, fire safety problems are more prominent. Aiming at the fire problem of high-rise buildings, based on the Internet of Things and big data technology, the intelligent fire risk perception system of high-rise buildings is studied. Through the functional modules of fire water monitoring system, intelligent electricity monitoring system and fire automatic alarm system, the hydraulic and water level of fire water, the current and voltage values of key nodes such as distribution cabinets, and the on-duty personnel in fire control room are mastered in real time, so as to grasp the specific location of fire in time and accurately, so as to realize the intelligent control of fire safety of high-rise buildings.
Research on the Application of 5G Network Slicing in Smart Grid
Authors: Dongwu  , Zhangtao  , Chenxiaojin  , Zhuhailong  , Pengdili  
Published: November 2021

Abstract
With the continuous construction and development of domestic power grids, the state has put forward many effective strategies to achieve the effectiveness and durability of energy supply, in order to ensure the stable operation of the power grid and the construction of smart grids. One of the most important components of the smart grid is various communication technologies. 5G network slicing is a typical application of the smart grid, because the wide-area distributed grid has greater requirements for low latency, high reliability and security. And 5G network slicing has the ability to meet its requirements. This paper analyzes the principle of 5G network slicing, analyzes the end-to-end isolation scheme of network slicing and the current smart grid slicing business model and existing problems, and proposes an effective solution for building a smart 5G slicing network.
Research on Intelligent Position Posture Detection and Control based on Multi-sensor Fusion Method
Authors: He Langning , Liu Shaowei , Guan Jiao 
Published: November 2021

Abstract
Aiming at the problems of low efficiency and high safety risks encountered in grabbing and placing the boxes by manual mode, the multi-sensor fusion method of vision and laser ranging sensor is used for position and posture detection, and the global rough positioning is detected by the global binocular camera. Precise positioning uses four local measurement modules, including a monocular camera and three laser ranging modules, to detect the position and posture of the target, and uses Move it in ROS for trajectory planning, obstacle avoidance detection and object grabbing. The intelligent position posture detection and control technology proposed in this paper can effectively improve the timeliness of the box grabbing operation, ensure the safety of the box grabbing operation, and enhance the adaptability to harsh environments.
The deformation and breakup of a droplet under the combined influence of electric field and shear flow
Authors: Chen Yanyan , Liang Yusheng , Chen Mengyuan 
Published: November 2021

Abstract
A lattice Boltzmann and finite-difference hybrid method is used to simulate the droplet deformation and breakup under the combined action of shear flow and electric field. The hybrid method is first used to validate for the droplet deformation in the combined action of shear flow and electric field. It is then used to simulate the droplet deformation and breakup in two different electric systems. Results of prolate droplets show that the droplet height and deformation both increase with increasing electric capillary number (

C

a
E

). In addition, for the breakup mode of prolate droplets, increasing

C

a
E

makes the long axis of the droplet incline more towards the wall electrodes and droplet breaks up into more daughter droplets. Results of oblate droplets show that the droplet height decreases with increasing

C

a
E

. However, the droplet deformation first decreases and then increases with increasing

C

a
E

, and its minima occurs at

C

a
E

=
0.01

. For the breakup mode of oblate droplets, the droplet deforms into a more oblate shape with a longer neck and finally breakup into more daughter droplets with increasing

C

a
E

.
Se-concentration dependent superstructure transformations of CuSe monolayer on Cu(111) substrate
Authors: Niu Gefei , Lu Jianchen , Wang Xingyue , Ruan Zilin , Zhang Hui , Gao Lei , Cai Jinming , Lin Xiao 
Published: November 2021

Abstract
As one of the most distinctive members of the monolayer transition metal monochalcogenides (TMM) family, the CuSe monolayer with a honeycomb structure has drawn much attention in the past few years. Depending on the Se concentration, the CuSe monolayer has two distinct superstructures on a Cu(111) substrate, a one dimensional (1D) moiré pattern, and two dimensional (2D) periodic nanopores. Here, we devise a strategy for simultaneous fabrication of the two superstructures of the CuSe monolayer on a Cu(111) substrate via artificially creating a density gradient of the Se concentration by an off-centered deposition method. At the boundary of the two superstructures, an intermediate state of the CuSe monolayer with a 2D hexagonal moiré pattern connected by six twisted petal-like stripes is observed. High-resolution scanning tunneling microscopy characterizations of three distinct CuSe monolayer superstructures demonstrate that the Se density can effectively modulate the stress in the CuSe monolayer formed by the lattice mismatch, driving the superstructure transformation from 1D moiré pattern through 2D intermediate states to 2D periodic nanopores. In addition, scanning tunneling spectroscopy measurements show that the intermediate state features a semiconducting behavior with a band gap of ∼2.0 eV. Our findings open up a new route for superstructure transformation control of 2D materials.
Microstructure and properties of ZrO2/Ni3Al-Ni3Al double-layer coating on stainless steel prepared by powder sintering one-step forming process
Authors: Li Xinxing , Yang Zirun , Wang Hongxia , Li Lin , Ren Naifei 
Published: November 2021

