Search results for: vehicle following models

Authors: Sabiu Bala Muhammad, Rosli Saad

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Multiple linear regression (MLR) models for fast estimation of true subsurface resistivity from apparent resistivity field measurements are developed and assessed in this study. The parameters investigated were apparent resistivity (ρₐ), horizontal location (X) and depth (Z) of measurement as the independent variables; and true resistivity (ρₜ) as the dependent variable. To achieve linearity in both resistivity variables, datasets were first transformed into logarithmic domain following diagnostic checks of normality of the dependent variable and heteroscedasticity to ensure accurate models. Four MLR models were developed based on hierarchical combination of the independent variables. The generated MLR coefficients were applied to another data set to estimate ρₜ values for validation. Contours of the estimated ρₜ values were plotted and compared to the observed data plots at the colour scale and blanking for visual assessment. The accuracy of the models was assessed using coefficient of determination (R²), standard error (SE) and weighted mean absolute percentage error (wMAPE). It is concluded that the MLR models can estimate ρₜ for with high level of accuracy.

Keywords: apparent resistivity, depth, horizontal location, multiple linear regression, true resistivity

Procedia PDF Downloads 276

Authors: Milica Ž. Karadžić, Lidija R. Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Z. Kovačević, Anamarija I. Mandić, Katarina Penov-Gaši, Andrea R. Nikolić, Aleksandar M. Oklješa

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This study considered selection of the in silico molecular descriptors and the models for newly synthesized steroid derivatives description and their characterization using chemometric techniques. Multiple linear regression (MLR) models were established and gave the best molecular descriptors for quantitative structure-retention relationship (QSRR) modeling of the retention of the investigated molecules. MLR models were without multicollinearity among the selected molecular descriptors according to the variance inflation factor (VIF) values. Used molecular descriptors were ranked using generalized pair correlation method (GPCM). In this method, the significant difference between independent variables can be noticed regardless almost equal correlation between dependent variable. Generated MLR models were statistically and cross-validated and the best models were kept. Models were ranked using sum of ranking differences (SRD) method. According to this method, the most consistent QSRR model can be found and similarity or dissimilarity between the models could be noticed. In this study, SRD was performed using average values of experimentally observed data as a golden standard. Chemometric analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their potential biological activity and further synthesis. This article is based upon work from COST Action (CM1105), supported by COST (European Cooperation in Science and Technology).

Keywords: generalized pair correlation method, molecular descriptors, regression analysis, steroids, sum of ranking differences

Procedia PDF Downloads 347

Authors: Prateek Saxena, Sanjeev Singh, Julius Roy

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The security of the power distribution grid remains a paramount to the utility professionals while enhancing and making it more efficient. The most serious threat to the system can be maintaining the transformers, as the load is ever increasing with the addition of elements like electric vehicles. In this paper, intelligent transformer monitoring and grid management has been proposed. The engineering is done to use the evolving data from the smart meter for grid analytics and diagnostics for preventive maintenance. The two-tier architecture for hardware and software integration is coupled to form a robust system for the smart grid. The proposal also presents interoperable meter standards for easy integration. Distribution transformer analytics based on real-time data benefits utilities preventing outages, protects the revenue loss, improves the return on asset and reduces overall maintenance cost by predictive monitoring.

Keywords: electric vehicle charging, transformer monitoring, data analytics, intelligent grid

Procedia PDF Downloads 790

Authors: Navid Daryasafar, Nima Farshidfar

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In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared.

Keywords: error steganography, unidirectional estimation, bidirectional estimation, AR linear estimation

Procedia PDF Downloads 539

Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Jeongmin Kim, Eunju Lee, Kwang Ryel Ryu

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This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis.

Keywords: predictive control, energy management, machine learning, optimization

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Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

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Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus

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One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, uncertainty, stochastic process, Monte Carlo simulation

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Authors: Nabila Dwi Agustin, Ria Septitis Mentari, Nugroho Adi Sasongko

