Search results for: intelligent late

Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

Procedia PDF Downloads 357

Authors: Y. Alrubyli

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Unmanned Arial Vehicles (UAVs), and more specifically, quadcopters need to be stable during their flights. Altitude stability is usually achieved by using a PID controller that is built into the flight controller software. Furthermore, the PID controller has gains that need to be tuned to reach optimal altitude stabilization during the quadcopter’s flight. For that, control system engineers need to tune those gains by using extensive modeling of the environment, which might change from one environment and condition to another. As quadcopters penetrate more sectors, from the military to the consumer sectors, they have been put into complex and challenging environments more than ever before. Hence, intelligent self-stabilizing quadcopters are needed to maneuver through those complex environments and situations. Here we show that by using online reinforcement learning with minimal background knowledge, the altitude stability of the quadcopter can be achieved using a model-free approach. We found that by using background knowledge instead of letting the online reinforcement learning algorithm wander for a while to tune the PID gains, altitude stabilization can be achieved faster. In addition, using this approach will accelerate development by avoiding extensive simulations before applying the PID gains to the real-world quadcopter. Our results demonstrate the possibility of using the trial and error approach of reinforcement learning combined with background knowledge to achieve faster quadcopter altitude stabilization in different environments and conditions.

Keywords: reinforcement learning, Q-leanring, online learning, PID tuning, unmanned aerial vehicle, quadcopter

Procedia PDF Downloads 164

Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

Procedia PDF Downloads 97

Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

Procedia PDF Downloads 128

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

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Authors: Orkide Donma, M. Metin Donma, Burcin Nalbantoglu, Birol Topcu, Feti Tulubas, Murat Aydin, Tuba Gokkus, Ahmet Gurel

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Increasing prevalence of childhood obesity has increased the interest in early and late indicators of gaining weight. Cell blood counts may be indicators of proinflammatory states. The aim was to evaluate associations of hematological parameters, including Hematocrit (HTC), hemoglobin, blood cell counts, and their indices with the degree of obesity in pediatric population. A total of 249; -139 morbidly obese (MO), 82 healthy Normal Weight (NW) and 28 Overweight (OW) children were included into the scope of the study. WHO BMI-for age percentiles were used to form age- and sex-matched groups. Informed consent forms and the Ethics Committee approval were obtained. Anthropometric measurements were performed. Hematological parameters were determined. Statistical analyses were performed using SPSS. The degree for statistical significance was p≤0.05. Significant differences (p=0.000) between waist-to-hip ratios and head-to-neck ratios (hnrs) of MO and NW children were detected. A significant difference between hnrs of OW and MO children (p=0.000) was observed. Red cell Distribution Width (RDW) was higher in OW children than NW group (p=0.030). Such finding couldn’t be detected between MO and NW groups. Increased RDW was prominent in OW children. The decrease in Mean Corpuscular Hemoglobin Concentration (MCHC) values in MO children was sharper than the values in OW children (p=0.006 vs p=0.042) compared to those in NW group. Statistically higher HTC levels were observed between MO-NW (p=0.014), but none between OW-NW. Though the cause-effect relationship between obesity and erythrocyte indices still needs further investigation, alterations in RDW, HTC, MCHC during obesity may be of significance in the early life.

Keywords: anthropometry, children, erythrocytes, obesity

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Authors: Tolulope Allo, Mofoluwake P. Ajayi, Adenike E. Idowu, Emmanuel O. Amoo, Fadeke Esther Olu-Owolabi

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Breast cancer which was more prevalent in Europe and America in the past is gradually being mirrored across the world today with greater economic burden on low and middle income countries (LMCs). Breast cancer is the most common cancer among women globally and current studies have shown that a woman dies with the diagnosis of breast cancer every thirteen minutes. The economic cost of breast cancer is overwhelming particularly for developing economies. While it causes billion of dollar in losses of national income, it pushes millions of people below poverty line. This study examined the economic burden of breast cancer on Nigerian women, its impacts on their standard of living and its effects on Nigeria’s socio economic development. The study adopts a qualitative research approach using the in-depth interview technique to elicit valuable information from respondents with cancer experience from the Southern part of Nigeria. Respondents constituted women in their reproductive age (15-49 years) that have experienced and survived cancer and also those that are currently receiving treatment. Excerpts from the interviews revealed that the cost of treatment is one of the major factors contributing to the late presentation of breast cancer incidences among women as many of them could not afford to pay for their own treatment. The study also revealed that many women prefer to explore other options such as herbal treatments and spiritual consultations which is less expensive and affordable. The study therefore concludes that breast cancer diagnosis and treatment should be subsidized by the government in order to facilitate easy access and affordability thereby promoting early detection and reducing the economic burden of treatment on women.

