bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study Of Cost And Emissions Considering Speed Optimization In Trans Eurasia Transportation Based On Intelligent Transportation
Authors: Dongmin Xu , Dingyi  , Youfang Huang , Bin Yang 
Published: February 2018

Abstract
People attach much importance to greenhouse gas emissions, and there is an increasing concern on cost and emissions on account of speed reduction for international shipping. Low steaming encountered these challenges of time constraint and technical requirements, while the cost and emissions of railway transportation distinctly differentiate from international shipping and it is a good substitute for international shipping to some extent. Intelligent transportation system (ITS) has proven to be a useful tool in providing efficient, environmentally friendly and safe transportation systems within inland and shipping. The paper focus on the selection and cooperative process between the shipping and railway, and discusses the cost and emissions by speed optimization for international shipping and railway transport aiming to select effective and environmental transportation mode based on ITS infrastructure. Firstly this paper reviews ITS and investigates the relationship of cost and speed along with the relationship of energy consumption and speed respectively for shipping and railway transportation, and then presents the speed optimization both shipping and railway transportation based on ITS so as to minimize cost and emissions. Subsequently it compared and analyzed the cost and emissions for mode selections, and it found that it is more effective and environmental to employ railway transport to some route leg. Finally A numerical case is presented and the results showed that it is necessary to shift model from sea to land on some section
Vibration Based Health Monitoring Of Honeycomb Core Sandwich Panels Using Support Vector Machine
Authors: Gupta Saurabh , Satpal Satish B , Banerjee Sauvik , Guha Anirban 
Published: February 2018

Abstract
Honeycomb sandwich structures are extensively used in aerospace, aeronautic, marine and automotive industries due to their high strength-to-weight ratios, high energy absorption capability and effective acoustic insulation. Unfortunately, either presence of disbond along the skin-core interface or emergence of disbond due to repeated loading, aging or an intensive load can jeopardize the integrity and safety of the whole structure The current work presents a new array based technique for health monitoring of these structures using support vector machine (SVM). The proposed technique is first used on simulated mode shape data of the structure and then the technique is validated using experimental mode shape data. The experimental set up has been developed in laboratory and Laser Doppler Vibrometer (LDV) is used to extract the experimental mode shapes. The results have been obtained using both support vector classification and regression analysis and it is found that that the former is better at prediction of debond location
Detection Of Moving Object By Fusion Of Color And Depth Information
Authors: Zhang T. T. , Zhao G. P. , Liu L. J. 
Published: February 2018

Abstract
Moving object detection based on color information is easily affected by illumination changes and shadows in complex scenes. Depth information can provide complementary information. In the paper, a novel method is presented by using color and depth information. Firstly, we improve the codebook algorithm by fusing the depth information as the fourth channel in the code word. Next, a compensation factor algorithm is presented to make the edges accurate. So the final detection result can be obtained by logic operation. Experiments adapt the public datasets, and experimental results show that the proposed method can successfully cope with the limitations of color-based or depth-based detection
Experimental And Theoretical Validation Method For Estimation Of Straightness Deviation And Associated Uncertainty In Cnc-Cmm Measurement
Authors: Ali Salah H.R. , Khalafalla M.A.H. , Naeim Ihab H. , Zahwi Sarwat Z.A. 
Published: February 2018

Abstract
Geometrical deviation in intelligent metrology system is an important factor in precision engineering. Estimating the deviation and associated uncertainty in straightness feature remains a necessary requirement to ensure highly accurate metrology method especially in CNC-CMM measurement. Optimization in straightness measurement using soft algorithm techniques is a widely encountered solution in coordinate metrology. In this work, straightness deviation has been measured precisely in 2D and in 3D using slab surface by CNC-CMM at the Egyptian national metrology institute (NIS). The work has been investigated experimentally and theoretically analyzed. The straightness deviation and its uncertainty results from 2D measurement have been estimated experimentally. The straightness deviation result of the 3D discrete points measurements have been analyzed theoretically using the standard Particle Swarm Optimization (PSO) algorithm. The probability density distribution of the measured straightness was calculated using a Sequential Monte Carlo (SMC) technique. A probability density histogram is obtained with an expanded measurement uncertainty based on coverage factor k equals 2 providing confidence level 95%. The computational results of straightness deviation and expanded uncertainty have been also estimated for 3D discrete point measurements. Comparison with relevant report showed agreement with our result since we used a computationally efficient modified SMC technique and PSO algorithm. The results of the straightness deviations and associated expanded uncertainties for both 2D and 3D measurements have been discussed and compared. They were found to be suitable for the proposed validation method. This work confirms that the developed strategic alternative methodology can be achieved successfully. Systematic acquisition of CNC-CMM data is another contributing factor for improving the required accuracy in measurement. Moreover, the confidence in the proposed hybrid validation method for estimating the straightness deviation with associated uncertainty has been achieved
Errors of Manometric Co2 Sorption Experiments On Coal Caused By Accuracy Of Pressure Sensor
Authors: Renxia Jiang , Hongguan Yu , Li Wang , Lihui Chen 
Published: February 2018

