Search results for: Yong Zhou

Authors: Ying Pan, Li Wu, Jing Zhou, Lan Li

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The coordination and development of ecological-living-industrial land uses are the premise foundations for the formulation and implementation of the current land space planning, and more attention should be paid to plateau mountainous areas. This research is based on spatial analysis technology and landscape pattern index method taking Yuxi city, a typical mountainous plateau as the research area. By using relevant software such as ArcGIS10.5, Fragstats 4.2 and the four remote sensing images of Yuxi city in 1980, 1995, 2005 and 2015, the temporal-spatial evolution and differentiation pattern of ecological-living-industrial land applications have been discussed. The research results show that: (1) From the perspective of land use type change, ecological land of Yuxi city has been the main source of land from 1980 to 2015, which totally occupies more than 78%. During this period, the spatial structure of the ecological-living-industrial land changed significantly, namely, the living land. Its land area increased significantly from 0.83% of the total area in 1980 to 1.25% in 2015, the change range of ecological land and industrial land is relatively small. (2) In terms of land use landscape pattern transfer matrix, from 1980 to 2015, the industrial land and ecological land in Yuxi city have been gradually transferred to living land. (3) In the aspect of landscape pattern changes, various landscape pattern indexes of Yuxi city indicate that the fragmentation degree of landscape pattern of the ecological-living-industrial land in this region is increasing. The degree of agglomeration goes down, and the landscape types have changed from being relatively simple to relatively rich. The landscape is more diverse, but the patch size is uneven, meanwhile, the integrity of the ecological space is destroyed.

Keywords: Ecological-living-industrial land, spatio-temporal evolution, landscape pattern, plateau mountainous area.

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Authors: Yingjun Ruan, Qingrong Liu, Weiguo Zhou, Toshiyuki Watanabe

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This paper has examined the energy consumption characteristics in six different buildings including apartments, offices, commercial buildings, hospitals, hotels and educational facilities. Then 5-hectare (50000m2) development site for respective building-s type has been assumed as case study to evaluate the introduction effect of Combined Heat and Power (CHP). All kinds of CHP systems with different distributed generation technologies including Gas Turbine (GT), Gas Engine (GE), Diesel Engine (DE), Solid Oxide Fuel Cell (SOFC) and Polymer Electrolyte Fuel Cell (PEFC), have been simulated by using HEATMAP, CHP system analysis software. And their primary energy utilization efficiency, energy saving ratio and CO2 reduction ratio have evaluated and compared respectively. The results can be summarized as follows: Various buildings have their special heat to power ratio characteristics. Matching the heat to power ratio demanded from an individual building with that supplied from a CHP system is very important. It is necessary to select a reasonable distributed generation technologies according to the load characteristics of various buildings. Distributed generation technologies with high energy generating efficiency and low heat to power ratio, like SOFC and PEFC is more reasonable selection for Building Combined Heat and Power (BCHP). CHP system is an attractive option for hotels, hospitals and apartments in Japan. The users can achieve high energy saving and environmental benefit by introducing a CHP systems. In others buildings, especially like commercial buildings and offices, the introduction of CHP system is unreasonable.

Keywords: Combined heat and power, distributed generation technologies, heat-tao-power ratio, energy saving ratio, CO2 reduction ratio

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: Continuous wavelet transform, convolution neural network, gated recurrent unit, health indicators, remaining useful life.

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Authors: Dongxiao Sun-Waterhouse, Shiji Nair, Reginald Wibisono, Sandhya S. Wadhwa, Carl Massarotto, Duncan I. Hedderley, Jing Zhou, Sara R. Jaeger, Virginia Corrigan

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Attempts to add fibre and polyphenols (PPs) into popular beverages present challenges related to the properties of finished products such as smoothies. Consumer acceptability, viscosity and phenolic composition of smoothies containing high levels of fruit fibre (2.5-7.5 g per 300 mL serve) and PPs (250-750 mg per 300 mL serve) were examined. The changes in total extractable PP, vitamin C content, and colour of selected smoothies over a storage stability trial (4°C, 14 days) were compared. A set of acidic aqueous model beverages were prepared to further examine the effect of two different heat treatments on the stability and extractability of PPs. Results show that overall consumer acceptability of high fibre and PP smoothies was low, with average hedonic scores ranging from 3.9 to 6.4 (on a 1-9 scale). Flavour, texture and overall acceptability decreased as fibre and polyphenol contents increased, with fibre content exerting a stronger effect. Higher fibre content resulted in greater viscosity, with an elevated PP content increasing viscosity only slightly. The presence of fibre also aided the stability and extractability of PPs after heating. A reduction of extractable PPs, vitamin C content and colour intensity of smoothies was observed after a 14-day storage period at 4°C. Two heat treatments (75°C for 45 min or 85°C for 1 min) that are normally used for beverage production, did not cause significant reduction of total extracted PPs. It is clear that high levels of added fibre and PPs greatly influence the consumer appeal of smoothies, suggesting the need to develop novel formulation and processing methods if a satisfactory functional beverage is to be developed incorporating these ingredients.