Keywords: breast cancer, development, economic burden, women

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Authors: Poonam A. Trambadia, Ashish V. Trambadia

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The architecture of sultanate period of Ahmedabad had evolved just before the establishment of Mughal rule in North India. After shifting the capital of the kingdom from Patan to Ahmedabad, when the buildings and structures were being built, an interesting cultural blend happened in architecture. Many sultanate buildings in Ahmedabad historic city have resemblance with Patan including the names. Outer fortification walls and Gates were built during the rule of the third ruler in the late 15th century. All the gates had sandstone slabs supported by three arched entrance in sandstone with wooden shutter. A restoration project of Khanpur Gate was initiated in 2016. The paper identifies some evidences and some hidden layers of structures as important elements of cultural interchange while some were just forgotten in the process. The recycling of pre-existing elements of structures are examined and compared. There were layers uncovered that were hidden behind later repairs using traditional brick arch, which was taken out in the process. As the gate had partially collapsed, the restoration included piece by piece dismantling and restoring in the same sequence wherever required. The recycled materials found in the process were recorded and provided the basis for this study. The gate after this discovery sets a new example of fortification Gate built in Sultanate era. The comparison excludes Maratha and British Period Gates to avoid further confusion and focuses on 15th – 16th century sultanate architecture of Ahmedabad.

Keywords: Ahmedabad World Heritage, fortification, Indo-Islamic style, Sultanate architecture, cultural interchange

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Authors: Marine Nozadze, George Japoshvili, George Melika

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In 2020-2021 we studied oaks gall wasps of different oak species in Georgia at 7 locations of their natural distribution: 1. Quercuse petrea subsp. iberica - Mtskheta municipality, village. Mukhattskaro; 2. Quercus subsp. pendunculifloria - Kvareli municipality, village. Gramy;3. Quercus robur subsp. imeretina -Baghdati Municipality, Ajameti Reserve; 4. Quercus pontica -Chokhatauri municipality, village. Tskhratskaro; 5. Quercus macranthera -Tetritskaro Municipality, Algeti National Park; 6. Quercuse petrea subsp. iberica - Shuakhevi municipality, village. Uchamba 7. Quercus hartwissiana - Baghdatis municipality, village. Dimi. Samples were collected from early spring to late autumn. As a result, 7 forms of galls were collected and described wich caused by different species of oak gall wasps: 1. Neuroterus numismalis asexual gall 2. Neuroterus quercusbaccarum asexual galls 3. Cynips korsakovi asexual gall 4. Biorhiza pallida sexual gall 5. Neuroterus quercusbaccarum asexual galls 6. Neuroterus numismalis sexual gall 7. Cynips quercusfolii. Neuroterus quercusbaccarum asexual galls form the most represented of them: In Algeti National Park; In Mtskheta municipality; In Shuakhevi municipality and Ajameti reserve. The most damaged locations by oak gall wasps were Algeti National Park and Mtskheta Municipality, whereas the most biodiversity according to galls form was represented In Algeti National Park.

Keywords: gall wasps, oak, cynipidae, species

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Ogunremi Beatrice, Oluwole David Adebayo

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Public secondary school students are face with some challenges from the parents, government and teachers in school. Some of the challenges that arises from the parents are lack of attention and adequate communication. From the government are unavailability of useful instructional materials, competent and professionally trained teachers for each subject the students do in school. The challenges that arise from the teachers most often are mismanagement of time, inability to understand the capacity of the student and lack class management and follow up. This study investigated self-regulation, emotional intelligence, self-efficacy and goal orientation as predictors of school engagement among public secondary school students in Ibadan. A structured questionnaire was administered on 258 students from six mixed secondary schools in Ibadan. Pearson Product Moment Correlation method was used for data analysis. Four hypothesis were raised and answered, the results showed there is positive and significant relationships between school engagement among public secondary school students and each of the independent variable: Self-regulation, Emotional intelligence, Self-efficacy, Goal orientation. On the basis of these findings, it was recommended that the parents have to encourage their children on how to be goal oriented ,build their self-efficacy skill, to be self-regulated and emotionally intelligent in order to be effective in school and be able to increase their intellectual ability.

