bluuebunny/crossref_metadata_2025 huggingface dataset:
TCF-1 limits the formation of Tc17 cells via repression of the MAF–RORγt axis
Authors: Mielke Lisa A. , Liao Yang , Clemens Ella Bridie , Firth Matthew A. , Duckworth Brigette , Huang Qiutong , Almeida Francisca F. , Chopin Michael , Koay Hui-Fern , Bell Carolyn A. , Hediyeh-Zadeh Soroor , Park Simone L. , Raghu Dinesh , Choi Jarny , Putoczki Tracy L. , Hodgkin Philip D. , Franks Ashley E. , Mackay Laura K. , Godfrey Dale I. , Davis Melissa J. , Xue Hai-Hui , Bryant Vanessa L. , Kedzierska Katherine , Shi Wei , Belz Gabrielle T.
Published: May 2019

Interleukin (IL)-17–producing CD8+ T (Tc17) cells have emerged as key players in host-microbiota interactions, infection, and cancer. The factors that drive their development, in contrast to interferon (IFN)-γ–producing effector CD8+ T cells, are not clear. Here we demonstrate that the transcription factor TCF-1 (Tcf7) regulates CD8+ T cell fate decisions in double-positive (DP) thymocytes through the sequential suppression of MAF and RORγt, in parallel with TCF-1–driven modulation of chromatin state. Ablation of TCF-1 resulted in enhanced Tc17 cell development and exposed a gene set signature to drive tissue repair and lipid metabolism, which was distinct from other CD8+ T cell subsets. IL-17–producing CD8+ T cells isolated from healthy humans were also distinct from CD8+IL-17− T cells and enriched in pathways driven by MAF and RORγt. Overall, our study reveals how TCF-1 exerts central control of T cell differentiation in the thymus by normally repressing Tc17 differentiation and promoting an effector fate outcome.
Image Super-Resolution based NCSs Model with Packet Loss
Authors: Jiang Changjiang , Li Hantao , Zhou Shangbo
Published: May 2019

There are many ways to super-resolution reconstruction of a single image. This method require the association between low-resolution images and high-resolution images, and has achieved good results in different applications. This article also pursues the correlation between low-resolution images and high-resolution images, but we combine images with linear networks to explore the possibilities of combining the two domains. In order to achieve the purpose of restoring image details, we used a method of combining image and network, different from the traditional single-picture super-resolution, we using network delay and packet loss compensation, fully retain the data packets before and after, and find the internal similarity from low-resolution images. Experiments show that inputting images into a network system is affected by system packet loss and has a large impact. It also shows that it is feasible and effective to combine images with network packet loss in the networked control systems model, and specific image effects will be shown in the paper. Our algorithm has achieve a certain expected effect, by superimposing the image super-resolution reconstruction is more extensive, and it is also convenient for future research based on image super-resolution.
New Methods on Image Analysis, Two Applications: An Augmented Reality Application for Heartbeat Sound Analysis And A MRI Brain Injury Image Analysis
Authors: Lechuga L J , Yamamoto T , López O , Lechuga G
Published: May 2019

Traditional statistical methods have become insufficient when applied to image analysis. The increasing size of data volume and its complexity demands new statistical approaches and algorithms. Current methods imply losing intrinsic data structures, for example when data comes from multiway arrays. In this work we concentrate in two applications i) A pre-diagnostic smartphone application for detection of cardiovascular abnormalities through the analysis of heartbeat sounds and the use of augmented reality for displaying valuable information to the end user in an immersive experience. Using the latest augmented reality smartphone applications, a digital stethoscope, heartbeat audios and classification using neural networks, we measure a user’s heartbeat and output in real time, a pre-diagnostic of their current cardiovascular health. ii) A study concerning comatose patients, based on a Diffusion Tensor Image Magnetic Resonance Imaging (MRI) dataset that predicts long-term outcome for patients having suffered a brain traumatic injury. MRI images were obtained from 104 comatose patients, 65 with positive outcome and 39 with negative outcome, 39 controls were used. The fact that each volumetric image led into a 143x255726x4 tensor input, is used to briefly explain how new multiway methods could be useful in image analysis methods.
Video-based Person Re-identification Based on Feature Learning of Valid Regions and Distance Fusion
Authors: Yang Danmei , Qi Meibin , Wu Jingjing , Jiang Jianguo
Published: May 2019

Video-based person re-identification (re-id) is a very challenging problem because of the occlusion and the changes of viewpoints, pedestrian postures and illumination. Most existing methods of video-based person re-id usually concatenates the extracted appearance and space-time features directly, which do not consider the discrepancy of different features fully. In order to deal with this problem well, we propose a simple but effective method based on feature learning of valid regions and distance fusion, which combines three distances. The first distance is local distance of valid regions calculated using the Gaussian of Gaussian(GOG) feature. Pedestrian images are divided in horizontal directions, and then regions with smaller distances are selected as valid ones to be reserved and regions with larger distances are as invalid ones to be removed due to occlusions and posture changes. The other two distances are obtained by independent metric learning using the histogram of oriented gradient 3D (HOG3D) feature and the Local Maximal Occurrence (LOMO) feature. The three distances are added as the final distance between pedestrian pair, so the matching rank of gallery is obtained. Extensive experiments are performed on the iLIDS-VID and PRID-2011 datasets, and the results prove the effectiveness of our methods.
Multi-info Fusion Based Video Recommendation System
Authors: Liu Yang , Zhang Guijuan , Jin Xiaoning , Yuan Haifeng
Published: May 2019