Keywords: Apple fibre, apple and blackcurrant polyphenols, consumer acceptability, functional foods, stability.

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Authors: Chris Phan, Yong Seok Park

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The Toyota Camry is one of the best-selling cars in America. It is economical, reliable, and most importantly, safe. These attributes allowed the Camry to be the trustworthy choice when choosing dependable vehicle. However, a new finding brought question to the Camry’s safety. Since 1997, the Camry received a “good” rating on its moderate overlap front crash test through the Insurance Institute of Highway Safety. In 2012, the Insurance Institute of Highway Safety introduced a frontal small overlap crash test into the overall evaluation of vehicle occupant safety test. The 2012 Camry received a “poor” rating on this new test, while the 2015 Camry redeemed itself with a “good” rating once again. This study aims to find a possible solution that Toyota implemented to reduce the severity of a frontal small overlap crash in the Camry during a mid-cycle update. The purpose of this study is to analyze and evaluate the performance of various A-pillar shapes as energy absorbing structures in improving passenger safety in a frontal crash. First, A-pillar structures of the 2012 and 2015 Camry were modeled using CAD software, namely SolidWorks. Then, a crash test simulation using ANSYS software, was applied to the A-pillars to analyze the behavior of the structures in similar conditions. Finally, the results were compared to safety values of cabin intrusion to determine the crashworthy behaviors of both A-pillar structures by measuring total deformation. This study highlights that it is possible that Toyota improved the shape of the A-pillar in the 2015 Camry in order to receive a “good” rating from the IIHS safety evaluation once again. These findings can possibly be used to increase safety performance in future vehicles to decrease passenger injury or fatality.

Keywords: A-pillar, crashworthiness, design synthesis, finite element analysis.

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: M. Gomaa Abdoelatef, Robert M. Field, Lee, Yong-Kwan

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Growing human population has placed increased demands on water supplies and spurred a heightened interest in desalination infrastructure. Key elements of the economics of desalination projects are thermal and electrical inputs. With growing concerns over use of fossil fuels to (indirectly) supply these inputs, coupling of desalination with nuclear power production represents a significant opportunity. Individually, nuclear and desalination technologies have a long history and are relatively mature. For desalination, Reverse Osmosis (RO) has the lowest energy inputs. However, the economically driven output quality of the water produced using RO, which uses only electrical inputs, is lower than the output water quality from thermal desalination plants. Therefore, modern desalination projects consider that RO should be coupled with thermal desalination technologies (MSF, MED, or MED-TVC) with attendant steam inputs to permit blending to produce various qualities of water. A large nuclear facility is well positioned to dispatch large quantities of both electrical and thermal power. This paper considers the supply of thermal energy to a large desalination facility to examine heat balance impact on the nuclear steam cycle. The APR1400 nuclear plant is selected as prototypical from both a capacity and turbine cycle heat balance perspective to examine steam supply and the impact on electrical output. Extraction points and quantities of steam are considered parametrically along with various types of thermal desalination technologies to form the basis for further evaluations of economically optimal approaches to the interface of nuclear power production with desalination projects. In our study, the thermodynamic evaluation will be executed by DE-TOP, an IAEA sponsored program. DE-TOP has capabilities to analyze power generation systems coupled to desalination plants through various steam extraction positions, taking into consideration the isolation loop between the nuclear and the thermal desalination facilities (i.e., for radiological isolation).

Keywords: APR1400, Cogeneration, Desalination, DE-TOP, IAEA, MED, MED-TVC, MSF, RO.