Keywords: emotional intelligence, self-efficacy, goal orientation, school engagement, self-regulation

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: I. L. Yakushev

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Economic sanctions are an integral part of the foreign policy repertoire of States. Increasingly, States and international organizations are resorting to sanctions to address a variety of issues -from fighting corruption to preventing the use of nuclear weapons. Over time, the ways in which economic sanctions have been used have changed, especially over the past two decades. In the late 1990s, the recognition of the humanitarian harm of economic sanctions and the "War on Terrorism" after the events of September 11, 2001, led to serious changes in the structure and mechanisms of their application. Questions about how these coercive tools work, when they are applied, what consequences they have, and when they are successful are still being determined by research conducted in the second half of the 20th century. The conclusions drawn from past cases of sanctions may not be fully applicable to the current sanctions policy. In the second half of the 20th century, most cases of sanctions were related to the United States, and it covered restrictions on international trade. However, over the past two decades, the European Union, the United Nations, and China have also been the main initiators of sanctions. Modern sanctions include targeted and financial restrictions and are applied against individuals, organizations, and companies. Changing the senders, targets, stakeholders, and economic instruments used in the sanctions policy has serious implications for effectiveness and results. The regulatory and bureaucratic infrastructure necessary to implement and comply with modern economic sanctions has become more reliable. This evolution of sanctions has provided the scientific community with an opportunity to study new issues of coercion and return to the old ones. The economic sanctions research program should be developed to be relevant for understanding the application of modern sanctions and their consequences.

Keywords: global analysis, economic sanctions, targeted sanctions, foreign policy, domestic policy, United Nations, European Union, USA, economic pressure

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Authors: Noraziah Nordin, Najihah Mohd Hashim, Nazia Abdul Majid, Mashitoh Abdul Rahman, Hamed Karimian, Hapipah Mohd Ali

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Enicosanthellum pulchrum belongs to family Annonaceae is also known as family of 'mempisang' in Malaysia. Liriodenine was isolated by prep-HPLC method. This method was first technique used for the isolation of this compound. The structure of the liriodenine was elucidated by 1D and 2D spectroscopy techniques. Liriodenine was tested on ovarian cancer cells line (CAOV-3) for MTT, AO/PI and cytotoxicity 3 assays. The MTT assay was performed to determine the cytotoxicity effect of lirodenine on CAOV-3 cells. The morphological changes on CAOV-3 cells were observed by AO/PI assay for the early and late stage of apoptosis, as well as necrosis. Meanwhile, the measurement of cell loss, nuclear morphology, DNA content, cell membrane permeability, mitochondrial membrane potential changes and cytochrome c release from mitochondria were detected through cytotoxicity 3 assay. The IC50 results showed liriodenine inhibits the growth of CAOV-3 cells after 24 h of treatment at 10.25 ± 1.06 µg/mL. After 48 and 72 h of treatments, the IC50 values were decreased to 7.65 ± 0:07 and 6.35 ± 1.62 µg/mL, respectively. The morphology changes can be seen on CAOV-3 with a production of cell membrane blebbing, cromatin condensation and apoptotic bodies with increasing time of treatment from 24 to 72 h. Evaluation of cytotoxicity 3 on CAOV-3 cells after treated with liriodenine, resulting loss of mitochondrial membrane potential and release of cytochrome c from mitochondria. The results demonstrated the capability of liriodenine as a promising anticancer agent, particularly on human ovarian cancer.

Keywords: Enicosanthellum pulchrum, ovarian cancer, apoptosis, cytotoxicity

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: hybrid meta-heuristic methods, substation construction, resource allocation, time-cost efficiency

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Authors: Umar Sambo Umar, Ahmad Isah Haruna, Abubakar Sadik Maigari, Muhammad Bello Abubakar

