Search results for: object detection with multimodal inputs

Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

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Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

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Authors: Hwan-Jun Choi, Sung-Bok Choi, Hyoung-Kyu Song

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Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMOOFDM system is important issue. In this paper, efficient iterative VBLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6 % less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication.

Keywords: MIMO-OFDM, V-BLAST, QR-decomposition, QRDM, DFE, iterative scheme, channel condition

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Authors: Evgeny A. Rybakov, Dmitry P. Starikov

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This article contains information about creating special unit for scanning 3D objects different nature, different materials, for example plastic, plaster, cardboard, wood, metal and etc. The main part of the unit is infrared transducer, which is sends the wave to the object and receive back wave for calculating distance. After that, microcontroller send to PC data, and computer program create model for printing from the plastic, gypsum, brass, etc.

Keywords: clutch, infrared, microcontroller, plastic, shaft, stage

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Authors: Babatunde Olumide Olawale, Oyebode Olumide Oyediran

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The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe.

Keywords: access control, multimodal biometrics, pattern recognition, security safe

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Authors: Majed Sanan, Mohammad Rammal, Wassim Rammal

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Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs.

Keywords: security, intrusion detection, intrusion prevention, honeypot, anomaly-based detection, signature-based detection, cloud computing, kfsensor

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Alexandre Faure, Yoko Mizokami, éRic Dinet

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These recent years, the potential of light in assisting visually impaired people in their indoor mobility has been demonstrated by different studies. Implementing smart lighting systems for selective visual enhancement, especially designed for low-vision people, is an approach that breaks with the existing visual aids. The appearance of the surface of an object is significantly influenced by the lighting conditions and the constituent materials of the objects. Appearance of objects may appear to be different from expectation. Therefore, lighting conditions lead to an important part of accurate material recognition. The main objective of this work was to investigate the effect of the spatial distribution of light on object identification in the context of low vision. The purpose was to determine whether and what specific lighting approaches should be preferred for visually impaired people. A psychophysical experiment was designed to study the ability of individuals to identify the smallest cube of a pair under different lighting diffusion conditions. Participants were divided into two distinct groups: a reference group of observers with normal or corrected-to-normal visual acuity and a test group, in which observers were required to wear visual impairment simulation glasses. All participants were presented with pairs of cubes in a "miniature room" and were instructed to estimate the relative size of the two cubes. The miniature room replicates real-life settings, adorned with decorations and separated from external light sources by black curtains. The correlated color temperature was set to 6000 K, and the horizontal illuminance at the object level at approximately 240 lux. The objects presented for comparison consisted of 11 white cubes and 11 black cubes of different sizes manufactured with a 3D printer. Participants were seated 60 cm away from the objects. Two different levels of light diffuseness were implemented. After receiving instructions, participants were asked to judge whether the two presented cubes were the same size or if one was smaller. They provided one of five possible answers: "Left one is smaller," "Left one is smaller but unsure," "Same size," "Right one is smaller," or "Right one is smaller but unsure.". The method of constant stimuli was used, presenting stimulus pairs in a random order to prevent learning and expectation biases. Each pair consisted of a comparison stimulus and a reference cube. A psychometric function was constructed to link stimulus value with the frequency of correct detection, aiming to determine the 50% correct detection threshold. Collected data were analyzed through graphs illustrating participants' responses to stimuli, with accuracy increasing as the size difference between cubes grew. Statistical analyses, including 2-way ANOVA tests, showed that light diffuseness had no significant impact on the difference threshold, whereas object color had a significant influence in low vision scenarios. The first results and trends derived from this pilot experiment clearly and strongly suggest that future investigations could explore extreme diffusion conditions to comprehensively assess the impact of diffusion on object identification. For example, the first findings related to light diffuseness may be attributed to the range of manipulation, emphasizing the need to explore how other lighting-related factors interact with diffuseness.

