Search results for: road damage detection

Authors: Seon Ho Kim, Minho Bang, Seok Min Choi, Young Moon Lee, Dong Kwan Kim, Hyung Hee Cho

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Gas turbines are heat engines that convert chemical energy into electrical energy through mechanical energy. Since their high energy density per unit volume and low pollutant emissions, gas turbines are classified as clean energy. In order to obtain better performance, the turbine inlet temperature of the current gas turbine is operated at about 1600℃, and thermal damage is a very serious problem. Especially, these thermal damages are more prominent in off-design conditions than in design conditions. In this study, the thermal damage characteristics of high temperature components of a gas turbine made of a single crystal material are studied numerically for the off-design operating conditions. The target gas turbine is configured as a reheat cycle and is operated in peak load operation mode, not normal operation. In particular, the target gas turbine features a lot of low-load operation. In this study, a commercial code, ANSYS 18.2, was used for analyzing the thermal-flow coupling problems. As a result, the flow separation phenomenon on the pressure side due to the flow reduction was remarkable at the off-design condition, and the high heat transfer coefficient at the upper end of the suction surface due to the tip leakage flow was appeared.

Keywords: gas turbine, single crystal blade, off-design, thermal analysis

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Authors: Daniel Alfonso Reséndiz-García, Luis Antonio García-Villanueva

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As a result of the high industrial activity, which arises from the search to satisfy the needs of products and services for society, several chemical accidents have occurred, causing serious damage to different sectors: human, economic, infrastructure and environmental losses. Historically, with the study of this chemical accidents, it has been determined that the causes are mainly due to human errors (inexperienced personnel, negligence, lack of maintenance and deficient risk analysis). The industries have the aim to increase production and reduce costs. However, it should be kept in mind that the costs involved in risk studies, implementation of barriers and safety systems is much cheaper than paying for the possible damages that could occur in the event of an accident, without forgetting that there are things that cannot be replaced, such as human lives.Therefore, it is of utmost importance to implement risk studies in all industries, which provide information for prevention and planning. The aim of this study is to compare risk methodologies by identifying the consequences of accidents related to the storage of flammable, dangerous goods for decision making and emergency response.The methodologies considered in this study are qualitative and quantitative risk analysis and consequence analysis. The latter, by means of modeling software, which provides radius of affectation and the possible scope and magnitude of damages.By using risk analysis, possible scenarios of occurrence of chemical accidents in the storage of flammable substances are identified. Once the possible risk scenarios have been identified, the characteristics of the substances, their storage and atmospheric conditions are entered into the software.The results provide information that allows the implementation of prevention, detection, control, and combat elements for emergency response, thus having the necessary tools to avoid the occurrence of accidents and, if they do occur, to significantly reduce the magnitude of the damage.This study highlights the importance of risk studies applying tools that best suited to each case study. It also proves the importance of knowing the risk exposure of industrial activities for a better prevention, planning and emergency response.

Keywords: chemical accidents, emergency response, flammable substances, risk analysis, modeling

Procedia PDF Downloads 89

Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

Procedia PDF Downloads 291

Authors: Bouba Oumarou Aboubakar, HongXia Li, Sardar Annes Farooq

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The Belt and Road Initiative’s success strongly depends on the safety of all the million workers on construction projects sites. As the new BRI is directed toward Africa and meets a completely different culture from the Chinese project managers, maintaining low risk for workers risks shall be closely related to cultural sharing and mutual understanding. This is why this work introduces a cultural-wise safety management framework for Chinese Construction projects in Africa. The theoretical contribution of this paper is an improved risk assessment framework that integrates language, culture and difficulty of controlling risk factors into one approach. Practically, this study provides not only a useful tool for project safety management practitioners but the full understanding of all risks that may arise in the BRI projects in Africa.

Keywords: cultural-wise, safety culture, risk assessment, Chinese construction, BRI projects, Africa

Procedia PDF Downloads 103

Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

Procedia PDF Downloads 237

Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

Procedia PDF Downloads 158

Authors: Michael Leontiev, Danil Zhilikov, Dmitry Lobanov, Lenar Klimov, Vyacheslav Chertan, Daniel Bobrov, Vladislav Maslov, Vasilii Vologdin, Ksenia Balabaeva

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Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, the human factor (improper PPE wearing) causes numerous losses to human health and material property. This research proposes a solid computer vision system for ensuring safety in pharmaceutical laboratories. For this, we have tested a wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached a high accuracy ([email protected]) ranging from 0.77 up to 0.98 in detecting all the elements of a common set of PPE used in pharmaceutical laboratories. Our system is a step towards safe medicine production.