The great progress in recommendation system help users discover more interesting items that satisfy their appetites. Considering the video recommendation is an increasing popular sub-field of recommendation, but the traditional recommendation techniques such as Collaborative Filtering and Content-based model simply exploit one information source that limits its performance. In this paper, we proposed a Multi-info fusion based recommendation system which integrates several different information sources to comprehensively model the similarity between videos. The information sources including the common user-item rating data and video’s textual content that consists of video’s genres and textual description. Experimental results on a public dataset show that the proposed system is of high quality and achieves significant improvements over the traditional Collaborative Filtering techniques.
An Improved Visual Background Extraction Algorithm Combining Depth Information
Authors: Yang Feng , Jin Shijun , Ye Wenwu
Published: May 2019

ViBe is one of the most commonly used background subtraction method, which conducts foreground detection on each frame pixel-by-pixel. When the algorithm is applied to video sequence with large depth differences such as forest fire surveillance, the problem of traditional ViBe is appearing. If the algorithm parameters of different depth are the same, it will inevitably lead to two situations: the nearby shaking of trees is easy to cause false detection because of the large pixel area, and the distant smoke is easy to miss as it occupies small pixels of the image. To solve this problem, an improved visual background extraction algorithm combining depth information is proposed. The method calculates the dark channel information as depth information, and a conversion function is designed to adjust sensitivity to accommodate moving targets with huge depth difference. The experimental results demonstrate that the improved ViBe algorithm has better performance than traditional ViBe algorithm in forest fire surveillance. In addition, the improvement of the algorithm does not excessively increase the computational time-consuming.
Correspondence Between 3D Shapes Based on Improved Functional Map
Authors: Yang Jun , Li Longjie , Obaseki Alexander Jesuorobo
Published: May 2019

We propose a shape matching algorithm based on an improved functional map in order to calculate correspondence between two given 3D non-rigid shapes, in which shape correspondence can be represented as a mapping function of mixed transformation matrix B and calibration matrix P of basis functions. First, Laplace matrix is calculated and a new matrix is constructed as basis matrix of function space by using eigenvectors of Laplace matrix after eigen-decomposition. Then, a calibration algorithm based on statistical covariance is proposed to calculate the matrix P that is used to calibrate basis matrices of function space of two shapes. Finally, the matrix B is optimized by an improved ICP(Iterative Closest Point) algorithm in order to calculate shape correspondence with the matrix P. Experimental results show that the proposed algorithm avoids excessive initial conditions, obtains accurate shape correspondence and significantly solves symmetry ambiguities of 3D shapes during matching process.
Real World Hand Gesture Interaction in Virtual Reality
Authors: Zhang Qi , Zhu Wenzhe , Zhu Qing
Published: May 2019

With the development of virtual reality, the appropriate interaction technology has become the focus of practitioners. Today’s virtual reality interaction is mostly through the interaction of traditional electronic devices, such as handles and other products, temporarily solve the problem of interaction, but also make the user from the virtual world is taken out, immersion greatly reduced. The paper uses hand gestures to interact with virtual reality. By using skin detection in YCbCr color space and indicates the location of hand to determine the approximate range of hand. Then, the histograms of oriented gradient (HOG) which has been widely used in target detection research in recent years is used to extract gesture features, and applies support vector machine (SVM) method to achieve the real-time hand gesture recognition. Finally, experiments prove that the proposed method is accurate and stable in virtual reality interaction.
An Efficient Method for Automatic Generation of Labanotation Based on Bi-Directional LSTM
Authors: Zhang Xueyan , Miao Zhenjiang , Yang Xiaonan , Zhang Qiang
Published: May 2019

Labanotation uses a variety of graphic symbols to analyse and record human movements accurately and flexibly, which is an important means to protect traditional dance. In this paper, we introduce an efficient method for automatic generation of Labanotation from motion capture data by identifying human movements with bidirectional LSTM network (Bi-LSTM). Up to our knowledge, this is the first time that Bi-LSTM network has been introduced to the field of Labanotation generation. Compared with previous methods, Bi-LSTM used in our human movements recognition system learns context information for sequential data from not only the past but also the future directions. Combined with a newly designed discriminative skeleton-topologic feature, our approach has the ability to generate more accurate Labanotation than previous work. Experiment results on two public motion capture datasets show that our method outperforms state-of-the-art methods, demonstrating its effectiveness.
DCRN: Densely Connected Refinement Network for Object Detection
Authors: Gao Shihui , Miao Zhenjiang , Zhang Qiang , Li Qingyu
Published: May 2019

