Search results for: vehicle classification

Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Salman Naseer, Usman Zafar, Iqra Zafar

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This study explores the implication of Vehicular Adhoc Network (VANET) - in the rural and urban scenarios that is one domain of Mobile Adhoc Network (MANET). VANET provides wireless communication between vehicle to vehicle and also roadside units. The Federal Commission Committee of United States of American has been allocated 75 MHz of the spectrum band in the 5.9 GHz frequency range for dedicated short-range communications (DSRC) that are specifically designed to enhance any road safety applications and entertainment/information applications. There are several vehicular related projects viz; California path, car 2 car communication consortium, the ETSI, and IEEE 1609 working group that have already been conducted to improve the overall road safety or traffic management. After the critical literature review, the selection of routing protocols is determined, and its performance was well thought-out in the urban and rural scenarios. Numerous routing protocols for VANET are applied to carry out current research. Its evaluation was conceded with the help of selected protocols through simulation via performance metric i.e. throughput and packet drop. Excel and Google graph API tools are used for plotting the graphs after the simulation results in order to compare the selected routing protocols which result with each other. In addition, the sum of the output from each scenario was computed to undoubtedly present the divergence in results. The findings of the current study present that DSR gives enhanced performance for low packet drop and high throughput as compared to AODV and DSDV in an urban congested area and in rural environments. On the other hand, in low-density area, VANET AODV gives better results as compared to DSR. The worth of the current study may be judged as the information exchanged between vehicles is useful for comfort, safety, and entertainment. Furthermore, the communication system performance depends on the way routing is done in the network and moreover, the routing of the data based on protocols implement in the network. The above-presented results lead to policy implication and develop our understanding of the broader spectrum of VANET.

Keywords: AODV, DSDV, DSR, Adhoc network

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Authors: Sahand Golmohammadi, Sana Hosseini Shirazi

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Because the status and mechanical parameters of discontinuities in the rock mass are included in the calculations, various methods of rock engineering classification are often used as a starting point for the design of different types of structures. The Q-system is one of the most frequently used methods for stability analysis and determination of support systems of underground structures in rock, including tunnel. In this method, six main parameters of the rock mass, namely, the rock quality designation (RQD), joint set number (Jn), joint roughness number (Jr), joint alteration number (Ja), joint water parameter (Jw) and stress reduction factor (SRF) are required. In this regard, in order to achieve a reasonable and optimal design, identifying the effective parameters for the stability of the mentioned structures is one of the most important goals and the most necessary actions in rock engineering. Therefore, it is necessary to study the relationships between the parameters of a system and how they interact with each other and, ultimately, the whole system. In this research, it has attempted to determine the most effective parameters (key parameters) from the six parameters of rock mass in the Q-system using the rock engineering system (RES) method to improve the relationships between the parameters in the calculation of the Q value. The RES system is, in fact, a method by which one can determine the degree of cause and effect of a system's parameters by making an interaction matrix. In this research, the geomechanical data collected from the water conveyor tunnel of Azad Dam were used to make the interaction matrix of the Q-system. For this purpose, instead of using the conventional methods that are always accompanied by defects such as uncertainty, the Q-system interaction matrix is coded using a technique that is actually a statistical analysis of the data and determining the correlation coefficient between them. So, the effect of each parameter on the system is evaluated with greater certainty. The results of this study show that the formed interaction matrix provides a reasonable estimate of the effective parameters in the Q-system. Among the six parameters of the Q-system, the SRF and Jr parameters have the maximum and minimum impact on the system, respectively, and also the RQD and Jw parameters have the maximum and minimum impact on the system, respectively. Therefore, by developing this method, we can obtain a more accurate relation to the rock mass classification by weighting the required parameters in the Q-system.

