bluuebunny/crossref_metadata_2025_split huggingface dataset:
Open-Circuit Fault Diagnosis of Power Rectifier Using Deep Convolutional Neural Network
Authors: Wang Ruoyue 
Published: September 2020

Abstract
Aimed to automatically provide accurate fault diagnosis of data from failed power electronics, various studies have been researched based on different approaches. Recently, data driven methods based on deep learning have required increasing attention because of their automatic feature learning abilities. Nonetheless, one of the challenges using these methods in practice is how to obtain the most representative fault features and ensure the better predication performances at the same time. This paper is in respect of the open-circuit fault diagnosis of the phase-controlled three-phase full-bridge power rectifier using deep convolutional neural network (DCNN) for extracting and further classifying fault features. The process mainly includes four steps. Firstly, a presupposed approach of DCNN which is applied to automatically capture the paramount fault features from the raw data is briefly introduced. Then a structure of DCNN is designed to extract the features from output data. Furthermore, the model and framework of the fault diagnosis system are developed to diagnose the open-circuit fault of the power rectifier. Finally, the effectiveness of the proposed method is validated using simulation results. Experiments illustrate that the DCNN model can achieve high accuracy in different fault cases and present great capability of diagnosing fault types in open-circuit fault of power rectifiers.
Matching Neural Network for Extreme Multi-Label Learning
Authors: Zhao Zhiyun , Li Fengzhi , Zuo Yuan , Wu Junjie 
Published: September 2020

AbstractMulti-label learning involving hundreds of thousands or even millions of labels is referred to as extreme multi-label learning, in which the labels often follow a power-law distribution with the majority occurring in very few data points as tail labels. As a promising solution of multi-label learning, however, the embedding-based methods face a problem that most of them have the basic low-rank assumption but the widespread of tail labels in data violates it. Recently, research efforts have been put on building tail label tolerant embedding-based models, however, for real-life datasets containing substantial data points with only tail labels, simply treating them as label matrix outliers will incur severe information loss, meanwhile accurately computing the pairwise distances between label vectors turns infeasible. In light of this, we present the Matching Neural Network (MNN), which learns two neural mapping functions that encode feature vectors and label vectors into their distributed representations, respectively. A noise contrastive loss is also proposed to guide the training of the functions so as to ensure matched features and labels have similar distributed representation measured by cosine similarity. Extensive experiments on various benchmark datasets with state-of-the-art baselines demonstrate the more accurate predictions of MNN.
Deep Attention Network for RGB-Infrared Cross-Modality Person Re-Identification
Authors: Li Yang , Xu Huahu 
Published: September 2020

AbstractRGB-Infrared cross-modality person re-identification is an important task for 24hour full-time intelligent video surveillance, the task is challenging because of cross modal heterogeneity and intra modal variation. A novel deep attention network is proposed in this paper to handle these challenges by increasing the discriminability of the learned person features. The method includes three elements: (1) dual-path CNN to extract the feature maps of the RGB images and infrared images respectively, (2) dual-attention mechanism combining spatial attention and channel attention to enhance the discriminability of extracted features, and (3) joint loss function joining bi-directional ranking loss and identity loss to constraint the training process to further increase the accuracy. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed method because the method achieves higher performance than state-of-the-arts methods.
The Analysis and Design of Unified Data Model for Electricity Market under the Background of Deepening Design
Authors: Dong Liu , Qian Zhang , Fei Ye , Lei Li , Chunyi Chen , Shuxian Qiu 
Published: September 2020

Abstract
With the deepening of market-oriented reform in China, the types of market entity are increasing. The existing data model of market entity in China’s power trading platform has been unable to meet the needs of registration and multidimensional statistical analysis under the new situation. Based on the design concept of the international standard IEC62325, this paper constructs the unified data mode with the wide range of adaptability including the market model and physical model, combining with the current registration needs of new market entity in China’s power market. The model takes into account the market registration of non-enterprise market entities (individuals or organizations) for the first time, and meets the registration needs of multi-identity market entity, realizing data sharing and market collaborative operation between two levels of platforms.
Application of Artificial Intelligence Technology in Pathological Image Analysis of Breast Tissue
Authors: Jia Mengmeng , Guo Xinjian , Tian Fang 
Published: September 2020

Abstract
Breast histopathological examination usually relies on experienced pathologists. In recent years, artificial intelligence technology has been applied to digital pathological images to help doctors diagnose breast tumor. This paper mainly introduces the research progress of artificial intelligence technology in pathological classification and histological grade of breast cancer. In view of the simplification of the pathological image analysis object of breast tissue, a multimodal breast cancer aided pathological diagnosis model is proposed. The proposed model combines the patient’s immunohistochemical diagnosis data and pathological image data with artificial intelligence to analyze and diagnose. To achieve a deeper evaluation of the patient’s condition on the basis of simple tumor screening, optimize the clinical treatment plan to make it more targeted. Discuss the main problems faced by artificial intelligence technology in the field of breast pathological image analysis, and prospect its development prospects.
Dynamic Complex Protein Detection using Binary Harris Hawks Optimization
Authors: Chellal Mouna , Benmessahel Ilyas 
Published: September 2020

