Search results for: analog signal processing

Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Anshu Anshu

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In the red corridor famed for the Left Wing Extremism, lies small district of Jamui in Bihar, India. The district lies at 24º20´ N latitude and 86º13´ E longitude, covering an area of 3,122.8 km2 The undulating topography, with widespread forests provides pristine environment for invigorating experience of tourists. Natural landscape in form of forests, wildlife, rivers, and cultural landscape dotted with historical and religious places is highly purposive for tourism. The study is primarily related to the identification of potential ecotourism sites, using Geographic Information System. Data preparation, analysis and finally identification of ecotourism sites is done. Secondary data used is Survey of India Topographical Sheets with R.F.1:50,000 covering the area of Jamui district. District Census Handbook, Census of India, 2011; ERDAS Imagine and Arc View is used for digitization and the creation of DEM’s (Digital Elevation Model) of the district, depicting the relief and topography and generate thematic maps. The thematic maps have been refined using the geo-processing tools. Buffer technique has been used for the accessibility analysis. Finally, all the maps, including the Buffer maps were overlaid to find out the areas which have potential for the development of ecotourism sites in the Jamui district. Spatial data - relief, slopes, settlements, transport network and forests of Jamui District were marked and identified, followed by Buffer Analysis that was used to find out the accessibility of features like roads, railway stations to the sites available for the development of ecotourism destinations. Buffer analysis is also carried out to get the spatial proximity of major river banks, lakes, and dam sites to be selected for promoting sustainable ecotourism. Overlay Analysis is conducted using the geo-processing tools. Digital Terrain Model (DEM) generated and relevant themes like roads, forest areas and settlements were draped on the DEM to make an assessment of the topography and other land uses of district to delineate potential zones of ecotourism development. Development of ecotourism in Jamui faces several challenges. The district lies in the portion of Bihar that is part of ‘red corridor’ of India. The hills and dense forests are the prominent hideouts and training ground for the extremists. It is well known that any kind of political instability, war, acts of violence directly influence the travel propensity and hinders all kind of non-essential travels to these areas. The development of ecotourism in the district can bring change and overall growth in this area with communities getting more involved in economically sustainable activities. It is a known fact that poverty and social exclusion are the main force that pushes people, resorting towards violence. All over the world tourism has been used as a tool to eradicate poverty and generate good will among people. Tourism, in sustainable form should be promoted in the district to integrate local communities in the development process and to distribute fruits of development with equity.

Keywords: buffer analysis, digital elevation model, ecotourism, red corridor

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Authors: Amara Rafik, Bougherara Maamar, Belhadj Aissa Mostefa

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Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques.

Keywords: ATM, GIS, radar data, air traffic simulation

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Authors: Aldilla Damayanti Purnama Ratri, Hendri Subakti, Buldan Muslim

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Ionosphere’s anomalies as an effect of earthquake activity is a phenomenon that is now being studied in seismo-ionospheric coupling. Generally, variation in the ionosphere caused by earthquake activity is weaker than the interference generated by different source, such as geomagnetic storms. However, disturbances of geomagnetic storms show a more global behavior, while the seismo-ionospheric anomalies occur only locally in the area which is largely determined by magnitude of the earthquake. It show that the earthquake activity is unique and because of its uniqueness it has been much research done thus expected to give clues as early warning before earthquake. One of the research that has been developed at this time is the approach of seismo-ionospheric-coupling. This study related the state in the lithosphere-atmosphere and ionosphere before and when earthquake occur. This paper choose the total electron content in a vertical (VTEC) in the ionosphere as a parameter. Total Electron Content (TEC) is defined as the amount of electron in vertical column (cylinder) with cross-section of 1m2 along GPS signal trajectory in ionosphere at around 350 km of height. Based on the analysis of data obtained from the LAPAN agency to identify abnormal signals by statistical methods, obtained that there are an anomaly in the ionosphere is characterized by decreasing of electron content of the ionosphere at 1 TECU before the earthquake occurred. Decreasing of VTEC is not associated with magnetic storm that is indicated as an earthquake precursor. This is supported by the Dst index showed no magnetic interference.