Keywords: Q-system, rock engineering system, statistical analysis, rock mass, tunnel

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid damage monitoring, 2015-Nepal earthquake

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Authors: Jeffrey Chase

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Historically the profession of Occupational Therapy was closely tied to the treatment of those suffering from mental illness; more recently, and especially in the U.S., the percentage of OTs identifying as working in the mental health area has declined significantly despite the estimate that by 2020 behavioral health disorders will surpass physical illnesses as the major cause of disability worldwide. In the U.S. less than 10% of OTs identify themselves as working with the mentally ill and/or practicing in mental health settings. Such a decline has implications for both those suffering from mental illness and the profession of Occupational Therapy. One reason cited for the decline of OT in mental health has been the limited research in the discipline addressing mental health practice. Despite significant advances in technology and growth in the field of neuroscience, major institutions and funding sources such as the National Institute of Mental Health (NIMH) have noted that research into the etiology and treatment of mental illness have met with limited success over the past 25 years. One major reason posited by NIMH is that research has been limited by how we classify individuals, that being mostly on what is observable. A new classification system being developed by NIMH, the Research Domain Criteria (RDoc), has the goal to look beyond just descriptors of disorders for common neural, genetic, and physiological characteristics that cut across multiple supposedly separate disorders. The hope is that by classifying individuals along RDoC measures that both reliability and validity will improve resulting in greater advances in the field. As a result of this change NIH and NIMH will prioritize research funding to those projects using the RDoC model. Multiple disciplines across many different setting will be required for RDoC or similar classification systems to be developed. During this shift in research methodology OT has an opportunity to reassert itself into the research and treatment of mental illness, both in developing new ways to more validly classify individuals, and to document the legitimacy of previously ill-defined and validated disorders such as sensory integration.

Keywords: global mental health and neuroscience, research opportunities for ot, greater integration of ot in mental health research, research and funding opportunities, research domain criteria (rdoc)

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Ahmed Elhussein, Hossam El-Zomor, Adel Alieldin, Mahmoud A. Afifi, Abdullah Elhusseiny, Hala Taha, Amal Refaat, Soha Ahmed, Mohamed S. Zagloul

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Background: A majority of children with retinoblastoma (60%) have a disease in one eye only (unilateral disease). This is a retrospective study to evaluate two different treatment modalities in those patients for saving their lives and vision. Methods: Four hundred and four patients were diagnosed with unilateral intraocular retinoblastoma at Children’s Cancer, Hospital Egypt (CCHE) through the period of July/2007 until December/2017. Management strategies included primary enucleation versus ocular salvage treatment. Results: Patients presented with mean age 24.5 months with range (1.2-154.3 months). According to the international retinoblastoma classification, Group D (n=172, 42%) was the most common, followed by group E (n=142, 35%), group C (n=63, 16%), and group B (n=27, 7%). All patients were alive at the end of the study except four patients who died, with 5-years overall survival 98.3% [CI, (96.5-100%)]. Patients presented with advanced disease and poor visual prognosis (n=241, 59.6%) underwent primary enucleation with 6 cycles adjuvant chemotherapy if they had high-risk features in the enucleated eye; only four patients out of 241 ended-up either with extraocular metastasis (n=3) or death (n=1). While systemic chemotherapy and focal therapy were the primary treatment for those who presented with favorable disease status and good visual prognosis (n=163, 40.4%); seventy-seven patients of them (47%) ended up with a pre-defined event (enucleation, EBRT, off protocol chemotherapy or 2ry malignancy). Ocular survival for patients received primary chemotherapy + focal therapy was [50.9% (CI, 43.5-59.6%)] at 3 years and [46.9% (CI,39.3-56%)] at 5 years. Comparison between upfront enucleation and primary chemotherapy for occurrence of extraocular metastasis revealed that there was no statistical difference between them except in group D (p value). While for occurrence of death, no statistical difference in all classification groups. Conclusion: In retinoblastoma, primary chemotherapy is a reasonable option and has a good probability for ocular salvage without increasing the risk of metastasis in comparison to upfront enucleation except in group D.

