Search results for: automated fault detection

Authors: Xinwen Zhu, Xingguang Li, Sun Yi

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Crack is one of the most common damages in buildings, bridges, roads and so on, which may pose safety hazards. However, cracks frequently happen in structures of various materials. Traditional methods of manual detection and measurement, which are known as subjective, time-consuming, and labor-intensive, are gradually unable to meet the needs of modern development. In addition, crack detection and measurement need be safe considering space limitations and danger. Intelligent crack detection has become necessary research. In this paper, an efficient method for crack detection and quantification using a 3D sensor, LiDAR, and depth camera is proposed. This method works even in a dark environment, which is usual in real-world applications. The LiDAR rapidly spins to scan the surrounding environment and discover cracks through lasers thousands of times per second, providing a rich, 3D point cloud in real-time. The LiDAR provides quite accurate depth information. The precision of the distance of each point can be determined within around  ±3 cm accuracy, and not only it is good for getting a precise distance, but it also allows us to see far of over 100m going with the top range models. But the accuracy is still large for some high precision structures of material. To make the depth of crack is much more accurate, the depth camera is in need. The cracks are scanned by the depth camera at the same time. Finally, all data from LiDAR and Depth cameras are analyzed, and the size of the cracks can be quantified successfully. The comparison shows that the minimum and mean absolute percentage error between measured and calculated width are about 2.22% and 6.27%, respectively. The experiments and results are presented in this paper.

Keywords: LiDAR, depth camera, real-time, detection and measurement

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Authors: Babak Kamrani, Nozar Kishi

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Stochastic catalog represents the events module of the earthquake loss estimation models. It includes series of events with different magnitudes and corresponding frequencies/probabilities. For the development of the stochastic catalog, random or uniform sampling methods are used to sample the events from the seismicity model. For covering all the Magnitude Frequency Distribution (MFD), a huge number of events should be generated for the above-mentioned methods. Characteristic Event (CE) method chooses the events based on the interest of the insurance industry. We divide the MFD of each source into bins. We have chosen the bins based on the probability of the interest by the insurance industry. First, we have collected the information for the available seismic sources. Sources are divided into Fault sources, subduction, and events without specific fault source. We have developed the MFD for each of the individual and areal source based on the seismicity of the sources. Afterward, we have calculated the CE magnitudes based on the desired probability. To develop the stochastic catalog, we have introduced uncertainty to the location of the events too.

Keywords: stochastic catalogue, earthquake loss, uncertainty, characteristic event

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Authors: Kay Thinzar Phu, Lwin Lwin Oo

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In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Muhammad Hassan Khalil, Xu Jiadong

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Early detection through screening is the best tool short of a perfect treatment against the malignant tumor inside the breast of a woman. By detecting cancer in its early stages, it can be recognized and treated before it has the opportunity to spread and change into potentially dangerous. Microwave tomography is a new imaging method based on contrast in dielectric properties of materials. The mathematical theory of microwave tomography involves solving an inverse problem for Maxwell’s equations. In this paper, we present designed antenna for breast cancer detection, which will use in microwave tomography configuration.

Keywords: microwave imaging, inverse scattering, breast cancer, malignant tumor detection

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Authors: Lenka Mynaříková, Hedvika Boukalová

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The study looks at the ability to detect nonverbal deception among police officers and management students in the Czech Republic. Respondents from police departments (n=197) and university students of human resources (n=161) completed a deception detection task and evaluated veracity of the statements of suspects in 21 video clips from real crime investigations. Their evaluations were based on nonverbal behavior. Voices in the video clips were modified so that words were not recognizable, yet paraverbal voice characteristics were preserved. Results suggest that respondents have a tendency to lie bias based on their profession. In the evaluation of video clips, stereotypes also played a significant role. The statements of suspects of a different ethnicity, younger age or specific visual features were considered deceitful more often. Research might be beneficial for training in professions that are in need of deception detection techniques.