Abstract
The accuracy of supercritical CO2 sorption determination on coal is important for estimating the CO2 sequestration potential of coal bed using CO2 injection into deep unminable coal seams. Based on the CO2 density change and CO2 sorption data of three selected coal samples, this paper investigates the effect of pressure-sensor accuracy on measurements of CO2 adsorption isotherms on coals with manometric equipment. The characteristic density-change of CO2 in proper pressure range determines a greater influence of pressure error on CO2 adsorption data on coal. The sensor accuracy has significant influence on Excess sorption increment of CO2 on coal in medium pressure (7-12 MPa), and on CO2 Excess-sorption isotherm at medium-and high pressure. The medium pressures with a greater CO2 density-change at a constant temperature in the sample cell are probably inevitable. A lower experiment-temperature will results in a greater measurement error of the sorption caused by pressure sensor accuracy. The pressure sensor accuracy may result in the less reproducibility and repeatability of CO2 adsorption on coals at medium- and high pressure, and negative sorption increment and even negative adsorption
Filter Bank Design Using Multiple Prototype Approach For Variable Granularity Bands
Authors: Prema S Chris , K Maneesha 
Published: February 2018

Abstract
In this paper, a multiple prototype based filter bank is proposed for variable granularity bands. This method makes use of channel combiners for variable granularity bands from a cosine modulated filter bank. In the proposed method, the filter bank is generated from proper choice of prototype filters and different bandwidth combinations are generated from different prototypes, where M determines the granularity of the uniform bandwidth from individual prototypes. The condition for generation of variable granularity bands from multiple prototype filters is also defined. The method is found to have less complexity and distortions as compared to a single prototype approach using channel combiners. The method allows different combinations of the uniform filter banks to generate the filter banks with variable granularity for integer powers of two. Simulations are done to obtain varying bandwidth channels, and analyzed for different input signals with different fidelity parameters. The results are found to be comparable with existing methods
A Hybrid Fuzzy Morphology And Connected Components Labeling Methods For Vehicle Detection And Counting System
Authors: Fatichah Chastine , Buliali Joko Lianto , Saikhu Ahmad , Tena Silvester 
Published: February 2018

Abstract
A hybrid fuzzy morphology and connected components labeling method is proposed for detecting and counting the number of vehicles in an image taken from a traffic monitoring camera. A fuzzy morphology approach in image segmentation method is used in the system to achieve faster computation time compared to the supervised learning. The connected components labeling method is combined with a fuzzy morphology method to determine the region and number of objects in an image. The processing phases in the proposed system are image preprocessing, image segmentation, and vehicle detection and counting the number of vehicles. Images are captured from the traffic monitoring cameras installed in highways. Results from testing phase using thirty images with varying brightness, contrast, and quality taken from different cameras during daylight showed that the accuracy of the system in counting the number of vehicles is 78.21%.
Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda
Authors: Wang Hua , Xiao Jiang , Zhang Junguo 
Published: February 2018

Abstract
Color which is one of the basic features of the image is widely used in image processing. The choice of color space is a primary issue for the color image segmentation based on color features. In this paper, giant pandas are chosen as the research objects. In order to achieve good segmentation results, different color spaces and the corresponding algorithms are chosen for image segmentation according to the color characteristics of different background of panda images. There are three kinds of color spaces introduced in detail and its advantages and disadvantages for the giant panda image segmentation are also summarized in this paper
Prediction Of Sewage Quality Based On Fusion Of Bpnetworks
Authors: Wang Lijuan 
Published: February 2018

Abstract
Sewage treatment system is a complicated nonlinear system with multi-variables, chemical reaction, biological process and altered loads, hard to describe mathematically. Thus prediction of the effluent quality parameters of sewage treatment plant has being a challenge. In this paper we adopt fusion of two BP networks to predict sewage quality parameters with a popular process Cyclic Activated Sludge System (CASS). We take use of SVM (support vector machine) to classify the input data into two kinds, and train the corresponding BP networks with the two kinds of data. Before using SVM to classify the input data, PCA (principle component analysis) is used to analyze the correlation between sewage quality parameters. Then we predict the value of sewage quality parameters with the fusion results of the two BP networks. Test results of the case study show that fusion of BP networks not only can improve the stability of BP networks but also can improve the prediction accuracy
An Improved Multi-Objective Evolutionary Optimization Algorithm For Sugar Cane Crystallization
Authors: Meng Yanmei , Li Wenxing , Chen Qingwei , Yu Xian , Zheng Kangyuan , Lu Guancheng 
Published: February 2018