Keywords: CCHE, chemotherapy, enucleation, retinoblastoma

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Authors: Bianca W. O. Silva, Adriana C. M. Andrade, Gustavo C. M. Lucena, Virna M. S. Lima

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Introduction: Basic life support (BLS) involves the immediate recognition of cardiopulmonary arrest. Each year, 359.400 and 275.000 individuals with cardiac arrest are attended in emergency departments in USA and Europe. Brazilian data shows that 200.000 cardiac arrests occur every year, and half of them out of the hospital. Medical schools around the world teach BLS in the first years of the course, but studies show that there is a decline of the knowledge as the years go by, affecting the chain of survival. The objective was to analyze the knowledge of medical students about BLS and the retention of this learning throughout the course. Methods: This study included 150 students who were at the second and third year of a medical school in Salvador, Bahia, Brazil. The instrument of data collection was a structured questionnaire composed of 20 questions based on the 2015 American Heart Association guideline. The Pearson Chi-square test was used in order to study the association between previous training, sex and semester with the degree of knowledge of the students. The Kruskal-Wallis test was used to evaluate the different yields obtained between the various semesters. The number of correct answers was described by average and quartiles. Results: Regarding the degree of knowledge, 19.6% of the female students reached the optimal classification, a better outcome than the achieved by the male participants. Of those with previous training, 33.33% were classified as good and optimal, none of the students reached the optimal classification and only 2.2% of them were classified as bad (those who did not have 52.6% of correct answers). The analysis of the degree of knowledge related to each semester revealed that the 5th semester had the highest outcome: 30.5%. However, the acquaintance presented by the semesters was generally unsatisfactory, since 50% of the students, or more, demonstrated knowledge levels classified as bad or regular. When confronting the different semesters and the achieved scores, the value of p was 0.831. Conclusion: It is important to focus on the training of medical professionals that are capable of facing emergency situations, improving the systematization of care, and thereby increasing the victims' possibility of survival.

Keywords: basic life support, cardiopulmonary ressucitacion, education, medical students

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Authors: Oscar E. Cariceo, Claudia V. Casal

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Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

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Authors: M. P. Tripathi, Priti Tiwari

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Soil and Water Assessment Tool (SWAT) is a distributed parameter continuous time model and was tested on daily and fortnightly basis for a small agricultural watershed (Dhangaon) of Chhattisgarh state in India. The SWAT model recently interfaced with ArcGIS and called as ArcSWAT. The watershed and sub-watershed boundaries, drainage networks, slope and texture maps were generated in the environment of ArcGIS of ArcSWAT. Supervised classification method was used for land use/cover classification from satellite imageries of the years 2009 and 2012. Manning's roughness coefficient 'n' for overland flow and channel flow and Fraction of Field Capacity (FFC) were calibrated for monsoon season of the years 2009 and 2010. The model was validated on a daily basis for the years 2011 and 2012 by using the observed daily rainfall and temperature data. Calibration and validation results revealed that the model was predicting the daily surface runoff and sediment yield satisfactorily. Sensitivity analysis showed that the annual sediment yield was inversely proportional to the overland and channel 'n' values whereas; annual runoff and sediment yields were directly proportional to the FFC. The model was also tested (calibrated and validated) for the fortnightly runoff and sediment yield for the year 2009-10 and 2011-12, respectively. Simulated values of fortnightly runoff and sediment yield for the calibration and validation years compared well with their observed counterparts. The calibration and validation results revealed that the ArcSWAT model could be used for identification of critical sub-watershed and for developing management scenarios for the Dhangaon watershed. Further, the model should be tested for simulating the surface runoff and sediment yield using generated rainfall and temperature before applying it for developing the management scenario for the critical or priority sub-watersheds.