Keywords: deception detection, police officers, human resources, forensic psychology, forensic studies, organizational psychology

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Authors: Majid Farsadrooh, Najmeh Sabbaghi, Seyed Mohammad Mostashari, Abolhasan Moradi

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Phenolic compounds are chief environmental contaminants on account of their hazardous and toxic nature on human health. The preparation of sensitive and potent chemosensors to monitor emerging pollution in water and effluent samples has received great consideration. A novel and versatile nanocomposite sensor based on poly pyrogallol is presented for the first time in this study, and its electrochemical behavior for simultaneous detection of hydroquinone (HQ), catechol (CT), and resorcinol (RS) in the presence of nitrite is evaluated. The physicochemical characteristics of the fabricated nanocomposite were investigated by emission-scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), and Brunauer-Emmett-Teller (BET). The electrochemical response of the proposed sensor to the detection of HQ, CT, RS, and nitrite is studied using cyclic voltammetry (CV), chronoamperometry (CA), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). The kinetic characterization of the prepared sensor showed that both adsorption and diffusion processes can control reactions at the electrode. In the optimized conditions, the new chemosensor provides a wide linear range of 0.5-236.3, 0.8-236.3, 0.9-236.3, and 1.2-236.3 μM with a low limit of detection of 21.1, 51.4, 98.9, and 110.8 nM (S/N = 3) for HQ, CT and RS, and nitrite, respectively. Remarkably, the electrochemical sensor has outstanding selectivity, repeatability, and stability and is successfully employed for the detection of RS, CT, HQ, and nitrite in real water samples with the recovery of 96.2%–102.4%, 97.8%-102.6%, 98.0%–102.4% and 98.4%–103.2% for RS, CT, HQ, and nitrite, respectively. These outcomes illustrate that poly pyrogallol is a promising candidate for effective electrochemical detection of dihydroxybenzene isomers in the presence of nitrite.

Keywords: electrochemical sensor, poly pyrogallol, phenolic compounds, simultaneous determination

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Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

Procedia PDF Downloads 621

Authors: Shilpa Khobragade, Carlos Da Silva Granja, Niklas Sandström, Igor Efimov, Victor P. Ostanin, Wouter van der Wijngaart, David Klenerman, Sourav K. Ghosh

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Rapid, label-free and direct detection of pathogenic bacteria is critical for the prevention of disease outbreaks. This paper for the first time attempts to probe the nonlinear acoustic response of quartz crystal resonator (QCR) functionalized with specific DNA aptamers for direct detection and quantification of viable E. coli KCTC 2571 bacteria. DNA aptamers were immobilized through biotin and streptavidin conjugation, onto the gold surface of QCR to capture the target bacteria and the detection was accomplished by shift in amplitude of the peak 3f signal (3 times the drive frequency) upon binding, when driven near fundamental resonance frequency. The developed nonlinear acoustic aptasensor system demonstrated better reliability than conventional resonance frequency shift and energy dissipation monitoring that were recorded simultaneously. This sensing system could directly detect 10⁽⁵⁾ cells/mL target bacteria within 30 min or less and had high specificity towards E. coli KCTC 2571 bacteria as compared to the same concentration of S.typhi bacteria. Aptasensor response was observed for the bacterial suspensions ranging from 10⁽⁵⁾-10⁽⁸⁾ cells/mL. Conclusively, this nonlinear acoustic aptasensor is simple to use, gives real-time output, cost-effective and has the potential for rapid, specific, label-free direction detection of bacteria.

Keywords: acoustic, aptasensor, detection, nonlinear

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Authors: N. Gayathri Devi, K. Batri

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The advent of technology has increased the traffic hazards and the road accidents take place. Collision detection system in automobile aims at reducing or mitigating the severity of an accident. This project aims at avoiding Vehicle head on collision by means of collision detection algorithm. This collision detection algorithm predicts the collision and the avoidance or minimization have to be done within few seconds on confirmation. Under critical situation collision minimization is made possible by turning the vehicle to the desired turn radius so that collision impact can be reduced. In order to avoid the collision completely, the turning of the vehicle should be achieved at reduced speed in order to maintain the stability.

Keywords: collision avoidance system, time to collision, time to turn, turn radius

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Authors: Anisha Mandal, Swambabu Varanasi

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Lead is one of the most prevalent toxic heavy metal ions, and its pollution poses a significant threat to the environment and human health. On the other hand, Ethylenediaminetetraacetic acid is a widely used metal chelating agent that, due to its poor biodegradability, is an incessant pollutant to the environment. For the first time, a green, simple, and cost-effective approach is used to hydrothermally synthesise photoluminescent carbon dots using Moringa Oleifera Gum in a single step. Then, using Moringa Oleifera Gum-derived carbon dots, a photoluminescent "ON-OFF-ON" mechanism for dual mode detection of trace Pb2+ and EDTA was proposed. MOG-CDs detect Pb2+ selectively and sensitively using a photoluminescence quenching mechanism, with a detection limit (LOD) of 0.000472 ppm. (1.24 nM). The quenched photoluminescence can be restored by adding EDTA to the MOG-CD+Pb2+ system; this strategy is used to quantify EDTA at a level of detection of 0.0026 ppm. (8.9 nM). The quantification of Pb2+ and EDTA in actual samples encapsulated the applicability and dependability of the proposed photoluminescent probe.