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to gather information in unknown environments to achieve simultaneous localization and mapping of the environment. This technology has a wide range of applications in autonomous driving, virtual reality, and other related fields. Currently, the research advancements related to VSLAM can maintain high accuracy in static environments. But in dynamic environments, the presence of moving objects in the scene can reduce the stability of the VSLAM system, leading to inaccurate localization and mapping, or even system failure. In this paper, a robust VSLAM method was proposed to effectively address the challenges in dynamic environments. We proposed a dynamic region removal scheme based on a semantic segmentation neural network and geometric constraints. Firstly, a semantic segmentation neural network is used to extract the prior active motion region, prior static region, and prior passive motion region in the environment. Then, the lightweight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static regions and dynamic regions. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under a high dynamic environment.

Keywords: Dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM.

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Authors: M. Qwamizadeh, K. Zhou, Z. Zhang, YW. Zhang

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Typical load-bearing biological materials like bone, mineralized tendon and shell, are biocomposites made from both organic (collagen) and inorganic (biomineral) materials. This amazing class of materials with intrinsic internally designed hierarchical structures show superior mechanical properties with regard to their weak components from which they are formed. Extensive investigations concentrating on static loading conditions have been done to study the biological materials failure. However, most of the damage and failure mechanisms in load-bearing biological materials will occur whenever their structures are exposed to dynamic loading conditions. The main question needed to be answered here is: What is the relation between the layout and architecture of the load-bearing biological materials and their dynamic behavior? In this work, a staggered model has been developed based on the structure of natural materials at nanoscale and Finite Element Analysis (FEA) has been used to study the dynamic behavior of the structure of load-bearing biological materials to answer why the staggered arrangement has been selected by nature to make the nanocomposite structure of most of the biological materials. The results showed that the staggered structures will efficiently attenuate the stress wave rather than the layered structure. Furthermore, such staggered architecture is effectively in charge of utilizing the capacity of the biostructure to resist both normal and shear loads. In this work, the geometrical parameters of the model like the thickness and aspect ratio of the mineral inclusions selected from the typical range of the experimentally observed feature sizes and layout dimensions of the biological materials such as bone and mineralized tendon. Furthermore, the numerical results validated with existing theoretical solutions. Findings of the present work emphasize on the significant effects of dynamic behavior on the natural evolution of load-bearing biological materials and can help scientists to design bioinspired materials in the laboratories.

Keywords: Load-bearing biological materials, nanostructure, staggered structure, stress wave decay.

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Authors: Bing Lu, Shuang Li, Hongyuan Zhou

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The steel-concrete hybrid structure has been extensively employed in high-rise buildings and super high-rise buildings. The light gauge steel-concrete hybrid structure, including light gauge steel structure and concrete hybrid structure, is a type of steel-concrete hybrid structure, which possesses some advantages of light gauge steel structure and concrete hybrid structure. The seismic behavior and loss assessment of three high-rise buildings with three different concrete hybrid structures were investigated through finite element software. The three concrete hybrid structures are reinforced concrete column-steel beam (RC-S) hybrid structure, concrete-filled steel tube column-steel beam (CFST-S) hybrid structure, and tubed concrete column-steel beam (TC-S) hybrid structure. The nonlinear time-history analysis of three high-rise buildings under 80 earthquakes was carried out. After simulation, it indicated that the seismic performances of three high-rise buildings were superior. Under extremely rare earthquakes, the maximum inter-story drifts of three high-rise buildings are significantly lower than 1/50. The inter-story drift and floor acceleration of high-rise building with CFST-S hybrid structure were bigger than those of high-rise buildings with RC-S hybrid structure, and smaller than those of high-rise building with TC-S hybrid structure. Then, based on the time-history analysis results, the post-earthquake repair cost ratio and repair time of three high-rise buildings were predicted through an economic performance analysis method proposed in FEMA-P58 report. Under frequent earthquakes, basic earthquakes and rare earthquakes, the repair cost ratio and repair time of three high-rise buildings were less than 5% and 15 days, respectively. Under extremely rare earthquakes, the repair cost ratio and repair time of high-rise buildings with TC-S hybrid structure were the most among three high rise buildings. Due to the advantages of CFST-S hybrid structure, it could be extensively employed in high-rise buildings subjected to earthquake excitations.

Keywords: seismic behavior, loss assessment, light gauge steel, concrete hybrid structure, high-rise building, time-history analysis

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Authors: E. L. Suarez, D. E. Meeroff, Y. Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: Community resilience, conflation, flood risk, nuisance flooding.

