Search results for: network diagnostic tool

Authors: Sedat Aktas, Egemen Ulusoy, Remzi Yildirim

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This article explains how to get a ram image from a computer with a Linux operating system and what steps should be followed while getting it. What we mean by taking a ram image is the process of dumping the physical memory instantly and writing it to a file. This process can be likened to taking a picture of everything in the computer’s memory at that moment. This process is very important for tools that analyze ram images. Volatility can be given as an example because before these tools can analyze ram, images must be taken. These tools are used extensively in the forensic world. Forensic, on the other hand, is a set of processes for digitally examining the information on any computer or server on behalf of official authorities. In this article, the protected mode architecture in the Linux operating system is examined, and the way to save the image sample of the kernel driver and system memory to disk is followed. Tables and access methods to be used in the operating system are examined based on the basic architecture of the operating system, and the most appropriate methods and application methods are transferred to the article. Since there is no article directly related to this study on Linux in the literature, it is aimed to contribute to the literature with this study on obtaining ram images. LIME can be mentioned as a similar tool, but there is no explanation about the memory dumping method of this tool. Considering the frequency of use of these tools, the contribution of the study in the field of forensic medicine has been the main motivation of the study due to the intense studies on ram image in the field of forensics.

Keywords: linux, paging, addressing, ram-image, memory dumping, kernel modules, forensic

Procedia PDF Downloads 118

Authors: Md Murshed Bhuyan, Hirotaka Okabe, Yoshiki Hidaka, Kazuhiro Hara

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Pectin-Acrylamide- (Vinyl Phosphonic Acid) Hydrogels were prepared from their blend by using gamma radiation of various doses. It was found that the gel fraction of hydrogel increases with increasing the radiation dose reaches a maximum and then started decreasing with increasing the dose. The optimum radiation dose and the composition of raw materials were determined on the basis of equilibrium swelling which resulted in 20 kGy absorbed dose and 1:2:4 (Pectin:AAm:VPA) composition. Differential scanning calorimetry reveals the gel strength for using them as the adsorbent. The FTIR-spectrum confirmed the grafting/ crosslinking of the monomer on the backbone of pectin chain. The hydrogels were applied in adsorption of Al, Ga, and In from multielement solution where the adsorption capacity order for those three elements was found as – In>Ga>Al. SEM images of hydrogels and metal adsorbed hydrogels indicate the gel network and adherence of the metal ions in the interpenetrating network of the hydrogel which were supported by EDS spectra. The adsorption isotherm models were studied and found that the Langmuir adsorption isotherm model was well fitted with the data. Adsorption data were also fitted to different adsorption kinetic and diffusion models. Desorption of metal adsorbed hydrogels was performed in 5% nitric acid where desorption efficiency was found around 90%.

Keywords: hydrogel, gamma radiation, vinyl phosphonic acid, metal adsorption

Procedia PDF Downloads 153

Authors: Katharina M. Rischer, Angelika Dierolf, Ana M. Gonzalez-Roldan, Pedro Montoya, Fernand Anton, Marian van der Meulen

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Even though age has been associated with increased and prolonged episodes of pain, little is known about potential age-related changes in the ˈtop-downˈ modulation of pain, such as cognitive distraction from pain. The analgesic effects of distraction result from competition for attentional resources in the prefrontal cortex (PFC), a region that is also involved in executive functions. Given that the PFC shows pronounced age-related atrophy, distraction may be less effective in reducing pain in older compared to younger adults. The aim of this study was to investigate the influence of aging on task-related analgesia and the underpinning neural mechanisms, with a focus on the role of executive functions in distraction from pain. In a first session, 64 participants (32 young adults: 26.69 ± 4.14 years; 32 older adults: 68.28 ± 7.00 years) completed a battery of neuropsychological tests. In a second session, participants underwent a pain distraction paradigm, while fMRI images were acquired. In this paradigm, participants completed a low (0-back) and a high (2-back) load condition of a working memory task while receiving either warm or painful thermal stimuli to their lower arm. To control for age-related differences in sensitivity to pain and perceived task difficulty, stimulus intensity, and task speed were individually calibrated. Results indicate that both age groups showed significantly reduced activity in a network of regions involved in pain processing when completing the high load distraction task; however, young adults showed a larger neural distraction effect in different parts of the insula and the thalamus. Moreover, better executive functions, in particular inhibitory control abilities, were associated with a larger behavioral and neural distraction effect. These findings clearly demonstrate that top-down control of pain is affected in older age, and could explain the higher vulnerability for older adults to develop chronic pain. Moreover, our findings suggest that the assessment of executive functions may be a useful tool for predicting the efficacy of cognitive pain modulation strategies in older adults.