Keywords: watershed, hydrologic and water quality, ArcSWAT model, remote sensing, GIS, runoff and sediment yield

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Authors: Saradha Priya

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Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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This paper presents the architecture of the “Intelligent cooperative integrated system for road safety and road infrastructure maintenance towards 2020” (ODOS2020) advanced infrastructure, which implements a number of cooperative ITS applications based on Internet of Things and Infrastructure-to-Vehicle (V2I) technologies with the purpose to enhance the active road safety level of vehicles through the provision of a fully automated V2I environment. The primary objective of the ODOS2020 project is to contribute to increased road safety but also to the optimization of time for maintenance of road infrastructure. The integrated technological solution presented in this paper addresses all types of vehicles and requires minimum vehicle equipment. Thus, the ODOS2020 comprises a low-cost solution, which is one of its main benefits. The system architecture includes an integrated notification system to transmit personalized information on road, traffic, and environmental conditions, in order for the drivers to receive real-time and reliable alerts concerning upcoming critical situations. The latter include potential dangers on the road, such as obstacles or road works ahead, extreme environmental conditions, etc., but also informative messages, such as information on upcoming tolls and their charging policies. At the core of the system architecture lies an integrated sensorial network embedded in special road infrastructures (strips) that constantly collect and transmit wirelessly information about passing vehicles’ identification, type, speed, moving direction and other traffic information in combination with environmental conditions and road wear monitoring and predictive maintenance data. Data collected from sensors is transmitted by roadside infrastructure, which supports a variety of communication technologies such as ITS-G5 (IEEE-802.11p) wireless network and Internet connectivity through cellular networks (3G, LTE). All information could be forwarded to both vehicles and Traffic Management Centers (TMC) operators, either directly through the ITS-G5 network, or to smart devices with Internet connectivity, through cloud-based services. Therefore, through its functionality, the system could send personalized notifications/information/warnings and recommendations for upcoming events to both road users and TMC operators. In the course of the ODOS2020 project pilot operation has been conducted to allow drivers of both C-ITS equipped and non-equipped vehicles to experience the provided added value services. For non-equipped vehicles, the provided information is transmitted to a smartphone application. Finally, the ODOS2020 system and infrastructure is appropriate for installation on both urban, rural, and highway environments. The paper presents the various parts of the system architecture and concludes by outlining the various challenges that had to be overcome during its design, development, and deployment in a real operational environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: infrastructure to vehicle, intelligent transportation systems, internet of things, road safety

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Semire Yekta

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Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

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Authors: Hala M. El-hanbuli, Mohammed F. Darweesh

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The incidence rates of any tumor vary hugely with geographical location. Basal Cell Carcinoma (BCC) is one of the most common skin cancer that has many histopathologic subtypes. Objective: The aim was to study the histopathological features of BCC cases that were received in the Pathology Department, Kasr El-Aini hospital, Cairo University, Egypt during the period from Jan 2004 to Dec 2013 and to evaluate the clinical characters through the patient data available in the request sheets. Methods: Slides and data of BCC cases were collected from the archives of the pathology department, Kasr El-Aini hospital. Revision of all available slides and histological classification of BCC according to WHO (2006) was done. Results: A total number of 310 cases of BCC representing about 65% from the total number of malignant skin tumors examined during the 10-years duration in the department. The age ranged from 8 to 84 years, the mean age was (55.7 ± 15.5). Most of the patients (85%) were above the age of 40 years. There was a slight male predominance (55%). Ulcerated BCC was the most common gross picture (60%), followed by nodular lesion (30%) and finally the ulcerated nodule (10%). Most of the lesions situated in the high-risk sites (77%) where the nose was the most common site (35%) followed by the periocular area (22%), then periauricular (15%) and finally perioral (5%). No lesion was reported outside the head. The tumor size was less than 2 centimeters in 65% of cases, and from 2-5 centimeters in the lesions' greatest dimension in the rest of cases. Histopathological reclassification revealed that the nodular BCC was the most common (68%) followed by the pigmented nodular (18.75%). The histologic high-risk groups represented (7.5%) about half of them (3.75%) being basosquamous carcinoma. The total incidence for multiple BCC and 2nd primary was 12%. Recurrent BCC represented 8%. All of the recurrent lesions of BCC belonged to the histologic high-risk group. Conclusion: Basal Cell Carcinoma is the most common skin cancer in the 10-year survey. Histopathological diagnosis and classification of BCC cases are essential for the determination of the tumor type and its biological behavior.