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Schist belts in Nigeria occur prominently west of longitude 80 E and sporadic to the east, they are upper Proterozioc low-medium grade deformed metasediments and metavolcanics that were intruded by Pan-African granitoids. The Wonaka schist belt, though reportedly distinctive in composition and metamorphism, is the least understood; the host for primary gold were not defined, structures which may control primary enrichment have not been delineated. The aim of this work is to determine the relationship between litho-structures and the gold around Wonaka schist belt through geological field mapping, petrographic studies and structural data analysis via ArcGis 10.2, Surfer 11.0 and Stereopro 2.0. The results show that the major rock types are mica schist and migmatites, muscovites detected during microstructural analysis suggests low-grade metamorphism in the metapelites. The shear zones identified were trending North Northeast – South Southwest (NNE-SSW), fractures trend mostly Northeast-Southwest (NE-SW) perpendicular to planes of gneissic foliations, these conform to the late Pan-African deformational episode. Pegmatite lodes, net self-cross cutting quartz veins as well as the quartz stringers hosted by both migmatites and schist are delineated as targets for primary gold mineralization, while major confluences of the streams serve as zones for secondary (placer) gold targets since the streams are dendritic and intermittent.

Keywords: gold mineralization, Nigeria, migmatites, Wonaka schist belt

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Authors: Su Xiaohan, Jin Chicheng, Liu Yijing, Burra Venkata Durga Kumar

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Energy-aware scheduling in real-time systems aims to minimize energy consumption, but issues related to resource reservation and timing constraints remain challenges. This study focuses on analyzing two scheduling algorithms, Fair-Share Scheduling (FFS) and Priority-Driven Preemptive Scheduling (PDPS), for solving these issues and energy-aware scheduling in real-time systems. Based on research on both algorithms and the processes of solving two problems, it can be found that Fair-Share Scheduling ensures fair allocation of resources but needs to improve with an imbalanced system load, and Priority-Driven Preemptive Scheduling prioritizes tasks based on criticality to meet timing constraints through preemption but relies heavily on task prioritization and may not be energy efficient. Therefore, improvements to both algorithms with energy-aware features will be proposed. Future work should focus on developing hybrid scheduling techniques that minimize energy consumption through intelligent task prioritization, resource allocation, and meeting time constraints.

Keywords: energy-aware scheduling, fair-share scheduling, priority-driven preemptive scheduling, real-time systems, optimization, resource reservation, timing constraints

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Authors: Vaibhav Barve

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Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

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Authors: A. Flammini, R. Morbidelli, C. Saltalippi

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The increase of air surface temperature at global scale is a fact, with values around 0.85 ºC since the late nineteen century, as well as a significant change in main features of rainfall regime. Nevertheless, the detected climatic changes are not equally distributed all over the world, but exhibit specific characteristics in different regions. Therefore, studying the evolution of climatic indices in different geographical areas with a prefixed standard approach becomes very useful in order to analyze the existence of climatic trend and compare results. In this work, a methodology to investigate the climatic change and its effects on a wide set of climatic indices is proposed and applied at regional scale in the case study of a Mediterranean area, Umbria region in Italy. From data of the available temperature stations, nine temperature indices have been obtained and the existence of trends has been checked by applying the non-parametric Mann-Kendall test, while the non-parametric Pettitt test and the parametric Standard Normal Homogeneity Test (SNHT) have been applied to detect the presence of break points. In addition, aimed to characterize the rainfall regime, data from 11 rainfall stations have been used and a trend analysis has been performed on cumulative annual rainfall depth, daily rainfall, rainy days, and dry periods length. The results show a general increase in any temperature indices, even if with a trend pattern dependent of indices and stations, and a general decrease of cumulative annual rainfall and average daily rainfall, with a time rainfall distribution over the year different from the past.

Keywords: climatic change, temperature, rainfall regime, trend analysis

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Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

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The optimal construction design used during the execution of a construction project is a key factor in determining high productivity and customer satisfaction, however, this management process sometimes is carried out without care and the systematic method that it deserves, bringing negative consequences. This study proposes a knowledge management (KM) approach that will enable the intelligent use of experienced and acknowledged engineers to improve the management of construction design works for a project. Then a knowledge-based application to support this decision-making process is proposed and described. To define and design the system for the application, semi-structured interviews were conducted within five construction consulting organizations with the purpose of studying the way that the method’ optimizing process is implemented in practice and the knowledge supported with it. A system of an optimizing construction design works (OCDW) based on knowledge was developed then validated with construction experts. The OCDW was liked as a valuable tool for construction design works’ optimization, by supporting organizations to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The benefits are described as provided by the performance support system, reducing costs and time, improving product design quality, satisfying customer requirements, expanding the brand organization.