Keywords: executive functions, cognitive pain modulation, fMRI, PFC

Procedia PDF Downloads 144

Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

Procedia PDF Downloads 160

Authors: Sarwat Jahan, Asker Rajin Rahman

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Management of offtake of any fluvial river is very sensitive in terms of long-term sustainability where the variation of water flow and sediment transport range are wide enough throughout a hydrological year. The Gorai River is a major distributary of the Ganges River in Bangladesh and is termed as a primary source of fresh water for the South-West part of the country. Every year, significant siltation of the Gorai offtake disconnects it from the Ganges during the dry season. As a result, the socio-economic and environmental condition of the downstream areas has been deteriorating for a few decades. To improve the overall situation of the Gorai offtake and its dependent areas, a study has been conducted by the Institute of Water Modelling, Bangladesh, in 2022. Using the mathematical morphological modeling tool MIKE 21C of DHI Water & Environment, Denmark, simulated results revealed the need for dredging/river training structures for offtake management at the Gorai offtake to ensure significant dry season flow towards the downstream. The dry season flow is found to increase significantly with the proposed river interventions, which also improves the environmental conditions in terms of salinity of the South-West zone of the country. This paper summarizes the primary findings of the analyzed results of the developed mathematical model for improving the existing condition of the Gorai River.

Keywords: Gorai river, mathematical modelling, offtake, siltation, salinity

Procedia PDF Downloads 97

Authors: Priyanka Agrawal, Janou Koskamp, Amir Raoof, Mariette Wolthers

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Calcite is the main mineral found in carbonate rocks, which form significant hydrocarbon reservoirs and subsurface repositories for CO2 sequestration. The injected CO2 mixes with the reservoir fluid and disturbs the geochemical equilibrium, triggering calcite dissolution. Different combinations of fluid chemistry and injection rate may therefore result in different evolution of porosity, permeability and dissolution patterns. To model the changes in porosity and permeability Kozeny-Carman equation K∝〖(∅)〗^n is used, where K is permeability and ∅ is porosity. The value of n is mostly based on experimental data or pore network models. In pore network models, this derivation is based on accuracy of relation used for conductivity and pore volume change. In fact, at a single pore scale, this relationship is the result of the pore shape development due to dissolution. We have prepared a new reactive transport model for a single pore which simulates the complex chemical reaction of carbonic-acid induced calcite dissolution and subsequent pore-geometry evolution at a single pore scale. We use COMSOL Multiphysics package 5.3 for the simulation. COMSOL utilizes the arbitary-Lagrangian Eulerian (ALE) method for the free-moving domain boundary. We examined the effect of flow rate on the evolution of single pore shape profiles due to calcite dissolution. We used three flow rates to cover diffusion dominated and advection-dominated transport regimes. The fluid in diffusion dominated flow (Pe number 0.037 and 0.37) becomes less reactive along the pore length and thus produced non-uniform pore shapes. However, for the advection-dominated flow (Pe number 3.75), the fast velocity of the fluid keeps the fluid relatively more reactive towards the end of the pore length, thus yielding uniform pore shape. Different pore shapes in terms of inlet opening vs overall pore opening will have an impact on the relation between changing volumes and conductivity. We have related the shape of pore with the Pe number which controls the transport regimes. For every Pe number, we have derived the relation between conductivity and porosity. These relations will be used in the pore network model to get the porosity and permeability variation.

Keywords: single pore, reactive transport, calcite system, moving boundary

Procedia PDF Downloads 374

Authors: K. Assis, K. P. Chong, A. S. Idris, C. M. Ho

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Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.