Abstract
The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization optimization refers to the simultaneous effects of both seeding characters and process variables on the final crystal size distribution (CSD) parameters, including mean size (MA) and coefficient of variation (CV). And the application of traditional multiobjective evolutionary algorithm in crystallization process could not optimize all of them. Therefore, this paper puts forward a different multi-objective framework, and correspondingly, an improved optimization algorithm is applied to intermittent sugar cane crystallization. This method combines the elitist non-dominated sorting genetic algorithm (NSGA-II) with technique for order preference which is similar to an ideal solution (TOPSIS), and it provides a quantitative way to analyze the effect of both seed characteristics and process variables on the trade-off between MA and CV. Furthermore, the proposed algorithm has been adapted here to be compared with the NSGA-II, and the comparing results demonstrate better Pareto-optimal solutions of the novel approach
Random Key Pre-Distribution Scheme Based On Key Updating
Authors: Ling-Zhi Zhu , Rui He , Jun-Ling Zhang 
Published: February 2018

Abstract
A random key pre-distribution scheme based on key updating (RKKU) was proposed, which is effective in wireless sensor networks. Firstly, the base station will randomly distribute some keys, a hash function and some code slices to each node. Furthermore, the RKKU scheme compares with the information of some random key to find the same key, and computes the communication key between two sensor nodes with one-way hash function. Since the one-way hash function can ensure that the attacker cannot use the obtained communication key to decipher the source key, it affects only two nodes communicate with each other. To assure the communication security, the key updating was designed based on code segment. The analysis shows that the proposed scheme can meet the security requirement of key management, and it also has less computation cost and storage cost than the existing schemes
Three-Step Registration And Multi-Thread Processing Based Image Mosaic For Unmanned Aerial Vehicle Applications
Authors: Li Hongguang , Ding Wenrui , Wang Yufeng 
Published: February 2018

Abstract
In the area of image mosaic for unmanned aerial vehicle (UAV) applications, the problems of precision and time consumption have drawn many scholars’ attention. To address above two problems, a novel algorithm based on three-step registration and multi-thread processing is proposed in this paper. This method divides the image registration into three steps to improve the precision. Firstly, based on the SIFT features, the fast index mechanism k-d tree and the Euclidean distance are utilized to determine the common points between two adjacent images; then, the linear slope constraint model is used to filter the mismatching point-pairs; finally, the RANSAC algorithm is adopted to remove outer points from the common points to ensure matching precision of inter frames. To accommodate the real time requirement of UAV application, a parallel data processing pattern is presented. The multi-core resources and multi-thread computing method of computers are employed adequately in the new pattern to speed up the whole algorithm. Extensive experiments on precision and time consumption show the superior performance of the proposed algorithm
Design Of A Non-Contact Infrared Thermometer
Authors: Long Guangli 
Published: February 2018

Abstract
In order to realize the human body temperature fast and non-contact measurement, an infrared thermometer is designed. The infrared human body temperature sensor is mainly used to convert the human body’s infrared into voltage signal, an operational amplifier to amplify the signal, filter circuit to filter the signal, the analog signal into digital signal by the A/D conversion circuit, data processing by the MCU, LCD display and voice reporting body temperature and time, so the human body non-contact measurement is realized. The experimental results show that: the device can realize the temperature and time of acquisition, the measurement error is not more than 0.5 °Ç voice broadcast and liquid crystal display the temperature and time, overrun alarm and other functions
Measuring Lung Abnormalities In Images-Based Ct
Authors: Lay-Ekuakille A. , Vergallo P. , Jabłoński I. , Casciaro S. , Conversano F. 
Published: February 2018

Abstract
Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper illustrates the use Hough transform in identifying pathological structures included in CT (Computer Tomography) and HRCT (High Resolution Computer Tomography) images related to patients suffering from lung disease. These abnormal areas appear as bulges of the trophic vessels and they are similar to circular structures with level of lighter gray near to white. Circular Hough transform (CHT) identifies regions with a circular shape. However, a metrics is defined in order to understand if the pointed out area has a pathological morphology. CHT is used here for helping to detect possible events of indolent tumors or undetermined significance pathologies for lung apparatus. For this aim, we use entropy approach with CHT because it measures the scatter of the directional elements in an image. In fact a high entropy value is related to areas with a strong contrast in grayscale, and abnormalities in the image are present as a set of points with more lighter than the dark background. The results have shown, by means of an accuracy true table, rendering a comparison between clinicians’diagnosis and CHT detection, it is possible to indicate, with a better accuracy, potential areas of undetermined significance pathologies. Finally, a receiver operational curve (ROC) is used as an accuracy index for evaluating the positive impact of entropy on diagnosis
Recognition of the Stacked Objects for Bin Picking
Authors: Hikizu M. , Mikami S. , Seki H. 
Published: February 2018