Keywords: basal cell carcinoma, high risk, histopathological features, statistical analysis

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Authors: Amina Akhtar, Rory O'Connor

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Introduction: There remains significant morbidity and mortality in young pedestrians hit by motor vehicles, even in the era of pedestrian crossings and speed limits. The aim of this study was to compare incidence and injury severity of motor vehicle-related pedestrian trauma according to time of day and season in a young population, based on the supposition that injuries would be more prevalent during dusk and dawn and during autumn and winter. Methods: Data was retrieved for patients between 10-25 years old from the National Trauma Audit and Research Network (TARN) database who had been involved as pedestrians in motor vehicle accidents between 2015-2020. The incidence of injuries, their severity (using the Injury Severity Score [ISS]), hospital transfer time, and mortality were analysed according to the hours of daylight, darkness, and season. Results: The study identified a seasonal pattern, showing that autumn was the predominant season and led to 34.9% of injuries, with a further 25.4% in winter in comparison to spring and summer, with 21.4% and 18.3% of injuries, respectively. However, visibility alone was not a sufficient factor as 49.5% of injuries occurred during the time of darkness, while 50.5% occurred during daylight. Importantly, the greatest injury rate (number of injuries/hour) occurred between 1500-1630, correlating to school pick-up times. A further significant relationship between injury severity score (ISS) and daylight was demonstrated (p-value= 0.0124), with moderate injuries (ISS 9-14) occurring most commonly during the day (72.7%) and more severe injuries (ISS>15) occurred during the night (55.8%). Conclusion: We have identified a relationship between time of day and the frequency and severity of pedestrian trauma in young people. In addition, particular time groupings correspond to the greatest injury rate, suggesting that reduced visibility coupled with school pick-up times may play a significant role. This could be addressed through a targeted public health approach to implementing change. We recommend targeted public health measures to improve road safety that focus on these times and that increase the visibility of children combined with education for drivers.

Keywords: major trauma, paediatric trauma, road traffic accidents, diurnal pattern

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Authors: Kisik Song, Sungjoo Lee

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With the growing importance of intangible assets recently, the impact of patent infringement on the business of a company has become more evident. Accordingly, it is essential for firms to estimate the risk of patent infringement risk before developing a technology and create new technology ideas to avoid the risk. Recognizing the needs, several attempts have been made to help develop new technology opportunities and most of them have focused on identifying emerging vacant technologies from patent analysis. In these studies, the IPC (International Patent Classification) system or keywords from text-mining application to patent documents was generally used to define vacant technologies. Unlike those studies, this study adopted F-term, which classifies patent documents according to the technical features of the inventions described in them. Since the technical features are analyzed by various perspectives by F-term, F-term provides more detailed information about technologies compared to IPC while more systematic information compared to keywords. Therefore, if well utilized, it can be a useful guideline to create a new technology idea. Recognizing the potential of F-term, this paper aims to suggest a novel approach to developing new technology ideas to avoid patent infringement based on F-term. For this purpose, we firstly collected data about F-term and then applied text-mining to the descriptions about classification criteria and attributes. From the text-mining results, we could identify other technologies with similar technical features of the existing one, the patented technology. Finally, we compare the technologies and extract the technical features that are commonly used in other technologies but have not been used in the existing one. These features are presented in terms of “purpose”, “function”, “structure”, “material”, “method”, “processing and operation procedure” and “control means” and so are useful for creating new technology ideas that help avoid infringing patent rights of other companies. Theoretically, this is one of the earliest attempts to adopt F-term to patent analysis; the proposed methodology can show how to best take advantage of F-term with the wealth of technical information. In practice, the proposed methodology can be valuable in the ideation process for successful product and service innovation without infringing the patents of other companies.