Object detection has made great progress in recent years, the two-stage approach achieves high accuracy and the one-stage approach achieves high efficiency. In order to inherit the advantages of both while improving detection performance, this manuscript present a useful method, named Densely Connected Refinement Network (DCRN). It adds the dense connection based on RefineDet. Compare to the RefineDet, our approach can take full advantage of the bottom feature information. DCRN is formed by three interconnected modules, the dense anchor refinement module (DARM), the dense object detection module (DODM) and the dense transfer connection block (DTCB). First module can make better use of the features from different layers to initially adjust anchors by attaching dense connection. The latter module takes the refined anchors to further improve the regression and predict multi-class label. Due to the dense connection in DCRN, the network parameters are reduced and the computing costs of this approach is also saved. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 demonstrate that DCRN achieves higher accuracy than the one-stage method and higher efficiency than the two-stage method.
Cerebral Microbleed Detection by Extracting Area and Number from Susceptibility Weighted Imagery Using Convolutional Neural Network
Authors: Sa-ngiem S , Dittakan K , Temkiatvises K , Yaisoongnern S , Kespechara K
Published: May 2019

Cerebral microbleed (CMB) the small vessels in the brain which is one of the major factors used to facilitate in the early stage diagnosis for Alzheimer’s disease detection. In traditional, CMBs detection can be done manually by the neurologists, doctors or specialists. However, the process is time-consuming and the results are not accurate depending on the doctor experiences. Therefore the efficient and reliable of the automatic detection of CMB is needed. This paper proposes a new framework for CMB detection which employs segmentation of the region of interests (ROIs), detection of the CMBs and identification of the area from SWI scan images. Convolutional Neural Network(CNN) is applied to generate the desired models for later prediction. Shape matching mechanism is also applied to identify locations of CMB in the brain. The experimental result shows that the CMB can be classified with a recorded accuracy value of 95.45%. The CMBs were discovered from three different locations include (i) cortical region, (ii) cerebellum and (iii) brainstem with an accuracy value of 100%.
Video Object Detection by Aggregating Features across Adjacent Frames
Authors: Zhang Ruyi , Miao Zhenjiang , Zhang Qiang , Hao Shanshan , Wang Shengbo
Published: May 2019

Video object detection is of great significance for video analysis. In contrast to object detection in still image, video object detection is more challenging which suffers from motion blur, varying view-points/pose, and occlusion. Existing methods utilized temporal information during detection in videos and show improvement over static-image detector. In this paper, we propose a novel method for video object detection that can aggregate features across adjacent frames adaptively as well as capture more global cues so as to be more robust to drastic appearance changes. Initially, current frame feature and warped feature from adjacent frames can be obtained via feature extraction network and optical flow network. Next, a coherence contribution module is designed for adaptively feature aggregation of the two kinds of features obtained from the first step. Finally, the still-image detector which included an extra instance-level module that agregates features from adjacent frames for capturing more global feature is adopted to get the final result. The experimental results evaluated on our method shows leading performance on the ImageNet VID dataset.
Light Corner Based Object Detector with Stacked-ENet Backbones
Authors: Li Qingyu , Miao Zhenjiang
Published: May 2019

Object detection algorithms have made great progress in the past few years because of the development of deep learning. From region selection to anchor box regression, the accuracy of the algorithms becoming more and more accurate, but they’re also far away from vision mechanism of human. Corner based is a new approach for object detection, but methods proposed nowadays are in huge wastage of time and resource, leading them not available in vast bulk of jobs. In this paper, we proposed a method which makes corner-based object detector could inference in real-time. We analyze the disadvantages of existing methods, found out the most consuming parts and the solution for improvements, which finally makes the algorithm efficiency and get a result which is competitive with other existing real-time methods.
Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and Grayscale
Authors: Castellanos Ari Ernesto Ortiz
Published: May 2019

In this paper, we use TensorFlow Mobile Lite for Object Detection with datasets of basic geometric figures on iOS mobile devices. Additionally, we trained 4 datasets in 2D and 3D and we compare the accuracy of detection between using colour and grayscale image data. Also, we evaluate the detection rate using 2D and 3D for some kind of normal objects in precision and label output. We used Convolutional Neural Networks (CNN) for build the datasets and OPENCV for convert into grayscale. We value the result relation between flat and volume datasets in way of label and numeric detection, also the affectation of TensorFlow Mobile with this kind of datasets. We make comparisons based on the results of the different experiments object the detection and, in this work, TensorFlow Mobile Lite implementation does not have pseudo boxes and the reason is explained for detection purposes and accuracy adjusted to this kind of experiments.
Weight Loss for Point Clouds Classification
Authors: Huang FangYuan , Xu Cheng , Tu XiaoHan , Li SiQi
Published: May 2019

