Search results for: road traffic noise

Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

Procedia PDF Downloads 207

Authors: N. Arunraj, C. P. V. Paul, D. M. D. Jayawardena, W. N. D. Fernando

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Traffic congestion with increased numbers of vehicles is already a serious issue for many countries. The absence of sufficient parking spaces adds to the issue. Motorists are forced to wait in long queues to park their vehicles. This adds to the inconvenience faced by a motorist, kept waiting for a slot allocation, manually done along with the parking payment calculation. In Sri Lanka, nowadays, parking systems use barcode technology to identify the vehicles at both the entrance and the exit points. Customer management is handled by the use of man power. A parking space is, generally permanently sub divided according to the vehicle type. Here, again, is an issue. Parking spaces are not utilized to the maximum. The current arrangement leaves room for unutilized parking spaces. Accordingly, there is a need to manage the parking space dynamically. As a vehicle enters the parking area, available space has to be assigned for the vehicle according to the vehicle type. The system, Automated Parking System (APS), provides an automated solution using RFID Technology to identify the vehicles. Simultaneously, an algorithm manages the space allocation dynamically. With this system, there is no permanent parking slot allocation for a vehicle type. A desktop application manages the customer. A Web application is used to manage the external users with their reservations. The system also has an android application to view the nearest parking area from the current location. APS is built using java and php. It uses LED panels to guide the user inside the parking area to find the allocated parking slot accurately. The system ensures efficient performance, saving precious time for a customer. Compared with the current parking systems, APS interacts with users and increases customer satisfaction as well.

Keywords: RFID, android, web based system, barcode, algorithm, LED panels

Procedia PDF Downloads 582

Authors: Davies Obaromi, Qin Yongsong, James Ndege

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To interpolate scattered or regularly distributed data, there are imprecise or exact methods. However, there are some of these methods that could be used for interpolating data in a regular grid and others in an irregular grid. In spatial epidemiology, it is important to examine how a disease prevalence rates are distributed in space, and how they relate with each other within a defined distance and direction. In this study, for the geographic and graphic representation of the disease prevalence, linear and biharmonic spline methods were implemented in MATLAB, and used to identify, localize and compare for smoothing in the distribution patterns of tuberculosis (TB) in Eastern Cape Province. The aim of this study is to produce a more “smooth” graphical disease map for TB prevalence patterns by a 3-D curve fitting techniques, especially the biharmonic splines that can suppress noise easily, by seeking a least-squares fit rather than exact interpolation. The datasets are represented generally as a 3D or XYZ triplets, where X and Y are the spatial coordinates and Z is the variable of interest and in this case, TB counts in the province. This smoothing spline is a method of fitting a smooth curve to a set of noisy observations using a spline function, and it has also become the conventional method for its high precision, simplicity and flexibility. Surface and contour plots are produced for the TB prevalence at the provincial level for 2012 – 2015. From the results, the general outlook of all the fittings showed a systematic pattern in the distribution of TB cases in the province and this is consistent with some spatial statistical analyses carried out in the province. This new method is rarely used in disease mapping applications, but it has a superior advantage to be assessed at subjective locations rather than only on a rectangular grid as seen in most traditional GIS methods of geospatial analyses.

Keywords: linear, biharmonic splines, tuberculosis, South Africa

Procedia PDF Downloads 219

Authors: Si-Geun Choi, Hoon-Jae Park, Jung-Woo Cho, Jin-Ho Lim, Jin-Young Park, Joo-Young Oh, Jae-Il Jeong Seock-Sam Kim, Young Tae Cho, Chan Gyu Kim, Jong-Hyoung Kim