Abstract
Identifying protein complexes brings new insights in the field of understanding of cellular life and the mechanisms underlying complex diseases. In order to solve the challenge of protein complex prediction, a large number of tools have been developed to create an efficient approach. Most existing methods do not take changing protein-protein interactions with time into consideration. To address these challenges, we propose a new binary technique of Harris Hawks Optimizer (BHHO) to improve the accuracy of protein complex detection. According to the core-attachment structure, BHHO is used to discover the potential cores of protein complexes by simulating the process of cooperative behavior in chasing by Harris’ hawks in nature. We design a new fitness function to detect protein complexes with various densities and modularities. The experimental results show that BHHO achieves an ideal performance PIN in term of the F1 and outperforms of the classical algorithms in yeast in term Jaccard measure.
A Hybrid Approach for Coronary Artery Anatomical Labeling in Cardiac CT Angiography
Authors: Zhou Chen 
Published: September 2020

Abstract
Automated anatomical labeling plays an essential role in the effort of developing a computer-aided diagnosing system for coronary artery diseases. The large variation between individuals, presence of vascular stenosis or blockages, and possible image degradations makes the problem extremely challenging. In this paper, we propose a hybrid approach that combines the strength of traditional rule-based methods and recent deep learning methods, ensuring interpretability and consistency of the labeling process while being capable of learning parameters in a data-driven scheme. Our method is composed of two major components. First, we present our method of artery tree building from possibly noisy initial segmentation. The proposed algorithm links missing vessel segments while reducing noise, resulting in a complete and clean centerline based artery tree representation. Next, our labeling algorithm combining gated graph convolutional network (GGCN) and logical rules is elaborated. Experiments have demonstrated encouraging results both for completeness of artery tree building and accuracy of anatomical artery labeling.
Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals
Authors: Zhang Yue , Xia Ming 
Published: September 2020

Abstract
Congestive heart failure (CHF) is a common serious heart disease that requires a number of clinical examinations to diagnose, which are costly and time-consuming. Electrocardiogram (ECG) is widely used in the diagnosis of various cardiovascular diseases due to its advantages of non-invasive, convenient and cheap, so the automatic CHF detection algorithm based on ECG signals can overcome the above shortcomings and has great application prospects. In this paper, inspired by the idea of DenseNet in computer version, we refined it to be applicable to CHF detection task, thus improving the diagnosis accuracy of the model. Secondly, to improve the robustness of the algorithm, we built a CHF database on PhysioBank, which contained more diverse data compared with similar studies, and conducted experiments on the built database. Finally, we presented an evaluation method based on the “inter-patient” pattern to evaluate the performance of the method more objectively. The results show that our algorithm can efficiently detect CHF with accuracy, sensitivity and specificity up to 94.97%, 89.38% and 99.50%, respectively. The algorithm proposed in this study can provide reliable references for doctors, and can be used in portable devices to realize real-time monitoring for patients.
In Silico Testing of a Closed-Loop Artificial Pancreas Based on Generalized Predictive Control
Authors: Liu Wenping , Jin Haoyu 
Published: September 2020

Abstract
The artificial pancreas (AP) system is a promising approach to maintain blood glucose concentration (BGC) of patients with type 1 diabetes (T1D) in a euglycemic range (70–180 mg/dl). A controller based on generalized predictive control (GPC) with an adaptive reference glucose trajectory and an adaptive softening factor (GPC+AA) was proposed for AP system in our previous research. It was built and tested with the University of Virginia (UVA)/Padova type 1 diabetes mellitus simulator (T1DMS), which embodies the biophysiological parameters of in-silico population accepted by the US Food and Drug Administration. It was showed that the GPC+AA controller realized effective control of the BGC of in-silico patients with T1D. Here, the effectiveness of the GPC+AA controller was further tested with strict conditions, including a pump with noise and error, a continuous glucose monitoring sensor and a high carbohydrate intake. Test results showed that the BGC of adolescent group was not effectively controlled with the GPC+AA controller. Then, the adaptive softening factor strategy was further optimized and a GPC controller with an adaptive reference glucose trajectory and an optimized adaptive softening factor (GPC+AO) was proposed here. It was demonstrated that the optimized adaptive softening factor strategy significantly improved the performance of the GPC controller, and the average BGC percentage within the euglycemic range of 9 adults and 10 adolescents increased to 80% and 70%, respectively. Thus, our GPC+AO controller is effective for patients with T1D and can be potentially applied in the AP systems.
A Parameter Classification Prediction Method Applied to LEAP Model of Electric Energy Substitution Forecasting
Authors: Cao Fang , Qian Ruixin 
Published: September 2020

