bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anomaly Detection of Manufacturing Process for Multi-Variety and Small Batch Production
Authors: Chen X , Chen F , Yin L 
Published: April 2020

Abstract
A series of quality control problems arise in multi-variety and small batch production due to the lack of data and other factors. In this paper, an integrated model in which control chart pattern (CCP) recognition is applied for anomaly detection is proposed to solve the problems. The integrated model is made up of four parts: feature extraction module, feature selection module, classifier module and anomaly diagnosis module. In the first module, thirteen shape features and eight statistical features of control charts are extracted. In the second module, the most representative feature set is selected by the sequential floating forward selection (SFFS) method. In the third module, a multiclass support vector machine (MSVM) which is optimized by beetle antennae search (BAS) algorithm is used to identify abnormal CCPs. In the last module, the results of pattern recognition are utilized to analyze the possible causes. The simulation results show that the CCP recognition method proposed in this paper has higher classification accuracy than other competing methods in the case of small sample with small amount of data. Finally, an example verifies that the proposed anomaly detection method is effective in multi-variety and small batch manufacturing environment.
Variation Method Overview of Image Segmentation
Authors: Tian Yuanyuan , Xue Yibing 
Published: April 2020

Abstract
With the research and development of image segmentation methods for several decades, the various theories and methods have been applied to the image segmentation. Among the many methods of image segmentation, the variation method is applied widely, because its model process is easier, the expansibility of the method is better and the implementation process is simple. On the basics of many achievements of variation method research of image segmentation recently, the variation method of image segmentation is divided into the model based on the boundary and the field according to various structural driving force based on energy functional. In this paper, we analyze these models based on these two methods and discuss their advantages and disadvantages. Relevant innovation algorithms and breakthrough progresses recently have been researched. Some development directions of image segmentation variation method have been given. The application field and development of variation image segmentation still have great research value.
Research on the Application of Artificial Intelligence in Judicial Trial: Experience from China
Authors: YAO Jie-jing , Hui Peng 
Published: April 2020

Abstract
The impact of artificial intelligence has penetrated into every aspect of life and work, and its value has been continuously explored and received great attention and recognition from business, government and academia. In general, artificial intelligence has begun to be applied in the judicial field in China, the development and practice of the intelligent assistant referee system will be of great significance and the key to the construction of a smart court, which can improve the judicial efficiency to a certain extent, prevent unjust and false cases, and ensure judicial justice. However, it is necessary to see clearly that artificial intelligence as a new thing has two sides. At present, it is still at a relatively preliminary stage and will create a series of risks. such as: the weakening of discretion, judicial discrimination, judicial dictatorship, privacy security, etc., which is essentially contrary to China’s judicial reform and rule of law construction. At the same time, in the face of the influence of artificial intelligence on judicial trial, we should have a correct attitude, prevent the worship of technology, and make clear the leading position of judges in the course of judicial trial.
A Survey on Video Dehazing Using Deep Learning
Authors: Feng Yue 
Published: April 2020

Abstract
With the fast development of object recognition and detection in autonomous driving and video monitoring, image with haze or raindrop can affect the result a lot. As deep learning develops, the hazed and raindrop image can lower the accuracy of object recognition and detection significantly. While hazed images cannot be managed using other image refining process. Since the noise in hazed image is signal-dependent. The object degradation in hazed image is related to object depth. So, the dehazing process depends on the input image. This paper provides a survey on single image and video dehazing methods, from end-to-end system to distributed system. General methods based on deep learning of state-of-art papers from 2010 to 2018 are summarized and compared, accompanied with their datasets of the current progress in this field. The application of these methods and relationship between these methos are also discussed in this paper.
A Novel Chinese Traditional Medicine Prescription Recommendation System based on Knowledge Graph
Authors: Wang Yinghui 
Published: April 2020