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The tilting pad bearing is a kind of the fluid film bearing and it can contribute to the high speed and the high load performance compared to other bearings including the rolling element bearing. Furthermore, the tilting bearing has many advantages such as high stability at high-speed performance, long life, high damping, high impact resistance and low noise. Therefore, it mostly used in mid to large size turbomachines, despite the high price disadvantage. Recently, manufacture and process employing laser techniques advancing at a fast-growing rate in mechanical industry, the dissimilar metal weld process employing laser techniques is actively studied. Moreover, also, Industry fields try to apply for welding the white metal and the back metal using laser cladding method for high durability. Furthermore, it has followed that laser cladding method has a lot better bond strength, toughness, anti-abrasion and environment-friendly than centrifugal casting method through preceding research. Therefore, the laser cladding method has a lot better quality, cost reduction, eco-friendliness and permanence of technology than the centrifugal casting method or the gravity casting method. In this study, we compare the mechanical properties of different bearing materials by evaluating the behavior of laser cladding layer with various materials (i.e. SS400, SCM440, S20C) under the same parameters. Furthermore, we analyze the porosity of various tilting pad bearing materials which white metal treated on samples. SEM, EDS analysis and hardness tests of three materials are shown to understand the mechanical properties and tribological behavior. W/D ratio, surface roughness results with various materials are performed in this study.

Keywords: laser cladding, tilting pad bearing, white metal, mechanical properties

Procedia PDF Downloads 360

Authors: D. Calogine, O. Chau, S. Dotti, O. Ramiarinjanahary, P. Rasoavonjy, F. Tovondahiniriko

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Mafate is a natural circus in the north-western part of Reunion Island, without an electrical grid and road network. A micro-grid concept is being experimented in this area, composed of a photovoltaic production combined with electrochemical batteries, in order to meet the local population for self-consumption of electricity demands. This work develops a discrete model as well as a stochastic model in order to reach an optimal equilibrium between production and consumptions for a cluster of houses. The management of the energy power leads to a large linearized programming system, where the time interval of interest is 24 hours The experimental data are solar production, storage energy, and the parameters of the different electrical devices and batteries. The unknown variables to evaluate are the consumptions of the various electrical services, the energy drawn from and stored in the batteries, and the inhabitants’ planning wishes. The objective is to fit the solar production to the electrical consumption of the inhabitants, with an optimal use of the energies in the batteries by satisfying as widely as possible the users' planning requirements. In the discrete model, the different parameters and solutions of the linear programming system are deterministic scalars. Whereas in the stochastic approach, the data parameters and the linear programming solutions become random variables, then the distributions of which could be imposed or established by estimation from samples of real observations or from samples of optimal discrete equilibrium solutions.

Keywords: photovoltaic production, power consumption, battery storage resources, random variables, stochastic modeling, estimations of probability distributions, mixed integer linear programming, smart micro-grid, self-consumption of electricity.

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: N. Benkaci, E. Oubaiche, J.-L. Chatelain, R. Bensalem, K. Abbes

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The ambient vibration H/V technique is widely used nowadays in microzonation studies, because of its easy field handling and its low cost, compared to other geophysical methods. However, in presence of complex geology or lateral heterogeneity evidenced by more than one peak frequency in the H/V curve, it is difficult to interpret the results, especially when soil information is lacking. In this work, we focus on the construction site of the Baraki 40000=place stadium, located in the north-east side of the Mitidja basin (Algeria), to identify the seismic wave amplification zones. H/V curve analysis leads to the observation of spatial and time variability of the H/V frequency peaks. The spatial variability allows dividing the studied area into three main zones: (1) one with a predominant frequency around 1,5 Hz showing an important amplification level, (2) the second exhibits two peaks at 1,5 Hz and in the 4 Hz – 10 Hz range, and (3) the third zone is characterized by a plateau between 2 Hz and 3 Hz. These H/V curve categories reveal a consequent lateral heterogeneity dividing the stadium site roughly in the middle. Furthermore, a continuous ambient vibration recording during several weeks allows showing that the first peak at 1,5 Hz in the second zone, completely disappears between 2 am and 4 am, and reaching its maximum amplitude around 12 am. Consequently, the anthropogenic noise source generating these important variations could be the Algiers Rocade Sud highway, located in the maximum amplification azimuth direction of the H/V curves. This work points out that the H/V method is an important tool to perform nano-zonation studies prior to geotechnical and geophysical investigations, and that, in some cases, the H/V technique fails to reveal the resonance frequency in the absence of strong anthropogenic source.

