Search results for: automatic vehicle location

Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

Procedia PDF Downloads 269

Authors: M. Hemnath, S. Kannan, R. Kiran, K. Thanigaivelu

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This paper is based on exploring the possible opportunities and applications using Power Line Communication (PLC) for security and surveillance operations. Various research works are done for introducing PLC into onboard vehicle communication and networking (CAN, LIN etc.) and various international standards have been developed. Wireless power transfer (WPT) is also an emerging technology which is studied and tested for recharging purposes. In this work we present a system which embeds the detection and the response into one which eliminates the need for dedicated network for data transmission. Also we check the feasibility for integrating wireless power transfer system into this proposed security system for transmission of power to detection unit wirelessly from the response unit.

Keywords: power line communication, wireless power transfer, surveillance

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Authors: Maureen Montalban

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The military is a predominantly male-dominated organisation that has entrenched hierarchical and patriarchal norms. Since 1975, women have been allowed to continue active service in the Australian Defence Force during pregnancy and after the birth of a child; prior to this time, pregnancy was grounds for automatic termination. The military and family, as institutions, make great demands on individuals with respect to their commitment, loyalty, time and energy. This research explores what it means to serve in the Australian Army as a woman through a gender lens, overlaid during a specific time period of their service; that is, during pregnancy, birth, and being a mother. It investigates the external demands faced by servicewomen who are mothers, whether it be from society, the Army, their teammates, their partners, or their children; and how they internally make sense of that with respect to their own identity and role as a mother, servicewoman, partner and as an individual. It also seeks to uncover how Australian Army servicewomen who are also mothers attempt to manage the dilemma of serving two greedy institutions when both expect and demand so much and whether this is, in fact, an impossible dilemma.

Keywords: women's health, gender studies, military culture, identity

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc).

Keywords: defuzzification, floating search, fuzzy clustering, Zernike moments

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Authors: Nneka Osuchukwu, Leonid Shpanin

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The concept of converting the kinetic energy of quadcopter propellers into electrical energy is considered in this contribution following the feasibility study of the propeller vibrations, theoretical energy conversion, and simulation techniques. Analysis of the propeller vibration performance is presented via graphical representation of calculated and simulated parameters, in order to demonstrate the possibility of recovering the harvested energy from the propeller vibrations of the quadcopter while the quadcopter is in operation. Consideration of using piezoelectric materials in such concept, converting the mechanical energy of the propeller into the electrical energy, is given. Photographic evidence of the propeller in operation is presented and discussed together with experimental results to validate the theoretical concept.

Keywords: energy harvesting, piezoelectric material, propeller vibration, unmanned aerial vehicle

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Authors: Anthony Credoz, Nathalie Nief, Remy Hedacq, Salvador Jordana, Laurent Cazes

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Groundwater monitoring is classically performed in a network of piezometers in industrial sites. Groundwater flow parameters, such as direction, sense and velocity, are deduced from indirect measurements between two or more piezometers. Groundwater sampling is generally done on the whole column of water inside each borehole to provide concentration values for each piezometer location. These flow and concentration values give a global ‘static’ image of potential plume of contaminants evolution in the shallow aquifer with huge uncertainties in time and space scales and mass discharge dynamic. TOTAL R&D Subsurface Environmental team is challenging this classical approach with an innovative dynamic way of characterization of shallow aquifer groundwater. The current study aims at optimizing the tools and methodologies for (i) a direct and multilevel measurement of groundwater velocities in each piezometer and, (ii) a calculation of potential flux of dissolved contaminant in the shallow aquifer. Lab-scale experiments have been designed to test commercial and R&D tools in a controlled sandbox. Multiphysics modeling were performed and took into account Darcy equation in porous media and Navier-Stockes equation in the borehole. The first step of the current study focused on groundwater flow at porous media/piezometer interface. Huge uncertainties from direct flow rate measurements in the borehole versus Darcy flow rate in the porous media were characterized during experiments and modeling. The structure and location of the tools in the borehole also impacted the results and uncertainties of velocity measurement. In parallel, direct-push tool was tested and presented more accurate results. The second step of the study focused on mass flux of dissolved contaminant in groundwater. Several active and passive commercial and R&D tools have been tested in sandbox and reactive transport modeling has been performed to validate the experiments at the lab-scale. Some tools will be selected and deployed in field assays to better assess the mass discharge of dissolved contaminants in an industrial site. The long-term subsurface environmental strategy is targeting an in-situ, real-time, remote and cost-effective monitoring of groundwater.