Keywords: sterility and safety in pharmaceutical development, personal protective equipment, computer vision, object detection, monitoring in pharmaceutical development, PPE

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Sneha Noble, Rahul Krishnan, Uma G., D. K. Vijaykumar

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Breast-Cancer related Lymphedema is a major problem that affects many women. Lymphedema is the swelling that generally occurs in the arms or legs caused by the removal of or damage to lymph nodes as a part of cancer treatment. Treating it at the earliest possible stage is the best way to manage the condition and prevent it from leading to pain, recurrent infection, reduced mobility, and impaired function. So, this project aims to focus on the multi-modal approaches to identify the risks of Lymphedema in post-surgical oncology patients and prevent it at the earliest. The Kinect IR Sensor is utilized to capture the images of the body and after image processing techniques, the region of interest is obtained. Then, performing the voxelization method will provide volume measurements in pre-operative and post-operative periods in patients. The formation of a mathematical model will help in the comparison of values. Clinical pathological data of patients will be investigated to assess the factors responsible for the development of lymphedema and its risks.

Keywords: Kinect IR sensor, Lymphedema, voxelization, lymph nodes

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Authors: Priyanka Bhatnagar, Malkeshkumar Patel, Joondong Kim, Joonpyo Hong

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Artificial skin and sensory memory platforms are produced using a flexible, transparent photovoltaic (TPV) device. The TPV device is composed of a metal oxide heterojunction (nZnO/p-NiO) and transmits visible light (> 50%) while producing substantial electric power (0.5 V and 200 μA cm-2 ). This TPV device is a transparent energy interface that can be used to detect signals and propagate information without an external energy supply. The TPV artificial skin offers a temperature detection range (0 C75 C) that is wider than that of natural skin (5 C48 °C) due to the temperature-sensitive pyrocurrent from the ZnO layer. Moreover, the TPV thermoreceptor offers sensory memory of extreme thermal stimuli. Much like natural skin, artificial skin uses the nociceptor mechanism to protect tissue from harmful damage via signal amplification (hyperalgesia) and early adaption (allodynia). This demonstrates the many features of TPV artificial skin, which can sense and transmit signals and memorize information under self-operation mode. This transparent photovoltaic skin can provide sustainable energy for use in human electronics.

Keywords: transparent, photovoltaics, thermal memory, artificial skin, thermoreceptor

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Authors: Ndje Ndje Mireille

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The improvement in the physical aspects of hysterectomy has tended to make us forget the psychological burden of this operation for many women. African women closely associate fertility and femininity, and they fear that their desire will diminish, that they will be less desirable after having undergone a hysterectomy. Medicine may be tempted to trivialize this surgical intervention by relying on the evolution of current surgery that leaves little or no marks. It is possible to think that the uterus is useless for a nun who has decided to freely disregard her motherhood. We used the clinical research method for this study. Through a semi-directive interview guide, we collected the verbatims of an hysterectomized catholic nun. The verbatims were transcribed and analyzed with the thematic content analysis. This analysis shows that the medical reality does not always correspond to the subjective experience of women, for whom hysterectomy can imply strong symbolic damage. The uterus is not essential to life, but it is essential to give life, and this lack can reactivate a desire for motherhood. The experience of hysterectomy is unique for each woman in relation to her history. This operation will eliminate all hope of pregnancy; it will be felt as intimate mutilation and an attack on femininity, it will bring up concerns about sexuality. Even if a woman has past the age of having children, has gone through menopause, or has freely decided not to have children, she still find it difficult to accept this procedure. The lack of uterus make a woman feel useless.

Keywords: hysterectomy, symbolic damage, desire for motherhood, feminity, nun

Procedia PDF Downloads 158

Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

Procedia PDF Downloads 127

Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is the ability to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle is vital in automotive engineering. Stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswind and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle especially with the worrying increase of vehicle collision every day. With better safety performance on a vehicle, every driver will be more confidence driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in four-wheel vehicle especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on performance of both suspension systems.

Keywords: automobile, camber thrust, cornering force, suspension

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Authors: Rajendra Kanojia

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Femoral components with inbuilt rotation require thicker flexion resection of the lateral femoral condyle and could potential risk to damage the popliteus tendon especially in the smaller Asian knees. We prospectively evaluated 10 patients with bilateral varus osteoarthritis knee to size the cuts and their location in relation to the popliteus tendon. Two different types of implant were used on either side, one side requires resection in 3° external rotation (group A) and other side femoral component with inbuilt external roation (group B). We had popliteus tendon injury in 3 knees all from group B. Risk of damaging the popliteus tendon was found higher in group B.