Abstract
In this paper, we propose recognition method of the stacked objects for pick-and-place motion. The situation that the objects are stacked miscellaneously in the home is assumed. In the home, the equipment to arrange the objects doesn’t exist. Therefore it’s necessary to recognize the stacked objects respectively. In this paper, Information on the objects are measured by a laser range finder (LRF). Those information is used as 3-D point cloud, and the objects are recognized by model-base. A local minimum problem exists in recognition of the objects. We propose the method to recognize the stacked objects statistically using multiple recognition result. Avoidance of the local minimum problem and the segmentation of each objects are performed by recognizing statistically.
User Behaviour Monitoring Using Mobile Phones to Improve 5G Services and Performance
Authors: Cui Zhihao , Yan Jize 
Published: February 2018

Abstract
4G has been widely commercialised, and 5G is currently under development. The expected data bandwidth for 5G is 100 times faster than 4G and 500 times faster than 3G; however, the evolution of telecommunication technologies involves both a boost in speed and the enhancement of user experience. The key word used to describe 5G is ‘user-centric’, rather than ‘service-centric’ for 4G, and thus user behaviours of mobile data usage should be further investigated. On the other hand, the testing equipment currently being used for base stations is limited to hardware devices, such as spectrum analysers and power meters. These testing methods do not include the considerable potential variations in data demands due to changes in user behaviours, which could be resolved by presuming that all data resources could be dynamically allocated by real-time events.
A complete system has been designed and implemented in this study to investigate current user behaviours regarding mobile data usage. The system consists of three individual parts, including a user iOS application, a web server and an administrative iOS application. Ten devices were tested within the two-month data collection period. Although the sample size was too small to produce any statistical results, it was found that data usage behaviours differ from user to user, with the exception of using more than 10 times the Wi-Fi over WWAN data at all times. The data also proved that some of the usage case families, which are described in the NGMN 5G white paper, do have strong demands, which could not be fulfilled using current telecommunication technologies due to technological gaps.
This paper shows that the system proposed is a feasible method to investigate user behaviours of mobile data usage. If the sample size of users involved could be increased in the future, it would be possible to develop a model for real-time simulations of mobile users in specific areas so that limited connection resources could be dynamically allocated. Moreover, the basic communication infra-structures, such as base stations, should be well-planned and developed in advance to fulfill the potential 5G demand.
Middleware for Smart Heterogeneous Critical Infrastructure Networks Intercommunication
Authors: Okathe Titus , Heydari Shahram Shah , Sood Vijay , Cole Orane , El-Khatib Khalil 
Published: February 2018

Abstract
Critical Infrastructures (CIs) are physical assets and organizations responsible for the production and distribution of society’s vital goods and services. The increasing interconnection of CIs has resulted in interdependencies which might lead to propagation of failure from one infrastructure to another. Most of current critical infrastructures are equipped with data collection and communication capabilities that can be used to inform and warn other CIs about such events and alarms. In this paper, a publish/subscribe-based communication system among dissimilar (heterogeneous) CIs is presented. The proposed system improves the manageability of CIs by providing an exchange medium for status information and alerts. It achieves this via a uniform architecture, within and across infrastructure boundaries, that maintains data restrictions that reflect real life organizational, administrative, and policy boundaries. Finally, the proposed system is modeled using the OMNET++ simulation framework, and a network performance study investigating scalability is presented. Simulation results showed that system scalability depends on service time per packet, subscription density, and number of clients per router.
PGSA-Based Localization Algorithm for Wireless Sensor Network
Authors: Qin Yuqiang , Ying Hui 
Published: February 2018

Abstract
This paper proposes a novel localization algorithm for wireless sensor network (WSN). Accurate localization is very important for WSN. WSN localization problem is sometimes regarded as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in natural way. In addition to the common characteristics of intelligent algorithms, PGSA show robustness and provides a global optimal solution, etc. In this paper, further enhancement of the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision has been reported. Comparing this algorithm with simulated annealing algorithm (SAA), simulation results show that this algorithm has a higher and more consistent localization precision and faster computational speed.