Keywords: earthquake, DST Index, ionosphere, seismoionospheric coupling, VTEC

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Authors: Sebastian Töpfer, Jorge Fuenzalida, Marta Gilaberte Basset, Juan P. Torres, Markus Gräfe

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This work presents an experimental and theoretical study about the noise resilience of quantum holography with undetected photons. Quantum imaging has become an important research topic in the recent years after its first publication in 2014. Following this research, advances towards different spectral ranges in detection and different optical geometries have been made. Especially an interest in the field of near infrared to mid infrared measurements has developed, because of the unique characteristic, that allows to sample a probe with photons in a different wavelength than the photons arriving at the detector. This promising effect can be used for medical applications, to measure in the so-called molecule fingerprint region, while using broadly available detectors for the visible spectral range. Further advance the development of quantum imaging methods have been made by new measurement and detection schemes. One of which is quantum holography with undetected light. It combines digital phase shifting holography with quantum imaging to extent the obtainable sample information, by measuring not only the object transmission, but also its influence on the phase shift experienced by the transmitted light. This work will present extended research for the quantum holography with undetected light scheme regarding the influence of external noise. It is shown experimentally and theoretically that the samples information can still be at noise levels of 250 times higher than the signal level, because of its information being transmitted by the interferometric pattern. A detailed theoretic explanation is also provided.

Keywords: distillation, quantum holography, quantum imaging, quantum metrology

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Authors: Hong Lu, Xin Chen, Zhengcheng Fan

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Prior research has claimed a close association between handwriting and mathematics attainment with the help of spatial cognition. However, the exact mechanism behind this relationship remains un-investigated. Focusing on visual-motor integration (VMI), one critical spatial skill, this meta-analysis aims to estimate the size of the handwriting- visual-motor integration relationship and examine the moderating effect of handwriting dimensions on the link. With a random effect model, a medium relation (r=.26, 95%CI [.22, .30]) between handwriting and VMI was summarized in 38 studies with 55 unique samples and 141 effect sizes. Findings suggested handwriting dimensions significantly moderated the handwriting- VMI relationship, with handwriting legibility showing a substantial correlation with VMI, but neither handwriting speed nor pressure. Identifying the essential relationship between handwriting legibility and VMI, this study adds to the literature about the key cognitive processing needs underlying handwriting, and spatial cognition thus highlights the cognitive mechanism regarding handwriting, spatial cognition, and mathematics performances.

Keywords: handwriting, visual-motor integration, legibility, meta-analysis

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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: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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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: Prafulla Kumar Choubey, Krishna Kishor Jha, S. B. Vaisakh Punnekkattu Chirayil

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In Graphics-display subsystem, frame buffers are shared between producer i.e. content rendering and consumer i.e. display. If a common buffer is operated by both producer and consumer simultaneously, their processing rates mismatch can cause tearing effect in displayed content. Therefore, Android OS employs triple buffered system, taking in to account an additional composition stage. Three stages-rendering, composition and display refresh, operate synchronously on three different buffers, which is achieved by using vsync pulses. This synchronization, however, brings in to the pipeline an additional latency of up to 26ms. The present study details about the existing synchronization mechanism of android graphics-display pipeline and discusses a new adaptive architecture which reduces the wait time to 5ms-16ms in all the use-cases. The proposed method uses two adaptive software vsyncs (PLL) for achieving the same result.

Keywords: Android graphics system, vertical synchronization, atrace, adaptive system

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Authors: Regita P. Permata, Ulfa S. Nuraini

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Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies.

Keywords: Euclidean, heuristics, simulation, TSP

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

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Authors: Diafat Abdelouahab, Ekhalfi A Hammoudia, Meribai Abdelmalek A, Bahloul Ahmedb

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The present study aimed to characterize 30 honey samples from the Bordj Bou Arreridj region (Algeria) regarding their floral origins, physicochemical parameters, mineral composition and microbial safety. Mean values obtained for physicochemical parameters were: pH 4.11, 17.17% moisture, 0.0061% ash, 370.57μS cm−1 electrical conductivity, 21.98 meq/kg free acidity, and 9.703 mg/kg HMF. The mineral content was determined by atomic absorption spectrometry. The mean values obtained were (mg/kg): Fe, 7.5714; Mg, 37.68; Na, 186,63; Zn, 3,86; Pb, 0,4869 × 10-3 ; Cd, 267 × 10-3. Aerobic mesophiles, fecal coliforms and sulphite-reducing clostridia were the microbial contaminants of interest studied. Microbiologically, the honey quality was considered good and all samples showed to be negative in respect to safety parameters. The results obtained for physicochemical characteristics of Bordj Bou Arreridj honey indicate a good quality level, adequate processing, good maturity and freshness.