Keywords: ambient vibrations, amplification, fundamental frequency, lateral heterogeneity, site effect

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Authors: Ghulamhossein Nezami

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Civilizations based in Iran, especially in the south, have always realized the all-around importance of the Persian Sea and for various reasons, have paid full attention to it. The first of these was the pre-Aryan government, Ilam in the coastal province of Sharihum and the city of Lian (now the port of Bushehr) in terms of trade, defense and religion. With the establishment of the Achaemenids on the entire plateau of Iran to the center of Persia, they created several communication routes from Parseh to the shores of the Persian Gulf, which ended in the present Bushehr province. This coastal area was extended by a road in the coastal plain to the more southern parts of the ports of Ausinze - according to Ptolemy the port of Siraf before the Sassanids - and Epstane and Hormozia in the present-day Strait of Hormuz. Meanwhile, the ancient city of Temukknana, whose new historical documents testify to its extraordinary importance in the Achaemenid period, especially Darius I of the Achaemenids, from a strategic position with the coastal areas, the coasts and on the other hand with the gamers, the political center. - Achaemenid administration, had. New archeological evidence, research, and excavations show that both the famous Achaemenid kings and courtiers paid special attention to Tamukknana. The discovery of a tomb and three Achaemenid palaces from before the reign of Cyrus to Xerxes in this region showed the importance of the strategic, security-defense and commercial position of this region, extraordinary for the Achaemenids. Therefore, the city of Temukkana in the Dashtestan region of present-day Bushehr province became an important Achaemenid center on the Persian Gulf coast and became the political-economic center of gravity of the Achaemenids and the regulator of communication networks on the Persian Gulf coast. This event showed that the Achaemenids attached importance to their economic goals and oversight of their vast territory by the Persian Gulf. Methods: Book resources and field study.

Keywords: Achaemenids, Bushehr, Persian Gulf, Tamukkana

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Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

Procedia PDF Downloads 384

Authors: Usman Ahmed Khan

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Urbanization directly affects the existing infrastructure, landscape modification, environmental contamination, and traffic pollution, especially if there is a lack of urban planning. Recently, the rapid urban sprawl has resulted in less developed green areas and has devastating environmental consequences. This study was aimed to study the past urban expansion rates and measure LST from satellite data. The land use land cover (LULC) maps of years 1996, 2010, 2013, and 2017 were generated using landsat satellite images. Four main classes, i.e., water, urban, bare land, and vegetation, were identified using unsupervised classification with iterative self-organizing data analysis (isodata) technique. The LST from satellite thermal data can be derived from different procedures: atmospheric, radiometric calibrations and surface emissivity corrections, classification of spatial changeability in land-cover. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, From 1996-2017, urban areas increased to about a considerable increase of about 48%. Few areas of the city also shown in a reduction in LST from the year 1996-2017 that actually began their transitional phase from rural to urban LULC. The mean temperature of the city increased averagely about 1ºC each year in the month of October. The green and vegetative areas witnessed a decrease in the area while a higher number of pixels increased in urban class.

Keywords: LST, LULC, isodata, urbanization

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Authors: Jahede Tekeykhah, Seyed Mohsen Hossini, Gholamali Jalali

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Industrial and traffic activities cause large amounts of heavy metals enter into the atmosphere and the use of plant species can be effective in assessing and reducing air pollution by metals. This study aimed to investigate the adsorption level of heavy metals in leaves of Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica trees in Abidar forest park. For this purpose, samples leaves of the trees were prepared from the contaminated and control areas in each region in 3 stations with 3 replicates in mid-August and finally 90 samples were sent to the laboratory. Then, the concentrations of heavy metals were measured by graphite furnace. To do this, factorial experiment based on a completely randomized design with two factors of location on two levels (contaminated area and control area) and the factor of species on five levels (Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica) with three replications was used. The analysis of collected data was performed by SPSS software and Duncan's multiple range test was used to compare the means. The results showed that the accumulation of all metals in the leaves of most species in the infected area with a significant difference at 95% level was higher than the control area. In the contaminated area, with a significant difference at 5% level, the highest accumulations of metals were observed as the following: lead, cadmium, zinc and manganese in Platanus orientalis, nickel in Fraxinus rotundifolia and copper in Platycladus orientalis.