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Indonesia is experiencing a crisis in the development of defense equipment. Most of Indonesia's defense equipment must import its parts from other countries. Moreover, the area of Indonesia is 2/3 of its territory is the sea areas. For the protection of marine areas, Indonesia relies solely on submarines in monitoring conditions and whether or not intruders enter their territory. In fact, we know the submarine has a large size so that the expenses are getting bigger, the time it takes longer and needs a big maneuver to operate the submarine. Indeed, the submarine can only be operated for deeper seas. Many other countries enter the underwater world of Indonesia but Indonesia could not do anything due to the limitations of underwater monitoring system. At the same time, reconnaissance and monitor for shallow seas cannot be done by submarine. Equipment that can be used for surveillance of shallow underwater areas shall be made. This study reviewed the current research and development initiative of the submarine unmanned vehicle (SUV) or unmanned undersea vehicle (UUV) in Indonesia. This can explore underwater without the need for an operator to operate in it, but we can monitor it from a long distance. UUV has several advantages that size can be reduced as we desired, rechargeable ship batteries, has a detection sonar commonly found on a submarine and agile movement to detect at shallow sea depth. In the sonar sensors consisted of MEMS (Micro Electro Mechanical System), the sonar system runs more efficiently and effectively to monitor the target. UUV that has been developed will be very useful if the equipment is used around the outlying islands and outer from Indonesia especially the island frequented by foreign submarines without us know. The impact of this may not be felt now but it will allow foreign countries to attack Indonesia from within for the future. In addition, UUV needs to be equipped with a anti-radar system so that submarines of other countries crossing borders cannot detect it and Indonesia anti-submarine vessels can take further security measures. As the recommendation, Indonesia should take decisive steps in the state border rules, especially submarines of other countries that deliberately cross the borders of the state. This decisive action not only by word alone but also action as well. Indonesia government should show the strength and sovereignty as the entire society unites and applies the principle of universal peace.

Keywords: submarine unmanned vehicle, submarine, development of defense equipment, the border of Indonesia

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Authors: Sudip Kumar Kundu, Charu Singh

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As a great physiographic divide, the Himalayas affecting a large system of water and air circulation which helps to determine the climatic condition in the Indian subcontinent to the south and mid-Asian highlands to the north. It creates obstacles by defending chill continental air from north side into India in winter and also defends rain-bearing southwesterly monsoon to give up maximum precipitation in that area in monsoon season. Nowadays extreme weather conditions such as heavy precipitation, cloudburst, flash flood, landslide and extreme avalanches are the regular happening incidents in the region of North Western Himalayan (NWH). The present study has been planned to investigate the suitable model(s) to find out the rainfall pattern over that region. For this investigation, selected models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and Coupled Model Intercomparison Project Phase 5 (CMIP5) has been utilized in a consistent framework for the period of 1976 to 2000 (historical). The ability of these driving models from CORDEX domain and CMIP5 has been examined according to their capability of the spatial distribution as well as time series plot of rainfall over NWH in the rainy season and compared with the ground-based Indian Meteorological Department (IMD) gridded rainfall data set. It is noted from the analysis that the models like MIROC5 and MPI-ESM-LR from the both CORDEX and CMIP5 provide the best spatial distribution of rainfall over NWH region. But the driving models from CORDEX underestimates the daily rainfall amount as compared to CMIP5 driving models as it is unable to capture daily rainfall data properly when it has been plotted for time series (TS) individually for the state of Uttarakhand (UK) and Himachal Pradesh (HP). So finally it can be said that the driving models from CMIP5 are better than CORDEX domain models to investigate the rainfall pattern over NWH region.

Keywords: global warming, rainfall, CMIP5, CORDEX, NWH

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Authors: Wang Qian, Bian Cheng Xiang

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In recent years, with the improvement of China's urbanization level, the number of urban motor vehicles has grown rapidly. As residents' daily commuting necessities, cars cause a lot of exhaust emissions and urban traffic congestion. In the "Fourteenth Five Year Plan" of China, it is proposed to strive to reach the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. Urban transport accounts for a high proportion of carbon emission sources. It is an important driving force for the realization of China's carbon peak strategy. Some cities have introduced and implemented the policy of "car restriction" to solve related urban problems by reducing the use of cars. This paper analyzes the implementation of the "automobile restriction" policy, evaluates the relevant effects of the automobile restriction policy, and discusses how to better optimize the "automobile restriction" policy in the process of urban governance.

Keywords: carbon emission, traffic jams, vehicle restrictions, evaluate

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Authors: C. Guven, M. Tufekci, E. Bayik, O. Gedik, M. Tas

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A sliding door system is used in commercial vehicles and passenger cars to allow a larger unobstructed access to the interior for loading and unloading. The movement of a sliding door on vehicle body is ensured by mechanisms and tracks having special cross-section which is manufactured by roll forming and stretch bending process. There are three tracks and three mechanisms which are called upper, central and lower on a sliding door system. There are static requirements as strength on different directions, rigidity for mechanisms, and door drop off, door sag; dynamic requirements as high energy slam opening-closing and durability requirement to validate these products. In addition, there is a kinematic requirement to find out force values from door handle during manual operating. In this study, finite element analysis and physical test results which are realized for sliding door systems will be shared comparatively.