Keywords: patent infringement, new technology ideas, patent analysis, F-term

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Authors: Amel Ltifi, Ahmed Zouinkhi, Mohamed Salim Bouhlel

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Security and trust management in Vehicular Ad-hoc NETworks (VANET) is a crucial research domain which is the scope of many researches and domains. Although, the majority of the proposed trust management systems for VANET are based on specific road infrastructure, which may not be present in all the roads. Therefore, road security should be managed by vehicles themselves. In this paper, we propose a new Self Organized Trust Management system (SOTM). This system has the responsibility to cut with the spread of false warnings in the network through four principal components: cooperation, trust management, communication and security.

Keywords: ative vehicle, cooperation, trust management, VANET

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Authors: George Zhou, Yunchan Chen, Candace Chien

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Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

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Authors: Szymon Kuczynski, Krystian Liszka, Mariusz Laciak, Andrii Oliinyk, Adam Szurlej

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Compressed natural gas (CNG) represents an excellent compromise between the availability of a technology that is proven and relatively easy to use in many areas of the automotive industry and incurred costs. This fuel causes a lower corrosion effect due to the lower content of products causing the potential difference on the walls of the engine system. Natural gas powered vehicles (NGVs) do not emit any substances that can contaminate water or land. The absence of carcinogenic substances in gaseous fuel extends the life of the engine. In the longer term, it contributes positively to waste management as well as waste disposal. Popularization of propulsion systems powered by natural gas CNG positively affects the reduction of heavy duty transport. For these reasons, CNG as a fuel stimulates considerable interest around the world. Over the last few years, technologies related to use of natural gas as an engine fuel have been developed and improved. These solutions have evolved from the prototype phase to the industrial scale implementation. The widespread availability of gaseous fuels has led to the development of a technology that allows the CNG fuel to be refueled directly from the urban gas network to the vehicle tank (ie. HYGEN - CNGHRS). Home refueling installations, although they have been known for many years, are becoming increasingly important in the present day. The major obstacle in the sale of this technology was, until recently, quite high capital expenditure compared to the later benefits. Home refueling systems allow refueling vehicle tank, with full control of fuel costs and refueling time. CNG Home Refueling Stations (such as HYGEN) allow gas value chain to overcome the dogma that there is a lack of refueling infrastructure allowing companies in gas value chain to participate in transportation market. Technology is based on one stage hydraulic compressor (instead of multistage mechanical compressor technology) which provides the possibility to compress low pressure gas from distribution gas network to 200 bar for its further usage as a fuel for NGVs. This boosts revenues and profits of gas companies by expanding its presence in higher margin of energy sector.

Keywords: alternative fuels, CNG (compressed natural gas), CNG stations, NGVs (natural gas vehicles), gas value chain

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Guoheng Liu, Zhilong Huang

Abstract:

For more than the past ten years, oil and gas production from marine shale such as the Barnett shale. In addition, in recent years, major breakthroughs have also been made in lacustrine shale gas exploration, such as the Yanchang Formation of the Ordos Basin in China. Lucaogou Formation shale, which is also lacustrine shale, has also yielded a high production in recent years, for wells such as M1, M6, and ML2, yielding a daily oil production of 5.6 tons, 37.4 tons and 13.56 tons, respectively. Lithologic identification and classification of reservoirs are the base and keys to oil and gas exploration. Lithology and lithofacies obviously control the distribution of oil and gas in lithological reservoirs, so it is of great significance to describe characteristics of lithology and lithofacies of reservoirs finely. Lithofacies is an intrinsic property of rock formed under certain conditions of sedimentation. Fine-grained sedimentary rocks such as shale formed under different sedimentary conditions display great particularity and distinctiveness. Hence, to our best knowledge, no constant and unified criteria and methods exist for fine-grained sedimentary rocks regarding lithofacies definition and classification. Consequently, multi-parameters and multi-disciplines are necessary. A series of qualitative descriptions and quantitative analysis were used to figure out the lithofacies characteristics and its effect on oil accumulation of Lucaogou formation fine-grained sedimentary rocks in Santanghu basin. The qualitative description includes core description, petrographic thin section observation, fluorescent thin-section observation, cathode luminescence observation and scanning electron microscope observation. The quantitative analyses include X-ray diffraction, total organic content analysis, ROCK-EVAL.II Methodology, soxhlet extraction, porosity and permeability analysis and oil saturation analysis. Three types of lithofacies were mainly well-developed in this study area, which is organic-rich massive shale lithofacies, organic-rich laminated and cloddy hybrid sedimentary lithofacies and organic-lean massive carbonate lithofacies. Organic-rich massive shale lithofacies mainly include massive shale and tuffaceous shale, of which quartz and clay minerals are the major components. Organic-rich laminated and cloddy hybrid sedimentary lithofacies contain lamina and cloddy structure. Rocks from this lithofacies chiefly consist of dolomite and quartz. Organic-lean massive carbonate lithofacies mainly contains massive bedding fine-grained carbonate rocks, of which fine-grained dolomite accounts for the main part. Organic-rich massive shale lithofacies contain the highest content of free hydrocarbon and solid organic matter. Moreover, more pores were developed in organic-rich massive shale lithofacies. Organic-lean massive carbonate lithofacies contain the lowest content solid organic matter and develop the least amount of pores. Organic-rich laminated and cloddy hybrid sedimentary lithofacies develop the largest number of cracks and fractures. To sum up, organic-rich massive shale lithofacies is the most favorable type of lithofacies. Organic-lean massive carbonate lithofacies is impossible for large scale oil accumulation.

Keywords: lithofacies classification, tuffaceous shale, oil enrichment, Lucaogou formation

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Authors: S. Ananthakrishnan, U. H. Acharya

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Whether shopping in a retail location or via the internet, consumers expect to receive their products intact. When products arrive damaged or over-packaged, the result can be customer dissatisfaction and increased cost for retailers and manufacturers. The packaging performance depends on both the transport situation and the packaging design. During transportation, the packaged products are subjected to the variation in vibration levels from transport vehicles that vary in frequency and acceleration while moving to their destinations. Spirits manufactured by this Company were being transported to various parts of the country by road. There were instances of package breaking and customer complaints. The vibration experienced on a straight road at some speed may not be same as the vibration experienced by the same vehicle on a curve at the same speed. This vibration may negatively affect the product or packing. Hence, it was necessary to conduct a physical road test to understand the effect of vibration in the packaged products. The field transit trial has to be done before the transportations, which results in high investment. The company management was interested in developing an in-house test environment which would adequately represent the transit conditions. With the objective to develop an in-house test condition that can accurately simulate the mechanical loading scenario prevailing during the storage, handling and transportation of the products a brainstorming was done with the concerned people to identify the critical factors affecting vibration rate. Position of corrugated box, the position of bottle and speed of vehicle were identified as factors affecting the vibration rate. Several packing scenarios were identified by Design of Experiment methodology and simulated in the in-house test facility. Each condition was observed for 30 minutes, which was equivalent to 1000 km. The achieved vibration level was considered as the response. The average achieved in the simulated experiments was near to the third quartile (Q3) of the actual data. Thus, we were able to address around three-fourth of the actual phenomenon. Most of the cases in transit could be reproduced. The recommended test condition could generate a vibration level ranging from 9g to 15g as against a maximum of only 7g that was being generated earlier. Thus, the Company was able to test the packaged cartons satisfactorily in the house itself before transporting to the destinations, assuring itself that the breakages of the bottles will not happen.

Keywords: ANOVA, Corrugated box, DOE, Quartile

Procedia PDF Downloads 125