Keywords: Lighting, Low Vision, Visual Aid, Object Identification, Psychophysical Experiment

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Authors: Joshi Manisha, Shivaram, Anand Vinekar, Tanya Susan Mathews, Yeshaswini Nagaraj

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Retinopathy of prematurity (ROP) is abnormal blood vessel development in the retina of the eye in a premature infant. The principal object of the invention is to provide a technique for detecting demarcation line and ridge detection for a given ROP image that facilitates early detection of ROP in stage 1 and stage 2. The demarcation line is an indicator of Stage 1 of the ROP and the ridge is the hallmark of typically Stage 2 ROP. Thirty Retcam images of Asian Indian infants obtained during routine ROP screening have been used for the analysis. A graphical user interface has been developed to detect demarcation line/ridge and to extract ground truth. This novel algorithm uses multilevel vessel enhancement to enhance tubular structures in the digital ROP images. It has been observed that the orientation of the demarcation line/ridge is normal to the direction of the blood vessels, which is used for the identification of the ridge/ demarcation line. Quantitative analysis has been presented based on gold standard images marked by expert ophthalmologist. Image based analysis has been based on the length and the position of the detected ridge. In image based evaluation, average sensitivity and positive predictive value was found to be 92.30% and 85.71% respectively. In pixel based evaluation, average sensitivity, specificity, positive predictive value and negative predictive value achieved were 60.38%, 99.66%, 52.77% and 99.75% respectively.

Keywords: ROP, ridge, multilevel vessel enhancement, biomedical

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Authors: Anton Stadler, Thorsten Ike

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In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.

Keywords: low density, optical flow, upward smoke motion, video based smoke detection

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Authors: Tomasz Sapiński, Dorota Kamińska, Adam Pelikant, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari

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People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman’s emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.

Keywords: body movement, emotion recognition, emotional corpus, facial expressions, gestures, multimodal database, speech

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Authors: Awa Diouf

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Since 2004, inputs specific to the agricultural sector have been exempt from VAT in Senegal. This paper measures, using the Naatal Mbay survey, the impact of this reform on agricultural productivity. The survey covers a sample of 3,122 rice, millet and maize farms for the 2016 crop year. The regressions show that tax incentives are ineffective in improving partial productivity of the land factor: the higher the share of the value of exemptions in the higher the production costs, the less productive the operation. The negative effect of the exemptions on productivity is accentuated for the most intensive agricultural area: the Senegal River Delta, and the most intensive crop: irrigated rice. This relationship could stem from a decrease in allocative efficiency: farmers have overinvested in the most accessible inputs. The loose budget constraint syndrome, therefore, explains this result: farmers who benefit more from exemptions reduce their managerial effort. The results suggest a removal of the VAT exemptions applied to finished products and agricultural inputs for a better efficiency of this tax, which typically taxes final consumption and should be neutral for the producer.

Keywords: agricultural productivity, agricultural taxation, Senegal, tax incentives

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Authors: Sergio Tovar-Pérez, Sebastian Töpfer, Markus Gräfe

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The work proposes a technique that decreases the detection acquisition time of quantum holography schemes down to one-third; this allows the possibility to image moving objects. Since its invention, quantum holography with undetected photon schemes has gained interest in the scientific community. This is mainly due to its ability to tailor the detected wavelengths according to the needs of the scheme implementation. Yet this wavelength flexibility grants the scheme a wide range of possible applications; an important matter was yet to be addressed. Since the scheme uses digital phase-shifting techniques to retrieve the information of the object out of the interference pattern, it is necessary to acquire a set of at least four images of the interference pattern along with well-defined phase steps to recover the full object information. Hence, the imaging method requires larger acquisition times to produce well-resolved images. As a consequence, the measurement of moving objects remains out of the reach of the imaging scheme. This work presents the use and implementation of a spatial light modulator along with a digital holographic technique called quasi-parallel phase-shifting. This technique uses the spatial light modulator to build a structured phase image consisting of a chessboard pattern containing the different phase steps for digitally calculating the object information. Depending on the reduction in the number of needed frames, the acquisition time reduces by a significant factor. This technique opens the door to the implementation of the scheme for moving objects. In particular, the application of this scheme in imaging alive specimens comes one step closer.