Keywords: airborne, tree species, heavy metals, absorption, Abidar Forest Park

Procedia PDF Downloads 282

Authors: K. Parandhama Gowd

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Space technologies have changed the way we live in the present day society and manage many aspects of our daily affairs through Remote sensing, Navigation & Communications. Further, defense and military usage of spacecraft has increased tremendously along with civilian purposes. The number of satellites deployed in space in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and the Geostationary Orbit (GEO) has gone up. The dependency on remote sensing and operational capabilities are most invariably to be exploited more and more in future. Every country is acquiring spacecraft in one way or other for their daily needs, and spacecraft numbers are likely to increase significantly and create spacecraft traffic problems. The aim of this research paper is to propose innovative design concepts for adaptive spacecraft. The main idea here is to improve existing design methods of spacecraft design and development to further improve upon design considerations for futuristic adaptive spacecraft with inbuilt features for automatic adaptability and self-protection. In other words, the innovative design considerations proposed here are to have future spacecraft with self-organizing capabilities for orbital control and protection from anti-satellite weapons (ASAT). Here, an attempt is made to propose design and develop futuristic spacecraft for 2030 and beyond due to tremendous advancements in VVLSI, miniaturization, and nano antenna array technologies, including nano technologies are expected.

Keywords: satellites, low earth orbit (LEO), medium earth orbit (MEO), geostationary earth orbit (GEO), self-organizing control system, anti-satellite weapons (ASAT), orbital control, radar warning receiver, missile warning receiver, laser warning receiver, attitude and orbit control systems (AOCS), command and data handling (CDH)

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: M. R. Ahmad, I. Musirin, M. M. Othman, N. A. Rahmat

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Plug-in Hybrid-Electric Vehicle (PHEV) has gained immense popularity in recent years. PHEV offers numerous advantages compared to the conventional internal-combustion engine (ICE) vehicle. Millions of PHEVs are estimated to be on the road in the USA by 2020. Uncoordinated PHEV charging is believed to cause severe impacts to the power grid; i.e. feeders, lines and transformers overload and voltage drop. Nevertheless, improper PHEV data model used in such studies may cause the findings of their works is in appropriated. Although smart charging is more attractive to researchers in recent years, its implementation is not yet attainable on the street due to its requirement for physical infrastructure readiness and technology advancement. As the first step, it is finest to study the impact of charging PHEV based on real vehicle travel data from National Household Travel Survey (NHTS) and at present charging rate. Due to the lack of charging station on the street at the moment, charging PHEV at home is the best option and has been considered in this work. This paper proposed a technique that comprehensively presents the impact of charging PHEV on power system networks considering huge numbers of PHEV samples with its traveling data pattern. Vehicles Charging Load Profile (VCLP) is developed and implemented in IEEE 30-bus test system that represents a portion of American Electric Power System (Midwestern US). Normalization technique is used to correspond to real time loads at all buses. Results from the study indicated that charging PHEV using opportunity charging will have significant impacts on power system networks, especially whereas bigger battery capacity (kWh) is used as well as for higher penetration level.

Keywords: plug-in hybrid electric vehicle, transportation electrification, impact of charging PHEV, electricity demand profile, load profile

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Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

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This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: cascade control, multi-Loop control systems, multiobjective optimization, optimal control

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Fereshteh M. Haskouei, Habib Alimohammadian

