Search results for: ring deep beam

Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Hyery Kang, Dong-Yeun Koh, Yun-Ho Ahn, Huen Lee

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Determination of the behavior of clathrate hydrate with inhibitor in the THz region will provide useful information about hydrate plug control in the upstream of the oil and gas industry. In this study, terahertz time-domain spectroscopy (THz-TDS) revealed the inhibition of the THF clathrate hydrate system with dosage of polyvinylpyrrolidone (PVP) with three different molecular weights. Distinct footprints of phase transition in the THz region (0.4–2.2 THz) were analyzed and absorption coefficients and real part of refractive indices are obtained in the temperature range of 253 K to 288 K. Along with the optical properties, ring breathing and stretching modes for different molecular weights of PVP in THF hydrate are analyzed by Raman spectroscopy.

Keywords: clathrate hydrate, terahertz spectroscopy, tetrahydrofuran, inhibitor

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Authors: Sy-Wei Lo, Chi-Heng Yu

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A new design of aerostatic thrust bearing is proposed for high static stiffness. The bearing body, which is mead of polymer covered with metallic membrane, is held by a circular ring. Such a support helps form a concave air gap to grasp the air pressure. The polymer body, which can be made rapidly by either injection or molding is able to provide extra damping under dynamic loading. The smooth membrane not only serves as the bearing surface but also protects the polymer body. The restrictor is a capillary inside a silicone tube. It can passively compensate the variation of load by expanding the capillary diameter for more air flux. In the present example, the stiffness soars from 15.85 N/µm of typical bearing to 349.85 N/µm at bearing elevation 9.5 µm; meanwhile the load capacity also enhances from 346.86 N to 704.18 N.

Keywords: aerostatic, bearing, polymer, static stiffness

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Authors: Rita Phillips, Mark Burgess, Maga Berlinski

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Although the positive impact of religiosity on well-being, health, and life-coping abilities is well known, up to date research has failed to provide scientific evidence for the relationship reasons. Therefore the present study took a qualitative approach by examining how religiosity interacts in coping with emotionally distressful situations, for which wedding preparations are an example. Wedding preparations, related to the experience of ambiguous emotions, can be the reason for phases of high distress. Although being per-se religious ceremonies, they are also socially-scripted and characterized by people’s striving for personally meaningful celebrations. The negotiation of these many influences can evoke conflicts. To reveal components of religiosity which contribute to stress-resolution, eight biographic-narrative interviews with recently married spouses were conducted. Participants were from different nationalities and Catholic deep-belief communities in order to determine factors independent from national-culture and social-subgroup. The audio-tape recorded, transcribed and translated interviews were analyzed by Interpretative Phenomenological Analysis. Opposing previous research on wedding-related conflicts but in-line with the quantitative account on the relation between stress-resilience and religiosity, the present study found participants reporting very low levels of distress and ambiguity. Although similar areas of potential conflicts were revealed, deep-belief Christians seemed to handle them in a different way. Participants freed themselves from own and others’ rigor mundane expectations by their spiritual preparation and the focus on a divine instance. This evoked a feeling of perceived closeness to God and of unconditional love, resulting in acceptance of oneself and others. Through relativizing mundane goods, participants perceived absolute freedom. Thus belief did not supplement coping strategies, previously defined in the literature, but substituted them. The paper implies that in explaining the connection between stress-resilience and religiosity, one’s perception and experience of unconditional love might outweigh other social or personal factors. However, further qualitative investigations are needed to fully explain the phenomenon.

Keywords: deep-belief, religiosity, resilience, wedding

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Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Mojtaba Heydarizad

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Andarokh basin is one of the main karstic regions in Khorasan Razavi province NE Iran. This basin is part of Kopeh-Dagh mega zone extending from Caspian Sea in the east to northern Afghanistan in the west. This basin is covered by Mozdooran Formation, Ngr evaporative formation and quaternary alluvium deposits in descending order of age. Mozdooran carbonate formation is notably karstified. The main surface karstic features in Mozdooran formation are Groove karren, Cleft karren, Rain pit, Rill karren, Tritt karren, Kamintza, Domes, and Table karren. In addition to surface features, deep karstic feature Andarokh Cave also exists in the region. Studying Ca, Mg, Mn, Sr, Fe concentration and Sr/Mn ratio in Mozdooran formation samples with distance to main faults and joints system using PCA analyses demonstrates intense meteoric digenesis role in controlling carbonate rock geochemistry. The karst evaluation in Andarokh basin varies from early stages 'deep seated karst' in Mesozoic to mature karstic system 'Exhumed karst' in quaternary period. Andarokh cave (the main cave in Andarokh basin) is rudimentary branch work consists of three passages of A, B and C and two entrances Andarokh and Sky.