Keywords: popliteaus tendon injury, TKA, orthopaedic surgery, biomechanics and clinical applications

Procedia PDF Downloads 333

Authors: Luís Miguel Cabeça Marques, Alberto Manuel Martinho Vale, José Pedro Miragaia Trancoso Vaz, Ana Sofia Baptista Fernandes, Rui Alexandre de Barros Coito, Tiago Miguel Prates da Costa

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The detection and localization of hidden radioactive sources are of significant importance in countering the illicit traffic of Special Nuclear Materials and other radioactive sources and materials. Radiation portal monitors are commonly used at airports, seaports, and international land borders for inspecting cargo and vehicles. However, these equipment can be expensive and are not available at all checkpoints. Consequently, the localization of SNM and other radioactive sources often relies on handheld equipment, which can be time-consuming. The current study presents the advantages of real-time analysis of gamma-ray count rate data from a mobile radiation detection system based on simulated data and field tests. The incorporation of profit functions and decision criteria to optimize the detection system's path significantly enhances the radiation field information and reduces survey time during cargo inspection. For source position estimation, a maximum likelihood estimation algorithm is employed, and confidence intervals are derived using the Fisher information. The study also explores the impact of uncertainties, baselines, and thresholds on the performance of the profit function. The proposed detection system, utilizing a plastic scintillator with silicon photomultiplier sensors, boasts several benefits, including cost-effectiveness, high geometric efficiency, compactness, and lightweight design. This versatility allows for seamless integration into any mobile platform, be it air, land, maritime, or hybrid, and it can also serve as a handheld device. Furthermore, integration of the detection system into drones, particularly multirotors, and its affordability enable the automation of source search and substantial reduction in survey time, particularly when deploying a fleet of drones. While the primary focus is on inspecting maritime container cargo, the methodologies explored in this research can be applied to the inspection of other infrastructures, such as nuclear facilities or vehicles.

Keywords: plastic scintillators, profit functions, path planning, gamma-ray detection, source localization, mobile radiation detection system, security scenario

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Authors: Elena Maria Scalisi

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The increasing production and the use of TiO₂ nanoparticles (NPs) have inevitably led to their release into the environment, thereby posing a threat to organisms and also for human. Human exposure to TiO₂-NPs may occur during both manufacturing and use. TiO₂-NPs are common in consumer products for dermal application, toothpaste, food colorants, and nutritional supplements, then oral exposure may occur during use of such products. Into the body, TiO₂-NPs thanks to their small size (<100 nm), can, through testicular blood barrier inducing effect on testis and then on male reproductive health. The nanoscale size of TiO₂ increase the surface-to-volume ratio making them more reactive in a cell, then TiO₂ NPs increase their ability to produce reactive oxygen species (ROS). In male germ cells, ROS may have important implications in maintaining the normal functions of mature spermatozoa at physiological levels, moreover, in spermatozoa they are important signaling molecules for their hyperactivation and acrosome reaction. Nevertheless, an excess of ROS by external inputs such as NPs can increased the oxidative stress (OS), which results in damage DNA and apoptosis. The aim of our study has been investigate the impact of TiO₂ NPs on human spermatozoa, evaluating DNA damage and the expression of proteins involved in cell stress. According WHO guidelines 2021, we have exposed human spermatozoa in vitro to TiO₂ NP at concentrations 50 ppm, 100 ppm, 250 ppm, and 500 ppm for 1 hour (at 37°C and CO₂ at 5%). DNA damage was evaluated by Sperm Chromatin Dispersion Test (SCD) and TUNEL assay; moreover, we have evaluated the expression of biomarkers of oxidative stress like Heat Shock Protein 70 (HSP70) and Metallothioneins (MTs). Also, sperm parameters as motility viability have been evaluated. Our results not report a significant reduction in motility of spermatozoa at the end of the exposure. On the contrary, the progressive motility was increased at the highest concentration (500 ppm) and was statistically significant compared to control (p <0.05). Also, viability was not changed by exposure to TiO₂-NPs (p <0.05). However, increased DNA damage was observed at all concentrations, and the TUNEL assay highlighted the presence of single strand breaks in the DNA. The spermatozoa responded to the presence of TiO₂-NPs with the expression of Hsp70, which have a protective function because they allow the maintenance of cellular homeostasis in stressful/ lethal conditions. A positivity for MTs was observed mainly for the concentration of 4 mg/L. Although the biological and physiological function of the metallothionein (MTs) in the male genital organs is unclear, our results highlighted that the MTs expressed by spermatozoa maintain their biological role of detoxification from metals. Our results can give additional information to the data in the literature on the toxicity of TiO₂-NPs and reproduction.