Keywords: pollen analysis, physicochemical analysis, mineral content, microbial contaminants

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Authors: K. O. Oriola, A. O. Raji, O. E. Akintola, O. T. Ismail

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Thermal properties of roots of three improved cassava varieties (TME419, TMS 30572, and TMS 0326) were determined on samples harvested at 12, 15 and 18 Months After Planting (MAP) conditioned to moisture contents of 50, 55, 60, 65, 70% (wb). Thermal conductivity at 12, 15 and 18 MAP ranged 0.4770 W/m.K to 0.6052W/m.K; 0.4804 W/m.K to 0.5530 W/m.K and 0.3764 to 0.6102 W/m.K respectively, thermal diffusivity from 1.588 to 2.426 x 10-7m2/s; 1.290 to 2.010 x 10-7m2/s and 0.1692 to 4.464 x 10-7m2/s and specific heat capacity from 2.3626 to 3.8991 kJ/kg.K; 1.8110 to 3.9703 kJ/kgK and 1.7311 to 3.8830 kJ/kg.K respectively within the range of moisture content studied across the varieties. None of the samples over the ages studied showed similar or definite trend in variation with others across the moisture content. However, second order polynomial models fitted all the data. Age on the other hand had a significant effect on the three thermal properties studied for TME 419 but not on thermal conductivity of TMS30572 and specific heat capacity of TMS 0326. Information obtained will provide better insight into thermal processing of cassava roots into stable products.

Keywords: thermal conductivity, thermal diffusivity, specific heat capacity, moisture content, tuber age

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Authors: Nomin-Erdene Erdenetsogt, Tseedulam Khuut, Batsaikhan Tserenpil, Bayarsaikhan Enkhee

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As the framework of 60 years of development of Astronomical and Geophysical science in modern Mongolia, various geophysical methods (electrical tomography, ground-penetrating radar, and high-resolution reflection seismic profiles) were used to image an active fault in-depth range between few decimeters to few tens meters. An active fault was fractured by an earthquake magnitude 7.6 during 1967. After geophysical investigations, trench excavations were done at the sites to expose the fault surfaces. The complex geophysical survey in the Mogod fault, Bulgan region of central Mongolia shows an interpretable reflection arrivals range of < 5 m to 50 m with the potential for increased resolution. Reflection profiles were used to help interpret the significance of neotectonic surface deformation at earthquake active fault. The interpreted profiles show a range of shallow fault structures and provide subsurface evidence with support of paleoseismologic trenching photos, electrical surveys.

Keywords: Mogod fault, geophysics, seismic processing, seismic reflection survey

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Reskino Resky

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This research purposes to create a model to detecting financial statement fraud. This research examines the variable of fraud triangle and auditor industry specialization with financial statement fraud. This research used sample of company which is listed in Indonesian Stock Exchange that have sanctions and cases by Financial Services Authority in 2011-2013. The number of company that were became in this research were 30 fraud company and 30 non-fraud company. The method of determining the sample is by using purposive sampling method with judgement sampling, while the data processing methods used by researcher are mann-whitney u and discriminants analysis. This research have two from five variable that can be process with discriminant analysis. The result shows the financial targets can be detect financial statement fraud, while financial stability can’t be detect financial statement fraud.

Keywords: fraud triangle analysis, financial targets, financial stability, auditor industry specialization, financial statement fraud

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Mary Josephine

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Waste of certain process can be the input source of other sectors in order to reduce environmental pollution. Today there are more and more solid wastes are generated, but only very small amount of those are recycled. So, the threatening of environmental pressure to public health is very serious. The methods considered for the treatment of solid waste are biogas tanks or processing to make animal feed and fertilizer, however, they did not perform well. An alternative approach is growing mushrooms on waste residues. This is regarded as an environmental friendly solution with potential economic benefit. The substrate producers do their best to produce quality substrate at low cost. Apart from other methods, this can be achieved by employing biologically degradable wastes used as the resource material component of the substrate. Mushroom growing is a significant tool for the restoration, replenishment and remediation of Earth’s overburdened ecosphere. One of the rational methods of waste utilization involves locally available wastes. The present study aims to find out the yield of mushroom grown on locally available waste for free and to conserve our environment by recycling wastes.

Keywords: biodegradable, environment, mushroom, remediation

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Ki Hwa Lee, Eunsu Kang, Jae Hong Park