Keywords: finite element analysis, sliding door, experimental, verification, vehicle tests

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Authors: T. Mohammed Chikouche, K. Hartani

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Based on the analysis of basic direct torque control, a parallel master slave for four in-wheel permanent magnet synchronous motors (PMSM) fed by two three phase inverters used in electric vehicle is proposed in this paper. A conventional system with multi-inverter and multi-machine comprises a three phase inverter for each machine to be controlled. Another approach consists in using only one three-phase inverter to supply several permanent magnet synchronous machines. A modified direct torque control (DTC) algorithm is used for the control of the bi-machine traction system. Simulation results show that the proposed control strategy is well adapted for the synchronism of this system and provide good speed tracking performance.

Keywords: electric vehicle, multi-machine single-inverter system, multi-machine multi-inverter control, in-wheel motor, master-slave control

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: H. C. Chinwenyi, H. D. Ibrahim, F. A. Ahmed

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In modern financial mathematics, valuing derivatives such as options is often a tedious task. This is simply because their fair and correct prices in the future are often probabilistic. This paper examines three different Stochastic Differential Equation (SDE) models in finance; the Constant Elasticity of Variance (CEV) model, the Balck-Karasinski model, and the Heston model. The various Martingales option price valuation formulas for these three models were obtained using the replicating portfolio method. Also, the numerical solution of the derived Martingales options price valuation equations for the SDEs models was carried out using the Monte Carlo method which was implemented using MATLAB. Furthermore, results from the numerical examples using published data from the Nigeria Stock Exchange (NSE), all share index data show the effect of increase in the underlying asset value (stock price) on the value of the European Put Option for these models. From the results obtained, we see that an increase in the stock price yields a decrease in the value of the European put option price. Hence, this guides the option holder in making a quality decision by not exercising his right on the option.

Keywords: equivalent martingale measure, European put option, girsanov theorem, martingales, monte carlo method, option price valuation formula

Procedia PDF Downloads 133

Authors: Adilson Elias Xavier, Otto Corrêa Rotunno Filho, Paulo Canedo De Magalhães

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This paper addresses the issue of automatic parameter estimation in conceptual rainfall-runoff (CRR) models. Due to threshold structures commonly occurring in CRR models, the associated mathematical optimization problems have the significant characteristic of being strongly non-differentiable. In order to face this enormous task, the resolution method proposed adopts a smoothing strategy using a special C∞ differentiable class function. The final estimation solution is obtained by solving a sequence of differentiable subproblems which gradually approach the original conceptual problem. The use of this technique, called Hyperbolic Smoothing Method (HSM), makes possible the application of the most powerful minimization algorithms, and also allows for the main difficulties presented by the original CRR problem to be overcome. A set of computational experiments is presented for the purpose of illustrating both the reliability and the efficiency of the proposed approach.

Keywords: rainfall-runoff models, automatic calibration, hyperbolic smoothing method

Procedia PDF Downloads 149

Authors: Mohkammad Nur Cahyadi, Imam Wahyu Farid, Ronny Mardianto, Agung Budi Cahyono, Eko Yuli Handoko, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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Unmanned aerial vehicle (UAV) technology has cost efficiency and data retrieval time advantages. Using technologies such as UAV, GNSS, and LiDAR will later be combined into one of the newest technologies to cover each other's deficiencies. This integration system aims to increase the accuracy of calculating the volume of the land stockpile of PT. Garam (Salt Company). The use of UAV applications to obtain geometric data and capture textures that characterize the structure of objects. This study uses the Taror 650 Iron Man drone with four propellers, which can fly for 15 minutes. LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetry and structural science principles from Motion point cloud technology. LiDAR can perform data acquisition that enables the creation of point clouds, three-dimensional models, Digital Surface Models, Contours, and orthomosaics with high accuracy. LiDAR has a drawback in the form of coordinate data positions that have local references. Therefore, researchers use GNSS, LiDAR, and drone multi-sensor technology to map the stockpile of salt on open land and warehouses every year, carried out by PT. Garam twice, where the previous process used terrestrial methods and manual calculations with sacks. Research with LiDAR needs to be combined with UAV to overcome data acquisition limitations because it only passes through the right and left sides of the object, mainly when applied to a salt stockpile. The UAV is flown to assist data acquisition with a wide coverage with the help of integration of the 200-gram LiDAR system so that the flying angle taken can be optimal during the flight process. Using LiDAR for low-cost mapping surveys will make it easier for surveyors and academics to obtain pretty accurate data at a more economical price. As a survey tool, LiDAR is included in a tool with a low price, around 999 USD; this device can produce detailed data. Therefore, to minimize the operational costs of using LiDAR, surveyors can use Low-Cost LiDAR, GNSS, and UAV at a price of around 638 USD. The data generated by this sensor is in the form of a visualization of an object shape made in three dimensions. This study aims to combine Low-Cost GPS measurements with Low-Cost LiDAR, which are processed using free user software. GPS Low Cost generates data in the form of position-determining latitude and longitude coordinates. The data generates X, Y, and Z values to help georeferencing process the detected object. This research will also produce LiDAR, which can detect objects, including the height of the entire environment in that location. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours. This study conducted an experimental process on the roof of a building with a radius of approximately 30 meters.