With the widely applying of 3D sensors such as LiDAR and RGBD camera in robots and driverless technology, 3D point clouds classification has also achieved some development. PointNet[1] is a competitive method in point clouds classification due to it’s fast speed and well performance in real applications. However, when dealing with easy-classified samples overwhelming in 3D object classification, the PointNet shows incompatibility due to its gradients are mainly determined by these easy-classified samples. In order to optimize the gradients in PointNet, we designed the SFL(Sigmoid Focal Loss) function for 3D object classification to instead of the standard softmax cross-entropy loss function. The proposed SFL function will automated refined the weights of the hard-classified samples in training, so that the new neural network can focus on these hard-classified samples to avoid the unbalance of the classifier. Our results show that the new loss function can achieve the best result in classification tasks that directly processes point clouds.
The Frontier of SGD and Its Variants in Machine Learning
Authors: Du Juan
Published: May 2019

A Numerical optimization is a classical field in operation research and computer science, which has been widely used in areas such as physics and economics. Although optimization algorithms have achieved great success for plenty of applications, handling the big data in the best fashion possible is a very inspiring and demanding challenge in the artificial intelligence era. Stochastic gradient descent (SGD) is pretty simple but surprisingly, highly effective in machine learning models, such as support vector machine (SVM) and deep neural network (DNN). Theoretically, the performance of SGD for convex optimization is well understood. But, for the non-convex setting, which is very common for the machine learning problems, to obtain the theoretical guarantee for SGD and its variants is still a standing problem. In the paper, we do a survey about the SGD and its variants such as Momentum, ADAM and SVRG, differentiate their algorithms and applications and present some recent breakthrough and open problems.
Trust-Based Neural Collaborative Filtering
Authors: Zeng Yejia , Qu Zehui
Published: May 2019

In order to maintain the strong representational learning of the deep neural network model to learn the interaction function between any users and items, combining the trust relationship between users as a local relationship to enhance the ability to supplement interactive data, to achieve a better recommendation effect. This paper proposes a Trust-based Neural Collaborative Filtering model (TNCF). Firstly, trust information and scoring information are merged through the Generalized Matrix Factorization model (GMF) to obtain recommendations based on trust friend preferences. Then, using the Multi-Layer Perceptron model (MLP), the nonlinear kernel is utilized to learn the interaction function from the data to obtain the recommendation based on the user’s personal taste. Finally, all the interaction results are aggregated for implicit prediction. Compared with the three different baselines on the FilmTrust and Epinions datasets, the experimental consequences reveal that the proposed model improves the recommendation and also performs well on sparse statistics.
An Efficient Character-Level and Word-Level Feature Fusion Method for Chinese Text Classification
Authors: Wenzhen Jin , Hong Zhu , Guocai Yang
Published: May 2019

In order to extract semantic feature information between texts more efficiently and reduce the effect of text representation on classification results, we propose a features fusion model C_BiGRU_ATT based on deep learning. The core task of our model is to extract the context information and local information of the text using Convolutional Neural Network(CNN) and Attention-based Bidirectional Gated Recurrent Unit(BiGRU) at character-level and word-level. Our experimental results show that the classification accuracies of C_BiGRU_ATT reach 95.55% and 95.60% on two Chinese datasets THUCNews and WangYi respectively. Meanwhile, compared with the single model based on character-level and word-level for CNN, the classification accuracies of C_BiGRU_ATT is increased by 1.6%, 2.7% on the THUCNews, and is increased by 0.6%, 5.2% on the WangYi. The results show that the proposed model C_BiGRU_ATT can extract text features more effectively.
Delay-sensitive Task Scheduling with Deep Reinforcement Learning in Mobile-edge Computing Systems
Authors: Meng Hao , Chao Daichong , Guo Qianying , Li Xiaowei
Published: May 2019

Mobile-edge computing(MEC) is considered to be a new network architecture concept that provides cloud-computing capabilities and IT service environment for applications and services at the edge of the network, and it has the characteristics of low latency, high bandwidth and real-time access to wireless network information. In this paper, we mainly consider task scheduling and offloading problem in mobile devices, in which the computation data of tasks that are offloaded to MEC server have been determined. In order to minimize the average slowdown and average timeout period of tasks in buffer queue, we propose a deep reinforcement learning (DRL) based algorithm, which transform the optimization problem into a learning problem. We also design a new reward function to guide the algorithm to learn the offloading policy directly from the environment. Simulation results show that the proposed algorithm outperforms traditional heuristic algorithms after a period of training.
ETA: A method of Dynamic Network Device Security Assessment
Authors: Mu CHEN , Zaojian DAI , Zhipeng SHAO , Liming WANG
Published: May 2019

The normal operation of network devices is an important cornerstone of network security. The security evaluation of network device is very important to prevent network security problem. In order to dynamically evaluate the security level of network device and quantify the state, a method of dynamic network device security assessment is proposed. Firstly, this method makes full use of the alert log which includes state information of network device and combines with the idea of TF-IDF algorithm to analyze the frequency and the distribution of alert. Then, it puts forward a new algorithm ETA to calculate the value of event threat. Finally, these values are used for calculating the security index of network device. The experiment shows that the proposed method in this paper can find the network device in low-level security and provide effective decision support for network security administrator.
A Key Sentences Based Convolution Neural Network for Text Sentiment Classification
Authors: Mohan Zhang , Yang Xiang
Published: May 2019