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Background: Interventional neuroradiology (INR) has been a rapidly growing and evolving neurosurgical part during the past few decades. Sevoflurane and propofol are both suitable anesthetics for INR procedure. Monitoring of depth of anesthesia is being used very widely. SEDLine™ monitor, a 4-channel processed EEG monitor, uses a proprietary algorithm to analyze the raw EEG signal and displays the Patient State Index (PSI) values. There are only a fewer studies examining the PSI in the neuro-anesthesia. We aimed to investigate the difference of PSI values and hemodynamic variables between sevoflurane and propofol anesthesia during INR procedure. Methods: We reviewed the medical records of patients who scheduled to undergo embolization of non-ruptured intracranial aneurysm by a single operator from May 2013 to December 2014, retrospectively. Sixty-five patients were categorized into two groups; sevoflurane (n = 33) vs propofol (n = 32) group. The PSI values, hemodynamic variables, and the use of hemodynamic drugs were analyzed. Results: Significant differences were seen between PSI values obtained during different perioperative stages in both two groups (P < 0.0001). The PSI values of propofol group were lower than that of sevoflurane group during INR procedure (P < 0.01). The patients in propofol group had more prolonged time of extubation and more phenylephrine requirement than sevoflurane group (p < 0.05). Anti-hypertensive drug was more administered to the patients during extubation in sevoflurane group (p < 0.05). Conclusions: The PSI can detect depth of anesthesia and changes of concentration of anesthetics during INR procedure. Extubation was faster in sevoflurane group, but smooth recovery was shown in propofol group.

Keywords: interventional neuroradiology, patient state index, propofol, sevoflurane

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Authors: Mimi Mohaffyza Mohamad, Yee Mei Heong, Nurfirdawati Muhammad Hanafi, Tee Tze Kiong

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This study is conducted to investigate the disparity of between learning styles and cognitive abilities specifically in Vocational Education. Felder and Silverman Learning Styles Model (FSLSM) was applied to measure the students’ learning styles while the content in Building Construction Subject consists; knowledge, skills and problem solving were taken into account in constructing the elements of cognitive abilities. There are four dimension of learning styles proposed by Felder and Silverman intended to capture student learning preferences with regards to processing either active or reflective, perception based on sensing or intuitive, input of information used visual or verbal and understanding information represent with sequential or global learner. The study discovered that students are tending to be visual learners and each type of learner having significant difference whereas cognitive abilities. The finding may help teachers to facilitate students more effectively and to boost the student’s cognitive abilities.

Keywords: learning styles, cognitive abilities, dimension of learning styles, learning preferences

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Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

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The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS

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Authors: Francielo Vendruscolo

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This review summarizes the potential of starch agroindustrial residues as substrate for biohydrogen production. Types of potential starch agroindustrial residues, recent developments and bio-processing conditions for biohydrogen production will be discussed. Biohydrogen is a clean energy source with great potential to be an alternative fuel, because it releases energy explosively in heat engines or generates electricity in fuel cells producing water as only by-product. Anaerobic hydrogen fermentation or dark fermentation seems to be more favorable, since hydrogen is yielded at high rates and various organic waste enriched with carbohydrates as substrate result in low cost for hydrogen production. Abundant biomass from various industries could be source for biohydrogen production where combination of waste treatment and energy production would be an advantage. Carbohydrate-rich nitrogen-deficient solid wastes such as starch residues can be used for hydrogen production by using suitable bioprocess technologies. Alternatively, converting biomass into gaseous fuels, such as biohydrogen is possibly the most efficient way to use these agroindustrial residues.

Keywords: biofuel, dark fermentation, starch residues, food waste

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Authors: Shweta K. Vyas, Rakesh Musale, Sanjeev R. Shukla

Abstract:

Eri silk is a non mulberry silk which is obtained without killing the silkworms and hence it is also known as Ahmisa silk. In the present study, the results on degumming of eri silk with alkaline peroxide have been compared with those obtained by using ionic liquid (IL) 1-Butyl-3-methylimidazolium chloride [BMIM]Cl. Experiments were designed to find out the optimum processing parameters for degumming of eri silk by response surface methodology. The statistical software, Design-Expert 6.0 was used for regression analysis and graphical analysis of the responses obtained by running the set of designed experiments. Analysis of variance (ANOVA) was used to estimate the statistical parameters. The polynomial equation of quadratic order was employed to fit the experimental data. The quality and model terms were evaluated by F-test. Three dimensional surface plots were prepared to study the effect of variables on different responses. The optimum conditions for IL treatment were selected from predicted combinations and the experiments were repeated under these conditions to determine the reproducibility.