Keywords: Andarokh basin, Andarokh cave, geochemical analyses, karst evaluation

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Marta Błażkiewicz, Adam Konefał

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Currently, boron-neutron therapy is carried out mainly with the use of a neutron beam generated in research nuclear reactors. This fact limits the possibility of realization of a BNCT in centers distant from the above-mentioned reactors. Moreover, the number of active nuclear reactors in operation in the world is decreasing due to the limited lifetime of their operation and the lack of new installations. Therefore, the possibilities of carrying out boron-neutron therapy based on the neutron beam from the experimental reactor are shrinking. However, the use of nuclear power reactors for BNCT purposes is impossible due to the infrastructure not intended for radiotherapy. Therefore, a serious challenge is to find ways to perform boron-neutron therapy based on neutrons generated outside the research nuclear reactor. This work meets this challenge. Its goal is to develop a BNCT technique based on commonly available neutron sources such as Cf-252 and PuBe, which will enable the above-mentioned therapy in medical centers unrelated to nuclear research reactors. Advances in the field of neutron source fabrication make it possible to achieve strong neutron fluxes. The current stage of research focuses on the development of virtual models of the above-mentioned sources using the Monte Carlo simulation method. In this study, the GEANT4 tool was used, including the model for simulating neutron-matter interactions - High Precision Neutron. Models of neutron sources were developed on the basis of experimental verification based on the activation detectors method with the use of indium foil and the cadmium differentiation method allowing to separate the indium activation contribution from thermal and resonance neutrons. Due to the large number of factors affecting the result of the verification experiment, the 10% discrepancy between the simulation and experiment results was accepted.

Keywords: BNCT, virtual models, neutron sources, monte carlo, GEANT4, neutron activation detectors, gamma spectroscopy

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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

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Authors: O. Arikan, C. Kocatepe, G. Ucar, Y. Hacialiefendioglu

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In this paper, influence of harmonics on medium voltage distribution system of Bogazici Electricity Distribution Inc. (BEDAS) which takes place at Istanbul/Turkey is investigated. A ring network consisting of residential loads is taken into account for this study. Real system parameters and measurement results are used for simulations. Also, probable working conditions of the system are analyzed for %50, %75 and %100 loading of transformers with similar harmonic contents. Results of the study are exhibited the influence of nonlinear loads on %THDV, P.F. and technical losses of the medium voltage distribution system.

Keywords: distribution system, harmonic, technical losses, power factor, total harmonic distortion, residential load, medium voltage

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Authors: K. Shaji Mon, P. R. John Sreenidhi

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In deep-submicrometer CMOS processes, time-domain resolution of a digital signal is becoming higher than voltage resolution of analog signals. This claim is nowadays pushing toward a new circuit design paradigm in which the traditional analog signal processing is expected to be progressively substituted by the processing of times in the digital domain. Within this novel paradigm, digitally controlled delay lines (DCDL) should play the role of digital-to-analog converters in traditional, analog-intensive, circuits. Digital delay locked loops are highly prevalent in integrated systems.The proposed paper addresses the glitches present in delay circuits along with area,power dissipation and signal integrity.The digitally controlled delay lines(DCDL) under study have been designed in a 90 nm CMOS technology 6 layer metal Copper Strained SiGe Low K Dielectric. Simulation and synthesis results show that the novel circuits exhibit no glitches for dual output coarse DCDL with less power dissipation and consumes less area compared to the glitch free NAND based DCDL.