Abstract
ZrO2/Ni3Al-Ni3Al double-layer coating on stainless steel was prepared by powder sintering one-step forming process. The surface-interface morphologies, chemical element distribution and phase compositions of the coatings before and after thermal oxidation or hot corrosion were analyzed using scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and X-ray diffractometer (XRD). The microstructure of the coating was Ni3Al matrix with dispersed ZrO2 particles, which was pure and compacted. The Ni3Al intermediate layer compactly formed between stainless steel substrate and ZrO2/Ni3Al composite coating, with good metallurgical bonding interfaces. The ZrO2/Ni3Al-Ni3Al double-layer coating exhibited a relatively high outmost microhardness of about 500 HV, and then gradually decreased from the coating to the substrate. When thermal oxidized or hot corroded at 1050 °C, a mixed oxide layer of NiO, Al2O3 and NiAl2O4 formed on the coating surface, which effectively acted as a diffusion barrier for oxygen and corrosive substances, and thus demonstrated the good protective effect of ZrO2/Ni3Al coating.
Assessing the dose rate delivery of helical TomoTherapy prostate and head & neck treatments
Authors: Kouris P , Moutsatsos A , Pappas E P , Beli I , Pantelakos P , Karaiskos P , Pantelis E 
Published: November 2021

Abstract
The dose rate distributions delivered to 55 prostate and head & neck (H&N) cancer patients treated with a helical TomoTherapy (HT) system were resolved and assessed with regard to pitch and field width defined during treatment planning. Statistical analysis of the studied cases showed that the median treatment delivery time was 4.4 min and 6.3 min for the prostate and H&N cases, respectively. Dose rate volume histogram data for the studied cases showed that the 25% and 12% of the volume of the planning target volumes of the prostate and H&N cases are irradiated with a dose rate of greater or equal to 1 Gy min−1. Quartile dose rate (QDR) data confirmed that in HT, where the target is irradiated in slices, most of the dose is delivered to each voxel of the target when it travels within the beam. Analysis of the planning data from all cases showed that this lasts for 68 s (median value). QDRs results showed that using the 2.5 cm field width, 75% of the prescribed dose is delivered to target voxels with a median dose rate of at least 3.2 Gy min−1 and 4.5 Gy min−1, for the prostate and H&N cases, respectively. Systematically higher dose rates were observed for the H&N cases due to the shallower depths of the lesions in this anatomical site. Delivered dose rates were also found to increase with field width and pitch setting, due to the higher output of the system which, in general, results in accordingly decreased total treatment time. The biological effect of the dose rate findings of this work needs to be further investigated using in-vitro studies and clinical treatment data.
Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks
Authors: Connan Mathilde , Sierotowicz Marek , Henze Bernd , Porges Oliver , Albu-Schäffer Alin , Roa Máximo A , Castellini Claudio 
Published: November 2021

Abstract

Objective. Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dexterity, makes them ideal assistive devices for upper-limb disabled persons, too. Indeed, teleoperating a bimanual robotic platform via muscle activation could revolutionize the way stroke survivors, amputees and patients with spinal injuries solve their daily home chores. Moreover, with respect to direct prosthetic control, teleoperation has the advantage of freeing the user from the burden of the prosthesis itself, overpassing several limitations regarding size, weight, or integration, and thus enables a much higher level of functionality. Approach. In this study, nine participants, two of whom suffer from severe upper-limb disabilities, teleoperated a humanoid assistive platform, performing complex bimanual tasks requiring high precision and bilateral arm/hand coordination, simulating home/office chores. A wearable body posture tracker was used for position control of the robotic torso and arms, while interactive machine learning applied to electromyography of the forearms helped the robot to build an increasingly accurate model of the participant’s intent over time. Main results. All participants, irrespective of their disability, were uniformly able to perform the demanded tasks. Completion times, subjective evaluation scores, as well as energy- and time- efficiency show improvement over time on short and long term. Significance. This is the first time a hybrid setup, involving myoeletric and inertial measurements, is used by disabled people to teleoperate a bimanual humanoid robot. The proposed setup, taking advantage of interactive machine learning, is simple, non-invasive, and offers a new assistive solution for disabled people in their home environment. Additionnally, it has the potential of being used in several other applications in which fine humanoid robot control is required.