Keywords: ganoderma, oil palm, regression model, yield loss, economic loss

Procedia PDF Downloads 389

Authors: Amós García Montaño

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In a democracy, a public policy must be developed within the regulatory framework and considering citizen participation in its planning, design, execution, and evaluation stages, necessary factors to have both legal support and sufficient legitimacy for its operation. However, the complexity and magnitude of certain public problems results in difficulties for the generation of consensus among society members, leading to unstable and unsuccessful scenarios for the exercise of the right to citizen participation and the generation of effective and efficient public policies. This is the case of public policies against corruption, an issue that in Mexico is difficult to define and generates conflicting opinions. To provide a possible solution to this delicate reality, this paper analyzes the principle of reasonableness as a tool for identifying the basic elements that guarantee a fundamental level of the exercise of the right to citizen participation in the fight against corruption, adopting elements of human rights indicator methodologies. In this sense, the relevance of having a legal framework that establishes obligations to incorporate proactive and transversal citizen participation in the matter is observed. It is also noted the need to monitor the operation of various citizen participation mechanisms in the decision-making processes of the institutions involved in the fight and prevention of corruption, which lead to an increase in the improvement of the perception of the citizen role as a relevant actor in this field. It is concluded that the principle of reasonableness is presented as a very useful tool for the identification of basic elements that facilitate the fulfillment of human rights commitments in the field of public policies.

Keywords: anticorruption, public participation, public policies, reasonableness

Procedia PDF Downloads 83

Authors: Amit Verma, Pardeep Kaur

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In software development organizations, Component based Software engineering (CBSE) is emerging paradigm for software development and gained wide acceptance as it often results in increase quality of software product within development time and budget. In component reusability, main challenges are the right component identification from large repositories at right time. The major objective of this work is to provide efficient algorithm for storage and effective retrieval of components using neural network and parameters based on user choice through clustering. This research paper aims to propose an algorithm that provides error free and automatic process (for retrieval of the components) while reuse of the component. In this algorithm, keywords (or components) are extracted from software document, after by applying k mean clustering algorithm. Then weights assigned to those keywords based on their frequency and after assigning weights, ANN predicts whether correct weight is assigned to keywords (or components) or not, otherwise it back propagates in to initial step (re-assign the weights). In last, store those all keywords into repositories for effective retrieval. Proposed algorithm is very effective in the error correction and detection with user base choice while choice of component for reusability for efficient retrieval is there.

Keywords: component based development, clustering, back propagation algorithm, keyword based retrieval

Procedia PDF Downloads 378

Authors: Thifhuriwi Emmanuel Madzunye, Patricia Harpur, Ephias Ruhode

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A review of the pertinent literature identified a gap concerning the hindrances and opportunities accompanying the implementation of real-time online learning systems (RTOLs) in rural areas. Whilst RTOLs present a possible solution to teaching and learning issues in rural areas, little is known about the implementation of digital strategies among schools in isolated communities. This study explores associated guidelines that have the potential to inform decision-making where Internet-based education could improve educational opportunities. A systematic literature review has the potential to consolidate and focus on disparate literature served to collect interlinked data from specific sources in a structured manner. During qualitative data analysis (QDA) of selected publications via the application of a QDA tool - ATLAS.ti, the following overarching themes emerged: digital divide, educational strategy, human factors, and support. Furthermore, findings from data collection and literature review suggest that signiant factors include a lack of digital knowledge, infrastructure shortcomings such as a lack of computers, poor internet connectivity, and handicapped real-time online may limit students’ progress. The study recommends that timeous consideration should be given to the influence of the digital divide. Additionally, the evolution of educational strategy that adopts digital approaches, a focus on training of role-players and stakeholders concerning human factors, and the seeking of governmental funding and support are essential to the implementation and success of RTOLs.

Keywords: communication, digital divide, digital skills, distance, educational strategy, government, ICT, infrastructures, learners, limpopo, lukalo, network, online learning systems, political-unrest, real-time, real-time online learning, real-time online learning system, pass-rate, resources, rural area, school, support, teachers, teaching and learning and training

Procedia PDF Downloads 334

Authors: M. Draman, S. Z. Muhamad Yassin, M. S. Alias, Z. Lambak, M. I. Zulkifli, S. N. Padhi, K. N. Baharim, F. Maskuriy, A. I. A. Rahim

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The performance of Malay automatic speech recognition (ASR) system for the call centre environment is presented. The system utilizes Kaldi toolkit as the platform to the entire library and algorithm used in performing the ASR task. The acoustic model implemented in this system uses a deep neural network (DNN) method to model the acoustic signal and the standard (n-gram) model for language modelling. With 80 hours of training data from the call centre recordings, the ASR system can achieve 72% of accuracy that corresponds to 28% of word error rate (WER). The testing was done using 20 hours of audio data. Despite the implementation of DNN, the system shows a low accuracy owing to the varieties of noises, accent and dialect that typically occurs in Malaysian call centre environment. This significant variation of speakers is reflected by the large standard deviation of the average word error rate (WERav) (i.e., ~ 10%). It is observed that the lowest WER (13.8%) was obtained from recording sample with a standard Malay dialect (central Malaysia) of native speaker as compared to 49% of the sample with the highest WER that contains conversation of the speaker that uses non-standard Malay dialect.