Keywords: optimizing construction design work, construction consultant organization, knowledge management, knowledge-based application

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Suhadiyana Hanapi, Alhassan Salami Tijani, W. A. N Wan Mohamed

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In this paper, a prototype PEM fuel cell vehicle integrated with a 1 kW air-blowing proton exchange membrane fuel cell (PEMFC) stack as a main power sources has been developed for a lightweight cruising vehicle. The test vehicle is equipped with a PEM fuel cell system that provides electric power to a brushed DC motor. This vehicle was designed to compete with industrial lightweight vehicle with the target of consuming least amount of energy and high performance. Individual variations in driving style have a significant impact on vehicle energy efficiency and it is well established from the literature. The primary aim of this study was to assesses the power and fuel consumption of a hydrogen fuel cell vehicle operating at three difference driving technique (i.e. 25 km/h constant speed, 22-28 km/h speed range, 20-30 km/h speed range). The goal is to develop the best driving strategy to maximize performance and minimize fuel consumption for the vehicle system. The relationship between power demand and hydrogen consumption has also been discussed. All the techniques can be evaluated and compared on broadly similar terms. Automatic intelligent controller for driving prototype fuel cell vehicle on different obstacle while maintaining all systems at maximum efficiency was used. The result showed that 25 km/h constant speed was identified for optimal driving with less fuel consumption.

Keywords: prototype fuel cell electric vehicles, energy efficient, control/driving technique, fuel economy

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Authors: H. Lee, J. Park

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In this present study, for the first time, we retrieved the Formaldehyde (HCHO) Vertical Column Density (HCHOVCD) using Pandora instruments in Seoul, a megacity in northeast Asia, for the period between 2012 and 2014 and investigated the temporal characteristics of HCHOVCD. HCHO Slant Column Density (HCHOSCD) was obtained using the Differential Optical Absorption Spectroscopy (DOAS) method. HCHOSCD was converted to HCHOVCD using geometric Air Mass Factor (AMFG) as Pandora is the direct-sun measurement. The HCHOVCDs is low at 12:00 Local Time (LT) and is high in the morning (10:00 LT) and late afternoon (16:00 LT) except for winter. The maximum (minimum) values of Pandora HCHOVCD are 2.68×1016 (1.63×10¹⁶), 3.19×10¹⁶ (2.23×10¹⁶), 2.00×10¹⁶ (1.26×10¹⁶), and 1.63×10¹⁶ (0.82×10¹⁶) molecules cm⁻² in spring, summer, autumn, and winter, respectively. In terms of seasonal variations, HCHOVCD was high in summer and low in winter which implies that photo-oxidation plays an important role in HCHO production in Seoul. In comparison with the Ozone Monitoring Instrument (OMI) measurements, the HCHOVCDs from the OMI are lower than those from Pandora. The correlation coefficient (R) between monthly HCHOVCDs values from Pandora and OMI is 0.61, with slop of 0.35. Furthermore, to understand HCHO mixing ratio within Planetary Boundary Layer (PBL) in Seoul, we converted Pandora HCHOVCDs to HCHO mixing ratio in the PBL using several meteorological input data from the Atmospheric InfraRed Sounder (AIRS). Seasonal HCHO mixing ratio in PBL converted from Pandora (OMI) HCHOVCDs are estimated to be 6.57 (5.17), 7.08 (6.68), 7.60 (4.70), and 5.00 (4.76) ppbv in spring, summer, autumn, and winter, respectively.

Keywords: formaldehyde, OMI, Pandora, remote sensing

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Authors: Haitham Nabil Zaghlol Hasan

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This research aims to study the means of securing graves in the Egyptian kingdom era, and revolves around many basic ideas used by the ancient Egyptian to protect his graves from thieves, which included architectural characteristics, which gave it importance only others. The most important of which was the choice of the place of the grave, which chose a kohl place in the desert to protect the graves, which is the valley of kings, and whether the choice of that place had an impact in protecting the grave or not, in addition to other elements followed in the architectural planning, which was in the valley of kings. The multiplicity of the tomb, the construction of the well chamber to deceive the thieves by the end of the graves suddenly, the construction of the wells of the tombs, which contained the burial chamber at the bottom of the main well and the effect of all these factors on the graves, and this shows the importance of the graves to the ancient Egyptian and his belief in resurrection and immortality. The Egyptian resorted to the elements of protection and was a religious worker by The protector gods and special texts to protect the deceased from any danger to protect the tomb. As for the human factor of securing the tomb through human guards (police) and security teams based on the guard and the words indicating the protection and the guard teams and the teams of the majai. The most important developments that arose on the cemetery from Tamit entrance, corridors, chambers, burial chamber and coffin, and the use of sand to close the well after from one cemetery to another and from time to time where it was built in the late period inside the temple campus to be under the attention of the priests and their protection, as the study dealt with an analytical study For the means of securing graves in the Egyptian kingdom period.