Keywords: LiDAR, unmanned aerial vehicle, low-cost GNSS, contour

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Authors: Mehdi Seifbarghy, Zahra Mansouri

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In this paper a few location-allocation models are developed in a multi-echelon supply chain including suppliers, manufacturers, distributors and retailers. The objectives are maximizing demand coverage, minimizing the total distance of distributors from suppliers, minimizing some facility establishment costs and minimizing the environmental effects. Since nature of the given models is multi-objective, we suggest a number of goal-based solution techniques such L-P metric, goal programming, multi-choice goal programming and goal attainment in order to solve the problems.

Keywords: location, multi-echelon supply chain, covering, goal programming

Procedia PDF Downloads 559

Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Nodelman V.

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Despite serious difficulties in the assimilation of the conceptual system of Calculus, software in the educational process is used only occasionally, and even then, mainly for illustration purposes. The following are a few reasons: The non-trivial nature of the studied material, Lack of skills in working with software, Fear of losing time working with software, The variety of the software itself, the corresponding interface, syntax, and the methods of working with the software, The need to find suitable models, and familiarize yourself with working with them, Incomplete compatibility of the found models with the content and teaching methods of the studied material. This paper proposes an active use of the developed non-commercial software VusuMatica, which allows removing these restrictions through Broad support for the studied mathematical material (and not only Calculus). As a result - no need to select the right software, Emphasizing the unity of mathematics, its intrasubject and interdisciplinary relations, User-friendly interface, Absence of special syntax in defining mathematical objects, Ease of building models of the studied material and manipulating them, Unlimited flexibility of models thanks to the ability to redefine objects, which allows exploring objects characteristics, and considering examples and counterexamples of the concepts under study. The construction of models is based on an original approach to the analysis of the structure of the studied concepts. Thanks to the ease of construction, students are able not only to use ready-made models but also to create them on their own and explore the material studied with their help. The presentation includes examples of using VusuMatica in studying the concepts of limit and continuity of a function, its derivative, and integral.

Keywords: counterexamples, limitations and requirements, software, teaching and learning calculus, user-friendly interface and syntax

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Authors: Meng Wu

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Unmanned aerial vehicle (UAV) landing technology is a common task that is required to be fulfilled by fly robots. In this paper, the crazyflie2.0 is located by ultra-wideband (UWB) localization system that contains 4 UWB anchors. Another UWB anchor is introduced and installed on a stationary platform. One cost function is designed to find the minimum distance between crazyflie2.0 and the anchor installed on the stationary platform. The coordinates of the anchor are unknown in advance, and the goal of the cost function is to define the location of the anchor, which can be considered as an optimal landing point. When the cost function reaches the minimum value, the corresponding coordinates of the UWB anchor fixed on the stationary platform can be calculated and defined as the landing point. The simulation shows the effectiveness of the method in this paper.

Keywords: UAV landing, UWB localization system, UWB anchor, cost function, stationary platform

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Authors: Siliang Luan, Zhongtai Jiang

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The risk of highway incidents is commonly recognized as a major concern for transportation authorities due to the hazardous consequences and negative influence. It is crucial to respond to these unpredictable events as soon as possible faced by emergency management decision makers. In this paper, we focus on path planning for emergency vehicles, one of the most significant processes to avoid congestion and reduce rescue time. A Mixed-Integer Linear Programming with Semi-Soft Time Windows Model (MIPSSTWM) is conducted to plan an optimal routing respectively considering the time consumption of arcs and nodes of the urban road network and the highway network, especially in developing countries with an enormous population. Here, the arcs indicate the road segments and the nodes include the intersections of the urban road network and the on-ramp and off-ramp of the highway networks. An attempt in this research has been made to develop a comprehensive and executive strategy for emergency vehicle routing in heavy traffic conditions. The proposed Cuckoo Search (CS) algorithm is designed by imitating obligate brood parasitic behaviors of cuckoos and Lévy Flights (LF) to solve this hard and combinatorial problem. Using a Chinese city as our case study, the numerical results demonstrate the approach we applied in this paper outperforms the previous method without considering the nodes of the road network for a real-world situation. Meanwhile, the accuracy and validity of the CS algorithm also show better performances than the traditional algorithm.