Keywords: silk degumming, ionic liquid, response surface methodology, ANOVA

Procedia PDF Downloads 575

Authors: Fitri D. Jaswar, Tharek A. Rahman, Yasser A. Ahmad

Abstract:

CubeSat in low earth orbit (LEO) mainly uses ultra high frequency (UHF) transmitter with fixed radio frequency (RF) output power to download the telemetry and the payload data. The transmitter consumes large amount of electrical energy during the transmission considering the limited satellite size of a CubeSat. A transmitter with power control ability is designed to achieve optimize the signal to noise ratio (SNR) and efficient power consumption. In this paper, the thermal vacuum chamber (TVAC) test is performed to validate the performance of the UHF band transmitter with power control capability. The TVAC is used to simulate the satellite condition in the outer space environment. The TVAC test was conducted at the Laboratory of Spacecraft Environment Interaction Engineering, Kyushu Institute of Technology, Japan. The TVAC test used 4 thermal cycles starting from +60°C to -20°C for the temperature setting. The pressure condition inside chamber was less than 10-5Pa. During the test, the UHF transmitter is integrated in a CubeSat configuration with other CubeSat subsystem such as on board computer (OBC), power module, and satellite structure. The system is validated and verified through its performance in terms of its frequency stability and the RF output power. The UHF band transmitter output power is tested from 0.5W to 2W according the satellite mode of operations and the satellite power limitations. The frequency stability is measured and the performance obtained is less than 2 ppm in the tested operating temperature range. The test demonstrates the RF output power is adjustable in a thermal vacuum condition.

Keywords: communication system, CubeSat, SNR, UHF transmitter

Procedia PDF Downloads 246

Authors: Hassam Muazzam

Abstract:

This paper devises an autonomous self-driven vehicle that is capable of taking a disabled person to his/her desired location using three different power sources (gasoline, solar, electric) without any control from the user, avoiding the obstacles in the way. The GPS co-ordinates of the desired location are sent to the main processing board via a GSM module. After the GPS co-ordinates are sent, the path to be followed by the vehicle is devised by Pythagoras theorem. The distance and angle between the present location and the desired location is calculated and then the vehicle starts moving in the desired direction. Meanwhile real-time data from ultrasonic sensors is fed to the board for obstacle avoidance mechanism. Ultrasonic sensors are used to quantify the distance of the vehicle from the object. The distance and position of the object is then used to make decisions regarding the direction of vehicle in order to avoid the obstacles using artificial neural network which is implemented using ATmega1280. Also the vehicle provides the feedback location at remote location.

Keywords: autonomous self-driven vehicle, obstacle avoidance, desired location, pythagoras theorem, neural network, remote location

Procedia PDF Downloads 396

Authors: Dimitriu Corneliu-Ioan, Gheorghe FrațIlă

Abstract:

Romania has one of the highest rates of road deaths among European Union Member States, and there is a concern that the country will not meet its goal of "zero deaths" by 2050. The European Union also aims to halve the number of people seriously injured in road accidents by 2030. Therefore, there is a need to improve road infrastructure safety management in Romania. The aim of this study is to analyze road accident data through statistical methods to assess the current state of road infrastructure safety in Bucharest. The study also aims to identify trends and make forecasts regarding serious road accidents and their consequences. The objective is to provide insights that can help prioritize measures to increase road safety, particularly in urban areas. The research utilizes statistical analysis methods, including exploratory analysis and descriptive statistics. Databases from the Traffic Police and the Romanian Road Authority are analyzed using Excel. Road risks are compared with the main causes of road accidents to identify correlations. The study emphasizes the need for better quality and more diverse collection of road accident data for effective analysis in the field of road infrastructure engineering. The research findings highlight the importance of prioritizing measures to improve road safety in urban areas, where serious accidents and their consequences are more frequent. There is a correlation between the measures ordered by road safety auditors and the main causes of serious accidents in Bucharest. The study also reveals the significant social costs of road accidents, amounting to approximately 3% of GDP, emphasizing the need for collaboration between local and central administrations in allocating resources for road safety. This research contributes to a clearer understanding of the current road infrastructure safety situation in Romania. The findings provide critical insights that can aid decision-makers in allocating resources efficiently and institutionally cooperating to achieve sustainable road safety. The data used for this study are collected from the Traffic Police and the Romanian Road Authority. The data processing involves exploratory analysis and descriptive statistics using the Excel tool. The analysis allows for a better understanding of the factors contributing to the current road safety situation and helps inform managerial decisions to eliminate or reduce road risks. The study addresses the state of road infrastructure safety in Bucharest and analyzes the trends and forecasts regarding serious road accidents and their consequences. It studies the correlation between road safety measures and the main causes of serious accidents. To improve road safety, cooperation between local and central administrations towards joint financial efforts is important. This research highlights the need for statistical data processing methods to substantiate managerial decisions in road infrastructure management. It emphasizes the importance of improving the quality and diversity of road accident data collection. The research findings provide a critical perspective on the current road safety situation in Romania and offer insights to identify appropriate solutions to reduce the number of serious road accidents in the future.

Keywords: road death rate, strategic objective, serious road accidents, road safety, statistical analysis

Procedia PDF Downloads 64