Keywords: dynamic characterization, groundwater flow, lab-scale, mass flux

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Vahid Rashtchi, Ashkan Pirooz

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This paper applies the Imperialist Competitive Algorithm (ICA) to find the optimal place and size of Static Compensator (STATCOM) in power systems. The output of the algorithm is a two dimensional array which indicates the best bus number and STATCOM's optimal size that minimizes all bus voltage deviations from their nominal value. Simulations are performed on IEEE 5, 14, and 30 bus test systems. Also some comparisons have been done between ICA and the famous Particle Swarm Optimization (PSO) algorithm. Results show that how this method can be considered as one of the most precise evolutionary methods for the use of optimum compensator placement in electrical grids.

Keywords: evolutionary computation, imperialist competitive algorithm, power systems compensation, static compensators, voltage profile

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Mazhar Hamayun

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Organizations need to take a holistic approach to their Zero Trust strategic and tactical security needs. This includes using a framework-agnostic model that will ensure all enterprise resources are being accessed securely, regardless of their location. Such can be achieved through the implementation of a security posture, monitoring the posture, and adjusting the posture through the Identify, Detect, Protect, Respond, and Recover Methods, The target audience of this document includes those involved in the management and operational functions of risk, information security, and information technology. This audience consists of the chief information security officer, chief information officer, chief technology officer, and those leading digital transformation initiatives where Zero Trust methods can help protect an organization’s data assets.

Keywords: ZTNA, zerotrust architecture, microsegmentation, NIST SP 800-207

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Authors: Jheanel E. Estrada, Larry A. Vea

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This study developed model to detect proper/improper sitting posture using the built in web camera which detects the upper body points’ location and distances (chin, manubrium and acromion process). It also established relationships of human body frames and proper sitting posture. The models were developed by training some well-known classifiers such as KNN, SVM, MLP, and Decision Tree using the data collected from 60 students of different body frames. Decision Tree classifier demonstrated the most promising model performance with an accuracy of 95.35% and a kappa of 0.907 for head and shoulder posture. Results also showed that there were relationships between body frame and posture through Body Mass Index.

Keywords: posture, spinal points, gyroscope, image processing, ergonomics

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Evgeny E. Tereshatov

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Astatine-211 is considered one of the most promising radionuclides for Targeted Alpha Therapy. In order to develop reliable procedures to label biomolecules and utilize efficient delivery vehicle principles, one should understand the main chemical characteristics of astatine. The short half-life of 211At (~7.2 h) and absence of any stable isotopes of this element are limiting factors towards studying the behavior of astatine. Our team has developed a procedure for rapid and efficient isolation of astatine from irradiated bismuth material in nitric acid media based on 3-octanone and 1-octanol extraction chromatography resins. This process has been automated and it takes 20 min from the beginning of the target dissolution to the At-211 fraction elution. Our next step is to consider commercially available chromatography resins and their applicability in astatine purification in the same media. Results obtained along with the corresponding sorption mechanisms will be discussed.

Keywords: astatine-211, chromatography, automation, mechanism, radiopharmaceuticals

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Authors: Mohammad Moonesun

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One of the most important future technologies is related to solar Autonomous Underwater Vehicles (AUVs). In this technical paper, some aspects of solar winged AUV design are mentioned. The case study is for Arya project. The submarine movement cyclograms, weight quotas for internal equipment, hydrodynamic test results are mentioned, and some other technical notes are discussed here. The main body is the SUBOFF type and has two hydroplanes on the both sides of the body with the NACA0015 cross section. On these two hydroplanes, two 50-W photovoltaic panel will be mounted. Four small hydroplanes with the same cross section of the NACA0015 are arranged at the stern of the body at a 90° angle to each other. This test is performed in National Iranian Marine Laboratory with the length of 402 m.

Keywords: AUV, solar, model test, hydrodynamic resistance

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Heung Sun Yoon, Jong Tae Kim

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In this paper, we present an activity diagram model for double-loop control self-adaptive braking system. Since activity diagram helps to improve visibility of self-adaption, we can easily find where improvement is needed on double-loop control. Double-loop control is adopted since the design conditions and actual conditions can be different. The system is reconfigured in runtime by using double-loop control. We simulated to verify and validate our model by using MATLAB. We compared single-loop control model with double-loop control model. Simulation results show that double-loop control provides more consistent brake power control than single-loop control.