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Magnetic susceptibility (χ) is operational to identify of late quaternary glacial-interglacial cycles in loess-paleosol sequences. It is well accepted that many loess-paleosol sequences bear witness to cold-dry/warm-humid periods, well known as glacial-interglacial cycles, respectively. For this study, loess-paleosol sequence of north-east of Iran was magnetically investigated. The study area is situated at about 8 km away of Neka city, on the main road of Sari-Behshahr, in Mazandaran Province, north of Iran. The youngest deposits of study area are the late Quaternary wind-blown accumulations. In this study, the total number of 117 samples was collected from loess-paleosols units. After that, the natural remnant magnetization (NRM) and magnetic susceptibility (MS) of the samples were measured. Variation of MS of more than 110 loess samples was plotted to reveal the correlation of the MS and paleoclimatic changes. This study aims reconstruction of climatic changes (glacial-interglacial and stadials-interstadials cycles). To confirm our results we compared MS (χ) and the curves of other investigations in paleoclimatology. This correspondence abled us to recognize worldly events in the study area such as: Younger Dryas, the Last Glacial Maximum (LGM), deglaciation of Northern Hemisphere etc. The obtained magnetic data indicate that during almost 50 ka, at least two glacial-interglacial periods occurred in north-east of Iran. Further, variation of χ values revealed short period of climatically cycles known as stadials-interstadials. We recognized 4 stadials and a single stadial as colder sub-periods for S₀ (recently soil-paleosol) and S₂ (lower paleosol), respectively, Moreover, we recognized 6 warmer sub-periods (interstadials) for L₁ (upper loess) and one interstadial L₂ (lower loess).

Keywords: glacial-interglacial cycles, Iran, last glacial maximum (LGM), loess, magnetic susceptibility (χ), Neka, stadials-interstadials sub-periods, younger dryas

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Authors: J. Alvarez, J. Pimienta, R. Sarmiento

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Diode lasers working in free mode present different shifting and broadening determined by external factors such as temperature, current or mechanical vibrations, and they are not more useful in applications such as spectroscopy, metrology, and cooling of atoms, among others. Different configurations can reduce the spectral width of a laser; one of the most effective is to extend the optical resonator of the laser diode and use optical feedback either with the help of a partially reflective mirror or with a diffraction grating; this latter configuration is not only allowed to reduce the spectral width of the laser line but also to coarsely adjust its working wavelength, within a wide range typically ~ 10nm by slightly varying the angle of the diffraction grating. Two settings are commonly used for this purpose, the Littrow configuration and the Littmann Metcalf. In this paper, we present the design, construction, and characterization of a compact extended laser cavity in Littrow configuration. The designed cavity is compact and was machined on an aluminum block using computer numerical control (CNC); it has a mass of only 380 g. The design was tested on laser diodes with different wavelengths, 650nm, 780nm, and 795 nm, but can be equally efficient at other wavelengths. This report details the results obtained from the extended cavity working at a wavelength of 780 nm, with an output power of around 35mW and a line width of less than 1Mhz. The cavity was used to observe the spectrum of the corresponding Rubidium D2 line. By modulating the current and with the help of phase detection techniques, a dispersion signal with an excellent signal-to-noise ratio was generated that allowed the stabilization of the laser to a transition of the hyperfine structure of Rubidium with an integral proportional controller (PI) circuit made with precision operational amplifiers.

Keywords: Littrow, Littman-Metcalf, line width, laser stabilization, hyperfine structure

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Authors: Nur Bella Octoria Bella, Ayomi Dita Rarasati

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DKI Jakarta is Indonesia's capital city with the 10th highest congestion rate in the world based on the 2019 traffic index. Other than that based on World Air Quality Report in 2019 showed DKI Jakarta's air pollutant concentrate 49.4 µg and the 5th highest air pollutant in the world. In the urban city nowadays, the mobility rate is high enough and the efficiency for sustainability assessment in transport infrastructure development is needed. This efficiency is the important key for sustainable infrastructure development. DKI Jakarta is nowadays in the process of constructing the railway infrastructure to support the transportation system. The problems appearing are the railway infrastructure networks and the service in DKI Jakarta already planned based on sustainability factors or not. Therefore, the aim of this research is to make the evaluation of railways infrastructure networks performance and services in DKI Jakarta regards on the railway sustainability key factors. Further, this evaluation will be used to make the railway sustainability assessment framework and to offer some of the alternative solutions to improve railway transportation sustainability in DKI Jakarta. Firstly a very detailed literature review of papers that have focused on railway sustainability factors and their improvements of railway sustainability, published in the scientific journal in the period 2011 until 2021. Regarding the sustainability factors from the literature review, further, it is used to assess the current condition of railway infrastructure in DKI Jakarta. The evaluation will be using a Likert rate questionnaire and directed to the transportation railway expert and the passenger. Furthermore, the mapping and evaluation rate based on the sustainability factors will be compared to the effect factors using the Analytical Hierarchical Process (AHP). This research offers the network's performance and service rate impact on the sustainability aspect and the passenger willingness for using the rail public transportation in DKI Jakarta.