Abstract
As the traditional Chinese medical approach, Chinese medicine plays an extremely important role in the field of medical treatment. With the development of computers, people tend to acquire medical knowledge from the Internet in daily life. Because of the complexity of online Chinese medicine knowledge, nowadays there is no good way to organize the existing knowledge to provide convenience for doctors and patients. This paper introduces the study on the recommendation system based on the Knowledge Graph (KG). Firstly, it conducts extraction of entities such as Traditional Chinese Medicine (TCM) diseases, prescription, Chinese herbal medicine, symptoms, etc. Secondly, it transforms KG into vector space using Node2vec. At last, based on the similarities between vectors the provided system recommends prescription by adopting the diagnostic process of Traditional Chinese Medicine. The result shows that the Hit Ratio (HR) of the recommend system is as high as 80%.
Research on the Image Annotation Technology for Product Quality and Safety Inspection Data
Authors: Ning Xiuli , Li Ying , Xu Yingcheng , Lu Xiaowei 
Published: April 2020

Abstract
In recent years, the vicious events about quality and safety in China have continued to bring serious impacts on people’s lives and property. The effective analysis and processing of product quality and safety inspection data will provide intellectual support for the overall improvement in product quality, and the effective control of prominent quality and safety problems in China. Aiming at the phenomenon that there is a large amount of image information in the quality detection data, this paper proposed an image annotation technology based on big data fusion, conducted weight fusion to image similarity and image user similarity, calculated the total similarity of images, and made denoising treatment. The experimental results showed that the method proposed in this study could annotate the quality test data well.
PickPatch: A Regularization Method for Deep Face Recognition
Authors: Sun Linjun , He Wei , Ning Xin , Li Weijun , Shi Yuan 
Published: April 2020

Abstract
This paper proposes a simple and efficient regularization method, called PickPatch, for face recognition based on a deep convolutional neural network (DCNN). The proposed method randomly selects patches in the input face image and the intermediate feature maps as the activation region according to facial landmarks during the training phase. PickPatch is an approximation method that trains a series of models for different face patches and provides a combined model. This strategy introduces the idea of model combination for multiple face patches but does not change the model structure, which is both simple and efficient. Experiments on the public LFW database demonstrate that the proposed regularization method based on current deep convolutional neural networks can achieve obvious improvements of face recognition accuracy.
Optimization of Energy Efficiency for FPGA-Based Convolutional Neural Networks Accelerator
Authors: Tang Yongming , Dai Rongshi , Xie Yi 
Published: April 2020

Abstract
Convolutional neural network (CNN) is widely applied to image recognition with high recognition accuracy. CNN has a wider implementation in general-purpose processors and can be accelerated on FPGA. CNN has a unique way of computing, but general-purpose processors are not efficient for CNN and cannot meet energy efficiency requirements. And the previous studies on FPGA did not involve an energy-efficient implementation on FPGA. We innovatively propose energy efficiency models and implement high energy efficiency CNN on FPGA. We implemented the LeNet-5 network model on the GENESYS 2 board and compared it to the traditional processor and previous studies. By comparison, the computing throughput of CPU, GPU and FPGA are 3.831GFLOPS, 27.143GFLOPS and 19.61GFLOPS respectively, and their powers are 32.15W, 52W, 4.152W respectively. The final energy efficiency (GFLOPS/W) is 0.119GFLOPS/W, 0.522 GFLOPS/W, 4.723 GFLOPS/W, so the energy efficiency of FPGA are far superior to that of CPU and GPU. Since the energy efficiency we achieved on FPGA is also higher than that of FPL2009 and FPGA2015, and we have achieved good experimental results in energy efficiency.
VRP Problem Solving Based on Adaptive Dynamic Search Ant Colony Algorithm
Authors: Zheng Li , He Zhiming , Liang Wen 
Published: April 2020

Abstract
Based on ant colony algorithm to solve the defect analysis of VRP problem, an adaptive dynamic search ant colony algorithm (ADACO) is proposed. Firstly, the model is established and the combination parameters is experimentally configured. Secondly, the strategy of combining pseudo-random and adaptive transition probability are used to help the group choose a higher quality path. When the group is in a local predicament, the segmented setting of the pheromone intensity induces the group to break out of the predicament in time. Finally, multiple groups of experimental tests are performed on the “Jia-hui Fresh” cargo delivery case. The results show that, compared with the original algorithm, the ADACO algorithm has respectively improved 17.65%, 16.13% and 16.10% in terms of delivery cost, convergence algebra and CPU running time.
An Action Recognition Method Based on Deformable Convolution Network
Authors: Dong Xu , Tan Li , Zhou Lina , Song Yanyan 
Published: April 2020