Existing research treated all sentences in the text on an equal basis during the training process and did not consider that key sentences tend to have a stronger influence. We propose a Convolutional Neural Network text sentiment classification model based on the key sentences enhancement. The proposed model can identify key sentences in the text and generate text representation based on these key sentences to reduce noise and improve the accuracy of the model sentiment classification. The experiment results show that the proposed model improves the accuracy of the text sentiment classification compared with other classic text sentiment classification models.
Development of a Multi-component Infrared Gas Sensor Detection System
Authors: Qing WANG , Yong-ping LIU , Wei-long LI
Published: May 2019

A multi-component infrared gas sensor detection system is designed based on the principle that the gas under test absorbs infrared radiation of a specific wavelength,periodic infrared radiation signals pass through the chamber to reach multiple sets of filters. The infrared sensitive component extracts an electrical signal to filter and zoom which having the same period as the light source. And in a period of signal acquisition repeatedly, which achieved the effect of improving the reliability of signal acquisition,Enhanceing the capability of anti-jamming,Reduce the response time of the sensor, etc.
FunPianoAR: A Novel AR Application for Piano Learning Considering Paired Play Based on Multi-Marker Tracking
Authors: Zeng Hong , He Xingxi , Pan Honghu
Published: May 2019

This paper describes the design and implementation of a novel augmented reality application, referred as FunPianoAR, that aiming to activate the interests of user and improve the experience of piano learning. The FunPianoAR with a user-friendly interface considers the paired play to further reduce the difficulty of playing the piano for adult novices. For the paired play, one user plays the melody and the other plays the harmony part. The app is developed by using Android Studio 3.0 and artoolkitX 1.0, an open source augmented reality SDK, and installed on the AR smart glasses, Epson Moverio BT300. Due to the textureless features of real piano and registration precision, the application is implemented by using the fiducial marker tracking instead of the markerless recognition. Besides, we divided the piano keyboard into four zones and each zone used a separate marker for tracking to solve the limited field of view (FOV) to some extent. The virtual keys can be accurately superimposed on the piano keys by using multi-marker tracking. An evaluation was also conducted to compare the effects of two types of augmented reality information superimposed, i.e., the instant way without the hints for the next note to be played and the FunPianoAR with the hints for the coming notes to be played. The correctness rate was calculated and the time differences between the notes that two players should play at the same time were collected. The FunPianoAR shows more advantages over the instant way based on the evaluation results.
Atrial Fibrillation Detection Based on EEMD and XGBoost
Authors: Yue Zhang , Jinjing Zhu
Published: May 2019

The electrocardiogram (ECG) is non-invasive, inexpensive and widely used in several applications, implemented to detect the physical condition and disease of the human body. Atrial fibrillation (AF) is the most common of many different forms of sustained arrhythmia. Therefore, early diagnosis of AF may help to improve doctor’s diagnostic efficiency and is essential to prevent further progression of Atrial fibrillation to other heart disease and stroke complications. With the popularity of the machine learning and deep learning, more and more researchers apply them in image recognition, speech recognition and so on. Naturally, there are also many studies which achieve the purpose of diagnosing diseases, such as detection of arrhythmia, biometric identification based on ECG signals and machine learning or deep learning. A novel approach to detect AF from ECG signals was developed on this study, we used great filter EEMD (Ensemble Empirical Mode Decomposition) and classifier XGBoost (eXtreme Gradient Boosting) to detect normal rhythm, AF and other rhythm. Finally, the great performance was achieved with an average F1 score of 0.84 and accuracy of 0.86.
Defining and assessing urban forests to inform management and policy
Authors: Pregitzer Clara C , Ashton Mark S , Charlop-Powers Sarah , D’Amato Anthony W , Frey Brent R , Gunther Bram , Hallett Richard A , Pregitzer Kurt S , Woodall Christopher W , Bradford Mark A
Published: May 2019

The context in which trees and forests grow in cities is highly variable and influences the provision of ecological, social, and economic benefits. Understanding the spatial extent, structure, and composition of forests is necessary to guide urban forest policy and management, yet current forest assessment methodologies vary widely in scale, sampling intensity, and focus. Current definitions of the urban forest include all trees growing in the urban environment, and have been translated to the design of urban forest assessments. However, such broad assessments may aggregate types of urban forest that differ significantly in usage and management needs. For example, street trees occur in highly developed environments, and are planted and cared for on an individual basis, whereas forested natural areas often occur in parkland, are managed at the stand level, and are primarily sustained by natural processes such as regeneration. We use multiple datasets for New York City to compare the outcomes from assessments of the entire urban forest, street trees, and forested natural areas. We find that non-stratified assessments of the entire urban forest are biased towards abundant canopy types in cities (e.g. street trees) and underestimate the condition of forested natural areas due to their uneven spatial arrangement. These natural areas account for one quarter of the city’s tree canopy, but represent the majority of trees both numerically and in terms of biomass. Non-stratified assessments of urban forest canopy should be modified to accurately represent the true composition of different urban forest types to inform effective policy and management.
Stamping of a Cross-shaped Part with 5052, 5754 and 6016 Aluminum Alloy Sheets – Experimental and Finite Element Analysis Comparison
Authors: Esmaeilpour R , Tiji S A Nazari , Kim H , Park T , Kim H , Pourboghrat F , Mohammed B
Published: May 2019