Keywords: transportation sustainability, railway transportation, sustainability, DKI Jakarta

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Neha Narayanan

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The paper tries to explore the role of language in determining spaces occupied by women in everyday lives. It is inspired from an ongoing action research work which employs ‘immersion’- arriving at a research problematic through community research, as a methodology in a Kondh adivasi village, Kirkalpadu located in Rayagada district of the Indian state of Odisha. In the dominant development discourse, language is associated with either preservation or conservation of endangered language or empowerment through language. Beyond these, is the discourse of language as a structure, with the hegemonic quality to organise lifeworld in a specific manner. This rigid structure leads to an experience of constriction of space for women. In Kirkalpadu, the action research work is with young and unmarried women of the age 15-25. During daytime, these women are either in the agricultural field or in the bari -the backyard of the house whose rooms are linearly arranged one after the other ending with the kitchen followed by an open space called bari (in Odia) which is an intimate and gendered space- where they are not easily visible. They justify the experience of restriction in mobility and fear of moving out of the village alone by the argument that the place and the men are nihi-aaeh (not good). These women, who have dropped out of school early to contribute to the (surplus) labour requirement in the household, want to learn English to be able to read signboards when they are on the road, to be able to fill forms at a bank and use mobile phones to communicate with their romantic partner(s). But the incapacity to have within one’s grasp the province of language and the incapacity to take the mobile phone to the kind of requirements marked by the above mentioned impossible transactions with space restricts them to the bari of the house. The paper concludes by seeking to explore the possibilities of learning and rethinking languages which takes into cognizance the gendered experience of women and the desire of women to cross the borders and occupy spaces restricted to them.

Keywords: action research, gendered experience, language, space

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

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Authors: Henar Martín-Sanz García, Eleni Chatzi, Eugen Brühwiler

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Located on the shore of Geneva Lake, in Switzerland, the Chillon Viaducts are two parallel structures consisted of post-tensioned concrete box girders, with a total length of 2 kilometers and 100m spans. Built in 1969, the bridges currently accommodate a traffic load of 50.000 vehicles per day, thereby holding a key role both in terms of historic value as well as socio-economic significance. Although several improvements have been carried out in the past two decades, recent inspections demonstrate an Alkali-Aggregate reaction in the concrete deck and piers reducing the concrete strength. In order to prevent further expansion of this issue, a layer of 40 mm of Ultra High Performance Fiber Reinforced cement-based Composite (UHPFRC) (incorporating rebars) was casted over the slabs, acting as a waterproof membrane and providing significant increase in resistance of the bridge structure by composite UHPFRC – RC composite action in particular of the deck slab. After completing the rehabilitation works, a Structural Monitoring campaign was installed on the deck slab in one representative span, based on accelerometers, strain gauges, thermal and humidity sensors. This campaign seeks to reveal information on the behavior of UHPFRC-concrete composite systems, such as increase in stiffness, fatigue strength, durability and long-term performance. Consequently, the structural monitoring is expected to last for at least three years. A first insight of the analyzed results from the initial months of measurements is presented herein, along with future improvements or necessary changes on the deployment.