Keywords: activity diagram, automotive, braking system, double-loop, self-adaptive, UML, vehicle

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Authors: Siva K. Bathina, Sudheer Siddapureddy

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Pool fires are formed when the flammable liquid accidentally spills on the ground or water and ignites. Pool fire is a kind of buoyancy-driven and diffusion flame. There have been many pool fire accidents caused during processing, handling and storing of liquid fuels in chemical and oil industries. Such kind of accidents causes enormous damage to property as well as the loss of lives. Pool fires are complex in nature due to the strong interaction among the combustion, heat and mass transfers and pyrolysis at the fuel surface. Moreover, the experimental study of such large complex fires involves fire safety issues and difficulties in performing experiments. In the present work, large eddy simulations are performed to study such complex fire scenarios using fire dynamic simulator. A 1 m diesel pool fire is considered for the studied cases, and diesel is chosen as it is most commonly involved fuel in fire accidents. Fire simulations are performed by specifying two different boundary conditions: one the fuel is in liquid state and pyrolysis model is invoked, and the other by assuming the fuel is initially in a vapor state and thereby prescribing the mass loss rate. A domain of size 11.2 m × 11.2 m × 7.28 m with uniform structured grid is chosen for the numerical simulations. Grid sensitivity analysis is performed, and a non-dimensional grid size of 12 corresponding to 8 cm grid size is considered. Flame properties like mass burning rate, irradiance, and time-averaged axial flame temperature profile are predicted. The predicted steady-state mass burning rate is 40 g/s and is within the uncertainty limits of the previously reported experimental data (39.4 g/s). Though the profile of the irradiance at a distance from the fire along the height is somewhat in line with the experimental data and the location of the maximum value of irradiance is shifted to a higher location. This may be due to the lack of sophisticated models for the species transportation along with combustion and radiation in the continuous zone. Furthermore, the axial temperatures are not predicted well (for any of the boundary conditions) in any of the zones. The present study shows that the existing models are not sufficient enough for modeling blended fuels like diesel. The predictions are strongly dependent on the experimental values of the soot yield. Future experiments are necessary for generalizing the soot yield for different fires.

Keywords: burning rate, fire accidents, fire dynamic simulator, pyrolysis

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Salim Belouettar, Kodjo Attipou

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The thermal wrinkling behavior of thin membranes is investigated. The Fourier double scale series are used to deduce the macroscopic membrane wrinkling equations. The obtained equations account for the global and local wrinkling modes. Numerical examples are conducted to assess the validity of the approach developed. Compared to the finite element full model, the present model needs only few degrees of freedom to recover accurately the bifurcation curves and wrinkling paths. Different parameters such as membrane’s aspect ratio, wave number, pre-stressed membranes are discussed from a numerical point of view and the properties of the wrinkles (critical load, wavelength, size and location) are presented.

Keywords: wrinkling, thermal stresses, Fourier series, thin membranes

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Authors: Rajat Kapoor

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India is a developing country and has experienced a rapid and tumultuous urban growth in the 20th century. The total urban population of the city increased ten-fold between 1901 and 2001. The share of urban population to the total population increased from less than 11 percent to over 28 percent in the same period. Except few examples, most of the Indian cities have grown in a haphazard manner; concentration of population followed by the planning exercises. In this era of global competitiveness and rapid urbanization there is no scope for malpractices in development strategies. It is expected that the Indian cities shall be planned comprehensively and holistically. The study reveals the land transformations the city of Faridabad is witnessing due to development which is largely boosted by the virtue of its location in the Delhi NCR.

Keywords: Delhi NCR, Faridabad, urban growth patterns, India

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Fangjing Zhou, Wen Chen, Brian J. Hall, Yu Wang, Carl Latkin, Li Ling, Joseph D. Tucker

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Background: Economic and social reforms designed to open China to the world has been successful, but also appear to have rapidly laid the foundation for the reemergence of STIs since 1980s. Changes in sexual behaviors, relationships, and norms among Chinese contributed to the STIs epidemic. As the massive population moved during the last 30 years, early coital debut, multiple sexual partnerships, and unprotected sex have increased within the general population. Our objectives were to assess associations between residences location, sexual risk behaviors and sexually transmitted infections (STIs) among adults living in Guangzhou, China. Methods: Stratified cluster sampling followed a two-step process was used to select populations aged 18-59 years in Guangzhou, China. Spatial methods including Geographic Information Systems (GIS) were utilized to identify 1400 coordinates with latitude and longitude. Face-to-face household interviews were conducted to collect self-report data on sexual risk behaviors and diagnosed STIs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of sexual risk behaviors and STIs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results: In this study, 1215 of 1400 households attempted surveys, with 368 refusals, resulting in a sample of 751 completed surveys. The prevalence of self-reported sexual risk behaviors was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STIs was 7.06%. Anal intercourse clustered in an area located along the border within the rural-urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou less than one year (p=0.007) overlapped this cluster. Excess cases for sex without a condom (p=0.031) overlapped the cluster for college students (p<0.001). Conclusions: Short-term migrants and college students reported greater sexual risk behaviors. Programs to increase safer sex within these communities to reduce the risk of STIs are warranted in Guangzhou. Spatial analysis identified geographical clusters of sexual risk behaviors, which is critical for optimizing surveillance and targeting control measures for these locations in the future.