Abstract
In order to solve the problem of behavior recognition in short video, this paper first proposes a key frame extraction algorithm based on mutual information entropy, which uses sliding window to preserve the timing information between frames. Based on the key frame extraction, a based on Deform-GoogLeNet, a two-stream CNN method for deformable convolutional networks, uses the two-stream network to extract the RGB features and optical Flow characteristics of the image separately, and uses the weighted average method to obtain the results of behavior recognition. On the public dataset Charades dataset, mAP is 22.9, which is higher than the similar fusion algorithm, which proves that the proposed algorithm has a good effect in short video behavior recognition.
Collective Intelligent Decision Making Method Based on Rationality Negotiation
Authors: Wentao Luo , Pingfa Feng , Jianfu Zhang , Dingwen Yu , Zhijun Wu , Haochen Liu 
Published: April 2020

Abstract
Collective intelligence without considering the rationality of the decision-making process often leads to unreasonable decisions, which brings immeasurable losses to intelligent system. To solve it, a collective intelligent decision-making method based on rationality negotiation is proposed. Each individual with decision-making right is endowed with wisdom by the adapted Q learning algorithm, so that each individual has the perceived ability and individual motivation. Rationality Negotiation Model algorithm is proposed to select a rational decision of group, which reflects collective motivation of group. Then the decision result generated by the collective motivation is transformed into knowledge feedback to each individual, so that the individual also learn the collective motivation. Finally with the repeated iterative optimization, individual action becomes reasonable in the long run. A case is studied based on industrial enterprises making decision in the face of unexpected situations influence production process. The actual verification shows that the proposed decision-making scheme for intelligent system is reasonable.
RFID as An Element of Economy 4.0 Used to Create Sustainable Supply Chains
Authors: Konecka S , Maryniak A 
Published: April 2020

Abstract
Currently, a necessity to create sustainable supply chains is stressed more and more often. The achievements of intelligent and innovative technologies significantly facilitate this process. Practically each of technological creations of industry 4.0 may, to a greater or lesser extent, contribute to the increase of environmental, economical and social effectiveness of supply chains. In this paper, the focus is placed on the RFID technology which has a broad scope of application in the logistics of business and non-business entities. The aim of the paper is specifying the role of RFID in the creation of sustainable supply chains as well as presenting the model connected with this issue which may be tested in various market conditions. The suggestions of empirical studies are placed within the background of other technologies of the fourth industrial revolution.
Development of Delaminate Inspection by Using Sound Sensor and Pattern Recognition Technique
Authors: Sukprasert Napaphan , Tangjitsitcharoen Somkiat 
Published: April 2020

Abstract
Windsurf manufacturing and inspection process have high influence for product quality. Recently windsurf manufacturer faced high nonconformity cost with delaminate problem. Current inspection process is performed by inspector skill which depends on the experience. The inspection method is proposed by using sound sensor and applying the Fast Fourier Transform to monitor the ranges of laminate and delaminate frequency. The pattern recognition technique is purposed to classify noise frequency in order to identify the laminate windsurf from the delaminate ones. This research investigates the delaminate among the polyvinyl chloride sheet, the biaxial carbon reinforced fabric and the fiber glass reinforced fabric. The area of laminate frequency in frequency domain shows the laminate point has high power spectrum density than the one from the delaminate point. The algorithm to detect the delaminate has been developed and proved by real windsurf. It’s proved that the method proposed can be identified the delaminate windsurf.
Analysis of Gap Time-Delay in Maglev Control System
Authors: Peng Lin , Hengkun Liu , Wenwu Zhou 
Published: April 2020

Abstract
In the maglev control system, signal filtering, A/D sampling and communication all bring time-delay to the gap signal, which will affect the performance of the levitation control. In order to analyze this effect, a single-degree-of-freedom maglev system model is established. The state feedback control method is used to control the maglev system, and the stability of the closed-loop system is analysed and he stable region of control parameters is studied. Gap time-delay is introduced into the closed-loop system model, and the influence of time-delay on the stability range of control parameters is analyzed. It is concluded that the gap time-delay will reduce the stability range of control parameters, the larger the time-delay of position signal is, the smaller the lower limit of the stability region of differential feedback coefficient is and the smaller the stability region is. The simulation results are used to verify the validity of the theoretical analysis.
Motion Analysis and Stability Control of Four-Wheeled Omnidirectional Mobile Robot under Limited Hardware Resource
Authors: Li Jun-nan , Tang Xiao-yu , Xu Fu-ren , Huang Rui-shan 
Published: April 2020