Abstract
As an energy substitution analysis & prediction tool with flexible parameter structure, LEAP model can provide strong support and guidance for guiding the electricity substitution work. On the basis of accurate parameters in LEAP model, this paper proposes a specific parameter classification prediction method. First, a targeted data structure is established, and the parameters that need to be input into the LEAP model are classified into general parameters and scenario parameters according to their degree of certainty. Secondly, predict the general parameters using improved GM(1,1) model by modifying background values and initial conditions. Thirdly, a Grey-Monte Carlo model was proposed to predict scenario parameters and their occurrence probability. Finally, the correctness of the parameter classification and the parameter prediction model are verified through example analysis, and it is proved that the co-application of them improves the accuracy of the parameters and further improves the accuracy of the electric energy substitution prediction.
Design of Low-Power Low-noise CMOS ECG Amplifier for Smart Wearable Device
Authors: Yang Yuze 
Published: September 2020

Abstract
The artificial intelligence health care devices have focused on the portability and accuracy and the most trending area of wearable biomedical application has become the electrocardiogram (ECG) recording device. The ECG signals have the characteristic of low amplitude, prone to be influenced by Power Line Interference (PLI), so it is essential to achieve high gain and high common-mode rejection ratio (CMRR),while the input-referred noise and power consumption need to be low to realize the cardiac screening system on chip (Soc).In this paper, a low-noise low-power analog front end (AFE) amplifier which based on Driven-Right-Leg circuit(DRL) has been proposed. It was implemented in CMOS 180 nm with bias current and supply voltage of 12µA and 0.7V, respectively. The simulation results showed that this front-end circuit can achieve a low input referred noise of 4.11µV/Hz and high common mode rejection ratio of 135dB. It also gave voltage gain of 41.8 dB with the bandwidth from 0.1Hz to 100Hz and the total power consumption was 4.32µW. Compared with recently relevant whole circuit design, we believe that it is suitable to be used in smart wearable device.
Cooperative 3-D relative localization for UAV swarm by fusing UWB with IMU and GPS
Authors: Qi Yang , Zhong Yisheng , Shi Zongying 
Published: September 2020

Abstract
A real-time 3-D relative position estimator is presented for fixed-wing UAV swarm system in formation flight, using measurements from IMU, compass, GPS, and a set of UWB ranging radios. Instead of using stationary UWB anchors to provide 3-D coordinate and fusing it with IMU, the estimator uses UWB measurements for the construction of a non-convex function, and calculates the global optimum of the non-convex function to get position estimation, while the GPS positions are used to provide bearing information. Actual flight experiments with seven fixed-wing UAVs are conducted outdoors, and the experimental results indicate that the proposed scheme achieves lower distance estimation error between neighbor UAVs compared to traditional GPS/IMU/UWB fusing algorithm using EKF.
Research and Application of Key Techniques for Renovation of Excellent Historic Buildings
Authors: Wang Bin , Luo Lei , Wang Feiran 
Published: September 2020

Abstract
China has a very long history and culture. Many historical relics and monuments have been preserved to this day. These are also representatives of Chinese civilization. Among them, the value and status of buildings are relatively high, and they have strong research significance. Based on previous work experience, this article summarizes the repair concepts of modern historical building protection units. The author discusses the application of key technologies for the preservation and repair of excellent historical buildings from the following four aspects. The four aspects respectively are the flat roof repair technology, clear water exterior wall repair technology, cobblestone exterior wall repair technology, and brushed exterior wall repair technology.
Discussion on Green Interior Design Creativity Based on 3R Concept
Authors: Fang Xiao 
Published: September 2020

Abstract
In recent years, the speed of economic development has accelerated. It has also brought a great impact on the global environment, especially the global warming, which has made people pay more attention to ecological and environmental protection issues. The awareness of environmental protection has increased significantly, which also puts forward new requirements for interior design. In recent years, green engineering has become the mainstream form. In addition, some scholars have put forward the 3R concept, which is committed to using the least resources and generating the least pollution to create a more comfortable and safe living space and reduce the impact of the construction industry.
The Strategy to Measure the Effectiveness of Smart City Growth Model
Authors: Tian Di 
Published: September 2020

Abstract
Nowadays, people attach greater importance to smart growth initiatives in an effort to consider long-range, sustainable city planning goals. Urban planning has become increasingly important and necessary to ensure that people have access to equitable and sustainable communities, resources, and jobs [1]. Our study analyzed the possible impact on current smart growth strategies and defined a metric to measure the success of the smart growth level of a city. Furthermore, this paper selected 9 indicators to measure the key factors affecting the system, and divide them into three categories, which refer to Economy, Equality, and Environment. Our model is broad enough to accommodate various situations in different countries. Lastly, this paper researched the current growth plan of the selected cities, Atlanta and Karamay, to discuss how the current growth plan of each city meets the smart growth principles.
Finite element analysis of unloading sheet pile wharf with ABAQUS
Authors: Na Wang , Gang Wang , guoqiang Chen , xing Li , Yifeng Ji , nianfei Liu 
Published: September 2020