Keywords: human spermatozoa, DNA damage, TiO₂-NPs, biomarkers

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Authors: Mohammadreza Salek Faramarzi, Touraj Taghikhany

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In this paper, seismic fragility assessment of a recently developed hybrid structural system, known as the strongback system (SBS) is investigated. In this system, to mitigate the occurrence of the soft-story mechanism and improve the distribution of story drifts over the height of the structure, an elastic vertical truss is formed. The strengthened members of the braced span are designed to remain substantially elastic during levels of excitation where soft-story mechanisms are likely to occur and impose a nearly uniform story drift distribution. Due to the distinctive characteristics of near-field ground motions, it seems to be necessary to study the effect of these records on seismic performance of the SBS. To this end, a set of 56 near-field ground motion records suggested by FEMA P695 methodology is used. For fragility assessment, nonlinear dynamic analyses are carried out in OpenSEES based on the recommended procedure in HAZUS technical manual. Four damage states including slight, moderate, extensive, and complete damage (collapse) are considered. To evaluate each damage state, inter-story drift ratio and floor acceleration are implemented as engineering demand parameters. Further, to extend the evaluation of the collapse state of the system, a different collapse criterion suggested in FEMA P695 is applied. It is concluded that SBS can significantly increase the collapse capacity and consequently decrease the collapse risk of the structure during its life time. Comparing the observing mean annual frequency (MAF) of exceedance of each damage state against the allowable values presented in performance-based design methods, it is found that using the elastic vertical truss, improves the structural response effectively.

Keywords: IDA, near-fault, probabilistic performance assessment, seismic fragility, strongback system, uncertainty

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Authors: Jeewan Rai, Kinzang, Yeshi Jigme Choden

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Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production.

Keywords: YOLO, openCV, dust detection, solar panels, computer vision, image processing

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Authors: Arnab Hazra

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Breath analysis is a quick, noninvasive and inexpensive technique for disease diagnosis can be used on people of all ages without any risk. Only a limited number of volatile organic compounds (VOCs) can be associated with the occurrence of specific diseases. These VOCs can be considered as disease markers or breath markers. Selective detection with specific concentration of breath marker in exhaled human breath is required to detect a particular disease. For example, acetone (C₃H₆O), ethanol (C₂H₅OH), ethane (C₂H₆) etc. are the breath markers and abnormal concentrations of these VOCs in exhaled human breath indicates the diseases like diabetes mellitus, renal failure, breast cancer respectively. Nanomaterial-based vapor sensors are inexpensive, small and potential candidate for the detection of breath markers. In practical measurement, selectivity is the most crucial issue where trace detection of breath marker is needed to identify accurately in the presence of several interfering vapors and gases. Current article concerns a novel technique for selective and lower ppb level detection of breath markers at very low temperature based on TiO₂ nanotube array based vapor sensor devices. Highly ordered and oriented TiO₂ nanotube array was synthesized by electrochemical anodization of high purity tatinium (Ti) foil. 0.5 wt% NH₄F, ethylene glycol and 10 vol% H₂O was used as the electrolyte and anodization was carried out for 90 min with 40 V DC potential. Au/TiO₂ Nanotube/Ti, sandwich type sensor device was fabricated for the selective detection of VOCs in low concentration range. Initially, sensor was characterized where resistive and capacitive change of the sensor was recorded within the valid concentration range for individual breath markers (or organic vapors). Sensor resistance was decreased and sensor capacitance was increased with the increase of vapor concentration. Now, the ratio of resistive slope (mR) and capacitive slope (mC) provided a concentration independent constant term (M) for a particular vapor. For the detection of unknown vapor, ratio of resistive change and capacitive change at any concentration was same to the previously calculated constant term (M). After successful identification of the target vapor, concentration was calculated from the straight line behavior of resistance as a function of concentration. Current technique is suitable for the detection of particular vapor from a mixture of other interfering vapors.

Keywords: breath marker, vapor sensors, selective detection, TiO₂ nanotube array

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Shahrokh Barati, Reza Ramezani

Abstract:

Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Rikson Gultom

Abstract:

Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

Procedia PDF Downloads 126

Authors: Purnima K Bajre, Rujula Talloo

Abstract:

A variety of vehicles are driven on roads such as private vehicles, commercial vehicles, public vehicles, and emergency service vehicles (EMV). Drivers driving different vehicles can have attitude differences towards emergency service vehicles which in turn affects their likelihood to give way to them. The present review aims to understand the factors that mediate this yielding behavior of drivers towards EMVs. Through extensive review of available literature, factors such as effects of lights and sirens, cognitive load, age of the driver, driving general experience, traffic load, drivers’ experience and training with EMVs and drivers’ attitude towards EMV drivers, have emerged as mediating factors. Whereas cognitive load is the most researched area and is observed to be associated negatively with on road drivers’ attitudes towards EMVs, there is a paucity of research to understand the relationships between empathy, pro-social skills, and on road drivers’ attitude towards EMVs.