Abstract
Since widely used in factories, hospitals and other workplaces, the motion stability of Mecanum omnidirectional mobile platform becomes more and more important. In this paper, an improved PID algorithm based on integral separation method, anti-integral saturation method and incomplete differential method was proposed to improve the control precision and motion stability of omnidirectional mobile robot (OMR) under limited hardware resource. From the analysis of the experimental data, it can be seen that by combining the Angle data with the motion equation, the attitude of the omnidirectional mobile platform can be adjusted in a short time, and the response speed can be greatly improved.
Design and Kinematics Analysis of a Continuum Throat Surgery Robot
Authors: Peng W L , Zhang Y N , Shen L Y , Qian J W 
Published: April 2020

Abstract
A cable-driven continuum throat surgery robot is designed in this paper, which is a new type of bionic robot with softness and flexibility. Throat surgery robot in the market has the disadvantage of large-scale wound and discomfort, robot in this can solve these problem. Firstly, the joint of the robot is modularized by simulating the skeleton of the snake, and the modules are connected by spherical joints to simulate the high flexibility of the snake. Secondly, each part of the robot is controlled by three cables to control the bending motion in the pitch and yaw directions. Lastly, the kinematics analysis is carried out by the D-H method, including the mapping of the driving space, joint space and operation space of the joint module. According to the entity we make, it is approved that the robot has the advantages of small structural size, high positioning accuracy, and strong space flexibility for that the structure of the snake robot is integrated with the drive of the continuous robot. It is ideal for transoral throat surgery, and the operation is performed through the natural cavity to ensure the accuracy and safety of the operation, and the postoperative recovery is also faster.
Variable Parameters Stiffness Identification and Modeling for Positional Compensation of Industrial Robots
Authors: Jiao Jiachen , Tian Wei , Zhang Lin , Li Bo , Hu Junshan 
Published: April 2020

Abstract
Due to the weak stiffness of robot structure, the positional accuracy of industrial robots under load can hardly meet the application requirements of high-precision machining. Predicting and compensating errors by accurate stiffness modeling is an effective method to improve robot positional accuracy. Existing stiffness modeling methods use theoretical kinematic parameters and approximate the joint stiffness to a fixed value, so that the modeling accuracy is poor. Thus, this paper proposes a regular sampling point selection method by space gridding. Then, combining Levenberg-Marquardt kinematics parameter calibration and static joint stiffness identification methods, a comprehensive identification method is proposed to achieve simultaneous identifying of robot kinematics and stiffness parameters. Next, a variable parameter stiffness model could be established, according to the identification results in different workspaces. Finally, a model-based error prediction and compensation method is put forward through online sensing of external load. The error compensation is performed on a KR500 robot, and experimental results verified that the average value of absolute positional errors caused by external load, could be reduced by 44.61%, compared with the traditional compensation method.
Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps
Authors: Jia Shenyue , Kim Seung Hee , Nghiem Son V , Doherty Paul , Kafatos Menas C 
Published: April 2020

Abstract
The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.
Dendrocalamus asper productivity after beginning thinning
Authors: Hani A 
Published: April 2020

Abstract

Dendrocalamus asper is one of bamboo species that is highly required for industrial purposes and community. Beside as the construction materials, shoot of D. asper has a good taste. However, D. asper cultivation faces some obstacles because its big stem and clump disturb other species. Planting in agroforestry model and an appropriate D. asper clump management are the efforts to attract people to plant D. asper. Thinning is applied in D. asper management. This is expected to be able to reduce its canopy impact on the under plants and to improve D. asper productivity. Objective of this research is to find out the influence of beginning thinning on D. asper clump productivity. The research used complete random design with blocks with three treatments of thinning, i.e. a. thinning by eliminating first generation (J1), b. thinning by eliminating first and second generation (J2), c. without thinning (control/J3). The bamboo clump used in the research is two years old. 2 years old clumps. The planting spaces were 10 m x 10 m with a total of 100 clump. The research showed that J2 give the best height growth (5.18 m). It is better than control/J3 (4.5 m), and there were no differences on parameter of diameter and numbers of stem in a clump, although J2 treatment showed the highest average number of stem in a clump.