The application of aluminum alloys is continuing to increase in automotive and aerospace industries. In this study, the stamping of a cross-shaped part, representing typical deep drawn automotive parts, was attempted with three different types of aluminum alloy sheets. To characterize the anisotropic properties of the material, uniaxial tensile tests were performed with ASTM standard dog-bone specimens cut from AA5052, AA5754 and AA6016 T4 alloy sheets. The stamping of the cross-shaped parts was conducted using a high capacity servo press with a servo-cushion system. Deformation strains developing in stamped parts were measured with the ARGUS system for comparison with finite element simulation results. Formed parts were also cut in two different directions using a water-jet machine, and thickness variations along the length of the cut section were measured. The measured surface strains and thickness distributions were compared with finite element simulations results obtained with LS-Dyna explicit code using the Yld2000-2D and Hill's 48 yield functions. Overall, experimental and numerical results correlated well, especially with the Yld2000-2D yield function, implying that this yield function is well suited for the modeling of the complex cross-shaped parts with aluminum alloys.
Thermomechanical Study and Fracture Properties of Silicon Wafer under Effect of Crystal Orientation
Authors: Romdhane Mohamed Cherif Ben , Mrad Hatem , Erchiqui Fouad , Mrad Ridha Ben
Published: May 2019

Designing and optimizing the performance of a piezoresistive pressure microsensor involves not only studying the piezoresistive properties of polysilicon but also analyse the thermomechanical behaviour of its most sensitive internal part. This work focuses on an analytical, numerical and experimental study of a silicon wafer used in a pressure sensor. A thermomechanical study was conducted to assess the effects of temperature and material orientation on the deflexion and failure mode of Silicon membrane. The chosen analytical approach is based on Kirchhoff theory in which a square Silicon membrane is subjected to a distributed pressure over all its surface. Thermomechanical simulations and fracture analysis are carried out using Abaqus software. An experimental thermomechanical technique to determine maximum wafer deflexion and its effect on crack initiation and fracture mode was implemented. Deflexions tests were carried out on a bending machine using specimens of p-Si doped wafers. Deflexion values are measured for each applied temperature. Some preliminary tests were performed to determine the impact of temperature and Silicon wafers orientation on maximum deflexion when fracture occurs. The test’s results confirmed that the failure mode is highly brittle and follow each time crystal orientations 0° and 45°. Furthermore, at room temperature, fracture occur at 389 μm of displacement, and grows by 5.2 % when temperature is increased over 40°C. Consequently, Silicon sensitivity will be increased by 4%.
Application of Technological System Based on Processes Modelling and Analysis: A Case Study in Italian Aquaculture Company
Authors: Corallo Angelo , Latino Maria Elena , Menegoli Marta , Cataldo Marco , Mancarella Luisa
Published: May 2019

Aquaculture is a growing sector and could satisfy the future worldwide fish demand. New sustainable consumption patterns arise from food market, based on the consumer’ need to know more information about food product. Technological and methodological innovations are needed to create more efficiency in aquaculture industry, but also to create product able to respond to the emerging market needs. The paper shows a case study in Aquaculture Company useful to demonstrate how collect product and production processes information and use it for traceability purpose. Starting from the as-is production process analysis and modelling, several strategically information for consumer are identified. The possibility of using the devices developed in S.E.A. research project has been exploited to collect some of this information and a new traceability layer was create in order to collect manually the missing data. At last the new production process was modelled. The benefits coming from the system adoption are multiple, for the final consumer that could see demonstrated the food quality and safety, for the company that could increase own environmental, economical and ethical sustainability.
Path Planning for Spray Painting Robot of Horns Surfaces in Ship Manufacturing
Authors: Zhou Yunzhong , Ma Shumei , Li Aiping , Yang Liansheng
Published: May 2019

Generating paint gun path for complex free-form surfaces in shipbuilding industry to ensure uniform paint deposition is highly challenging due to their complex geometry, especially for horns surfaces. In this paper, a tool trajectory optimization method was proposed based on dip angle spray in order to solve the problems of poor coating uniformity and material waste for spraying horns surface with curved patches. A dip angle spray model was developed based on the Beta distribution model on the planar surface by principle of differential geometry. The spray path optimization models for dip angle spraying on cylindrical surfaces of three cases were proposed and compared first to define the best case which yields the best painting quality. Then we used the best strategy on a horns surface in a shipyard to evaluate the effectiveness and robustness of designed algorithm. This algorithm can also be extended to other applications.
Soil water characteristic curves for laterite soil at different water contents and methods as lining system
Authors: Bello Yamusa Yamusa , Azril Hezmi Muhammad , Ahmad Kamarudin , Anuar Kassim Khairul , Sa’ari Radzuan , Alias Noraliani , Mustaffar Mushairry , Kassim Azman , Safuan A. Rashid Ahmad
Published: May 2019