Keywords: cognitive load, emergency service vehicle, empathy, traffic load

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Authors: Racholsan Raj Nirmal, B. S. V. Patnaik, R. Jayaganthan

Abstract:

The deformation behaviour of additively manufactured AlSi10Mg alloy under low strains, high strain rates and elevated temperature conditions is essential to analyse and predict its response against dynamic loading such as impact and thermomechanical fatigue. The constitutive relation of Johnson-Cook is used to capture the strain rate sensitivity and thermal softening effect in AlSi10Mg alloy. Johnson-Cook failure model is widely used for exploring damage mechanics and predicting the fracture in many materials. In this present work, Johnson-Cook material and damage model parameters for additively manufactured AlSi10Mg alloy have been determined numerically from four types of uniaxial tensile test. Three different uniaxial tensile tests with dynamic strain rates (0.1, 1, 10, 50, and 100 s^-1) and elevated temperature tensile test with three different temperature conditions (450 K, 500 K and 550 K) were performed on 3D printed AlSi10Mg alloy in ABAQUS/Explicit. Hexahedral elements are used to discretize tensile specimens and fracture energy value of 43.6 kN/m was used for damage initiation. Levenberg Marquardt optimization method was used for the evaluation of Johnson-Cook model parameters. It was observed that additively manufactured AlSi10Mg alloy has shown relatively higher strain rate sensitivity and lower thermal stability as compared to the other Al alloys.

Keywords: ABAQUS, additive manufacturing, AlSi10Mg, Johnson-Cook model

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Authors: M. Mansouri, P. Jaklitsch, E. Teiniker

Abstract:

SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection.

Keywords: Linux system calls, web attack detection, interception, SQL

Procedia PDF Downloads 356

Authors: Xiaocong He

Abstract:

Self-pierce riveting (SPR) is a new high-speed mechanical fastening technique which is suitable for point joining dissimilar sheet materials, as well as coated and pre-painted sheet materials. Mechanical structures assembled by SPR are expected to possess a high damping capacity. In this study, experimental measurement techniques were proposed for the prediction of vibration behavior of single-lap cantilevered SPR beams. The dynamic test software and the data acquisition hardware were used in the experimental measurement of the dynamic response of the single-lap cantilevered SPR beams. Free and forced vibration behavior of the single-lap cantilevered SPR beams was measured using the LMS CADA-X experimental modal analysis software and the LMS-DIFA Scadas II data acquisition hardware. The frequency response functions of the SPR beams of different rivet number were compared. The main goal of the paper is to provide a basic measuring method for further research on vibration based non-destructive damage detection in single-lap cantilevered SPR beams.

Keywords: self-piercing riveting, dynamic response, experimental measurement, frequency response functions

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Authors: Xufei Liu, Shouxiang Lu, Kim Meow Liew

Abstract:

Because a relatively small fire could potentially cause damage by smoke corrosion far exceed thermal fire damage, it has been realized that the corrosion of metal exposed to smoke atmospheres is a significant fire hazard, except for toxicity or evacuation considerations. For the burning materials in an actual fire may often be the mixture of combustible matters, a quantitative study on the corrosivity of smoke produced by the combustion of mixture is more conducive to the application of the basic theory to the actual engineering. In this paper, carbon steel samples were exposed to smoke generated by polyvinyl chloride and polypropylene, two common combustibles in industrial plants, with different mixing ratios in high humidity for 120 hours. The separate and combined corrosive effects of smoke were examined subsequently by weight loss measurement, scanning electron microscope, energy dispersive spectroscopy and X-ray diffraction. It was found that, although the corrosivity of smoke from polypropylene was much smaller than that of smoke from polyvinyl chloride, smoke from polypropylene enhanced the major corrosive effect of smoke from polyvinyl chloride to carbon steel. Furthermore, the corrosion kinetics of carbon steel under smoke were found to obey the power function. Possible corrosion mechanisms were also proposed. All the analysis helps to provide basic information for the determination of smoke damage and timely rescue after fire.

Keywords: corrosion kinetics, corrosion mechanism, mixed combustible, SEM/EDS, smoke corrosivity, XRD

Procedia PDF Downloads 213