Keywords: carbon dots, photoluminescence, sensor, moringa oleifera gum

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Gaokai Liu

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The distribution of material grain sizes reflects the strength, fracture, corrosion and other properties, and the grain size can be acquired via the grain boundary. In recent years, the automatic grain boundary detection is widely required instead of complex experimental operations. In this paper, an effective solution is applied to acquire the grain boundary of material images. First, the initial superpixel segmentation result is obtained via a superpixel approach. Then, a region merging method is employed to merge adjacent regions based on certain similarity criterions, the experimental results show that the merging strategy improves the superpixel segmentation result on material datasets.

Keywords: grain boundary detection, image segmentation, material images, region merging

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Shashank Bhatnagar, W. Wilfred Godfrey

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We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network.

Keywords: community detection, community rank, Hawkes process, influencer node, pagerank, trend detection

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Authors: Jiaqi Huang, Yuheng Wang

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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Toleen Alaqqad, Kadi Alshabramiy, Suad Zaafarany, Basma Musallam

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CADmapper is a significant tool of software for transforming geographic data into realistic CAD drawings. It speeds up and simplifies the conversion process by automating it. This allows architects, urban planners, engineers, and geographic information system (GIS) experts to solely concentrate on the imaginative and scientific parts of their projects. While the future incorporation of AI has the potential for further improvements, CADmapper's current capabilities make it an indispensable asset in the business. It covers a combination of 2D and 3D city and urban area models. The user can select a specific square section of the map to view, and the fee is based on the dimensions of the area being viewed. The procedure is straightforward: you choose the area you want, then pick whether or not to include topography. 3D architectural data (if available), followed by selecting whatever design program or CAD style you want to publish the document which contains more than 200 free broad town plans in DXF format. If you desire to specify a bespoke area, it's free up to 1 km2.

Keywords: cadmaper, gdata, 2d and 3d data conversion, automated cad drawing, urban planning software

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Authors: Mohammad Sarchami, Mohsen Zeinalkhani

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Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

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Authors: Rao Singh Priyanka, Jayangondaperumal R.

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The Himalayan active fault has the potential to generate great earthquakes in the future, posing a biggest existential threat to humans in the Himalayan and adjacent region. Quantitative evaluation of accumulated and released interseismic strain is crucial to assess the magnitude and spatio-temporal variability of future great earthquakes along the Himalayan arc. To mitigate the destruction and hazards associated with such earthquakes, it is important to understand their recurrence cycle. The eastern Himalayan and Indo-Burman plate boundary systems offers an oblique convergence across two orthogonal plate boundaries, resulting in a zone of distributed deformation both within and away from the plate boundary and clockwise rotation of fault-bounded blocks. This seismically active region has poorly documented historical archive of the past large earthquakes. Thus, paleoseismologicalstudies confirm the surface rupture evidences of the great continental earthquakes (Mw ≥ 8) along the Himalayan Frontal Thrust (HFT), which along with the Geodetic studies, collectively provide the crucial information to understand and assess the seismic potential. These investigations reveal the rupture of 3/4th of the HFT during great events since medieval time but with debatable opinions for the timing of events due to unclear evidences, ignorance of transverse segment boundaries, and lack of detail studies. Recent paleoseismological investigations in the eastern Himalaya and Mishmi ranges confirms the primary surface ruptures of the 1950 C.E. great earthquake (M>8). However, a seismic gap exists between the 1714 C.E. and 1950 C.E. Assam earthquakes that did not slip since 1697 C.E. event. Unlike the latest large blind 2015 Gorkha earthquake (Mw 7.8), the 1950 C.E. event is not triggered by a large event of 1947 C.E. that occurred near the western edge of the great upper Assam event. Moreover, the western segment of the eastern Himalayadid not witness any surface breaking earthquake along the HFT for over the past 300 yr. The frontal fault excavations reveal that during the 1950 earthquake, ~3.1-m-high scarp along the HFT was formed due to the co-seismic slip of 5.5 ± 0.7 m at Pasighat in the Eastern Himalaya and a 10-m-high-scarp at a Kamlang Nagar along the Mishmi Thrust in the Eastern Himalayan Syntaxis is an outcome of a dip-slip displacement of 24.6 ± 4.6 m along a 25 ± 5°E dipping fault. This event has ruptured along the two orthogonal fault systems in the form of oblique thrust fault mechanism. Approx. 130 km west of Pasighat site, the Himebasti village has witnessed two earthquakes, the historical 1697 Sadiya earthquake, and the 1950 event, with a cumulative dip-slip displacement of 15.32 ± 4.69 m. At Niglok site, Arunachal Pradesh, a cumulative slip of ~12.82 m during at least three events since pre 19585 B.P. has produced ~6.2-m high scarp while the youngest scarp of ~2.4-m height has been produced during 1697 C.E. The site preserves two deformational events along the eastern HFT, providing an idea of last serial ruptures at an interval of ~850 yearswhile the successive surface rupturing earthquakes lacks in the Mishmi Range to estimate the recurrence cycle.