Keywords: cluster analysis, migrant, sexual risk behaviors, spatial distribution

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Authors: Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut

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In recent years, large unmanned underwater vehicles such as the Boeing Voyager and Anduril Ghost Shark have been developed. These vessels can be structured to contain onboard internal payload bays. These payload bays can serve a variety of purposes – including the launch and recovery (LAR) of smaller underwater vehicles. The LAR of smaller vessels is extremely important, as it enables transportation over greater distances, increased time on station, data transmission and operational safety. The larger vessel and its payload bay structure complicate the LAR of UUVs in contrast to static docks that are affixed to the seafloor, as they actively impact the local flow field. These flow field impacts require analysis to determine if UUV vessels can be safely launched and recovered inside the motherships. This research seeks to determine the hydrodynamic forces exerted on a vertically over-actuated, small, unmanned underwater vehicle (OUUV) during an internal LAR manoeuvre and compare this to an under-actuated vessel (UUUV). In this manoeuvre, the OUUV is navigated through the stern wake region of the larger vessel to a set point within the internal payload bay. The manoeuvre is simulated using ANSYS Fluent computational fluid dynamics models, covering the entire recovery of the OUUV and UUUV. The analysis of the OUUV is compared against the UUUV to determine the differences in the exerted forces. Of particular interest are the drag, pressure, turbulence and flow field effects exerted as the OUUV is driven inside the payload bay of the larger vessel. The hydrodynamic forces and flow field disturbances are used to determine the feasibility of making such an approach. From the simulations, it was determined that there was no significant detrimental physical forces, particularly with regard to turbulence. The flow field effects exerted by the OUUV are significant. The vertical thrusters exert significant wake structures, but their orientation ensures the wake effects are exerted below the UUV, minimising the impact. It was also seen that OUUV experiences higher drag forces compared to the UUUV, which will correlate to an increased energy expenditure. This investigation found no key indicators that recovery via a mothership payload bay was not feasible. The turbulence, drag and pressure phenomenon were of a similar magnitude to existing static and towed dock structures.

Keywords: underwater vehicles, submarine, autonomous underwater vehicles, AUV, computational fluid dynamics, flow fields, pressure, turbulence, drag

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Authors: Dmitry Fedoseyev

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The ultimate success of unmanned aerial vehicles (UAVs) depends largely on the effective control of their flight, especially in variable wind conditions. This paper investigates different approaches to selecting the optimal flight level to maximize the range of UAVs. We propose to consider methods based on mathematical models of atmospheric conditions, as well as the use of sensor data and machine learning algorithms to automatically optimize the flight level in real-time. The proposed approaches promise to improve the efficiency and range of UAVs in various wind conditions, which may have significant implications for the application of these systems in various fields, including geodesy, environmental surveillance, and search and rescue operations.

Keywords: drone, UAV, flight trajectory, wind-searching, efficiency

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Authors: Elysia Barker, Jian Guo Zhou, Ling Qian, Steve Decent

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A method of modelling topography used in the simulation of riverbeds is proposed in this paper, which removes the need for datapoints and measurements of physical terrain. While complex scans of the contours of a surface can be achieved with other methods, this requires specialised tools, which the proposed method overcomes by using fractional Brownian motion (FBM) as a basis to estimate the real surface within a 15% margin of error while attempting to optimise algorithmic efficiency. This removes the need for complex, expensive equipment and reduces resources spent modelling bed topography. This method also accounts for the change in topography over time due to erosion, sediment transport, and other external factors which could affect the topography of the ground by updating its parameters and generating a new bed. The lattice Boltzmann method (LBM) is used to simulate both stationary and steady flow cases in a side-by-side comparison over the generated bed topography using the proposed method and a test case taken from an external source. The method, if successful, will be incorporated into the current LBM program used in the testing phase, which will allow an automatic generation of topography for the given situation in future research, removing the need for bed data to be specified.

Keywords: bed topography, FBM, LBM, shallow water, simulations

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Authors: Pijitra Jomsri

Abstract:

Currently, the sources of cultures and tourist attractions are presented in online documentary form only. In order to make them more virtual, the researcher then collected and presented them in the form of Virtual Temple. The prototype, which is a replica of the actual location, was developed to the website and allows people who are interested in Rattanakosin Island can see in form of Panorama Pan View. By this way, anyone can access the data and appreciate the beauty of Rattanakosin Island in the virtual model like the real place. The result from the experiment showed that the levels of the knowledge on Thai temples in Rattanakosin Island increased; moreover, the users were highly satisfied with the systems. It can be concluded that virtual temples can support to publicize Thai arts, cultures and travels, as well as it can be utilized effectively.

Keywords: virtual sight-seeing, Rattanakosin Island, Thai temples, virtual temple

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