Keywords: Egypt, archaeology, civilization, Egyptian

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Authors: Oum Elkhir Mahmoudi, Nebiha Ben Haj Ali

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Micropaleontological, sedimentological and statistical studies were carried out on the late Paleocene-early Eocene succession of Sra Ouertaine and Dyr El Kef in Northern open phosphatic Basin of Tunisia. Based on the abundance and stratigraphic distribution of planktic foraminiferal species, five planktic zones have been recognized from the base to the top of the phosphatic layers. The El Acarinina sibaiyaensis Zone, the E2 Pseudohastigerina wilcoxensis Zone, the E3 Morozovella marginodentata Zone, the E4 Morozovella formosa Zones and the E5 Morozovella subbotinae Zone. The placement of Paleocene-Eocene boundary (PETM) is just below the base of the phosphatic interval. The ETM-2 event may be detectable in the analyzed biotic record of Sra Ouertaine. Based on benthic assemblages, abundances, cluster and multivariate statistical analyses, two biofacies were recognized for each section. The recognized ecozones are typical of warm and shallow water inner neritic setting (dominance of epifaunal fauna Anomalinoides, Dentalina and Cibicidoides associated with Frondicularia phosphatica, Trochamminoides globigeriniformis and Eponides elevatus). The paleoenvironment is eutrophic (presence of several bolivinitids and verneuilinids). For the Dyr El Kef section and P5 and E2 of Sra Ouertaine section, our records indicate that paleoenvironment is influenced by coastal upwelling without oxygen-deficiency, the paleodepth is estimated to be around 50 m. The paleoecosystem is diversified and balanced with a general tendency to stressed condition. While the upper part of Sra Ouertaine section is more eutrophic, influenced by coastal upwelling with oxygen-deficiency, the paleodepth is estimated to be less than 50 m and the ecosystem is unsettled.

Keywords: Tunisia, Sra ouertaine Dyr el kef, early Eocene, foraminifera, chronostratigraphy, paleoecology, paleoenvironment

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Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

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Authors: Anna Keuchenius

Abstract:

Broadly speaking, scientific development is studied in either a qualitative manner with a focus on the behavior and interpretations of academics, such as the sociology of science and science studies or in a quantitative manner with a focus on the analysis of publications, such as scientometrics and bibliometrics. Both come with a different set of methodologies and few cross-references. This paper contributes to the bridging of this divide, by on the on hand approaching the process of scientific progress from a qualitative sociological angle and using on the other hand quantitative and computational techniques. As a case study, we analyze the diffusion of Granovetter's hypothesis from his 1973 paper 'On The Strength of Weak Ties.' A network is constructed of all scientists that have referenced this particular paper, with directed edges to all other researchers that are concurrently referenced with Granovetter's 1973 paper. Studying the structure and growth of this network over time, it is found that Granovetter's hypothesis is used by distinct communities of scientists, each with their own key-narrative into which the hypothesis is fit. The diffusion within the communities shares similarities with the diffusion of an innovation in which innovators, early adopters, and an early-late majority can clearly be distinguished. Furthermore, the network structure shows that each community is clustered around one or few hub scientists that are disproportionately often referenced and seem largely responsible for carrying the hypothesis into their scientific subfield. The larger implication of this case study is that the diffusion of scientific hypotheses and ideas are not the spreading of well-defined objects over a network. Rather, the diffusion is a process in which the object itself dynamically changes in concurrence with its spread. Therefore it is argued that the methodology presented in this paper has potential beyond the scientific domain, in the study of diffusion of other not well-defined objects, such as opinions, behavior, and ideas.

Keywords: diffusion of innovations, network analysis, scientific development, sociology of science

Procedia PDF Downloads 299