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Authors: Caixia Gao, Chuanrong Li, Lingli Tang, Lingling Ma, Yaokai Liu, Xinhong Wang, Yongsheng Zhou

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Performances analysis of remote sensing sensor is required to pursue a range of scientific research and application objectives. Laboratory analysis of any remote sensing instrument is essential, but not sufficient to establish a valid inflight one. In this study, with the aid of the in situ measurements and corresponding image of three-gray scale permanent artificial target, the in-flight radiometric performances analyses (in-flight radiometric calibration, dynamic range and response linearity, signal-noise-ratio (SNR), radiometric resolution) of self-developed short-wave infrared (SWIR) camera are performed. To acquire the inflight calibration coefficients of the SWIR camera, the at-sensor radiances (L_i) for the artificial targets are firstly simulated with in situ measurements (atmosphere parameter and spectral reflectance of the target) and viewing geometries using MODTRAN model. With these radiances and the corresponding digital numbers (DN) in the image, a straight line with a formulation of L = G × DN + B is fitted by a minimization regression method, and the fitted coefficients, G and B, are inflight calibration coefficients. And then the high point (L_H) and the low point (L_L) of dynamic range can be described as L_H= (G × DN_H + B) and L_L= B, respectively, where DN_H is equal to 2ⁿ − 1 (n is the quantization number of the payload). Meanwhile, the sensor’s response linearity (δ) is described as the correlation coefficient of the regressed line. The results show that the calibration coefficients (G and B) are 0.0083 W·sr⁻¹m⁻²µm⁻¹ and −3.5 W·sr⁻¹m⁻²µm⁻¹; the low point of dynamic range is −3.5 W·sr⁻¹m⁻²µm⁻¹ and the high point is 30.5 W·sr⁻¹m⁻²µm⁻¹; the response linearity is approximately 99%. Furthermore, a SNR normalization method is used to assess the sensor’s SNR, and the normalized SNR is about 59.6 when the mean value of radiance is equal to 11.0 W·sr⁻¹m⁻²µm⁻¹; subsequently, the radiometric resolution is calculated about 0.1845 W•sr^-1m^-2μm^-1. Moreover, in order to validate the result, a comparison of the measured radiance with a radiative-transfer-code-predicted over four portable artificial targets with reflectance of 20%, 30%, 40%, 50% respectively, is performed. It is noted that relative error for the calibration is within 6.6%.

Keywords: Calibration, dynamic range, radiometric resolution, SNR.

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Authors: Han-Mei Chen, Rongxin Zhou, Taige Wang

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Glass is widely used in everyday life, from glass bottles for beverages, to architectural glass for various forms of glazing. Although the mainstream of used glass is recycled in the UK, the single-use and then recycling procedure results in a lot of waste as it incorporates intact glass with smashing, re-melting and remanufacturing. These processes bring massive energy consumption with a huge loss of high embodied energy and economic value, compared to re-use which’s towards a ‘zero carbon’ target. As a tourism city, Liverpool has more glass bottle consumption than most less leisure focused cities. It is therefore vital for Liverpool to find an upgrading approach for the single-use glass bottles with a low carbon output. This project aims to assess the feasibility of an industrial symbiosis and upgrading framework of glass and to investigate the ways of achieving them. It is significant to Liverpool’s future industry strategy since it provides an opportunity to target on economy recovery for post-COVID by industry symbiosis and an up-grading waste management in Liverpool to respond to the climate emergency. In addition, it will influence the local government policy for glass bottle reuse and recycling in North West England, and as a good practice to be further recommended to other areas of the UK. First, critical literature review of glass waste strategies has been conducted in the UK, and world-wide industrial symbiosis practices. Second, mapping, data collection and analysis have shown the current life cycle chain and the strong links of glass reuse and upgrading potentials via site visits to 16 local waste recycling centres. The results of this research have demonstrated the understanding the influence of key factors on the development of a circular industrial symbiosis business model for beverage glass bottles. The current waste management procedures of glass bottle industry, its business model, supply chain and the material flow have been reviewed. The various potential opportunities for glass bottle up-valuing have been investigated towards an industrial symbiosis in Liverpool. Finally, an up-valuing business model has been developed for an industrial symbiosis framework of glass in Liverpool. For glass bottles, there are two possibilities: 1) focus on upgrading processes towards re-use rather than single-use and recycling, 2) focus on ‘smart’ re-use and recycling leading to optimised values in other sectors to create a wider industry symbiosis for a multi-level and circular economy.

Keywords: Glass bottles, industry symbiosis, smart reuse, waste upgrading.

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: Diesel engine, machine learning, NOx emission, semi-empirical.

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