Keywords: emergency vehicle, path planning, cs algorithm, urban traffic management and urban planning

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: H. Djebar, L. Khene, M. Boucenna, M. R. Djebar, M. N. Khebbeb, M. Djekoun

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Currently in toxicology, use of alternative models permits to understand the mechanisms of toxicity at different levels of cells. Objectives of our research concern the determination of NPs ZnO, TiO2, AlO2, and FeO2 effect on ciliate protist freshwater Paramecium sp and Helix aspersa. The result obtained show that NPs increased antioxidative enzyme activity like catalase, glutathione –S-transferase and level GSH. Also, cells treated with high concentrations of NPs showed a high level of MDA. In conclusion, observations from growth and enzymatic parameters suggest on one hand that treatment with NPs provokes an oxidative stress and on the other that snale and paramecium are excellent alternatives models for ecotoxicological studies.

Keywords: NPs, GST, catalase, GSH, MDA, toxicity, snale and paramecium

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Authors: Feri̇t Çakici, M. Kemal Leblebi̇ci̇oğlu

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In this study, an unmanned aerial vehicle (UAV) with level flight, vertical take-off and landing (VTOL) and mode-transitioning capability is designed and analyzed. The platform design combines both multirotor and fixed-wing (FW) conventional airplane structures and control surfaces; therefore named as VTOL-FW. The aircraft is modeled using aerodynamical principles and linear models are constructed utilizing small perturbation theory for trim conditions. The proposed method of control includes implementation of multirotor and airplane mode controllers and design of an algorithm to transition between modes in achieving smooth switching maneuvers between VTOL and FW flight. Thus, VTOL-FW UAV’s flight characteristics are expected to be improved by enlarging operational flight envelope through enabling mode-transitioning, agile maneuvers and increasing survivability. Experiments conducted in simulation and real world environments shows that VTOL-FW UAV has both multirotor and airplane characteristics with extra benefits in an enlarged flight envelope.

Keywords: aircraft design, linear analysis, mode transitioning control, UAV

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

Abstract:

Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: Raninder Kaur Dhillon, Mayur Jethwa, Hardeep Singh Rai

Abstract:

Information technology has made a pivotal progress across disparate disciplines, one of which is AEC (Architecture, Engineering and Construction) industry. CAD is a form of computer-aided building modulation that architects, engineers and contractors use to create and view two- and three-dimensional models. The AEC industry also uses building information modeling (BIM), a newer computerized modeling system that can create four-dimensional models; this software can greatly increase productivity in the AEC industry. BIM models generate open source IFC (Industry Foundation Classes) files which aim for interoperability for exchanging information throughout the project lifecycle among various disciplines. The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a Building Information Modeling (BIM) authoring tool, to extract a partial or complete IFC instance model. This paper proposes an efficient algorithm for extracting a partial and total model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD).

Keywords: BIM, CAD, IFC, MVD

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

Abstract:

In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Thiago Victor Cordeiro Marcos, Joao Felipe de Araujo Martos, Ronaldo de Lima Cardoso, David Romanelli Pinto, Paulo Gilberto de Paula Toro, Israel da Silveira Rego, Antonio Carlos de Oliveira

Abstract:

Currently, the use of 3D rapid prototyping, also known as 3D printing, has been investigated by some universities around the world as an innovative technique, fast, flexible and cheap for a direct plastic models manufacturing that are lighter and with complex geometries to be tested for hypersonic shock tunnel. Initially, the purpose is integrated prototyped parts with metal models that actually are manufactured through of the conventional machining and hereafter replace them with completely prototyped models. The mechanical design models to be tested in hypersonic shock tunnel are based on conventional manufacturing processes, therefore are limited forms and standard geometries. The use of 3D rapid prototyping offers a range of options that enables geometries innovation and ways to be used for the design new models. The conception and project of a prototyped model for hypersonic shock tunnel should be rethought and adapted when comparing the conventional manufacturing processes, in order to fully exploit the creativity and flexibility that are allowed by the 3D prototyping process. The objective of this paper is to compare the conception and project of a 3D rapid prototyping model and a conventional machining model, while showing the advantages and disadvantages of each process and the benefits that 3D prototyping can bring to the manufacture of models to be tested in hypersonic shock tunnel.

Keywords: 3D printing, 3D prototyping, experimental research, hypersonic shock tunnel

Procedia PDF Downloads 469