Keywords: conversational speech recognition, deep neural network, Malay language, speech recognition

Procedia PDF Downloads 322

Authors: A. Khan, H. Mahmood

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In Underwater Wireless Sensor Networks (UWSNs), sensor nodes close to water surface (final destination) are often preferred for selection as forwarders. However, their frequent selection makes them depleted of their limited battery power. In consequence, these nodes die during early stage of network operation and create energy holes where forwarders are not available for packets forwarding. These holes severely affect network throughput. As a result, system performance significantly degrades. In this paper, a routing protocol is proposed to avoid energy holes during packets forwarding. The proposed protocol does not require the conventional position information (localization) of holes to avoid them. Localization is cumbersome; energy is inefficient and difficult to achieve in underwater environment where sensor nodes change their positions with water currents. Forwarders with the lowest water pressure level and the maximum number of neighbors are preferred to forward packets. These two parameters together minimize packet drop by following the paths where maximum forwarders are available. To avoid interference along the paths with the maximum forwarders, a packet holding time is defined for each forwarder. Simulation results reveal superior performance of the proposed scheme than the counterpart technique.

Keywords: energy holes, interference, routing, underwater

Procedia PDF Downloads 409

Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

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One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

Procedia PDF Downloads 264

Authors: Tania Viju, Bimal P., Naseer M. A.

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This study aims to develop a conceptual framework for the decision support system (DSS), that helps the decision-makers to dynamically choose appropriate safety measures for each identified accident blackspot. An accident blackspot is a segment of road where the frequency of accident occurrence is disproportionately greater than other sections on roadways. According to a report by the World Bank, India accounts for the highest, that is, eleven percent of the global death in road accidents with just one percent of the world’s vehicles. Hence in 2015, the Ministry of Road Transport and Highways of India gave prime importance to the rectification of accident blackspots. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying and rectifying accident blackspots is of great importance. This study helps to understand and evaluate the existing methods in accident blackspot identification and prediction that are used around the world and their application in Indian roadways. The decision support system, with the help of IoT, ICT and smart systems, acts as a management and planning tool for the government for employing efficient and cost-effective rectification strategies. In order to develop a decision criterion, several factors in terms of quantitative as well as qualitative data that influence the safety conditions of the road are analyzed. Factors include past accident severity data, occurrence time, light, weather and road conditions, visibility, driver conditions, junction type, land use, road markings and signs, road geometry, etc. The framework conceptualizes decision-making by classifying blackspot stretches based on factors like accident occurrence time, different climatic and road conditions and suggesting mitigation measures based on these identified factors. The decision support system will help the public administration dynamically manage and plan the necessary safety interventions required to enhance the safety of the road network.

Keywords: decision support system, dynamic management, road accident blackspots, road safety

Procedia PDF Downloads 144

Authors: M. Zaitoun, S. Cumming, A. Purcell

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This study aims to determine if audiologists' experience characteristics in ABR (Auditory Brainstem Response) reading is associated with their performance in interpreting ABR results. Fifteen ABR traces with varying degrees of hearing level were presented twice, making a total of 30. Audiologists were asked to determine the hearing threshold for each of the cases after completing a brief survey regarding their experience and training in ABR administration. Sixty-one audiologists completed all tasks. Correlations between audiologists’ performance measures and experience variables suggested significant associations (p < 0.05) between training period in ABR testing and audiologists’ performance in terms of both sensitivity and accuracy. In addition, the number of years conducting ABR testing correlated with specificity. No other correlations approached significance. While there are relatively few significant correlations between ABR performance and experience, accuracy in ABR reading is associated with audiologists’ length of experience and period of training. To improve audiologists’ performance in reading ABR results, an emphasis on the importance of training should be raised and standardized levels and period for audiologists training in ABR testing should also be set.