Keywords: composite materials, rehabilitation, structural health monitoring, UHPFRC

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Authors: Ballout Amor, Yasmina Bouchahm, Lacheheb Dhia Eddine Zakaria

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Several studies all around the world were conducted on the phenomenon of the urban heat island, and referring to the results obtained, one of the most important factors that influence this phenomenon is the mineralization of the cities which means the reducing of evaporative urban surfaces, replacing vegetation and wetlands with concrete and asphalt. The use of vegetation and water can change the urban environment and improve comfort, thus reduce the heat island. The trees act as a mask to the sun, wind, and sound, and also as a source of humidity which reduces air temperature and surrounding surfaces. Water also acts as a buffer to noise; it is also a source of moisture and regulates temperature not to mention the psychological effect on humans. Our main objective in this paper is to determine the impact of vegetation, ponds and fountains on the urban micro climate in general and on the thermal comfort of people along the Independence square in the Algerian city of Sétif, which is a semi-arid climate, in particularly. In order to reach this objective, a comparative study between different scenarios has been done; the use of the Envi-met program enabled us to model the urban environment of the Independence Square and to study the possibility of improving the conditions of comfort by adding an amount of vegetation and water ponds. After studying the results obtained (temperature, relative humidity, wind speed, PMV and PPD indicators), the efficiency of the additions we've made on the square was confirmed and this is what helped us to confirm our assumptions regarding the terms of comfort in the studied site, and in the end we are trying to develop recommendations and solutions which may contribute to improve the conditions for greater comfort in the Independence square.

Keywords: comfort in outer space, urban environment, scenarisation, vegetation, water ponds, public square, simulation

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Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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Authors: A. S. Adegoke, O. N. Popoola

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The central purpose of management in any organization is that of coordinating the efforts of people towards the achievement of its goal. Effective and productive management is the function of leadership. Leadership plays a critical role in helping groups, organizations and societies to achieve their goals. Factors considered to make leadership to be effective are intelligence, social maturity, inner motivation and achievement drives and lastly, human relations attitudes. The factors affecting leadership style and effectiveness were examined. Also, the study examined which of the various leadership style best befits an organization and discussed the ways in which the style was determined. In order to meet the objectives of this study, different types of methods of data gathering were carried out. The methods include data from primary and secondary sources. The primary sources include personal interview, personal observation, and questionnaire while data from secondary sources were derived from various books, journal write up and other documentary records. Data were collected from respondents through questionnaire, and the field research carried out through oral interview to test each of the related hypotheses. From the data analysed it was determined that 45% strongly agreed that leadership traits are inborn not acquired and 28.3% agreed that leadership traits are inborn, while 11.7% and 10% strongly disagreed and disagreed respectively and 5% were undecided. 48.4% strongly agreed, and 43.3% agreed that environmental factors determined the appropriate style of leadership to be employed while 3.3% strongly disagreed, 1.7% disagreed and 3.3% were undecided. From the study, no single style of leadership is appropriate in any situation instead of concentrating on single leadership style; leader can vary approaches depending on forces in the leaders, characteristic of the subordinates, situational forces of the organization, lastly the expectations and behaviour of superior.

Keywords: hypothesis, leadership, management, organization

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Authors: Wang Qiaowei, Gao Yujiang

Abstract:

With the economic growth and rapid urbanization after the reform and openness, some of China's fast-growing cities have demolished former dwellings and built modern residential quarters. The blind, incomplete reference to western modern cities and the one-off construction lacking feedback mechanism have intensified such phenomenon, causing the citizen gradually expanded their living scale with the popularization of car traffic, and the peer-to-peer lifestyle gradually settled. The construction of large-scale commercial centers has caused obstacles to small business around the residential areas, leading to space for residents' interaction has been compressed. At the same time, the advocated Central Business District (CBD) model even leads to the unsatisfactory reconstruction of many historical blocks such as the Hangzhou Southern Song Dynasty Imperial Street. However, the popularity of historical spaces such as Wuzhen and Hongcun also indicates the collective memory and needs of the street space for Chinese residents. The evolution of Shanghai TianZiFang also proves the importance of the motivation of space participants in space construction in the context of the “top-down” construction model in China. In fact, there are frequent occurrences of “reconstruction”, which may redefine the space, in various residential areas. If these activities can be selectively controlled and encouraged, it will be beneficial to activate the public space as well as the residents’ intercourse, so that the traditional Chinese street space can be reconstructed in the context of modern cities.

Keywords: rapid urbanization, traditional street space, space re-construction, bottom-up design

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