Preventing groundwater contamination from the most common method of solid waste disposal technique i.e. landfilling is by proving hydraulic barriers. Accordingly, water movement in unsaturated soil is important in the analysis of leachate migration in soil material used as hydraulic barrier in waste containment systems. A full-scale experiment would be prohibitively costly and time consuming. The only feasible recourse therefore is to construct a model, which reasonably portray the behaviour of the full-scale system and simulate the relevant physical parameters and describes the overall significant characteristics of the transport phenomena. This paper presents the trends and patterns of soil water characteristic curves (SWCC) in terms of volumetric water content versus soil suction developed for compacted laterite soil specimens using data from pressure plate tests. Specimens were prepared at three different water contents corresponding to dry of optimum, optimum, and wet of optimum conditions. Models suggested by Brooks and Corey (BC), van Genuchten (VG), and Fredlund and Xing (FX) were used to obtain curve fitting parameters using the program “SWRC Fit” which performed nonlinear fitting of soil water retention curves. The SWRC Fit can simultaneously calculate the fitting parameters of these models with Root Mean Square Error values and draw the fitting curves. By comparing the results of the models using SWRC Fit, the model that best fits the laterite soil investigated will be chosen to be used for further analysis. The results show that the BC model represents the soil water retention curves better than the VG and FX models when the soil has distinct air entry suction. On the other hand, the VG and FX models can fit most soil water retention curves very well when discussion on the pore-size distribution is desired.
Bottom ash utilization: A review on engineering applications and environmental aspects
Authors: Abdullah M H , Rashid A S A , Anuar U H M , Marto A , Abuelgasim R
Published: May 2019

Bottom ash produced from coal-fired power plants has been utilized in engineering applications for the purpose of recycling and solving disposal issue of bottom ash. This study reviews various applications of bottom ash in engineering fields. The engineering properties exhibited by bottom ash in terms of free draining, granular, lightweight and interlocking nanostructures allow them to be used for different functions. The bottom ash used is not limited to its raw state, but can be mixed and modified for advanced applications without neglecting the impacts on the environmental. The presence of metals in bottom ash are well known and being discussed whether they are harmful to the environment. Many countries have their own threshold limit of maximum contaminants, hence TCLP result will determine the hazardous status of bottom ash. This review helps in promoting the solution and potential usage of bottom ash, since coal is one of the dominant source of energy in power generation sector.
Compaction characteristics of lime-treated tropical soil
Authors: Azril Hezmi Muhammad , Ahmad Kamarudin , Mohd Yunus Nor Zurairahetty , Anuar Kassim Khairul , Rashid Ahmad Safuan A. , Abdul Hassan Norhidayah
Published: May 2019

Like soils of other regions in the world, some tropical soils are also associated with problems of low strength. To improve engineering characteristics of soils, several methods have been employed worldwide which are categorized as mechanical, chemical, thermal and electrical. The aim of this research is to compare the compaction characteristics of untreated and lime-treated soils. Soil samples obtained from three different points within a project site in Klang, Selangor were utilized in this study. Each sample was subjected to Eades and Grimm’s test in accordance with ASTM D6276-99a to determine the appropriate lime dosage for soil stabilization. The required lime dosage was found between 1.3% and 3.4%. Then each untreated soil and soil sample mixed with 5% lime, higher than that obtained from Eades and Grimm’s tests were compacted in accordance with BS 1377:1990. Generally, each soil sample achieved lower values of maximum dry density after treated with 5% lime than those of the untreated soils. Such observations were in good agreement with those obtained by previous researchers working on lime-treated soils. Nonetheless, the optimum moisture content of the lime-treated sample from Points 1 was slightly higher than that of the untreated sample. This finding was different from those obtained by previous researchers. Such contradicting observation might be due to the type and quantity of mineralogical contents of the soils studied.
Empirical equations for predicting pile/soil setup effect
Authors: Min Lee Lee , Sze Ming Jasmine Wong , Sien Ti Kok , Teck Leong Lau , Tuck Wai Yeong
Published: May 2019

Pile/soil setup effect is a natural phenomenon where pile load capacity increases over time as the results of dissipation of pore-water pressure and soil aging. The magnitude of pile/soil setup is governed by three main factors, i.e. pile slenderness ratio, elapsed time and type of surrounding soil. In this study, empirical correlations were established based on data compiled from previous case studies. Two forms of empirical equations were proposed to predict the pile/soil setup effect subjected to different slenderness ratios and elapsed times. The proposed equations were subsequently verified by actual field data collected from two sites in Malaysia. Results showed that 80% of the pile capacities increased by 1.150-1.875 times per log time cycle. Piles in fine-grained soils generally exhibited a greater pile/soil setup effect than coarse-grained soils.
An overview of traditional and non traditional stabilizer for soft soil
Authors: Md Zahri Azura , Zainorabidin Adnan
Published: May 2019