Keywords: quasi-parallel phase shifting, quantum imaging, quantum holography, quantum metrology

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Sunil Tumkur Dakshinamurthy, Mamootil Zachariah Kurian

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Change requirement traceability in object oriented software systems is one of the challenging areas in research. We know that the traces between links of different artifacts are to be automated or semi-automated in the software development life cycle (SDLC). The aim of this paper is discussing and implementing aspects of dynamically linking the artifacts such as requirements, high level design, code and test cases through the Extensible Markup Language (XML) or by dynamically generating Object Oriented (OO) metrics. Also, non-functional requirements (NFR) aspects such as stability, completeness, clarity, validity, feasibility and precision are discussed. We discuss this as a Fifth Taxonomy, which is a system vulnerability concern.

Keywords: artifacts, NFRs, OO metrics, SDLC, XML

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Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

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An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone

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Authors: Carlos Alberto Riveros, Edwin Fabián García, Javier Enrique Rivero

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The measured data obtained from sensors in continuous monitoring of civil structures are mainly used for modal identification and damage detection. Therefore when modal identification analysis is carried out the quality in the identification of the modes will highly influence the damage detection results. It is also widely recognized that the usefulness of the measured data used for modal identification and damage detection is significantly influenced by the number and locations of sensors. The objective of this study is the numerical implementation of two widely known optimum sensor placement methods in beam-like structures

Keywords: optimum sensor placement, structural damage detection, modal identification, beam-like structures.

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Authors: N. Dunleavy, E. Lovemore, K. Siljeur, D. Jackson, M. Hendricks, M. Hoosain, N. Plastow, S. Marais

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Background: The work requirements of sewing machine operators cause physical and emotional strain. Past ergonomic interventions have been provided to alleviate physical concerns; however, a holistic, multimodal intervention was needed to improve these factors. Aim: The study aimed to examine the effect of an occupational therapy programme on sewing machine operators’ pain, mental health, and productivity within a factory in the South African context. Methods: A pilot randomised control trial was conducted with 22 sewing machine operators within a single factory. Stratified randomisation was used to determine the experimental (EG) and control groups (CG), using measures for pain intensity, level of depression (mental health), and productivity rates as stratification variables. The EG received the multimodal intervention, incorporating education, seating adaptations, and mental health intervention. In three months, the CG will receive the same intervention. Pre- and post-intervention testing have occurred with upcoming three- and six-month follow-ups. Results: Immediate results indicate a statistically significant decrease in pain in both experimental and control groups; no change in productivity scores and depression between the two groups. This may be attributed to external factors. The values for depression further showed no statistical significance between the two groups and within pre-and post-test results. The Statistical Program for Social Sciences (SPSS) version-24 was used as the data analysis testing, where all the tests will be evaluated at a 5% significance level. Contribution of research: The research adds to the body of knowledge informing the Occupational Therapy role in work settings, providing evidence on the effectiveness of workplace-based multimodal interventions. Conclusion: The study provides initial data on the effectiveness of a pilot randomised control trial on pain and mental health in South Africa. Results indicated no quantitative change between the experimental and control groups; however, qualitative data suggest a clinical significance of the findings.

Keywords: ergonomics programme, occupational therapy, sewing machine operators, workplace-based multimodal interventions

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Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

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Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

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Authors: Yusuke Kuramitsu, Tomoaki Hashimoto, Hirokazu Tahara

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The control problem of underactuated spacecrafts has attracted a considerable amount of interest. The control method for a spacecraft equipped with less than three control torques is useful when one of the three control torques had failed. On the other hand, the quantized control of systems is one of the important research topics in recent years. The random dither quantization method that transforms a given continuous signal to a discrete signal by adding artificial random noise to the continuous signal before quantization has also attracted a considerable amount of interest. The objective of this study is to develop the control method based on random dither quantization method for stabilizing the rotational motion of a rigid spacecraft with two control inputs. In this paper, the effectiveness of random dither quantization control method for the stabilization of rotational motion of spacecrafts with two torque inputs is verified by numerical simulations.