Keywords: glitch free, NAND-based DCDL, CMOS, deep-submicrometer

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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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

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Authors: Khansa Hayat

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Organizational stress is one of the vital phenomena which is having its roots deep down in has deep roots in management, psychology, and organizational behavior research. In the meanwhile, keeping its focus on the positive strength of humans rather than the traditional negativity oriented research, positive psychology has emerged as a separate branch of organizational behavior. The current study investigates the interactive effects of Challenge and hindrance stressors and core Self Evaluations (CSE’s) of the individual on job performances including the in-role performance and extra role performances. The study also aims to investigate the supporting/buffering role of the human dispositions (i.e., self esteem, self efficacy, locus of control and emotional stability). The results show that Challenge stressors have a significant positive effect on in role performance and extra role performance of the individual. The findings of the study indicate that Core Self evaluations strengthen the relationship between challenge stressors and in role performance of the individual. In case of Hindrance Stressors the Core self Evaluations lessen the negative impact of Hindrance stressors and they let the individual perform at a better and normal position even when the Hindrance stressors are high. The relationship and implication of conservation of resource theory are also discussed. The limitations, future research directions and implications of the study are also discussed.

Keywords: challenge-hindrance stressors, core self evaluations, in-role performance, extra-role performance

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Authors: I. Wlasny, Z. Klusek, A. Wysmolek

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Hexagonal boron nitride is a representative of a widely popular class of two-dimensional Van Der Waals materials. It finds its uses, among others, in construction of complexly layered heterostructures. Hexagonal boron nitride attracts great interest because of its properties characteristic for wide-gap semiconductors as well as an ultra-flat surface.Van Der Waals heterostructures composed of two-dimensional layered materials, such as transition metal dichalcogenides or graphene give hope for miniaturization of various electronic and optoelectronic elements. In our presentation, we will show the results of our investigations of the not previously reported modification of the hexagonal boron nitride layers with focused laser beam. The electrostatic force microscopy (EFM) images reveal that the irradiation leads to changes of the local electric fields for a wide range of laser wavelengths (from 442 to 785 nm). These changes are also accompanied by alterations of crystallographic structure of the material, as reflected by Raman spectra. They exhibit high stability and remain visible after at least five months. This behavior can be explained in terms of photoionization of the defect centers in h-BN which influence non-uniform electrostatic field screening by the photo-excited charge carriers. Analyzed changes influence local defect structure, and thus the interatomic distances within the lattice. These effects can be amplified by the piezoelectric character of hexagonal boron nitride, similar to that found in nitrides (e.g., GaN, AlN). Our results shed new light on the optical properties of the hexagonal boron nitride, in particular, those associated with electron-phonon coupling. Our study also opens new possibilities for h-BN applications in layered heterostructures where electrostatic fields can be used in tailoring of the local properties of the structures for use in micro- and nanoelectronics or field-controlled memory storage. This work is supported by National Science Centre project granted on the basis of the decision number DEC-2015/16/S/ST3/00451.

Keywords: atomic force microscopy, hexagonal boron nitride, optical properties, raman spectroscopy

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Authors: Cristina Lavilla, Andreas Heise

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The synthesis of sequence-controlled polymers has received increasing attention in the last years. Well-defined polyacrylates, polyacrylamides and styrene-maleimide copolymers have been synthesized by sequential or kinetic addition of comonomers. However this approach has not yet been introduced to the synthesis of polypeptides, which are in fact polymers developed by nature in a sequence-controlled way. Polypeptides are natural materials that possess the ability to self-assemble into complex and highly ordered structures. Their folding and properties arise from precisely controlled sequences and compositions in their constituent amino acid monomers. So far, solid-phase peptide synthesis is the only technique that allows preparing short peptide sequences with excellent sequence control, but also requires extensive protection/deprotection steps and it is a difficult technique to scale-up. A new strategy towards sequence control in the synthesis of polypeptides is introduced, based on the sequential addition of α-amino acid-N-carboxyanhydrides (NCAs). The living ring-opening process is conducted to full conversion and no purification or deprotection is needed before addition of a new amino acid. The length of every block is predefined by the NCA:initiator ratio in every step. This method yields polypeptides with a specific sequence and controlled molecular weights. A series of polypeptides with varying block sequences have been synthesized with the aim to identify structure-property relationships. All of them are able to adopt secondary structures similar to natural polypeptides, and display properties in the solid state and in solution that are characteristic of the primary structure. By design the prepared polypeptides allow selective modification of individual block sequences, which has been exploited to introduce functionalities in defined positions along the polypeptide chain. Poly(ethylene glycol)(PEG) was the functionality chosen, as it is known to favor hydrophilicity and also yield thermoresponsive materials. After PEGylation, hydrophilicity of the polypeptides is enhanced, and their thermal response in H2O has been studied. Noteworthy differences in the behavior of the polypeptides having different sequences have been found. Circular dichroism measurements confirmed that the α-helical conformation is stable over the examined temperature range (5-90 °C). It is concluded that PEG units are the main responsible of the changes in H-bonding interactions with H2O upon variation of temperature, and the position of these functional units along the backbone is a factor of utmost importance in the resulting properties of the α-helical polypeptides.