Keywords: paleoseismology, surface rupture, recurrence interval, Eastern Himalaya

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Authors: Xincheng Jiang, Yanyan An, Yaoyao Huang, Wei Ding, Manli Sun, Hong Li, Huaili Zheng

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The residual antibiotics in the environment will pose a threat to the environment and human health. Thus, efficient removal and rapid detection of norfloxacin (NOR) in wastewater is very important. The main sources of NOR pollution are the agricultural, pharmaceutical industry and hospital wastewater. The total consumption of NOR in China can reach 5440 tons per year. It is found that neither animals nor humans can totally absorb and metabolize NOR, resulting in the excretion of NOR into the environment. Therefore, residual NOR has been detected in water bodies. The hazards of NOR in wastewater lie in three aspects: (1) the removal capacity of the wastewater treatment plant for NOR is limited (it is reported that the average removal efficiency of NOR in the wastewater treatment plant is only 68%); (2) NOR entering the environment will lead to the emergence of drug-resistant strains; (3) NOR is toxic to many aquatic species. At present, the removal and detection technologies of NOR are applied separately, which leads to a cumbersome operation process. The development of simultaneous adsorption-flocculation removal and FTIR detection of pollutants has three advantages: (1) Adsorption-flocculation technology promotes the detection technology (the enrichment effect on the material surface improves the detection ability); (2) The integration of adsorption-flocculation technology and detection technology reduces the material cost and makes the operation easier; (3) FTIR detection technology endows the water treatment agent with the ability of molecular recognition and semi-quantitative detection for pollutants. Thus, it is of great significance to develop a smart water treatment material with high removal capacity and detection ability for pollutants. This study explored the feasibility of combining NOR removal method with the semi-quantitative detection method. A magnetic Cu-Ni-S/Ni foam was synthesized by in-situ loading Cu-Ni-S nanostructures on the surface of Ni foam. The novelty of this material is the combination of adsorption-flocculation technology and semi-quantitative detection technology. Batch experiments showed that Cu-Ni-S/Ni foam has a high removal rate of NOR (96.92%), wide pH adaptability (pH=4.0-10.0) and strong ion interference resistance (0.1-100 mmol/L). According to the Langmuir fitting model, the removal capacity can reach 417.4 mg/g at 25 °C, which is much higher than that of other water treatment agents reported in most studies. Characterization analysis indicated that the main removal mechanisms are surface complexation, cation bridging, electrostatic attraction, precipitation and flocculation. Transmission FTIR detection experiments showed that NOR on Cu-Ni-S/Ni foam has easily recognizable FTIR fingerprints; the intensity of characteristic peaks roughly reflects the concentration information to some extent. This semi-quantitative detection method has a wide linear range (5-100 mg/L) and a low limit of detection (4.6 mg/L). These results show that Cu-Ni-S/Ni foam has excellent removal performance and semi-quantitative detection ability of NOR molecules. This paper provides a new idea for designing and preparing multi-functional water treatment materials to achieve simultaneous removal and semi-quantitative detection of organic pollutants in water.