Keywords: spacecraft control, quantized control, nonlinear control, random dither method

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: Yi Xiong, Tao Kong

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Traditional safety, risk and reliability analysis approaches are problem-oriented, which make it great workload when analyzing complicated and huge system, besides, too much repetitive work would to do if the analyzed system composed by many similar components. It is pressing need an object and function oriented approach to maintain high consistency with problem domain. A new approach is proposed to overcome these shortcomings of traditional approaches, the concepts: class, abstract, inheritance, polymorphism and encapsulation are introduced into FTA and establish the professional class library that the abstractions of physical objects in real word, four areas relevant information also be proposed as the establish help guide. The interaction between classes is completed by the inside or external methods that mapping the attributes to base events through fully search the knowledge base, which forms good encapsulation. The object oriented fault tree analysis system that analyze and evaluate the system safety and reliability according to the original appearance of the problem is set up, where could mapped directly from the class and object to the problem domain of the fault tree analysis. All the system failure situations can be analyzed through this bottom-up fault tree construction approach. Under this approach architecture, FTA approach is developed, which avoids the human influence of the analyst on analysis results. It reveals the inherent safety problems of analyzed system itself and provides a new way of thinking and development for safety analysis. So that object oriented technology in the field of safety applications and development, safety theory is conducive to innovation.

Keywords: FTA, knowledge base, object-oriented technology, reliability analysis

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Authors: Mojtaba Valinataj

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Reversible logic as a new favorable design domain can be used for various fields especially creating quantum computers because of its speed and intangible power consumption. However, its susceptibility to a variety of environmental effects may lead to yield the incorrect results. In this paper, because of the importance of multiplication operation in various computing systems, some novel reversible logic array multipliers are proposed with error detection capability by incorporating the parity-preserving gates. The new designs are presented for two main parts of array multipliers, partial product generation and multi-operand addition, by exploiting the new arrangements of existing gates, which results in two signed parity-preserving array multipliers. The experimental results reveal that the best proposed 4×4 multiplier in this paper reaches 12%, 24%, and 26% enhancements in the number of constant inputs, number of required gates, and quantum cost, respectively, compared to previous design. Moreover, the best proposed design is generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: array multipliers, Baugh-Wooley method, error detection, parity-preserving gates, quantum computers, reversible logic

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: Vedant Subhash

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This paper discusses the physics of the detection of thermal neutrons using thin-film coated semiconductor detectors. The thermal neutron detection efficiency as a function of film thickness is calculated for the front and back irradiation detector devices coated with ¹⁰B, ⁶LiF, and pure Li thin films. The detection efficiency for back irradiation devices is 4.15% that is slightly higher than that for front irradiation detectors, 4.0% for ¹⁰B films of thickness 2.4μm. The theoretically calculated thermal neutron detection efficiency using ¹⁰B film thickness of 1.1 μm for the back irradiation device is 3.0367%, which has an offset of 0.0367% from the experimental value of 3.0%. The detection efficiency values are compared and proved consistent with the given calculations.

Keywords: detection efficiency, neutron detection, semiconductor detectors, thermal neutrons

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

Abstract:

With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

Abstract:

In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

Procedia PDF Downloads 112

Authors: Sijia Guo

Abstract:

The development of new technologies and the falling cost of high-speed Internet access have made it easier for institutes and language teachers to opt different ways to communicate with students at distance. The emergence of web-conferencing applications, which integrate text, chat, audio / video and graphic facilities, offers great opportunities for language learning to through the multimodal environment. This paper reports on data elicited from a Ph.D. study of using web-conferencing in the teaching of first-year Chinese class in order to promote learners’ collaborative learning. Firstly, a comparison of four desktop videoconferencing (DVC) tools was conducted to determine the pedagogical value of the videoconferencing tool-Blackboard Collaborate. Secondly, the evaluation of 14 campus-based Chinese learners who conducted five one-hour online sessions via the multimodal environment reveals the users’ choice of modes and their learning preference. The findings show that the tasks designed for the web-conferencing environment contributed to the learners’ collaborative learning and second language acquisition.

Keywords: computer-mediated communication (CMC), CALL evaluation, TBLT, web-conferencing, online Chinese teaching

Procedia PDF Downloads 301

Authors: S. Ghasemi, K. Khorasani

Abstract:

In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

Procedia PDF Downloads 426