Abstract
Sheet pile wharf with separated relieving platform is so complex with little relatively engineering application that influencing factors of structure characteristics are lack of deeply studying. This paper researches it’s influencing factors by finite element numerical simulation finite element numerical simulation finite element numerical simulation the finite element numerical simulation of the Sheet pile wharf with separated relieving platform with ABAQUS, Referring to the partial dimensions and geological parameters of the centrifuge test for the separation unloading sheet pile wharf of the South Academy of Sciences, after proper simplification and reasonable assumption, The working mechanism and characteristics of the separated unloading sheet pile wharf are studied from a new angle, and the main influencing factors are further analyzed, which provides reference for the design calculation and optimization of the separated unloading sheet pile wharf.
Research and application of viscosity optimization to reduce pressure drop of single well
Authors: Na Su , Zhi Ma , Yan-mei Xu , Ai-jun Wei , Bo Zhao , Ke-jia Wang , Hui-li Du , Li-li Wei , Ying Liu , Zhong-Hua Shao 
Published: September 2020

Abstract
Due to the long service life and corrosion problems of the pipeline in the fourth oil production plant of Huabei Oilfield, the increase of back pressure will increase the probability of pipeline perforation and lead to potential safety accidents. In view of this problem, based on the analysis of the composition and rheological law of crude oil, according to the scientific and reasonable experimental evaluation method, a new type of viscosity reducer system was developed by using the unique performance of surfactant. The viscosity reducer concentration of this system was 2%, compared with the blank crude oil, the average decrease of the freezing point of the crude oil was 5°C, The viscosity reduction rate is more than 60%, yield value reduction rate is more than 75%, and the low temperature fluidity of crude oil is improved. The expected target has been achieved through the dosing operation of two oil wells on site.
A mixing model for comprehensive evaluation on the removal performance of portable air cleaners
Authors: Yang Rancun , Yin Keke , Xu Peng , Wu Peipei 
Published: September 2020

Abstract
A mixing model based on the indoor air quality obtained from statistics was developed to evaluate the comprehensive removal performance of Portable air cleaners (PACs), which have been inadequate in current research. PM0.3-2.5, formaldehyde, acetaldehyde, acetone, toluene, n-butyl acetate, ethylbenzene, xylene, styrene, and n-undecane were selected as the typical components, and initial concentrations and emission procedure were optimized. The relative standard deviations of repeatability tests were under 10%, which make the model exhibit fairly good reproducibility, stability and reusability, and are considered suitable for the estimation of the comprehensive removal performance of PACs.
Optimization of Frequency Conversion Operation of Solar Coupled Ground Source Heat Pumps System
Authors: Wang Chengshun , Liu Yufeng , Liu Jiaqi , Wang Chen 
Published: September 2020

Abstract
Taking an office building in Qingdao as the research object, the optimal operation of the solar coupled ground source heat pumps system using frequency conversion technology at load side and source side is studied, The possibility of variable flow operation of air conditioning water system and the control strategy of variable frequency pumps are analyzed. The selection of the equipmets is completed based on the hourly load of the office building through the year. Based on the dynamic simulation software, the simulation model of solar coupled ground source heat pumps is established. The results show that the energy saving rate of variable frequency pumps is 53.18%, the energy saving rate of the heat pumps unit is-12.91%, the total energy saving rate of the system is 9.00%, and the annual performance coefficient of the system is 3.86, and the overall energy saving effect is remarkable.
Mechanical properties of frozen mixed soils with different coarse-grained contents
Authors: Xiao Wei , Zhang Chaofeng , Liu Enlong , Yu Yihao , De Zhang 
Published: September 2020

Abstract
For the purpose of investigating the mechanical properties of frozen mixed soils, a series of triaxial compression tests were conducted with the confining pressures ranging from 0.3 to 6.0MPa and coarse-grained contents from 100:0 to 100:80 at the temperature of -6°C. The experimental results indicate that the strength presents an increasing tendency at first and then decreases with increase of confining pressures, whereas appearing to downtrend with increasing coarse-grained contents. And thus the mechanical behaviours are analyzed in detail, including the stress-strain characteristics and strength criterion. Additionally, it is interpreted for the pressure melting phenomenon using the modified Mohr-Coulomb strength criterion. Finally, laboratory investigations demonstrate that different fractions of coarse grains have great influences on the mechanical features of frozen mixed soils.