Keywords: adsorption-flocculation, antibiotics detection, Cu-Ni-S/Ni foam, norfloxacin

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Authors: Ergün Şakalar, Şeyma Özçirak Ergün

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Pistachio nuts, the fruits of the pistachio tree (Pistacia vera), are edible tree nuts highly valued for their organoleptic properties. Pistachio nuts used in snack foods, chocolates, baklava, meat products, ice-cream industries and other gourmet products as ingredients. Undeclared pistachios may be present in food products as a consequence of fraudulent substitution. Control of food samples is very important for safety and fraud. Mix of pistachio, peanut (Arachis hypogaea), pea (Pisum sativum L.) used instead of pistachio in food products, because pistachio is a considerably expensive nut. To solve this problem, a sensitive polymerase chain reaction PCR has been developed. A real-time PCR assay for the detection of pea, peanut and pistachio in baklava was designed by using EvaGreen fluorescence dye. Primers were selected from powerful regions for identification of pea, peanut and pistachio. DNA from reference samples and industrial products were successfully extracted with the GIDAGEN® Multi-Fast DNA Isolation Kit. Genomes were identified based on their specific melting peaks (Mp) which are 77°C, 85.5°C and 82.5°C for pea, peanut and pistachio, respectively. Homogenized mixtures of raw pistachio, pea and peanut were prepared with the ratio of 0.01%, 0.1%, 1%, 10%, 40% and 70% of pistachio. Quantitative detection limit of assay was 0.1% for pistachio. Also, real-time PCR technique used in this study allowed the qualitative detection of as little as 0.001% level of peanut DNA, 0,000001% level of pistachio DNA and 0.000001% level of pea DNA in the experimental admixtures. This assay represents a potentially valuable diagnostic method for detection of nut species adulterated with pistachio as well as for highly specific and relatively rapid detection of small amounts of pistachio in food samples.

Keywords: pea, peanut, pistachio, real-time PCR

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Authors: Minh-Phuong Ngoc Bui, Abdennour Abbas

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Microbial spoilage of food/drug has been a constant nuisance and an unavoidable problem throughout history that affects food/drug quality and safety in a variety of ways. A simple and rapid test of fungi and bacteria in food/drugs and environmental clinical samples is essential for proper management of contamination. A number of different techniques have been developed for detection and enumeration of foodborne microorganism including plate counting, enzyme-linked immunosorbent assay (ELISA), polymer chain reaction (PCR), nucleic acid sensor, electrical and microscopy methods. However, the significant drawbacks of these techniques are highly demand of operation skills and the time and cost involved. In this report, we introduce a rapid method for detection of bacteria and fungi in food/drug products using a specific interaction between a reducing agent (tris(2-carboxylethyl)phosphine (TCEP)) and the microbial surface proteins. The chemical reaction was transferred to a transduction system using gold nanoplates-enhanced chemiluminescence. We have optimized our nanoplates synthetic conditions, characterized the chemiluminescence parameters and optimized conditions for the microbial assay. The new detection method was applied for rapid detection of bacteria (E.coli sp. and Lactobacillus sp.) and fungi (Mucor sp.), with limit of detection as low as single digit cells per mL within 10 min using a portable luminometer. We expect our simple and rapid detection method to be a powerful alternative to the conventional plate counting and immunoassay methods for rapid screening of microorganisms in food/drug products.

Keywords: microorganism testing, gold nanoplates, chemiluminescence, reducing agents, luminol

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Authors: Manisha Mukherjee, Diptarka Saha

Abstract:

Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.

Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function

Procedia PDF Downloads 158

Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

Abstract:

The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-doppler features

Procedia PDF Downloads 116

Authors: Haq Nawaz, Muhammad Amjad Iqbal, Kamran Malik

Abstract:

"Altasreef" is an automated system available for Web and Android users which provide facility to the users to learn the Quran verbs. It provides the facility to the users to practice the learned material and also provide facility of exams of Arabic verbs variation focusing on Quran text. Arabic is a highly inflectional language. Almost all of its words connect to roots of three, four or five letters which approach the meaning of all their inflectional forms. In Arabic, a verb is formed by inserting the consonants into one of a set of verb patterns. Suffixes and prefixes are then added to generate the meaning of number, person, and gender. The active/passive voice and perfective aspect and other patterns are than generated. This application is designed for learners of Quranic Arabic who already have learn basics of Arabic conjugation. Application also provides the facility of translation of generated patterns. These translations are generated with the help of rule-based approach to give 100% results to the learners.

Keywords: NLP, Quran, Computational Linguistics, E Learning

Procedia PDF Downloads 163

Authors: Weitao Lin

Abstract:

To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method.

Keywords: natural language processing, Chinese event detection, rules matching, dependency parsing

Procedia PDF Downloads 134