Keywords: ABR, audiology, performance, training, experience

Procedia PDF Downloads 166

Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

Procedia PDF Downloads 88

Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

Procedia PDF Downloads 158

Authors: David Calderon Villegas, Thomas Kaltizky

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Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an IRR 1.5 times higher than the discount rate.

Keywords: small hydropower, renewable energy, RoR schemes, optimal sizing, objective function

Procedia PDF Downloads 132

Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

Procedia PDF Downloads 176

Authors: T. Gomes, J. Manzi

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The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

Procedia PDF Downloads 246

Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 183

Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

Procedia PDF Downloads 372

Authors: Tarek Dandashi

Abstract:

WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

Procedia PDF Downloads 262

Authors: H. Yousefnia, A. Aghanejad, A. Mirzaei, R. Enayati, A. R. Jalilian, S. Zolghadri

Abstract:

Bone metastases occur in many cases at an early stage of the tumour disease; however, their symptoms are recognized rather late. The aim of this study was the preparation and quality control of 111In-BPAMD for diagnostic purposes. 111In was produced at the Agricultural, Medical, and Industrial Research School (AMIRS) by means of 30 MeV cyclotron via natCd(p,x)111In reaction. Complexion of In‐111 with BPAMD was carried out by using acidic solution of 111InCl3 and BPAMD in absolute water. The effect of various parameters such as temperature, ligand concentration, pH, and time on the radiolabeled yield was studied. 111In-BPAMD was prepared successfully with the radiochemical purity of 95% at the optimized condition (100 µg of BPAMD, pH=5, and at 90°C for 1 h) which was measured by ITLC method. The final solution was injected to wild-type mice and biodistribution was determined up to 72 h. SPECT images were acquired after 2 and 24 h post injection. Both the biodistribution studies and SPECT imaging indicated high bone uptake while accumulation in other organs was approximately negligible. The results show that 111In-BPAMD can be used as an excellent tracer for diagnosis of bone metastases by SPECT imaging.

Keywords: biodistribution, BPAMD, 111In, SPECT

Procedia PDF Downloads 561

Authors: Andika Bagaskara, Andhika Rizki Pratama, Lalu Arya Repatmaja, Septhian Ditaputra Raharja

Abstract:

The power transformer is one of the critical things in system transmission. Regular testing of the power transformer is very important to maintain the reliability of the power. One of the causes of the failure of the transformer is the breakdown of insulation caused by the presence of voids in the equipment that is electrified. As a result of the voids that occur in this power transformer equipment, it can cause partial discharge. Several methods were used to determine the occurrence of damage to the power transformer equipment, such as Sweep Frequency Response Analysis (SFRA) and Tan Delta. In Inter Bus Transformer (IBT) 500/150 kV Cirata Extra High Voltage (EHV) Substation, a breakdown occurred in the T-phase tertiary bushing. From the lessons learned in this case, a complete electrical test was carried out. From the results of the complete electrical test, there was a suspicion of deterioration in the post-breakdown SFRA results. After overhaul and inspection, traces of voids were found on the tertiary bushing, which indicated a breakdown in the tertiary bushing of the IBT 500/150kV Cirata Substation transformer.

Keywords: void, bushing, SFRA, Tan Delta

Procedia PDF Downloads 141

Authors: Ghodhbani Salah, Jemili Farah

Abstract:

With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

Procedia PDF Downloads 404

Authors: Ganesh B. Shinde, Vijaya B. Musande

Abstract:

Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

Procedia PDF Downloads 318

Authors: Zeradam Yeshiwas, A. Krishnaia

Abstract:

The aim of this study is to verify the resulting surface roughness of parts formed by the Single-Point Incremental Forming (SPIF) process for an ISO 3574 Drawing Quality Cold Rolled CR2 Steel. The chemical composition of drawing quality Cold Rolled CR2 steel is comprised of 0.12 percent of carbon, 0.5 percent of manganese, 0.035 percent of sulfur, 0.04 percent phosphorous, and the remaining percentage is iron with negligible impurities. The experiments were performed on a 3-axis vertical CNC milling machining center equipped with a tool setup comprising a fixture and forming tools specifically designed and fabricated for the process. The CNC milling machine was used to transfer the tool path code generated in Mastercam 2017 environment into three-dimensional motions by the linear incremental progress of the spindle. The blanks of Drawing Quality Cold Rolled CR2 steel sheets of 1 mm of thickness have been fixed along their periphery by a fixture and hardened high-speed steel (HSS) tools with a hemispherical tip of 8, 10 and 12mm of diameter were employed to fabricate sample parts. To investigate the surface roughness, hyperbolic-cone shape specimens were fabricated based on the chosen experimental design. The effect of process parameters on the surface roughness was studied using three important process parameters, i.e., tool diameter, feed rate, and step depth. In this study, the Taylor-Hobson Surtronic 3+ surface roughness tester profilometer was used to determine the surface roughness of the parts fabricated using the arithmetic mean deviation (Rₐ). In this instrument, a small tip is dragged across a surface while its deflection is recorded. Finally, the optimum process parameters and the main factor affecting surface roughness were found using the Taguchi design of the experiment and ANOVA. A Taguchi experiment design with three factors and three levels for each factor, the standard orthogonal array L9 (3³) was selected for the study using the array selection table. The lowest value of surface roughness is significant for surface roughness improvement. For this objective, the ‘‘smaller-the-better’’ equation was used for the calculation of the S/N ratio. The finishing roughness parameter Ra has been measured for the different process combinations. The arithmetic means deviation (Rₐ) was measured via the experimental design for each combination of the control factors by using Taguchi experimental design. Four roughness measurements were taken for a single component and the average roughness was taken to optimize the surface roughness. The lowest value of Rₐ is very important for surface roughness improvement. For this reason, the ‘‘smaller-the-better’’ Equation was used for the calculation of the S/N ratio. Analysis of the effect of each control factor on the surface roughness was performed with a ‘‘S/N response table’’. Optimum surface roughness was obtained at a feed rate of 1500 mm/min, with a tool radius of 12 mm, and with a step depth of 0.5 mm. The ANOVA result shows that step depth is an essential factor affecting surface roughness (91.1 %).

Keywords: incremental forming, SPIF, drawing quality steel, surface roughness, roughness behavior

Procedia PDF Downloads 62

Authors: Saba. T. Suliman, Alaa. H. Osman, Sara. T. Ahmed, Zeinab. A. Mustafa, Akram. I. Omara, Banazier. A. Ibraheem

Abstract:

Diagnosis by sweat is one of the emerging methods whereby sweat can identify many diseases in the human body. Sweat contains many elements that help in the diagnostic process. In this research, we analyzed sweat samples by using a Colorimeter device to identify the disease of kidney failure in its various stages. This analysis is a non-invasive method where the sample is collected from outside the body, and then this sample is analyzed. Urea refers to the disease of kidney failure when its quantity is high in the blood and then in the sweat, and by experience, we found that the amount of urea for males differs from its quantity for females, where there is a noticeable increase for males in normal and pathological cases. In this research, we took many samples from a normal group that does not suffer from renal failure and another who suffers from the disease to compare the percentage of urea, and after analysis, we found that the urea percentage is high in people with kidney failure disease. with an accuracy of results of 85%.

Keywords: sweat analysis, kidney failure, urea, non-invasive, eccrine glands, mineral composition, sweat test

Procedia PDF Downloads 42

Authors: A. Szabó Nagy, J. Szabó, I. Vass

Abstract:

Water and sediment samples from the Danube River and Moson Danube Arm (Hungary) have been collected and analyzed for contamination by 18 polycyclic aromatic hydrocarbons (PAHs) and eight trace metal(loid)s (As, Cu, Pb, Ni, Cr, Cd, Hg and Zn) in the period of 2014-2015. Moreover, the trace metal(loid) concentrations were measured in the Rába and Marcal rivers (parts of the tributary system feeding the Danube). Total PAH contents in water were found to vary from 0.016 to 0.133 µg/L and concentrations in sediments varied in the range of 0.118 mg/kg and 0.283 mg/kg. Source analysis of PAHs using diagnostic concentration ratios indicated that PAHs found in sediments were of pyrolytic origins. The dissolved trace metal and arsenic concentrations were relatively low in the surface waters. However, higher concentrations were detected in the water samples of Rába (Zn, Cu, Ni, Pb) and Marcal (As, Cu, Ni, Pb) compared to the Danube and Moson Danube. The concentrations of trace metals in sediments were higher than those found in water samples.

Keywords: surface water, sediment, PAH, trace metal

Procedia PDF Downloads 315