Keywords: α-amino acid N-carboxyanhydrides, multiblock copolymers, poly(ethylene glycol), polypeptides, ring-opening polymerization, sequence control

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Indunil Jayatilake, Warna Karunasena

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Fiber Reinforced Polymer (FRP) composites enjoy an array of applications ranging from aerospace, marine and military to automobile, recreational and civil industry due to their outstanding properties. A structural glass fiber reinforced polymer (GFRP) composite sandwich panel made from E-glass fiber skin and a modified phenolic core has been manufactured in Australia for civil engineering applications. One of the major mechanisms of damage in FRP composites is skin-core debonding. The presence of debonding is of great concern not only because it severely affects the strength but also it modifies the dynamic characteristics of the structure, including natural frequency and vibration modes. This paper deals with the investigation of the dynamic characteristics of a GFRP beam with single and multiple debonding by finite element based numerical simulations and analyses using the STRAND7 finite element (FE) software package. Three-dimensional computer models have been developed and numerical simulations were done to assess the dynamic behavior. The FE model developed has been validated with published experimental, analytical and numerical results for fully bonded as well as debonded beams. A comparative analysis is carried out based on a comprehensive parametric investigation. It is observed that the reduction in natural frequency is more affected by single debonding than the equally sized multiple debonding regions located symmetrically to the single debonding position. Thus it is revealed that a large single debonding area leads to more damage in terms of natural frequency reduction than isolated small debonding zones of equivalent area, appearing in the GFRP beam. Furthermore, the extents of natural frequency shifts seem mode-dependent and do not seem to have a monotonous trend of increasing with the mode numbers.

Keywords: debonding, dynamic response, finite element modelling, novel FRP beams

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Authors: Fatima Zohra Ibn Majdoub Hassani, Ivan Lavandera, Joseph Kreit

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This study explored the effects of different organic solvents, temperature, and the amount of glycerol on the alcohol dehydrogenase (ADH)-catalysed stereoselective reduction of different ketones. These conversions were then analyzed by gas chromatography. It was found that when the amount of deep eutectic solvents (DES) increases, it can improve the stereoselectivity of the enzyme although reducing its ability to convert the substrate into the corresponding alcohol. Moreover, glycerol was found to have a strong stabilizing effect on the ADH from Ralstonia sp. (E. coli/ RasADH). In the case of organic solvents, it was observed that the best conversions into the alcohols were achieved with DMSO and hexane. It was also observed that temperature decreased the ability of the enzyme to convert the substrates into the products and also affected the selectivity. In addition to that, the recycling of DES up to three times gave good conversions and enantiomeric excess results and glycerol showed a positive effect in the stability of various ADHs. Using RasADH, a good conversion and enantiomeric excess into the S-alcohol were obtained. It was found that an enhancement of the temperature disabled the stabilizing effect of glycerol and decreased the stereoselectivity of the enzyme. However, for other ADHs a temperature increase had an opposite positive effect, especially with ADH-T from Thermoanaerobium sp. One of the objectives of this study was to see the effect of cofactors such as NAD(P) on the biocatlysis activities of ADHs.