Soft soil always deal with high compressibility, high water content, low shear strength and low permeability. All these challenges lead to soil failure such as excessive settlement, failure of sub structure which is lead to superstructure damage and many others failure. Hence, soil stabilization is one of the option in ground improvement technique by adding other stabilization agent either natural basis or chemical basis. This paper critically reviewed about advantages and disadvantages of chemical stabilizer namely traditional and non traditional. Also closely reviewed on polymer and lignosulfonate as soil stabilization agent in various type of soil. Reviewed and analysing of some references revealed that using non traditional stabilizer have more advantages than traditional stabilizer in term of engineering properties as well as chemical composition. Polymer and lignosulfonate also have their on specialties in order to corporate with various type of soil.
Estimation of Soil Water Characteristic Curves (SWCC) of mining sand using soil suction modelling
Authors: Azmi M , Ramli M. H , Hezmi M. A , Mohd Yusoff S.A.N , Alel M. N. A
Published: May 2019

Soil water characteristic curve (SWCC) is widely known as the relationship between matric suction and water content. It is used as a tool to determine the hydraulic and mechanical behaviour of unsaturated soils and to predict soil water storage. Direct measurement is difficult and time-consuming. Many empirical models have been developed to represent SWCC. The objective of this study is to validate the Van Genuchten and Fredlund and Xing models. The SWCC was obtained from pressure plate tests for different soil gradations of mining tailing sand samples taken from Kuala Trong, Taiping, Perak, Malaysia. The results presented include moisture content and degree of saturation versus matric suction. The constructed SWCCs were fitted using the Van Genuchten and Fredlund and Xing equations, and the behaviour of the parameters analysed and discussed. The study found that this method is good for the SWCC for mining tailing sand, and that SWCC is greatly affected by initial water content and the grain size of the sample.
Soil anisotropic stress-strain prediction using Normalised Rotational Multiple Yield Surface Framework (NRMYSF) for compacted tropical residual sandy soils
Authors: Alias A , Noor M J Md. , Mohamed Jais I B
Published: May 2019

Rotational Multiple Yield Surface Framework (RMYSF) is an anisotropic soil volume change model developed to integrate shear strength in characterising the soil volume change behaviour from the standpoint of the stress-strain response. The anisotropic soil volume change behaviour is described from the interaction between applied stress represented by the Mohr-Coulomb circle and shear strength in terms of curved-surface mobilized shear strength envelope. This research work is to enhance the current method of RMYSF using normalisation of axial strain to predict stress-strain behaviour. A series of triaxial tests has been conducted on Malaysian sedimentary sandy residual soil grade VI. The stress-strain curves and volume change behaviours were analysed using this enhanced method. Subsequently, the enhance method is apply to predict the soil stress strain respond and make comparison with the actual laboratory stress-strain curve. Essentially the normalised method of RMYSF has improved the accuracy of the prediction as presented in the paper.
Risk assessment at open dumping area using Monte Carlo simulation
Authors: Mohd Pauzi N I , Razali Z Che
Published: May 2019

Open dumping area has a potential hazard such as the settlement of the dumping sites, the release of hazardous gas and pollution of the groundwater to the environment. The risk assessment are conducted on the open dumping area in order to evaluate the potential risk that occur at the open dumping area. The purpose of this paper is to evaluate the bearing capacity of the waste soil at the open dumping area. The risk assessment on the bearing capacity at the open dumping area are simulate using the Monte Carlo Simulations. The methodology starts from collecting the soil samples, conducting experiments such as sieve analysis test, specific gravity test and direct shear box test, and finally the analysis using Monte Carlo Simulation. Monte Carlo Simulation was used to decide or to make conclusion on the best probabilities for the bearing capacity at the open dumping area.
Effects of cementation reagent on the precipitation of calcium carbonate induced by Bacillus Megaterium
Authors: Muhammed A S , Zango M U , Kassim K A , Ahmad K , Umar M
Published: May 2019

A laboratory experiment was carried out to determine the concentration of cementation reagent that will produce the maximal amount of calcium carbonate induced by Bacillus Megaterium. The optimum condition for calcium carbonate precipitation was evaluated for its application in improving the geotechnical properties of soil. The process was studied using the test-tube experiment and evaluating the amount of calcium carbonate precipitated and subsequently verified using X-ray diffraction test. Five different concentrations of cementation reagent (0.25, 0.5, 0.75, 1.0 and 1.5M) were used in the study. Results showed calcium carbonate was higher with increase in concentration of cementation reagent irrespective of the curing period. Furthermore, the XRD scan confirmed the precipitate formed was calcium carbonate. Calcite formed acts ad bio-cement which is responsible for improving the geotechnical properties of various soil.