Keywords: alcohol dehydrogenases, DES, gas chromatography, RasADH

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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

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Authors: Arifuzzaman Nayeem, Tafannum Torsha, Tanvir Manzur, Shaurav Alam

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Performance evaluation of reinforced concrete (RC) beams subjected to accidental fire is significant for post-fire capacity measurement. Mechanical properties of any RC beam degrade due to heating since the strength and modulus of concrete and reinforcement suffer considerable reduction under elevated temperatures. Moreover, fire-induced thermal dilation and shrinkage cause internal stresses within the concrete and eventually result in cracking, spalling, and loss of stiffness, which ultimately leads to lower service life. However, conducting full-scale comprehensive experimental investigation for RC beams exposed to fire is difficult and cost-intensive, where the finite element (FE) based numerical study can provide an economical alternative for evaluating the post-fire capacity of RC beams. In this study, an attempt has been made to study the fire behavior of RC beams using FE software package ABAQUS under different durations of fire. The damaged plasticity model of concrete in ABAQUS was used to simulate behavior RC beams. The effect of temperature on strength and modulus of concrete and steel was simulated following relevant Eurocodes. Initially, the result of FE models was validated using several experimental results from available scholarly articles. It was found that the response of the developed FE models matched quite well with the experimental outcome for beams without heat. The FE analysis of beams subjected to fire showed some deviation from the experimental results, particularly in terms of stiffness degradation. However, the ultimate strength and deflection of FE models were similar to that of experimental values. The developed FE models, thus, exhibited the good potential to predict the fire behavior of RC beams. Once validated, FE models were then used to analyze several RC beams having different strengths (ranged between 20 MPa and 50 MPa) exposed to the standard fire curve (ASTM E119) for different durations. The post-fire performance of RC beams was investigated in terms of load-deflection behavior, flexural strength, and deflection characteristics.

Keywords: fire durations, flexural strength, post fire capacity, reinforced concrete beam, standard fire

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Authors: M. R. Akbari , H. Yousefnia, E. Mirrezaei

Abstract:

Range uncertainties of protons are currently a topic of interest in proton therapy. Two of the parameters that are often used to specify proton range are water equivalent thickness (WET) and water equivalent ratio (WER). Since WER values for a specific material is nearly constant at different proton energies, it is a more useful parameter to compare. In this study, WER values were calculated for different proton energies in polymethyl methacrylate (PMMA), polystyrene (PS) and aluminum (Al) using FLUKA and TRIM codes. The results were compared with analytical, experimental and simulated SEICS code data obtained from the literature. In FLUKA simulation, a cylindrical phantom, 1000 mm in height and 300 mm in diameter, filled with the studied materials was simulated. A typical mono-energetic proton pencil beam in a wide range of incident energies usually applied in proton therapy (50 MeV to 225 MeV) impinges normally on the phantom. In order to obtain the WER values for the considered materials, cylindrical detectors, 1 mm in height and 20 mm in diameter, were also simulated along the beam trajectory in the phantom. In TRIM calculations, type of projectile, energy and angle of incidence, type of target material and thickness should be defined. The mode of 'detailed calculation with full damage cascades' was selected for proton transport in the target material. The biggest difference in WER values between the codes was 3.19%, 1.9% and 0.67% for Al, PMMA and PS, respectively. In Al and PMMA, the biggest difference between each code and experimental data was 1.08%, 1.26%, 2.55%, 0.94%, 0.77% and 0.95% for SEICS, FLUKA and SRIM, respectively. FLUKA and SEICS had the greatest agreement (≤0.77% difference in PMMA and ≤1.08% difference in Al, respectively) with the available experimental data in this study. It is concluded that, FLUKA and TRIM codes have capability for Bragg curves simulation and WER values calculation in the studied materials. They can also predict Bragg peak location and range of proton beams with acceptable accuracy.

Keywords: water equivalent ratio, dosimetric materials, proton therapy, Monte Carlo simulations

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

Abstract:

The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

Procedia PDF Downloads 50

Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

Abstract:

Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

Procedia PDF Downloads 294

Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

Procedia PDF Downloads 186

Authors: Tsega Asresa Mengistu, Getahun Tigistu

Abstract:

Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

Procedia PDF Downloads 438

Authors: Zhuang Hou, Xiaolei Cao

Abstract:

The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

Procedia PDF Downloads 134